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Best Artificial Intelligence Tools for Image and Video Generation
|
Best Artificial Intelligence Tools for Image and Video Generation
|
https://amadine.com
|
[
"Belight Software",
"The Design Of The Article Was Created Olha Voronova",
"Revised Max Lazor."
] |
The emergence of new AI tools is transforming the world of graphic design. Artificial intelligence design tools are giving designers new ways to do anything in ...
|
Stable Diffusion
Stable Diffusion is a text-to-image model using deep learning launched in 2022. Initially not an online service, it got its online version over time, but previously you had to install it locally on the computer. It will not come as a surprise that it has most of the possibilities. It can function on the majority of consumer hardware outfitted with a modest GPU and at least 8 GB VRAM, and its code and model weights have also been made freely available.
The issue here is that it is difficult to come by if you prefer to install the non-online version. You will need a lot of additional work to install the latest app onto your device with the help of Github using Terminal. Moreover, the problem with working on your device also arises from the need to get an additional interface for the model. Over time different third-party interfaces appeared to make the work on your device possible. Among the most popular ones are Automatic1111, Comfyui, Fooocus v2, InvokeAI. But qualitatively Stable Diffusion is a departure from earlier publicly available proprietary text-to-image models like DALL-E and Midjourney. These were previously only available via cloud services, so all this hard work is fully worth the effort.
While Stable Diffusion does not yet have a user-friendly interface like some AI picture generators, it is free for personal and commercial use on both PC and Mac and has a fairly permissive license, only prohibiting a number of drawing scenarios and use cases. When it comes to inspiration, you can get prompts from Lexica to get a better picture as a final result. The service has been so popular that the internet is full of different models and prompts to adjust to the most efficient work with the tool.
You can get the beginner’s guide to Stable Diffusion’s models as well as numerous examples for all the use cases you can imagine.
| 2023-02-01T00:00:00 |
https://amadine.com/useful-articles/ai-graphic-design-tools
|
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|
Text-to-image AI | Google Cloud
|
Text-to-image AI
|
https://cloud.google.com
|
[] |
Google Cloud text-to-AI tools and resources, including pre-trained AI models like Imagen, Parti, and Muse, available in Vertex AI, are designed to help ...
|
Generate images from text descriptions in seconds using Google Cloud AI-powered image generation with available APIs in Python, Java, and Go programming languages.
New customers get up to $300 in free credits to generate images and more using Imagen on Vertex AI.
| 2023-02-01T00:00:00 |
https://cloud.google.com/use-cases/text-to-image-ai
|
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|
Top 11 AI Graphic Design Tools to Boost Your Efficiency
|
Top 11 AI Graphic Design Tools to Boost Your Efficiency
|
https://influencermarketinghub.com
|
[
"Geri Mileva",
"About The Author",
"An Experienced Ip Network Engineer",
"Distinguished Writer At Influencer Marketing Hub",
"Specializes In The Realms Of The Creator Economy",
"Ai",
"Blockchain",
"The Metaverse. Her Articles",
"Featured In The Huffington Post",
"Ravishly"
] |
Top 11 AI Graphic Design Tools to Boost Your Efficiency · 1. Jasper.ai · 2. Designs.ai · 3. Adobe Sensei · 4. Uizard · 5. Fronty · 6. Khroma · 7. AutoDraw · 8.
|
Graphic design is an essential part of marketing, but it can also be resource-intensive. A Piktochart survey of over 1,000 business professionals found that 55% of respondents use graphic design to communicate better with audiences. On the other hand, 67% of companies that don’t use graphic design said they would do so if it were easier and more cost-effective.
Fortunately, artificial intelligence (AI) is rapidly changing the digital marketing industry. In fact, businesses have been utilizing AI tools to optimize their processes and hit their goals faster. Similarly, AI is learning how to design and perform creative tasks in a quick span of time, making the life of designers easier.
Here are some AI graphic design software to help you create professional-looking assets within minutes.
What is an AI Graphic Design Tool?
AI simulates human intelligence processes in machines and computer-controlled robots. This allows computer systems to perform laborious tasks, helping humans focus on more important things. As a result, the demand for AI integrations in the workplace has increased over the years. In fact, experts predict that the overall AI software market will reach $791.5 billion in revenue in 2025.
Likewise, AI is rapidly becoming part of the design industry. In graphic design, AI helps optimize work processes, enhance creative assets, and analyze customer preferences. AI graphic design tools are trained to determine the visual asset that you need and provide it in a single click. This is a win for marketers, who don’t need a design background to create visuals that boost brand engagement.
Why You Need an AI Graphic Design Tool
1. Reduce manual work
Last year, almost 18% of surveyed marketers revealed that they spend an average of over 20 hours per week creating visual content. AI graphic design tools can ease up the workload by automatically refining your work and generating design ideas. Many can also suggest design alternatives, which eliminate repetitive tasks like removing backgrounds and creating mockups.
2. Automatically generate multiple designs
In a survey, marketers revealed that autogenerating multiple design types is the biggest design workflow that they want to automate. AI tools generate multiple versions of graphics based on your preferences, needs, and previous designs. They can also create unique and alternate versions of visual assets without compromising your brand’s integrity.
3. Approach design from a data perspective
AI can help you analyze your audience’s profile and suggest designs based on that data. Some AI tools can also identify best-converting designs, user preferences, and usability metrics that can work well for your brand. This not only personalizes your design but also improves user engagement. You can then choose an effective design from the suggestion and run it with your audience through an A/B test.
What Makes a Great AI Graphic Design Tool?
1. Intuitive interface
Most designers and content creators start with no background in design. In fact, more marketers use an online tool or graphic maker than hire a designer or use advanced design software. For this reason, it’s best to choose an AI graphic design tool with little learning curve. Many AI applications are easily navigable, even for beginners.
2. User-friendly design assets
A Venngage survey reveals that producing consistent content is the biggest struggle in creating highly engaging visuals. Other challenges included finding the right layout and creating great designs. A robust graphic design tool will not only help you generate a variety of outstanding visual assets but also wow your audiences while professionally representing your brand.
3. Ideation tools
Over 36% of marketers agree that original graphics is the most popular visual content. Moreover, it was the best-performing type of visual in 2021. This means that many marketers are designing from scratch to boost engagement with customers. Some AI tools are capable of handling creative ideations, while others perform various tasks to let you focus on conceptualization.
4. Desktop and mobile optimization
41% of content creators are churning out visual content between two to five times a week. A good AI graphic design tool helps you efficiently create and edit artwork from any platform and device. Some tools allow you to edit or update your designs with just a tap so that you can repurpose your content across multiple channels and share it on the go.
Top AI Graphic Design Tool
Top ai graphic design tools 2025 1. Jasper.ai Jasper.ai is an AI art generator that can create unique images and photos in seconds, based on user prompts. It offers high-resolution 2k images with royalty-free commercial use, no watermark, and unlimited generations. Jasper.ai has been highly praised by users who find it faster and easier to use than searching for stock images or creating images themselves. Jasper.ai also offers an AI writing tool that can generate content and improve writing. Jasper.ai’s pricing starts at $24/month per user with a 5-day free trial, and custom plans are available for enterprises. Users can generate up to 200 images for free during the trial period. The generated images can be used commercially without any limitations, and users own the commercial copyrights. Jasper.ai has received positive testimonials from its users, who find it helpful in creating content and improving their design process. The generated images have been highly rated and praised for their quality. Jasper.ai’s FAQs cover topics such as pricing, image ownership, commercial copyrights, biases in image generation, and refund and cancellation policies. Users are allowed to publish the AI-generated images on their sites/blogs and have the rights to use the images for commercial purposes. However, the images are not exclusive to the users and can be used by others as well. Features: Boss Mode
Chrome Extensions
Business Features
Jasper Chat
Surfer SEO Integraton Pricing per month: Starter: $24
Boss Mode: $49
Business: Custom Pros Cons
Assist in rapid content creation Conducting research is essential The interface is simple and easy to use Additional fees may be required for plagiarism detection Offers significant flexibility in usage Low-quality content can result in financial losses Boss Mode is particularly impressive Overly technical subjects can be challenging to comprehend Natively supports Grammarly 2. Designs.ai Designs.ai is an online-based design software that is making design accessible to all. It promises to help you create your marketing portfolio in less than two minutes, even if you don’t have a design background. Moreover, its smart editor and wizard tool save you time brainstorming design ideas for your marketing content. With Designs.ai, you can create logos, graphics, videos, mockups, and speeches. You can choose from a library of over 20,000 templates and 10,000 icons. The platform also uses AI to generate thousands of design variations and millions of design assets. You can create a logo and design template just by putting your brand information and preferences. Features: Assistive tools
Brand identity package
Color matcher
Font pairer
Scalable Vector Graphics
Team collaboration
Unlimited downloads Pricing per month: Basic: $29
Pro: $69
Enterprise: Customized Pros Cons Clean and intuitive interface No mobile app Works on multiple browsers and devices Website tends to be slow No software installation required 3. Adobe Sensei Adobe Sensei uses AI and machine learning to enhance creative assets, speed up business operations, and deepen marketing insights. Adobe introduced the application in 2016 to make businesses work faster and smarter. This helps designers focus on the more important things, like ideating, creating, and engaging with clients. Adobe Sensei makes images discoverable and lets you create high-quality graphics that automatically adjust to screen size and resolution. Aside from streamlining work processes, Adobe Sensei also helps you make better design decisions for marketing. Its performance forecasting feature predicts your future performance and helps you optimize your campaign strategies. Features: Automated Forms Conversion
Business and real-time intelligence
Content creation and intelligence
Image discovery and manipulation
One-to-one marketing personalization
Workflow automation Pricing per month: Prices are available upon request from the vendor. Pros Cons Reliable integration with various software Geared more toward design professionals Comprehensive and high-quality design features Not included in the Adobe Creative Cloud Features are accessible to most Adobe programs 4. Uizard Uizard began as a machine learning research project in 2017. Today, the AI-powered prototyping tool has over 400,000 users and more than 8,000 user-created projects each week. Its goal is to democratize design by helping both designers and non-designers create digital products that audiences can interact with. With Uizard, you can professionally design websites, desktop interfaces, and web and mobile apps within seconds. It automatically converts your scanned hand-drawn sketches into a prototype and applies them to your project. Moreover, Uizard’s pre-made design templates and drag-and-drop components allow you to edit your designs quickly. Features: Rapid product prototyping and ideation
Digital product wireframing
Pre-made and personalized templates
Real-time design collaboration
AI-powered design assistant Pricing per creator per month: Free: $0
Pro2: $15 ($12 per month if billed yearly)
Enterprise: $39 (billed yearly) Pros
Cons Intuitive interface One misclick can mess up the entire design Fast collaboration Limited export options No software installation required Clunky drag and drop Reliable customer service 5. Fronty Fronty is an AI-powered tool that creates source codes based on a user-uploaded image. It offers a new way of creating websites. The AI graphic design tool has over two decades of front-end experience and currently uses 11 technology products and services, including HTML5 and Google Analytics. Fronty generates an HTML CSS code based on your draft. Simply upload an image of your webpage design. The AI identifies the different elements of the image and then automatically generates your HTML/CSS code. You can also use its online user interface (UI) editor to modify your webpages. Fronty takes pride in its clean, speed-optimized, and accessible codes. Features: AI-powered image-to-code converter
Online layout editor
Website hosting
Custom domain
Customized Bootstrap Theme
Sassy Cascading Style Sheets
Search Engine Optimization Pricing per month: Freemium: $0
Pro: $4.52
Advanced: $9 Pros Cons Provided codes are 100% W3C valid and ISO/EIC compliant Still in its developmental stage 95% customer service uptime HTML processing can be long Variety of templates to choose from 6. Khroma Multi-disciplinary designer George Hastings set out to create Khroma when he realized there weren’t any tools that allowed him to browse and compare color combinations easily. Khroma is an AI-based color combination generator that draws from your selection of colors and thousands of human-made color palettes across the internet. To generate a personalized list of color combinations, you must first choose 50 colors on the Khroma website. You can view them as gradient, palette, typography, and image. You can also discover and search for new combinations, as well as build your collection. In addition, you can upload your own custom image and test the colors on it. Features: Color search option
Infinite combinations and viewing options
Personalized algorithm
Search and filter
Unlimited favorites library Pricing per month: Khroma is free to use. Pros Cons You can use different parameters to search for colors Choosing from a huge selection of colors can be tedious The color combinations are limitless Infinite scroll UI can be overwhelming Straightforward design 7. AutoDraw AutoDraw is a web-based AI-powered drawing tool. It was created by Google Creative Lab’s Dan Motzenbecker and Kyle Phillips to make drawing accessible and enjoyable for everyone. It helps you draw fast and refine your artwork by pairing machine learning with drawings from various artists. Simply make a doodle on AutoDraw. Its suggestion tool will then attempt to guess what you’re drawing. Then it will offer drawing suggestions created by different artists and designers to make your artwork look better. You can download your work as a PNG file and share it on social media from any device. Some of the designs are also available for you to download. Features: AI-based suggestion tool
Basic drawing tools, including freehand drawing, color picker, shapes, fill, text, resize, zoom, and rotate tools
Keyboard shortcuts
Download and share options Pricing per month: AutoDraw is free to use. Pros Cons Absolutely free Not collaborative Straightforward interface Lacks more advanced drawing features Availability on any device 8. Deep Art Effects Deep Art Effects is an AI-based image processing tool that turns your design into works of art with a single click. With over 120 art styles, this graphic design software has more than 2 million global users and 200 million created artworks under its belt. Some of its partners include Samsung, Huawei, and Globus. Deep Art Effects not only learns different art styles but also refines your images. You can edit just the background or foreground of an image, create your own art styles, render images at any resolution, and design using your favorite artists’ styles. You can even integrate filters and image technology into your apps using the software’s Application Programming Interface (API). Features: Automatic cropping and bokeh
Automatic grayscale
Desktop software and mobile app availability
Generative Adversarial Networks
Intelligent scaling
Offline and German data protection
Selective render for background and foreground Pricing: 1 Month Subscription: $9.90
3 Month Subscription: $25 ($8.33 per month)
1 Year Subscription: $80 ($6.66 per month)
One-Time Purchase: $129 Pros Cons Intuitive and easy to use Image processing takes time Responsive customer support Plenty of design varieties 9. Let’s Enhance Let’s Enhance is an image enhancer and upscaler. It was founded in 2017 to meet the challenges of user-created images. While many brands want to maximize user-generated content (UGC), most of these are shot on mobile phones by ordinary people. When uploaded online, these pictures compress and pixelate because of their size. Thanks to Let’s Enhance’s cutting-edge image processing algorithm, you can upscale images up to 16 times without compromising their quality. You can cleanse your images, resize your logo, and adjust tones and colors with one click. You can do all that from any online platform. This AI tool for graphic designers also lets you process up to 20 visual assets at a time, speeding up your workflow. Features: Accurate face detection
Automated presets
Batch processing
Color and tone enhancement
Cloud storage
Custom algorithm
Image upscale
Smart Enhance algorithm Pricing per month: 10 free credits: $0
100 credits: $12 ($9 billed annually)
300 credits: $32 ($24 billed annually)
500 credits: $45 ($34 billed annually) Pros Cons Availability on any device Lacks advanced functions for complex images Intuitive interface Pricier than other software High-quality processing Image processing takes time 10. remove.bg Whether you’re processing 50 or 1,000 images at a time, remove.bg can automatically remove each photo’s background with just a drag and drop. It even handles challenging edges, giving your photos a professional look. Over 28,000 customers from more than 200 countries are using the application. Some of its clients include AT&T Sportsnet, Canva, and Samsung. Aside from removing backgrounds in photos, remove.bg also lets you edit your work within seconds so that you can create highly effective visuals on the go. You can even replace background images and create professional-looking product photos using its design templates. Furthermore, the software fully integrates with Photoshop, which can hasten your work process. Features: Adobe Photoshop extension
API documentation
Design templates
Downloadable app for Windows, Mac, Linux, and Android
Integrations, tools, and apps Pricing per month: Free Account: $0
Subscription Plan: Starts at 40 credits for $0.23 per image
Pay as You Go: Starts at one credit for $1.99 per image Pros Cons Free unlimited previews for all plans May remove other photo elements that you can’t tweak Fast and easy to use Cost-effective 11. Movavi Movavi is a software powerhouse that excels in crafting multimedia tools and applications for a variety of platforms such as Windows, Mac, and mobile devices. Among their innovative creations is Movavi Photo Editor, a state-of-the-art, AI-infused photo-editing software designed for every stage of expertise. Its user-friendly interface and intelligent features empower you to produce breathtaking visuals effortlessly. The AI Auto Enhance feature fine-tunes colors and contrast in a flash, while the Precise Object Removal tool effortlessly banishes unwanted elements from your photos, leaving them looking pristine and untouched. The AI Enlargement tick box revolutionizes image resizing, ensuring that your photos retain their original quality, even as their dimensions increase. Other AI-driven tool is the Auto Correction Slider, which intuitively adjusts exposure, highlights, shadows, brightness, and contrast. The Denoise Slider eliminates digital noise, leaving your photos looking polished and professional. Features: AI Auto Enhance
AI Enlargement
Auto Correction Slider
Denoise Slider Pricing per month: Video Suite License: $89.95/year
Movavi Unlimited License: $69.95/lifetime
Video Suit + Photo Editor License: $144.95/lifetime Pros Cons User-friendly interface Limited free version AI-driven tools for efficient editing Regular updates and improvements
Frequently Asked Questions Is AI used in graphic design? AI is used in graphic design to simplify your creative workflow. Machine learning helps make less manual work and allows people to focus on the bigger picture. You can create full marketing campaigns in 2 minutes with AI graphic design tools. What is AI design tool? An AI design software tool are computer programs that help power artificial intelligence, or AI. These AI design tools use machine learning to save you time, provide inspiration, or simply do the work for you. What is the best AI tools? The top artificial intelligence tools and frameworkers you need to know: Jasper.ai Designs.ai Adobe Sensei Uizard Fronty Khroma AutoDraw Deep Art Effects Let’s Enhance remove.bg Movavi What is AI in graphics? AI files are native vector file type for Adobe Illustrator. An AI file can help designers scale their graphics, drawings, and images infinitely with no impact on resolution.
| 2022-10-19T00:00:00 |
2022/10/19
|
https://influencermarketinghub.com/ai-graphic-design-tools/
|
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NVIDIA GeForce RTX AI PCs | Powering Advanced AI
|
NVIDIA GeForce RTX AI PCs
|
https://www.nvidia.com
|
[] |
NVIDIA NIM microservices deliver cutting-edge AI models optimized for RTX GPUs. With one click, you can access tools for language, vision, speech, design, and ...
|
When it comes to AI PCs, the best have NVIDIA GeForce RTX™ GPUs inside. That’s because the same technology powering world-leading AI innovation is built into every RTX GPU, giving you the power to do the extraordinary. From class to work to entertainment, with RTX-powered AI, you’re getting the most advanced AI experiences available on Windows PCs.
| 2023-02-01T00:00:00 |
https://www.nvidia.com/en-us/ai-on-rtx/
|
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|
Graphic Designer - ChatGPT
|
Graphic Designer
|
https://chatgpt.com
|
[] |
GPT Icon. Graphic Designer. By community builder. Expert in crafting visual designs and graphics. Sign up to chat. Sign up or Log in to chat.
|
Sign up to chat
Sign up or Log in to chat
| 2023-02-01T00:00:00 |
https://chatgpt.com/g/g-lPeUc0axE-graphic-designer
|
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"date": "2025/02/01",
"position": 87,
"query": "AI graphic design"
},
{
"date": "2025/05/01",
"position": 88,
"query": "AI graphic design"
},
{
"date": "2025/06/01",
"position": 87,
"query": "AI graphic design"
}
] |
|
Union Collective Bargaining Agreement Strategies in ...
|
Union Collective Bargaining Agreement Strategies in Response to Technology
|
https://escholarship.org
|
[
"Kresge"
] |
by L Kresge · 2020 · Cited by 13 — This paper reviews strategies that unions have used to leverage their collective bargaining agreements to address technological change, both past and present.
|
This paper reviews strategies that unions have used to leverage their collective bargaining agreements to address technological change, both past and present. It groups these approaches into three categories: those focused on establishing rights and roles regarding the decision to adopt new technology, those designed to mitigate the introduction of new technology, and those related to the use of technology in workforce management.
| 2020-12-03T00:00:00 |
2020/12/03
|
https://escholarship.org/uc/item/1x43h4jr
|
[
{
"date": "2023/02/01",
"position": 4,
"query": "artificial intelligence labor union"
}
] |
Artificial Intelligence Technology: Threat or Opportunity for Humans?
|
Faculty of Economics and Business Syarif Hidayatullah State Islamic University
|
https://feb.uinjkt.ac.id
|
[] |
... work professions. It is feared that artificial intelligence (AI) technology will make humans unemployed in the future. However, this did not take long to ...
|
Rapid developments in the field of artificial intelligence (AI) have brought profound transformation in various aspects of human life, including the world of work. Although AI promises increased efficiency and innovation, behind its potential it also holds threats to various work professions. It is feared that artificial intelligence (AI) technology will make humans unemployed in the future. However, this did not take long to become a reality. Several companies have started to cut employees. It is estimated that there will be a wave of layoffs of at least 44 percent throughout 2024 due to AI efficiency.
One of the main threats facing the world of work is the automation of routine and repetitive jobs. Intelligent AI systems can replace jobs that require repetitive and standardized tasks, such as in the manufacturing and administrative sectors. Machines equipped with artificial intelligence are able to perform these tasks more quickly and accurately than humans. AI also has the potential to result in job reductions in certain sectors.
Not only routine jobs, but AI can also threaten creative jobs. Computer programs trained to produce works of art or write news stories could replace the work of human artists and journalists. AI's success in imitating human creativity is a serious challenge that needs to be overcome. Companies that adopt AI technology may tend to reduce demand for human workers. In an effort to increase efficiency, some companies may turn to cheaper and more efficient automation systems, resulting in a decline in traditional employment.
Although advances in AI bring efficiency, the presence of intelligent machines can bring challenges in aspects of socialization and human interaction. Jobs that require empathy, sensitivity to social context, and communication skills may be less suited to automation, but remain crucial to the success of many professions.
So what solutions can be done to overcome the AI threat? The first solution to overcome the threat of AI is through education and training. Governments, educational institutions and companies need to work together to develop training programs that provide new skills to workers who may be impacted by automation. A focus on developing human skills, such as creativity, emotional intelligence, and problem solving, can help workers compete in the AI era.
The development and preservation of jobs that are difficult to automate could be a strategy to reduce the negative impact of AI on employment. Professions that require interpersonal intelligence, creative skills, and moral judgment may be more resistant to automation. The government needs to take an active role in shaping policies that support society's adaptation to the changes brought by AI. This includes regulations that ensure worker protection and the creation of a business environment that promotes sustainability and fairness.
Integrating AI technology into the workplace in a way that supports human and machine collaboration is a positive approach. Workers can work together with AI systems to increase productivity and innovation, while maintaining their critical role in complex decision making. Innovation and entrepreneurship can drive economic growth in the AI era. Encouraging the development of new technologies and creating previously unthinkable jobs can be a strategy to address shifts in the employment landscape.
A holistic approach through education, policy formation, and the integration of technology with human wisdom can help reduce negative impacts and ensure that humans remain the primary driver of economic and social development. This challenge requires cross-sector collaboration and global commitment to achieve sustainable and inclusive solutions.
| 2023-02-01T00:00:00 |
https://feb.uinjkt.ac.id/en/artificial-intelligence-technology-threat-or-opportunity-for-humans
|
[
{
"date": "2023/02/01",
"position": 97,
"query": "artificial intelligence workers"
},
{
"date": "2023/04/01",
"position": 98,
"query": "artificial intelligence workers"
},
{
"date": "2023/08/01",
"position": 94,
"query": "artificial intelligence workers"
},
{
"date": "2023/09/01",
"position": 98,
"query": "artificial intelligence workers"
},
{
"date": "2023/11/01",
"position": 89,
"query": "artificial intelligence workers"
},
{
"date": "2024/01/01",
"position": 96,
"query": "artificial intelligence workers"
},
{
"date": "2024/02/23",
"position": 54,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 98,
"query": "artificial intelligence workers"
}
] |
|
How To Be “Smart” About Using Artificial Intelligence In ...
|
How To Be “Smart” About Using Artificial Intelligence In The Workplace
|
https://www.jdsupra.com
|
[] |
If adequately designed and applied, AI can help employees find employment, match employers with valuable employees, and advance diversity, inclusion, and ...
|
Artificial Intelligence (AI) is undoubtedly revolutionizing the workplace. More and more employers are relying on algorithms or automated tools to determine who gets interviewed, hired, promoted, compensated, disciplined, or terminated. If adequately designed and applied, AI can help employees find employment, match employers with valuable employees, and advance diversity, inclusion, and accessibility in the workplace. Yet, despite its positive impacts, AI poses new risks for employment discrimination, especially when designed or used improperly, and has become a focal point of targeted efforts by federal and state enforcement agencies and lawmakers. Employers must be smart, transparent, and knowledgeable about how they use AI in their workplaces. When used properly, AI tools could potentially make employment processes faster and more efficient, while eliminating both conscious and unconscious bias.
EEOC’s Concerns with Artificial Intelligence – Recruitment and Hiring
The use of AI in the workplace has been on the radar screen of federal regulators such as the U.S. Equal Employment Opportunity Commission (EEOC), for several years. The EEOC is now taking aim to develop technical assistance, guidance, audit tools, or other parameters to ensure that AI is developed, understood, and used responsibly. AI is a “priority” subject matter in the EEOC’s 2023-2027 Strategic Enforcement Plan (SEP), which we recently summarized. The EEOC has signaled that, for the first time, it will take into account “employers’ increasing use of automated systems, including artificial intelligence or machine learning,” to make hiring and recruiting decisions. The EEOC plans to scrutinize employers’ “use of software that incorporates algorithmic decision-making or machine learning, including artificial intelligence; use of automated recruitment, selection, or production and performance management tools; or other existing or emerging technological tools used in employment decisions.” This is a warning shot for employers to be deliberate and cautious when employing new technologies to assist with decision-making.
One of the EEOC’s priorities is eliminating barriers in recruitment and hiring. The use of AI, in the EEOC’s view, may result in discrimination in recruitment and hiring in three specific ways:
First, the potential that AI is used unlawfully to “target job advertisements, recruit applicants, or make or assist in hiring decisions where such systems intentionally exclude or adversely impact protected groups.” The EEOC cited an example where it believed a company specifically programmed its application software to automatically reject applicants over a certain age.
Second, the potential that use of AI results in “restrictive application processes or systems, including online systems that are difficult for individuals with disabilities or other protected groups to access.” The EEOC previously issued guidance, “The Americans with Disabilities Act and the Use of Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees” (dated May 12, 2022), which is designed to help private employers comply with the Americans with Disabilities Act (ADA) when using AI. The EEOC claims that ADA liability can arise in three cases: (i) the employer fails to provide a reasonable accommodation necessary for an individual to be rated fairly and accurately by the tool, (ii) the tool screens out an individual with a disability even though the individual can do the job with a reasonable accommodation, or (iii) the tool violates the ADA’s restrictions on disability-related inquiries and medical examinations.
Third, the risk that the use of AI tools “disproportionately impact workers based on their protected status.” Title VII of the Civil Rights Act of 1964, the Age Discrimination in Employment Act (ADEA), and the ADA have long proscribed selection procedures such as pre-employment tests, interviews, and promotion tests that disparately impact workers based on their protected status. The EEOC intends to prioritize its scrutiny of selection tools that use AI, applying long-standing uniform hiring and selection guidelines, and more modern criteria or guidelines that may need to be formulated.
What’s Next?
The EEOC conducted a public hearing on January 31, 2023, entitled “Navigating Employment Discrimination in AI and Automated Systems: A New Civil Rights Frontier.” The hearing is part of the EEOC’s ongoing “Artificial Intelligence and Algorithmic Fairness Initiative” in which the Commission reportedly seeks to ensure that technological resources are used in a manner to further the interests of accessibility, diversity, equity, and inclusion. The EEOC ultimately seeks to “guide employers, employees, job applicants, and vendors to ensure that these [AI] technologies are used fairly and consistently with federal equal employment opportunity laws.” According to the EEOC Chair, Charlotte A. Burrows, the Commission is currently evaluating just how exactly to do that. The EEOC intends to “gather additional information, educate stakeholders about the use of AI tools, and combat algorithm discrimination where they find it.”
The EEOC is not the only federal agency with workplace technology concerns on the horizon. We previously alerted about the NLRB General Counsel’s plan to crack down on electronic monitoring in the workplace based upon concerns of infringement upon employees’ rights to engage in protected concerted activity. If successful, the General Counsel’s new framework could potentially slow the growth of “smart” workplaces in the United States.
Many states have also already tackled the issue of surveillance and other privacy concerns in the workplace driven by the use of technology, and we expect that trend will continue to develop. Some state and local governments have implemented or proposed robust laws and rules targeting automated employment decision tools which could potentially shift the legal landscape even further. As a result, it is important for employers to be aware of state and local laws regarding the use of AI, electronic monitoring, and other technologies.
Employer Best Practices for Using AI
Ultimately, the legal issues and potential liability associated with the use of AI in employment decisions will continue to emerge as the technologies become more advanced. That being said, as the legal implications remain somewhat unknown, there are a number of best practices employers can follow to manage the risk of AI tools:
(1) Know Your Data. It is important that employers exercise caution when developing, applying, and modifying data to train and operate AI used for employment decision-making. Incomplete or erroneous data will negatively impact the AI tool’s machine learning. Ask vendors about the technology being used and ensure you understand the algorithms and mechanics behind the automated processes.
(2) Disclose the Topics and Methodology. Be transparent and explain how AI is utilized with applicants and employees, as this will foster trust, credibility, and, as a result, a greater appreciation of the merits of AI systems.
(3) Consider Undergoing a Bias Audit. Monitor and audit AI uses and processes to proactively identify intentional misuse or potential discriminatory outcomes.
(4) Implement Human Oversight. Discern the point at which humans must be involved in the employment decision-making process. Employers should assign a team to oversee the processes and results of AI tools to ensure they are performing their legitimate objectives, and also avoiding discriminatory outcomes.
(5) Review Vendor Agreements. Carefully review vendor agreements that provide automated-decision systems to ensure vendors attest to the fairness and integrity of the AI tool.
Take Note: This is just a glimpse of the interaction between artificial intelligence and anti-discrimination laws, not an exhaustive summary, and this topic is a moving target.
| 2023-02-01T00:00:00 |
https://www.jdsupra.com/legalnews/how-to-be-smart-about-using-artificial-3826623/
|
[
{
"date": "2023/02/01",
"position": 1,
"query": "artificial intelligence employers"
},
{
"date": "2023/02/01",
"position": 16,
"query": "artificial intelligence hiring"
}
] |
|
100 Top Artificial Intelligence (AI) Companies in 2024
|
100 Top Artificial Intelligence (AI) Companies in 2024
|
https://www.linkedin.com
|
[
"Amazon Web Services",
"Aws",
"David Linthicum",
"Saad Alismaili",
"Geophysicist",
"Bsc",
"Squ",
"Mud Logger At Oman Sea Petroservices",
"Business Development Specialist",
"Automata Intelligence"
] |
100 Top Artificial Intelligence (AI) Companies in 2024 · 1. Google Cloud · 2. IBM Cloud · 3. Alibaba Cloud · 4. Amazon Web Services (AWS) · 5. DataRobot · 6.
|
As artificial intelligence (AI) has become a growing force in business, today’s top AI companies are leaders in this emerging technology. Often leveraging cloud computing and edge computing, AI companies mix and match various technologies to meet and exceed use case expectations in the home, the workplace, and the greater community.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Machine learning (ML) leads the pack in this realm, but today’s leading AI firms are expanding their capabilities through other technologies, from predictive analytics to business intelligence (BI) to data warehouse tools to the deep learning (DL) segment of AI, alleviating several industry pain points. To help organizations keep up with the AI market, see this breakdown of top companies playing a key role in shaping the future of AI — by industry:
Top AI Companies by Industry
Cloud AI Companies
Health Care AI Companies
Vehicle/Transportation AI Companies
Security AI Companies
E-Commerce AI Companies
Financial AI Companies
Education AI Companies
Manufacturing/Engineering AI Companies
Energy/Environment AI Companies
Robotics AI Companies
Entertainment AI Companies
Cloud AI Companies
Major cloud companies, such as Microsoft and Google, have created their own cloud AI tools, along with competitors, including DataRobot.
Here are eight the top cloud AI companies:
1. Google Cloud
Google, a leader in AI and data analytics, is on a massive AI acquisition binge, having acquired a number of AI startups in the last several years. Google is deeply invested in furthering artificial intelligence capabilities. In addition to using AI to improve its services, Google Cloud sells several AI and machine learning services to businesses. It has an industry-leading software project in TensorFlow as well as its own Tensor AI chip project.
2. IBM Cloud
IBM is a leader in the field of artificial intelligence. Its efforts in recent years center around IBM Watson, an AI-based cognitive service, AI software as a service, and scale-out systems designed for delivering cloud-based analytics and AI services. It has been acquisitive, purchasing several startups over several years. It benefits from having a strong cloud platform.
3. Alibaba Cloud
A leading cloud platform in Asia, Alibaba offers clients a sophisticated machine learning platform for AI. Significantly, the platform offers a visual interface for ease of use, so companies can drag and drop various components into a canvas to assemble their AI functionality. Also included in the platform are scores of algorithm components that can handle any number of chores, enabling customers to use pre-built solutions.
4. Amazon Web Services (AWS)
AWS provides AI services that use the tools for a company’s applications and workloads. Their AI services integrate with their infrastructure to help a company address their recommendations, modernizing contact centers, security, and customer engagement. The service gives quality ML services to give accurate APIs. AWS AI requires no formal experience with ML or AI, which is a great feature for businesses who are beginning to update their systems.
5. DataRobot
A high-profile emerging cloud AI company, DataRobot provides the experienced data scientist with a platform for building and deploying machine learning models. The software helps business analysts build predictive analytics with no knowledge of machine learning or programming and uses automated ML to build and deploy accurate predictive models quickly.
6. Baidu AI Cloud
China-based Baidu is a company with a focus on AI and the cloud. Baidu supports AI platform-as-a-service (PaaS) and AI SaaS solutions across many industries, such as transportation, finance, manufacturing, and media. To help their customers, Baidu uses AI, machine learning, deep learning, language processing, video, and data analysis. Baidu is mostly used by developers.
7. Microsoft Azure
Microsoft offers a mix of consumer-facing and business AI projects. On its Azure cloud service, Microsoft sells a range of AI services, such as bot services, machine learning, and cognitive services. Recently, Microsoft has invested in OpenAI to further their partnership and create new AI technology. “Azure’s unique architecture design has been crucial in delivering best-in-class performance and scale for our AI training and inference workloads,” says a representative from OpenAI about their partnership.
8. Salesforce
In recent years, Salesforce has acquired a handful of AI startups and sharpened the features of Salesforce Einstein, their artificial intelligence service. The initiative, which includes an extensive team of data scientists, uses machine learning to help employees more efficiently perform tasks by simplifying and speeding them up. In addition to Salesforce’s employees, Einstein is available for customers who can build their own applications and are interested in features, like recommendation builder, scorecards, and in-depth navigation insights.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Healthcare AI Companies
Artificial intelligence within healthcare has become a helpful tool to catch early signs of disease, what medicine works best for a patient, and speed up vaccination creation and processes.
Here are 12 of the top healthcare AI companies:
9. Tempus
Tempus, specializing in “data-driven precision medicine,” uses an AI application strategy to fight disease and bolster patient outcomes. It gathers and analyzes massive pools of medical and clinical data at scale to provide precision medicine that personalizes and optimizes treatments to each individual’s specific health needs. Applications include neurology, psychiatry, and oncology.
10. Suki.Ai
It’s not enough that Suki offers an AI-powered software solution that assists doctors as they make voice notes on a busy day. Suki’s aim — using the power of AI to learn over time — is to mold and adapt to users with repeated use, so the solution becomes more of a time saver and efficiency booster for physicians over time. As a sign of the times, Suki was delivered with COVID-19 data and templates to speed up the vaccination and health tracking processes.
11. Nanox
Nanox has completed its acquisition of Zebra Medical Systems, an Israeli company that applied deep learning techniques to the field of radiology. It claims it can predict multiple diseases with better than human accuracy, by examining a huge library of medical images and specialized examination technology. It also moved its AI algorithms to Google Cloud to help it scale and offer inexpensive medical scans.
12. Freenome
Freenome uses artificial intelligence to conduct cancer screenings and diagnostic tests to spot signs of cancer earlier than possible with traditional testing methods. It uses non-invasive blood tests to recognize disease-associated patterns. The company’s solution has trained on cancer-positive blood samples, which enables it to detect problems using specific biomarkers.
13. Neurala
Neurala claims that it helps users improve visual inspection problems using AI technology. The company manages The Neurala Brain, a deep learning neural network software that makes devices, like cameras, phones, and drones, smarter and easier to use. AI tends to be power hungry, but the Neurala Brain uses audio and visual input in low-power settings to make simple devices more intelligent.
14. ICarbonX
iCarbonX is a Chinese biotech startup that uses artificial intelligence to provide personalized health analyses and health index predictions. It has formed an alliance with several technology companies from around the world that specialize in gathering different types of healthcare data and will use algorithms to analyze genomic, physiological, and behavioral data. It also works to provide customized health and medical advice.
15. Flatiron Health
Using machine learning to mine health data for cancer research, Flatiron finds cancer research information in near real-time, drawing on a variety of sources. The company raised more than $175 million in Series C funding before being acquired by cancer research giant Roche.
16. Deep 6
Deep 6 uses AI to, in its own words, “find more patients in minutes, not months.” The patients in this sense are participants in clinical trials — a critical part of the research process in developing new medicine. Certainly one of the challenging issues that were faced during the quest for a COVID-19 vaccine was finding a community of appropriate candidates. Deep 6 finds these kinds of communities by using an AI-powered system to scan through medical records, with the ability to understand patterns in human health.
17. Butterfly Network
Using AI to make healthcare more affordable and accessible, Butterfly Network provides a handheld medical diagnostic device that connects with a user’s smartphone. This device, powered by Butterfly iQ, allows an ultrasound examination of the entire body, at a far lower cost than legacy systems. This is especially helpful for underserved communities where health care resources are scarce.
18. K Health
There’s a gray area in our lives in terms of health care; we ask ourselves, does this problem I’m having really require making a doctor’s appointment, or could a major dose of simple information be enough? K Health’s AI solution operates in this area. Users can text a doctor or find similar cases near them, which has been particularly useful for COVID-19. Using a model built from a vast store of anonymous health records, its system offers help based on how a user’s complaint correlates with this vast history of other patients. Think of K Health as the advanced edge of telemedicine.
19. Insitro
Insitro operates at “the convergence of human biology and machine learning.” More specifically, it uses artificial intelligence to build models of various human illnesses, using those models to forecast previously unknown solutions beyond human intuition. These models use the power of ML to improve drug discovery and development. Founded by Daphne Koller, Insitro has drawn investment from an exhaustive array of VC and financial firms.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Vehicle/Transportation AI Companies
Artificial intelligence is being used by vehicle and transportation companies to help create safer streets, railways, and air travel.
Here are eight of the top vehicle and transportation AI companies:
20. Anduril Industries
Palmer Luckey is one of the most intriguing figures in today’s emerging tech. He co-founded Oculus, which Facebook bought for a cool $2 billion. Post-Facebook and at the ripe age of 27, he launched Anduril with co-founder Brian Schimpf. Anduril adds sophisticated sensors, vehicles, and drones to create a threat protection zone. Products include Sentry Tower for autonomous awareness, Ghost 4 sUAS for intelligent air support, and Anvil sUAS for precision kinetic intercept.
21. AEye
AEye builds the vision algorithms, computer vision strategy, software, and hardware used to guide autonomous vehicles, or self-driving cars. Its LiDAR technology focuses on the most important information in a vehicle’s sightline, such as people, other cars, and animals, while putting less emphasis on other landscape features, like the sky, buildings, and surrounding vegetation. AEye has also entered into a merger agreement with CF Finance Acquisition Corp. III.
22. Pony.Ai
Pony.ai develops software for autonomous vehicles. The company was created by ex-Google and Baidu engineers who felt that the big companies were moving too slowly in this arena. It has already made its first fully autonomous driving demonstration and now operates a self-driving ride-sharing fleet in Guangzhou, China, using cars from a local automaker. The company raised $400 million in funding from Toyota.
23. Nauto
Nauto offers an AI-powered driver behavior learning platform. So instead of self-driving cars, Nauto is an AI model designed to improve the safety of commercial fleets and autonomous fleets. The platform assesses how drivers interact with the vehicle and the road ahead to reduce distracted driving and prevent collisions.
24. Nuro
Nuro makes small self-driving electric delivery trucks designed for local deliveries, such as groceries or takeout. Its founders previously worked on Google’s Waymo self-driving car project. Overall, the company’s goal is to boost the value of robotics in daily life.
25. Zoox
Acquired in a $1.2 billion deal by Amazon, Zoox still operates as an independent company within Amazon. Zoox focuses on building a self-driving fleet, hence Amazon’s interest. Their AI-based vehicle is geared for the robo-taxi market.
26. DJI
Based in China, DJI is a big player in the rapidly growing drone market. The company is leveraging AI and image recognition to track and monitor the landscape, and it’s expected that the company will play a role in the self-driving car market. DJI has partnered with Microsoft for a drone initiative.
27. Orbital Insight
Orbital Insight uses satellite geospatial imagery and artificial intelligence to gain insights not visible to the human eye. It uses data from satellites, drones, balloons, and other aircraft to look for answers or insight on things related to the agriculture and energy industries that normally wouldn’t be visible. The company describes itself as a leader in geospatial analytics.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Security AI Companies
Companies are adding AI to their software to help identify, predict, and respond to cybersecurity threats. Many AI security products are working to detect vulnerabilities based on previous threats.
Here are eight of the top security AI companies:
28. CrowdStrike
This cloud-based SaaS company focuses on endpoint security. Leveraging AI, CrowdStrike’s Falcon platform can identify what it calls active indicators of attack to detect malicious activity before a breach actually happens. It presents the network administrators with actionable intelligence of real-time findings for them to take necessary action.
29. BlackBerry
BlackBerry has acquired the AI cybersecurity company Cylance. The two joined forces to develop security apps that prevent, instead of reactively detect, viruses and other malware. Using a mathematical learning process, BlackBerry Cybersecurity identifies what is safe and what is a threat rather than operating from a blacklist or whitelist. The company claims its machine learning has an understanding of a hacker’s mentality to predict their behavior.
30. DataVisor
DataVisor uses machine learning to detect fraud and financial crime, using unsupervised machine learning to identify attack campaigns before they result in any damage. DataVisor protects companies from attacks, such as account takeovers, fake account creation, money laundering, fake social posts, and fraudulent transactions.
31. Sherpa.Ai
Sherpa is a virtual personal assistant that works with a user’s entire array of devices, inferring and predicting their needs that allow the assistant to learn about the users and anticipate their needs before they ask. It works with many consumer devices and any accessory that could use some kind of intelligence in privacy or cybersecurity. Tapping a growth market, Sherpa sells white label digital assistants for consumer applications.
32. BigPanda
The goal of BigPanda is to leverage AI to lessen or stop IT outages before they take down a full business, an e-commerce operation, or a mission-critical application. In essence, this company’s goal is the magic of AIOps, using AI to improve admin and IT operation, which is a major growth area.
33. Symphony AyasdiAI
Ayasdi was acquired by the SymphonyAI Group. Symphony AyasdiAI is a machine intelligence software company that offers intelligent applications to its clients around the world for big data and complex data analytics problems. Its goal is to help customers automate what would be manual processes of using their own unique data. Symphony AyasdiAI also partnered with Sionic, leading to a greater focus on financial crime detection.
34. Dataminr
Dataminr is a global real-time information discovery company that monitors news feeds for high-impact events and critical breaking news. It cuts through the clutter of non-news or irrelevant news to specific industries and only provides highly relevant news when it happens. For news-sensitive vendors, its goal is to detect early risks from media coverage.
35. Darktrace
Cybersecurity company Darktrace is based in the U.K., focusing on how to help customers keep their data and infrastructure secure. Using self-learning AI, Darktrace can detect specific needs of their customers. Darktrace works to prevent, detect, respond, and heal from cyberattacks all at once.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
E-Commerce AI Companies
From marketing to sales, AI e-commerce providers are helping companies use big data to increase revenue through better demand planning and real-time optimizations and targeting.
Here are 24 of the top e-commerce AI companies:
36. Algorithmia
Is there a better name for an AI company than Algorithmia? Now a DataRobot company after an acquisition, Algorithmia’s goal is to help data scientists find and use algorithms. It was initially an exchange for algorithms on a one-off, single-user basis. As it has grown, it has set its sights on the enterprise market.
37. The Trade Desk
A company designed to help digital advertisers run targeted digital advertising campaigns, The Trade Desk uses AI to optimize its customers’ advertising campaigns for their appropriate audiences. Their AI, known as Koa, was built to analyze data across the internet to figure out what certain audiences are looking for and where ads should be placed to optimize reach and cost. The Trade Desk also allows you to launch your digital ads independently but uses its AI to offer performance suggestions while your campaign is live.
38. Swim.Ai
Swim.ai’s goal is to enable businesses to mine continuously streaming data into actionable insights. Leveraging machine learning, the company’s “open core platform” augments the decision-making process by providing streaming data and contextualizing data sources. The SwimOS is open source.
39. Phrasee
Phrasee specializes in natural language generation for marketing copy. Its natural language generation system can generate millions of human-sounding variants of marketing at the touch of a button, allowing customers to tailor their copy to targeted customers. Retail, marketing, and AI are a combination of a rapid growth curve in the AI sector. During the COVID-19 pandemic, several retailers, such as Walgreens, used Phrasee to boost customer engagement related to vaccination.
40. Pymetrics
Based in New York City, Pymetrics leverages AI to help companies hire optimal candidates by examining more than what’s traditionally included in a resume scan. Customers have their best employees fill out the Pymetrics assessment, which then creates a model for what future ideal candidates should bring to the table. In essence, the AI-based system is attempting to find more new staff that will fit in well with the existing top staff, using AI and behavioral science.
41. People.Ai
People.ai’s goal is to streamline the life of salespeople, assisting them in putting the reams of small details into relevant CRM systems, chiefly Salesforce. Think of all those pesky info bits from texting, your calendar, and endless Slack conversations — the company aims to help you with all of that. Plus, the system attempts to coach sales reps on the most effective ways to manage their time.
42. AlphaSense
AlphaSense is an AI-powered search engine designed for investment firms, banks, and Fortune “500” companies. The search engine focuses on searching for important information within earnings call transcripts, SEC filings, news, and research. The technology also uses artificial intelligence to expand keyword searches for relevant content.
43. Icertis
The remarkable truth about AI is that it keeps moving up the food chain in terms of the sophisticated tasks it can handle. Taking a big step up from simple automation, Icertis, with a decade under its belt, handles millions of business contracts through a method they call contract intelligence. Leveraging the cloud, the company’s solution automates certain tasks and scans previous contract details. The company has gained some big clients , like Microsoft, and was named a Gartner leader.
44. Bizzabo
Bizzabo acquired X.ai. Geared to assist the busiest of people, X.ai’s intelligent virtual assistant “Amy” helps users schedule meetings. The concept is simple: If you receive a meeting request but don’t have time to work out logistics, you copy Amy in the email, and she handles it. Through machine learning and natural language processing, Amy schedules the best time and location for your meeting based on your preferences and schedule.
45. One Model
Human resources can be a bifurcated digital workspace, with different apps for each task that HR handles. OneModel is a talent analytics accelerator that helps HR departments handle employees, career pathing, recruiting, succession, exits, engagement, surveys, HR effectiveness, payroll, planning, and other HR features all in one place and in a uniform way. The company’s core goal is to equip HR pros with machine learning smarts.
46. CopyAI
A fairly new startup in the AI copywriting space, Copy.ai uses basic inputs from users to generate marketing copy in seconds. It can create copy for a variety of different formats, including article outlines, meta descriptions, digital ads, social media content, and sales copy. Copy.ai has raised $2.9 million in funding from Craft Ventures and several other smaller investors. With its use of the GPT-3 language model to generate words, Copy.ai is a content-driven AI tool to keep an eye on.
47. C3.Ai
Focusing on enterprise AI, C3.ai offers a wide array of pre-built applications, along with a PaaS solution, to enable the development of enterprise-level AI, IoT applications, and analytics software. These AI-fueled applications serve a wide array of sectors and industry verticals, from supply chains to health care to anti-fraud efforts. The goal is to speed up and optimize the process of digital transformation.
48. Accubits
Accubits, a top-rated AI development company, focuses most of its energy on helping businesses enable AI for new efficiencies in their existing systems. Some of their AI solutions include intelligent chatbots in CRMs and predictive health diagnostics, both of which are designed to mesh with your existing software infrastructure. Accubits works across industries, like consumer technology, automotive, cybersecurity, health care, and fashion.
49. SS&C Blue Prism
SS&C Technologies completed an acquisition of Blue Prism, a leading RPA company. Blue Prism uses AI-fueled automation to do an array of repetitive, manual software tasks, which frees human staff up to focus on more meaningful work. The company’s AI laboratory researches automated document reading and software vision. To further boost its AI functionality, Blue Prism bought Thoughtonomy, which offers AI-based cloud solutions.
50. DocuSign
A well-known technology company in the contract world, DocuSign uses e-signature technology to digitize the contracting process across a multitude of industries. Many users don’t realize some of the AI features that DocuSign powers, such as AI-powered contract and risk analysis that is applied to a contract before you sign. This AI process lends itself to more efficient contract negotiations and/or renegotiations.
51. Tetra Tech
Tetra Tech uses AI to take notes on phone calls, so people working in call centers can focus on discussions with the callers. It uses AI to generate a detailed script of dialogues using its speech recognition technology. Given the large market for call centers, and the need to make them more effective at low cost, this is a big market for AI.
52. Nvidia
Nvidia’s emergence as an AI leader was hardly overnight. It has been promoting its CUDA GPU programming language for nearly two decades. AI developers have come to see the value in the GPU’s massively parallel processing design and embraced Nvidia GPUs for machine learning and artificial intelligence.
53. ViSenze
ViSenze’s artificial intelligence visual recognition technology works by recommending visually similar items to users when shopping online. Its advanced visual search and image recognition solutions help businesses in e-commerce, m-commerce, and online advertising by recommending visually similar items to online shoppers.
54. ServiceNow
Element AI was acquired by ServiceNow. Originally based in Montreal, Element AI provides a platform for companies to build AI-powered solutions, particularly for companies that may not have the in-house talent to do it. Element AI says it supports app-building for predictive modeling, forecasting modeling, conversational AI and NLP, image recognition, and automatic tagging of attributes based on images.
55. Pointr
Pointr is an indoor positioning and navigation company with analytics and messaging features that help people navigate busy locations, like train stations and airport terminals. Its modules include indoor navigation, contextual notifications, location-based analytics, and location tracking. Its Bluetooth beacons use customer phones to help orient them around the building.
56. Directly
Considered one of the best AI-driven customer support tools on the market, Directly counts Microsoft as a customer. It helps its customers by intelligently routing their questions to chatbots to answer their questions personally or to customer support personnel. It prides itself on intelligent automation.
57. Rulai
You have surely encountered the limited conversational style of a chatbot; a few stock phrases delivered in a monotone. Rulai is working to change this using the flexibility and adaptability of AI. The company claims its level 3 AI dialog manager can create “multi-round” conversations without requiring code from customers. Clearly a major growth area.
58. Tamr
In a world run by data, in many cases, someone, or some system, has to prep that data so it’s usable. Data preparation is unglamorous but absolutely essential. Tamr combines machine learning and human tech staff to help customers optimize and integrate the highest value datasets into operations. Referred to as an enterprise-scale data unification company, Tamr enables cloud-native, on-premise, or hybrid scenarios — truly a good fit for today’s data-driven, multicloud world.
59. Aurea Software
Aurea Software acquired Xant and returned the brand to its original and widely recognized name, InsideSales, that same year. InsideSales is a sales acceleration platform with a predictive and prescriptive self-learning engine, assisting in a sale and providing guidance to the salesperson to help close the deal. At its core is machine learning.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Financial AI Companies
AI in finance is being used to help reduce debt, eliminate fraud, and offer higher approval rates. Both banks and consumers can benefit from AI in financial offerings.
Here are seven of the top financial AI companies:
60. HighRadius
Based in Houston, HighRadius is a finance AI platform to help many large companies across the world to transform their organization’s cash, treasury, and records. HighRadius works to deliver measurable business outcomes for working capital optimization, debt reduction, reduce month-long timelines, and improve employee productivity within six months.
61. Signifyd
Based in San Jose, California, Signifyd is an AI financial company with a goal to provide an end-to-end commerce protection platform for their customers that can leverage its commerce network to maximize conversion, eliminate fraud, and avoid consumer abuse.
62. Numberai
San Francisco-based Numerai is a financial AI company that manages an institutional grade global equity strategy for investors. Using machine learning to transform and regulate their global network of data scientists. Numberai created the first encrypted data science tournament for stock market predictions.
63. Cleo
London-based Cleo is a financial AI company that uses an AI assistant to help their customers improve their relationship with money and financial health. Cleo’s AI assistant gives customers deep insights into their money while also helping customers save and budget their finances. Cleo aims to grow and develop with their customers.
64. Fount
Fount, an AI investment company based in Seoul, provides AI asset management services for their customers stretching across over 20 global financial institutions. Fount aims to pursue stable returns for their customers by diversifying investments. Fount provides sensitivity to global trends as well.
65. Upstart
Upstart, based in San Mateo, California, is an AI lending company that partners with banks and credit unions to offer more affordable credit. The banks and credit union customers that work with Upstart are more likely to have higher approval rates and lower loss rates. After being a public company, Upstart plans to leverage domain expertise and change aspects of leading and credit risk evaluation.
66. Brighterion
Once a stand-alone company and now a division of MasterCard, Brighterion offers AI for the financial services industry, specifically designed to block fraud rates. The company’s AI Express is a fast-to-market solution, within six to eight weeks, that is custom designed for customer use cases. Its solution is used by a majority of the 100 largest banks.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Education AI Companies
The education industry is usingAI to help students and teachers alike with tutoring, transcription, personalization, and real-time feedback.
Here are six of the top education AI companies:
67. Riiid
Riiid is a leading AI education platform to empower global education outside of traditional ways of learning. Based in Mountain View, California, Riiid is a tutoring service based on deep-learning algorithms while replacing traditional textbooks and lectures. Riiid can be more affordable than human tutoring, drawing international success.
68. Iris.Ai
Iris.ai helps researchers sort through cross-disciplinary research to find relevant information, and as it is used more often, the tool learns how to return better results. Since its launch, many people have tried the service with some becoming regular users. Its Iris.ai release includes the Focus tool, an intelligent mechanism to refine and collate a reading list of research literature, cutting out a huge amount of manual effort.
69. Rev.Com
In a world with a vast ocean of podcasts and videos to transcribe, Rev uses AI to find its market. An AI-powered, but human-assisted, transcription provider, the company also sells access to developers, so tech-savvy folks can use its speech recognition technology. But the key part here is the combination of humans with AI, which is a sweet spot in the effective use cases for artificial intelligence. With a growing need for accessibility features in audiovisual production especially, expect more AI competitors to take advantage of a similar business model in the future.
70. Clarifai
Clarifai is an image recognition platform that helps users organize, filter, and search their image database. Images and videos are tagged, teaching the technology to find similarities in images. Its AI solution is offered via mobile, on-premises, or API interfaces. Beyond image recognition, Clarifai also offers solutions in computer vision, natural language processing, and automated machine learning.
71. HyperScience
HyperScience is designed to cut down on the tedium of mundane tasks, like filling out forms or data entry of handwritten forms. It also processes the relevant information from forms rather than requiring that a human read through the whole form. It touts itself as intelligent document processing.
72. Narrative Science
Narrative Science, a Salesforce company since its acquisition, creates natural language generation technology to translate data from multiple silos into what it calls stories. AI highlights only the most relevant and interesting information, to turn data into easy-to-understand reports, transform statistics into stories, and convert numbers into knowledge. To be sure, data storytelling is a key trend to watch.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Manufacturing/Engineering AI Companies
AI manufacturing companies are working to revolutionize production methods and equipment to increase output while making factories faster and safer.
Here are eight of the top manufacturing and engineering AI companies:
73. CognitiveScale
CognitiveScale builds customer service AI apps for the health care, insurance, financial services, and digital commerce industries. Its products are built on its Cortex-augmented intelligence platform for companies to design, develop, deliver, and manage an enterprise-grade AI system. It also has an AI marketplace, which is an online AI collaboration system where business experts, researchers, data scientists, and developers can collaborate to solve problems.
74. Lobster Media
AI meets social media. Lobster Media is an AI-powered platform that helps brands, advertisers, and media outlets find and license user-generated social media content. Its process includes scanning major social networks and several cloud storage providers for images and video, using AI-tagging and machine learning algorithms to identify the most relevant content. It then provides those images to clients for a fee.
75. SenseTime
Based in Asia, SenseTime develops facial recognition technology that can be applied to payment and picture analysis. It is used in banks and security systems. Its valuation is impressive, racking several billion dollars in recent years. The company specializes in deep learning, education, and fintech.
76. Bright Machines
Automation in factories has been progressing for years, even decades, but Bright Machines is working to push it a quantum leap forward. Based in San Francisco, the AI company is leveraging advances in robotics like machine learning and facial recognition to create an AI platform for digital manufacturing. Its solutions can accomplish any number of fine-grain tasks that might previously have required the exactitude of a skilled human.
77. Graphcore
Graphcore makes what it calls the Intelligence Processing Unit (IPU), a processor specifically for machine learning used to build high-performance machines. The IPU’s unique architecture allows developers to run current machine learning models orders of magnitude faster and undertake entirely new types of work not possible with current technologies.
78. Deepmind
Acquired by Alphabet, Deepmind is a research firm that focuses on AI research, covering everything from climate change to healthcare and finance. Its goal is to build “safe” AI that evolves in its abilities to solve problems. The company is based in London and recruits heavily from Oxford and Cambridge, which are leading universities in Europe for AI and ML research.
79. Domino Data Lab
Certainly an AI company with a certain buzz about it, Domino is a SaaS solution that helps tech and data professionals program and test AI models. Think of it as a gathering place, an aggregation of sorts, for the AI community. Expect Domino to grow rapidly in the years ahead. Based in San Francisco, the company touts itself as a platform for data science.
80. OpenAI
OpenAI is a nonprofit research firm that operates under an open-source type of model to allow other institutions and researchers to freely collaborate, making its patents and research open to the public. The founders say they are motivated in part by concerns about existential risk from artificial general intelligence. ChatGPT is a recent part of OpenAI that allows users to generate text from poetry to short stories. However, despite OpenAI being nonprofit, ChatGPT is now its own for-profit company.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Energy/Environment AI Companies
AI is being used by companies to better plan real-world projects and efficiently use resources as well as produce both energy and food..
Here are eight of the top energy and environmental-focused AI companies:
81. SenSat
SenSat builds digital copies of physical environments and applies AI modeling to understand the parameters of that environment and provide valuable feedback. For example, it can give spatial and volume statistics about a roadway that is about to undergo repair work. Boosting SenSat’s fortunes, Tencent led a $10 million investment in the company.
82. Blue River Technology
Blue River Technology is a subsidiary of Deere & Co. that combines artificial intelligence and computer vision to build smart farm tech, a growing need given population growth. The company’s See & Spray technology can detect individual plants and apply herbicide to the weeds only. This is designed to reduce the number of chemicals sprayed by up to 90% over traditional methods.
83. Stem
Stem is a veteran energy storage firm that has adopted AI to help automate energy management. It uses its industry-leading AI platform, Athena, to determine when to charge energy storage systems and when to draw on them. Athena focuses on energy forecasting and automated control.
84. Xanadu
Based in Canada, Xanadu is a quantum hardware and technology outfit that is developing a type of quantum computer based on photonic technology. Instead of transmitting energy via electrons, Xanadu’s system employs laser light to move data. That means no more energy-hungry, overheating electric machines, among other advantages.
85. Ambyint
A Canadian-based startup, Ambyint, is working towards reducing cost within the oil exploration market. Amybyint plans to do this by using AI-powered management with the ability to analyze platforms with on-site equipment. Ambyint’s AI management works to deliver real-time control and optimize a company’s production.
86. VIA
VIA, a startup in the United States, uses AI to connect smart meters, drones, and sensors on energy assets that are processed and checked to predict energy demands, grid loads, outages, and how much renewable energy is generated by solar panels and wind turbines. This is done by using machine learning algorithms to process all the information needed.
87. Siemens
Munich-based Siemens focuses on areas like energy, electrification, digitalization, and automation. They also work to develop resource-saving and energy-efficient technologies and are considered a leading provider of devices and systems for medical diagnosis, power generation, and transmission. The Siemens website also refers to “AI at the beer garden.”
88. Zymergen
AI biotech company Zymergen describes itself as a “biofacturer.” One of their offerings is called Hyline, a bio-based polyimide film. Their work includes applications for pharmaceutical companies, agriculture, and industrial uses. The company is based in Emeryville, California.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Robotics AI Companies
Robotics companies are working to integrate AI into machines to support companies and their workers through automation, with everything from manufacturing to customer service.
Here are seven of the top robotics AI companies
89. Bossa Nova Robotics
The robots imagined by 1950s futurists were tin people that could walk and talk. It hasn’t quite turned out that way yet), but Bossa Nova Robotics is using AI to make today’s robots more effective. Indeed, modern robots are rarely shaped like humans; Bossa Nova’s robots resemble tall vacuum cleaners. Ironically, Bossa Nova started as a robotic toy maker but now has full-scale robots in retailers, like Walmart. The robots roll up and down the shelves, spotting inventory problems and allowing cost savings on human employment.
90. CloudMinds
CloudMinds is an AI cloud robotics company. Founded in Irvine, California, CloudMinds uses cloud AI to create humanoid robots that can be helpful to both companies and average households. CloudMinds set a goal that by 2025, they will create affordable robots for customers and reach out internationally to help people and markets everywhere.
91. Vicarious
With backing from some real tech heavyweights — Jeff Bezos, Elon Musk, and Mark Zuckerberg — Vicarious’s goal is nothing less than to develop a robot brain that can think like a human. It hasn’t been particularly forthcoming with details, but its AI robots, geared for industrial automation, are known to learn as they do more tasks.
92. HiSilicon
Running AI is exceptionally data-intensive, the more data the better, and so today’s chipmakers, like Intel and Nvidia, are star players. Add to that list HiSilicon. The company fabricated the first AI chip for mobile units. Impressively, the chip accomplishes tasks like high-speed language translation and facial recognition.
93. UiPath
Arguably the top vendor in the robotic process automation sector, UiPath makes an enterprise software platform that includes tools for robot licensing, provisioning, scheduling, monitoring, and alerting. Its robots do the mundane work of communication between legacy apps, so developers can focus on new AI-oriented apps.
94. Smart Eye
Arguably, the two final frontiers in artificial intelligence are ethics and emotion. Can software decide between right and wrong, in a moral sense? And can software “feel” emotions? Affectiva is dealing with this latter issue by using AI to help systems understand the emotions in a human face and conversation. Affectiva was acquired by Smart Eye, a supplier of driver monitoring systems for automakers.
95. Qualcomm
Driving the AI revolution with the highly capable smartphone chips it makes, Qualcomm leverages a signal processor for image and sound capabilities. Qualcomm acquired NUVIA, a competitive CPU and technology design company, ultimately enhancing CPU opportunities for the future. Given its market size and power, it’s likely that Qualcomm will continue to be a key driver of AI functionality in the all-important consumer device market.
Be part of the most informative LinkedIn group and community for AI: https://www.linkedin.com/groups/12772000/
Entertainment AI Companies
The entertainment industry is using AI to advance augmented reality (AR) experiences and voice-based apps through natural language processing (NLP) as well as to screen social media content.
Here are five of the top entertainment AI companies:
96. Discord
The gaming chat app company Discord acquired Ubiquity6, an augmented reality startup. Ubiquity6 has built a mobile app that enables augmented reality (AR) for several people at once. Users see and interact with objects presented by the fully dimensioned visual world of the Ubiquity app, immersing themselves in a creative or educational environment.
97. Facebook
While Facebook is certainly better known in other areas as one of the largest social media networks in the world, the company is making great strides in its AI capabilities, especially in self-teaching for its news feed algorithms. Most significantly, the Facebook team has started using AI to screen for hate speech, fake news, and potentially illegal actions across posts on the site.
98. Tencent
One of the largest social media companies to come out of China, Tencent has an advanced AI lab that develops tools to process information across its ecosystem, including NLP, news aggregators, and facial recognition. They also have one of China’s top video streaming platforms, Tencent Music. A giant in the field, they fund several AI efforts.
99. SoundHound
SoundHound started as a Shazam-like song recognition app called Midomi, but it has expanded to answering complex voice prompts like Siri. Instead of converting language into text like most virtual assistants, the app’s AI combines voice recognition and language understanding in a single step.
100. AIBrain
AIBrain is an artificial intelligence company that builds AI solutions for smartphones and robotics applications. Its products include AICoRE, the AI agent, iRSP, an intelligent robot software platform, and Futurable, a future simulation AI game where every character is a fully autonomous AI. The focus of their work is to develop artificial intelligence infused with the human skill sets of problem solving, learning, and memory.
Bottom Line: Top AI Companies Continue To Expand AI’s Capabilities
Entire industries are being reshaped by AI. For example, RPA companies are working to completely advance their software with machine learning and AI to improve their automation capabilities. AI in healthcare is changing patient care in major ways, such as using AI to increase the scale and efficiency of medical imaging to analyze and diagnose patients.
Companies in various industries are increasing their interest and investment in AI, hoping to propel internal operations and customer experiences forward by using machine learning and in some cases, deep learning to apply big data to enhance products, create new products, and solve everyday business use cases.
| 2023-02-01T00:00:00 |
https://www.linkedin.com/pulse/100-top-artificial-intelligence-ai-companies-2023-george-oliveira
|
[
{
"date": "2023/02/01",
"position": 2,
"query": "artificial intelligence employers"
}
] |
|
What Business Leaders Need To Know About Artificial Intelligence
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What Business Leaders Need To Know About Artificial Intelligence
|
https://online.isb.edu
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[] |
Most leaders perceive AI as an opportunity, despite its potential disruption. According to research conducted by the business consulting firm Kearney, over 90% ...
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Data-Driven Decision-Making (DDDM) refers to the practice of using facts, indicators, and data to guide strategic decisions that align with business objectives. Data has become the cornerstone of the modern technological revolution. Recent studies indicate that approximately 402.74 million terabytes of data is generated daily. To put this into perspective, in 2010-coinciding with the rise of social media, mobile devices, and increased internet usage-around two zettabytes of data was created. By 2025, global data generation is projected to reach nearly 200 zettabytes, representing a hundred-fold increase since 2010. Notably, 90% of the world’s data has been produced within the past few years, with AI playing a crucial role in this surge.
With such abundance and fast exponential increase of data in the world, leaders have the potential to make more informed decisions. However, this is only achievable if they are proficient in utilising the necessary tools and technologies to collect, clean, and analyse data effectively. Through DDDM, leaders can improve decision quality, enhance agility and adaptability, and ensure greater accountability. Many top firms have embedded data-driven strategies into their business models. For instance, Amazon's customer-centric approach relies on the collection and analysis of customer feedback, reviews, and behavioural data to enhance user experience. Similarly, Netflix leverages AI-driven data analytics to assess viewing habits, preferences, and ratings, thereby curating personalised content recommendations. Leaders at these organisations capitalise on AI’s analytical capabilities to drive strategic business decisions.
| 2025-05-14T00:00:00 |
2025/05/14
|
https://online.isb.edu/perspectives/article/what-business-leaders-need-to-know-about-artificial-intelligence
|
[
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Seven Skills Business Leaders Need in the New Era of Artificial ...
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Seven Skills Business Leaders Need in the New Era of Artificial Intelligence
|
https://www.waldenu.edu
|
[] |
Artificial intelligence (AI) is quickly transforming the way we live and work. A recent Goldman Sachs report found that 300 million jobs worldwide stand to be ...
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Walden University is fortunate to have insightful faculty members like Dr. Steven Verrone. Dr. Verrone teaches MBA capstone courses as well as MBA courses in marketing and innovation. In this article, he shares his thoughts about the critical skills business leaders need for success in the age of artificial intelligence.
Artificial intelligence (AI) is quickly transforming the way we live and work. A recent Goldman Sachs report found that 300 million jobs worldwide stand to be impacted by artificial intelligence and automation, such as office and administrative support roles.1 As AI becomes more integrated into our daily lives, leaders must determine how to navigate this new landscape.
Artificial intelligence is already revolutionizing a wide range of industries. The demand for Master of Business Administration (MBA) graduates with well-defined skills will only grow as businesses embrace AI technologies. As the use of artificial intelligence becomes more widespread, businesses are seeking professionals who can combine the skills of an MBA with the knowledge of AI.
These are the seven skills needed for success in the new era of artificial intelligence:
The ability to clearly communicate the vision. One of the critical skills leaders need in an integrated AI world is the ability to communicate their vision transparently. MBA graduates who can clearly and concisely explain complex AI systems and the results they produce to non-technical stakeholders are highly valued. Collaboration and teamwork. In order to develop and manage AI systems, teamwork is essential. Working in teams and collaborating with others are always key business leadership skills, but they are even more critical in the era of artificial intelligence. Strategic thinking. In an AI-driven world, where business models and strategies constantly evolve, individuals who can think strategically and adapt to new challenges are highly valued. Identifying long-term business goals, assessing risks, and developing plans to achieve those goals are all components of strategic thinking. Adaptability. As artificial intelligence and its uses rapidly evolve, adaptability is a critical skill for anyone who wants to thrive in a business career. Leaders must demonstrate adaptability by always being ready to innovate and to quickly respond to opportunities and challenges.2 Being comfortable with technology. No longer can technology solely be the realm of engineers. Today’s business leaders need to be hands-on with the technology that runs their businesses. And that technology will constantly change, so leaders need to be technology users who understand how people interact with that technology. Understanding people. Even more important than technical skills are people skills. Successful MBA graduates will understand people’s behaviors, motivations, and perceptual and cognitive capabilities and limitations. Those skills will help managers lead a team of people as well as expand the abilities and applications of AI. In fact, psychologists who study intelligence in humans are now helping unlock ways to enhance intelligence in machines.3 Trust-building. When integrating artificial intelligence into a business, a leader must explain the goal, the changes needed, how it will be rolled out, and over what timeline, in order to build trust in AI. These human skills cannot be “botsourced.” That means that leaders must develop the ability to create a human-centric organization with superhuman intelligence.4 To do so, they must use all of the skills already highlighted in order to build trust with their employees, their customers, and their stakeholders.
Artificial intelligence is changing our world quickly. Business leaders must be able to communicate transparently, collaborate, think strategically, be adaptable, understand technology, understand human behavior, and use all of those skills to build trust. By developing these skills, leaders can successfully navigate the integrated AI world and lead their organizations to success.
If you envision a career in business leadership, prepare yourself for a successful future with a Master of Business Administration (MBA) degree from Walden University. You can feel confident that when you choose Walden, you’re choosing a top MBA online program and a university committed to student success. Walden’s MBA program is accredited by the Accreditation Council for Business Schools and Programs (ACBSP). In 2019, Walden’s College of Management and Technology and School of Management received the first-ever ACBSP Accreditation Plus Award, which recognizes high standards in business education.
An online master’s degree in business from Walden can support you in becoming a capable, confident leader. An MBA is a highly recognized master’s degree that can help you develop the skills that are critical for leaders. Walden’s curriculum includes courses in fostering innovation, promoting collaboration, and communicating as a leader. Embrace technology and earn an MBA online from Walden.
Dr. Steven Verrone is a core faculty member in Walden University’s College of Management and Human Potential Master of Business Administration (MBA) program. Dr. Verrone’s background is in marketing management, strategy building, leadership, and higher education. At Walden, he teaches MBA marketing, innovation, and capstone courses. He is a lifelong learner and believes education is the key to success.
Walden University is an accredited institution offering a Master of Business Administration degree program online. Expand your career options and earn your degree in a convenient, flexible format that fits your busy life.
Walden University is accredited by The Higher Learning Commission, www.hlcommission.org.
1Source: www.forbes.com/sites/jackkelly/2023/03/31/goldman-sachs-predicts-300-million-jobs-will-be-lost-or-degraded-by-artificial-intelligence
2Source: hbr.org/2018/01/as-ai-makes-more-decisions-the-nature-of-leadership-will-change
3Source: www.apa.org/monitor/2021/11/cover-artificial-intelligence
4Source: hbr.org/2020/08/the-secret-to-ai-is-people
| 2023-02-01T00:00:00 |
https://www.waldenu.edu/news-and-events/seven-skills-business-leaders-need-in-the-new-era-of-artificial-intelligence
|
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"date": "2025/02/01",
"position": 42,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/06/01",
"position": 43,
"query": "artificial intelligence business leaders"
}
] |
|
Deeply Responsible Leadership: The Business of AI
|
Deeply Responsible Leadership: The Business of AI
|
https://business.cornell.edu
|
[
"Cornell Sc Johnson"
] |
In this panel, industry leaders explore the intersection of deeply responsible leadership and artificial intelligence (AI) in driving business transformation.
|
“Deeply Responsible Leadership” is a modern-day realignment within our corporate ethos, urging business leaders to think beyond the sole pursuit of profit and instead towards a more holistic consideration of societal and environmental impacts. The 2024 Lifelong Learning Experience, created for alumni, will address the profound impact of artificial intelligence (AI) on society. This year will emphasize the importance of understanding AI technologies and their opportunities, offering insights into the future of AI, dealing with ethical issues, and more while responsibly navigating these complexities. Companies leveraging AI face ethical dilemmas surrounding data privacy, algorithmic bias, and the possible societal ramifications of AI-driven decision-making. Ensuring AI technologies are developed and deployed responsibly is essential. Innovation in AI demands ethical foresight, balancing progress with societal well-being.
The programming includes curriculum-based content presented by our faculty as well as conversations about challenges and opportunities in this dynamic space.
The lifelong learning experience is supported by the Cornell SC Johnson College of Business Office of Alumni Affairs and Development and the Innovation, Entrepreneurship, and Technology (IET) theme, in partnership with eCornell.
| 2023-02-01T00:00:00 |
https://business.cornell.edu/alumni/lifelong-learning/deeply-responsible-leadership-the-business-of-ai/
|
[
{
"date": "2023/02/01",
"position": 48,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/05/01",
"position": 50,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/06/01",
"position": 47,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/11/01",
"position": 50,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/01/01",
"position": 48,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/02/01",
"position": 48,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/05/01",
"position": 48,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/07/01",
"position": 47,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/09/01",
"position": 48,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/10/01",
"position": 48,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/11/01",
"position": 48,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/01/01",
"position": 48,
"query": "artificial intelligence business leaders"
}
] |
|
What business leaders need to know about AI | Epicor ANZ
|
What business leaders need to know about AI
|
https://www.epicor.com
|
[] |
As AI becomes more established, business executives need to keep pace by understanding its implications and potential benefits.
|
What business leaders need to know about AI
Artificial Intelligence (AI) is fast becoming a critical component of business operations for many industries. As AI becomes more established, business executives need to keep pace by understanding its implications and potential benefits.
In this ebook, Mark Jensen, Director, Product Marketing at Epicor, looks at:
The potential value of AI to your business
What to consider when implementing AI
AI terms
Executing AI for success
With the right measures in place, you can help ensure a successful implementation of this revolutionary technology.
| 2023-02-01T00:00:00 |
https://www.epicor.com/en-au/resources/library/manufacturing/ebook-what-managers-need-to-know-about-ai/
|
[
{
"date": "2023/02/01",
"position": 70,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/05/01",
"position": 69,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/06/01",
"position": 69,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/11/01",
"position": 68,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/12/01",
"position": 68,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/01/01",
"position": 67,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/02/01",
"position": 64,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/05/01",
"position": 73,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/07/01",
"position": 73,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/09/01",
"position": 73,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/10/01",
"position": 73,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/11/01",
"position": 73,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/01/01",
"position": 73,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/02/01",
"position": 63,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/06/01",
"position": 64,
"query": "artificial intelligence business leaders"
}
] |
|
"AI Fundamentals for Business Leaders" by Eya Mahouachi
|
Book Review: AI [Artificial Intelligence] Fundamentals for Business Leaders
|
https://scholar.valpo.edu
|
[
"Eya Mahouachi",
"Mahouachi"
] |
This book is organized around understanding AI, Data and Data Management, Machine Learning, Deep Learning, Model Selection and Evaluation, and Generative AI.
|
Abstract
Artificial Intelligence Fundamentals for Business Leaders: Up to Date with Generative AI (Byte-sized Learning Book 1) is a recent book in a series of four on Artificial Intelligence. It is authored by I. Almeida, the Chief Transformation Officer at Now Next Later AI, an AI advisory, training, and publishing business that supports organizations with their AI strategy, transformation, and governance.
This book is organized around understanding AI, Data and Data Management, Machine Learning, Deep Learning, Model Selection and Evaluation, and Generative AI. The last chapter incorporates an assignment for leaders. This book employs an effective approach to help readers grasp the fundamentals of Artificial Intelligence and Generative AI. The book also guides readers on how to apply these concepts practically in real-world situations for optimal results. Although it doesn't explore a global perspective on AI adoption or the effects of various leadership styles and organizational cultures on Generative AI, it provides detailed explanations along with practical guidance for general readers and leaders in particular.
| 2023-02-01T00:00:00 |
https://scholar.valpo.edu/jvbl/vol18/iss1/20/
|
[
{
"date": "2023/02/01",
"position": 71,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/05/01",
"position": 82,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/06/01",
"position": 70,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/11/01",
"position": 87,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/12/01",
"position": 70,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/01/01",
"position": 69,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/02/01",
"position": 80,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/05/01",
"position": 72,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/07/01",
"position": 72,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/09/01",
"position": 72,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/10/01",
"position": 72,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/11/01",
"position": 72,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/01/01",
"position": 72,
"query": "artificial intelligence business leaders"
}
] |
|
AI Strategy for Business Leaders: From Hype to Impact
|
AI Strategy for Business Leaders: From Hype to Impact
|
https://pdpreg.dce.harvard.edu
|
[
"Modern Campus - Https"
] |
Leverage new technologies to build value for your organization. Topics Covered Converging technologies and the rise of the fourth industrial revolution ...
|
Converging technologies and the rise of the fourth industrial revolution
Artificial intelligence and artificial general purpose intelligence
Emerging technologies
Big data vs. scattered data
Deterministic technologies vs. probabilistic technologies
Machine learning (supervised/unsupervised)
Deep learning
Natural language processing
The magna carta for the global AI economy
Leverage new technologies to build value for your organization.Want to reread the full program description? Visit our website. Ready to register? Click the "Add to Cart" button below!
| 2023-02-01T00:00:00 |
https://pdpreg.dce.harvard.edu/index.cfm?method=ClassInfo.ClassInformation&int_class_id=856
|
[
{
"date": "2023/02/01",
"position": 73,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/05/01",
"position": 73,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/06/01",
"position": 63,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/11/01",
"position": 75,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/12/01",
"position": 71,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/01/01",
"position": 70,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/02/01",
"position": 69,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/05/01",
"position": 75,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/07/01",
"position": 75,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/09/01",
"position": 75,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/10/01",
"position": 75,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/11/01",
"position": 75,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/01/01",
"position": 75,
"query": "artificial intelligence business leaders"
}
] |
|
AI and Leadership: How Artificial Intelligence changes Leadership
|
AI and Leadership: How Artificial Intelligence changes Leadership
|
https://triangility.com
|
[
"Robert Schaffner",
"Robert Is An Entrepreneur With Over Years Of Experience In International Training",
"Consulting Programmes. He Works As An International Futurist",
"Keynote Speaker",
"Moderator. Robert Is Passionate About Technology",
"Its Possibilities",
"He Inspires Many Leaders With His Enthusiasm. His Motto",
"Lifelong Learning",
"Creativity",
"Constant New Challenges"
] |
While AI can assist in data evaluation and trend analysis, strategic vision and the ability to set and communicate long-term goals remains a central role for ...
|
Leadership in change
The concept of leadership has changed completely in recent years. Away from the authoritarian boss, towards the coach. The goal: to support employees in their personal development and to tap their full potential.
Creating a shared culture, independent, location-independent working: This is what employees want from their bosses today. But it’s not just the employees who are influencing the leadership role – technology is also having an impact. First and foremost: artificial intelligence.
How AI influences the leadership role
The integration of AI technologies into everyday management opens up a broad spectrum of new possibilities and at the same time puts proven concepts to the test. This is because artificial intelligence is encroaching on the managers’ area of responsibility in many areas.
Artificial intelligence as an assistant
AI is your tireless assistant. It analyses data in real time and provides recommendations for action. This allows you to make better-informed decisions and react faster to changes.
Relief for routine tasks
Routine tasks eat up valuable time, but are often an important part of everyday work. AI can help – and take over unpleasant tasks. Managers can thus concentrate more on strategic planning, innovative idea development and, above all, on the individual support of their employees.
Broadening the spectrum of knowledge
Humans make mistakes – and that is unavoidable. AI is not perfect either, but its ability to analyse and recognise patterns is superior to ours many times over. The insights gained from this then support managers in decision-making. Nevertheless, team leaders must also learn to critically question AI. The balance between technological precision and human intuition is crucial.
Improved human resources development
Employees’ demands have changed – individuality and further development are high on the list of requirements. AI can help identify individual employee strengths and development areas more precisely. As a result, customised training and development plans can be created that specifically promote professional development. What is important, however, is that the human being must remain at the centre. Artificial intelligence can support, but interpersonal interaction, empathy and emotional intelligence remain essential elements of successful leadership.
Ethics and society
AI in the boardroom is revolutionising corporate management. But it also raises new questions. Questions about data security, privacy and fair use of AI systems. Tomorrow’s leadership must ensure responsible use of AI – and ensure that technological advances are in line with corporate values and the needs of employees.
Opportunities through the integration of AI
The integration of AI into leadership practice opens up a multitude of opportunities that have the potential to take leadership effectiveness and efficiency to a new level.
Data analysis
AI systems are able to process huge amounts of data in real time, identifying patterns and trends that often remain hidden to human eyes.
This enables more informed and data-driven decision-making – leaders are better informed and can respond more quickly to changing market conditions.
Automation
Repetitive tasks are time-consuming and annoying. AI can take these tasks away from you – allowing you to focus more on strategic planning, creative thinking and interpersonal relationships. This allows you to focus on your core responsibilities: supporting, motivating and developing your team.
Human resources development
The shortage of staff is a burden on companies. One more reason to rely on AI for personnel development. It recognises employees’ individual strengths and weaknesses and thus makes targeted further training and career planning possible. One example: AI-supported learning platforms can create personalised training programmes tailored to the individual needs of each employee. This not only improves employees’ skills and performance, but also promotes the success of the entire company.
Challenges and risks
While the integration of AI undoubtedly brings many opportunities for leadership practice, leaders must also keep the challenges in mind.
Integration and transparency
All employees involved must be actively involved in the design and use of AI systems, and the responsibilities between AI and management must be clearly defined. It must be clearly recognisable for your employees how decisions are made – and where the boundaries of responsibility run.
Bias
Another risk is that AI systems could have unconscious biases due to algorithms and data sources. For example, when the AI analyses applications for a certain position, it takes successful employees as references. If more men are currently employed in the company, the AI could prefer men based on its references. Managers must therefore ensure that the AI systems used work transparently, ethically and responsibly.
Fears and worries
Not everyone likes artificial intelligence. The fear of job losses or of machines taking over tasks is not unjustified – and must be taken seriously by managers. Managers face the challenge of addressing these fears, communicating transparently and involving employees in the change process.
Further education
In order to be able to use AI safely and reliably, training and further education are indispensable. This is the only way to ensure that the system actually enriches your company – and does not hinder it.
Data protection
AI needs sensitive, personal data from your employees to function properly. Many people fear data protection violations here – and not without good reason. It is essential to comply with the various national and European data protection directives such as the GDPR.
AI and leadership: What skills are in demand?
The integration of artificial intelligence is changing the weighting of the skills in demand. Because while technological know-how is certainly an advantage, some of the traditional leadership skills remain indispensable.
Critical analysis
AI can do a lot, but it also makes mistakes. Managers must therefore be able to question and validate the data and insights provided by AI systems. A healthy scepticism towards automated recommendations and the ability to bring in human instinct and experience are essential.
Emotional intelligence
In the age of digitalisation, emotional intelligence is more important than ever. The risk: Human relationships take a back seat through the use of AI. Therefore, it is of great importance to maintain empathy, empathy and the ability to communicate effectively. Managers must be able to understand and respond to the needs, concerns and expectations of their employees.
Strategic planning
While AI can assist in data evaluation and trend analysis, strategic vision and the ability to set and communicate long-term goals remains a central role for leaders. They must be able to integrate the use of AI into corporate strategy and ensure that technological developments are in line with long-term goals.
Flexibility
Flexibility and agility are important key competencies. In the face of ever-changing technologies and ways of working, leaders must be willing to adapt and upskill. A willingness to continuously evolve and an openness to innovation are essential to keep pace.
Ethical responsibility
AI can support, but not guide. It is therefore imperative that leaders are able to identify and address ethical issues related to the use of AI. This includes ensuring data protection, avoiding discrimination by algorithms and communicating transparently about the use of AI technologies.
| 2023-10-09T00:00:00 |
2023/10/09
|
https://triangility.com/ai-and-leadership-how-artificial-intelligence-is-changing-the-leadership-role/
|
[
{
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{
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{
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{
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{
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},
{
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{
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},
{
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},
{
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},
{
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{
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},
{
"date": "2024/11/01",
"position": 71,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/01/01",
"position": 71,
"query": "artificial intelligence business leaders"
},
{
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"position": 68,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/06/01",
"position": 71,
"query": "artificial intelligence business leaders"
}
] |
Taking the Lead with Artificial Intelligence (AI)
|
Taking the Lead with Artificial Intelligence (AI)
|
https://cmcoutperform.com
|
[] |
Develop and lead an effective AI strategy to avoid risks and seize opportunities. Leaders in today's organizations must have a clear and realistic ...
|
Develop and lead an effective AI strategy to avoid risks and seize opportunities.
Leaders in today’s organizations must have a clear and realistic understanding of the pitfalls, risks and rewards that come with the integration of AI in their organization. It must go beyond a general awareness of AI to a more strategic perspective of the steps your business needs to take to prevent AI from having a negative impact on your competitive edge—and to make sure you instead get the maximum benefit from it. In this course, you will learn how organizations are applying AI to existing and new business models and explore ways they are successfully building, scaling and refining what they do in the context of AI. You’ll also discover tools for accelerating your organization’s digital strategy to improve enterprise outcomes and weigh appropriate strategies for your business while you factor in potential benefits and constraints. In addition, you’ll create a specific implementation road map for your best possible AI solution.
| 2023-02-01T00:00:00 |
https://cmcoutperform.com/Artificial-Intelligence-Strategy-Business-Leaders
|
[
{
"date": "2023/02/01",
"position": 80,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/04/01",
"position": 89,
"query": "AI business leaders"
},
{
"date": "2023/05/01",
"position": 81,
"query": "AI business leaders"
},
{
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"query": "artificial intelligence business leaders"
},
{
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"query": "artificial intelligence business leaders"
},
{
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},
{
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},
{
"date": "2023/11/01",
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"query": "artificial intelligence business leaders"
},
{
"date": "2023/12/01",
"position": 78,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/01/01",
"position": 81,
"query": "AI business leaders"
},
{
"date": "2024/01/01",
"position": 75,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/02/01",
"position": 71,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/05/01",
"position": 79,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/07/01",
"position": 81,
"query": "AI business leaders"
},
{
"date": "2024/07/01",
"position": 80,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/09/01",
"position": 79,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/10/01",
"position": 94,
"query": "AI business leaders"
},
{
"date": "2024/10/01",
"position": 79,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/11/01",
"position": 79,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/12/01",
"position": 81,
"query": "AI business leaders"
},
{
"date": "2025/01/01",
"position": 80,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/02/01",
"position": 97,
"query": "AI business leaders"
},
{
"date": "2025/02/01",
"position": 78,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/04/13",
"position": 29,
"query": "AI business leaders"
},
{
"date": "2025/04/13",
"position": 21,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/06/01",
"position": 90,
"query": "AI business leaders"
},
{
"date": "2025/06/01",
"position": 79,
"query": "artificial intelligence business leaders"
}
] |
|
AI for Leaders Certificate Program - UChicago Professional Education
|
AI for Leaders Certificate Program
|
https://professional.uchicago.edu
|
[] |
In today's dynamic business landscape, leaders equipped with AI knowledge drive innovation, optimize decision-making, and maintain a competitive edge.
|
This transformative learning experience comprises foundational courses, live online sessions, and a series of AI leadership presentations, providing a holistic approach to leadership in the AI domain.
After completing the certificate program, you will be able to:
Demonstrate a comprehensive understanding of how AI aligns with business strategy, enabling you to make informed decisions and drive organizational success.
Implement AI strategies practically, ensuring you are ready to drive real-world impact.
Address biases, promote transparency, and lead responsibly to ensure that you not only harness the power of AI but also contribute to ethical and responsible implementation.
Earn a certificate of completion from the University of Chicago and become part of the UChicago network.
| 2023-02-01T00:00:00 |
https://professional.uchicago.edu/find-your-fit/certificates/ai-leaders-certificate-program?language_content_entity=en
|
[
{
"date": "2023/02/01",
"position": 89,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/06/01",
"position": 90,
"query": "AI business leaders"
},
{
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"position": 86,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/12/01",
"position": 87,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/05/01",
"position": 90,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/07/01",
"position": 89,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/09/01",
"position": 90,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/10/01",
"position": 90,
"query": "artificial intelligence business leaders"
},
{
"date": "2024/11/01",
"position": 90,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/01/01",
"position": 89,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/02/01",
"position": 72,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/06/01",
"position": 73,
"query": "artificial intelligence business leaders"
}
] |
|
The Effectiveness of Applying Artificial Intelligence in ...
|
The Effectiveness of Applying Artificial Intelligence in Recruitment in Private Sectors
|
https://link.springer.com
|
[
"Almajthoob",
"Abdulla Mohamed Husain",
"College Of Business",
"Hamdan",
"Ahlia University",
"Hakami",
"University Of Business",
"Finance",
"Manama",
"Abdulla Mohamed Husain Almajthoob"
] |
by AMH Almajthoob · 2022 · Cited by 3 — The trend in recruitment is moving forward applying artificial intelligence. This paper focus on impact of Artificial intelligence (AI) on private sectors.
|
Companies seek for improving quality in order to survive in the competitive market. To survive, you must search for the right candidates and hire them in the best-fit position at the right time. Due to the rapid development in the world, several methods (traditional methods) are no longer efficient. People and companies are looking forward deploying more efficient methods in recruiting candidates. Currently, the trend in recruitment is moving forward applying artificial intelligence. This paper focus on impact of Artificial intelligence (AI) on private sectors. Since this industry is introduced recently, various companies are interested in applying AI. But the dilemma is that the advantages and disadvantages of applying AI in HR is still not commonly known for all of them. Also, the long-term effects for this industry still not revealed.
| 2023-07-14T00:00:00 |
2023/07/14
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https://link.springer.com/chapter/10.1007/978-3-031-26953-0_58
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[
{
"date": "2023/02/01",
"position": 12,
"query": "artificial intelligence hiring"
},
{
"date": "2023/03/01",
"position": 87,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/01",
"position": 33,
"query": "artificial intelligence hiring"
}
] |
Recruitment Analytics: Hiring in the Era of Artificial ...
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Recruitment Analytics: Hiring in the Era of Artificial Intelligence
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https://www.emerald.com
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by VR Uma · 2023 · Cited by 22 — AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidate's perception of ...
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Introduction: Traditional recruitment system relied heavily on the applicants’ curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be ‘misfits’. CVs were the only source of candidates’ data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process.
Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages.
Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes.
Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles.
Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidate’s perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees’ queries. Resume screening techniques can save the recruiter’s time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team.
| 2023-02-01T00:00:00 |
https://www.emerald.com/insight/content/doi/10.1108/978-1-80382-027-920231008/full/html
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[
{
"date": "2023/02/01",
"position": 23,
"query": "artificial intelligence hiring"
}
] |
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Will agency recruiting become a thing of the past with ...
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The heart of the internet
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https://www.reddit.com
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AI would have to get to the point where it could make phone calls, conduct interviews, handle objections, funnel information between the candidate and client, ...
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Welcome, Recruiting & Talent Acquisition Professionals! Industry discussion space - Recruiters only. No candidates, ads, or research. If you are a candidate/job seeker and have a question for recruiters, please post in our weekly "Ask Recruiters" Megathread
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| 2023-02-01T00:00:00 |
https://www.reddit.com/r/recruiting/comments/10qlyiu/will_agency_recruiting_become_a_thing_of_the_past/
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[
{
"date": "2023/02/01",
"position": 31,
"query": "artificial intelligence hiring"
}
] |
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Artificial Intelligence for Recruitment and Selection
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Artificial Intelligence for Recruitment and Selection
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https://www.emerald.com
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by A Gupta · 2023 · Cited by 21 — Introduction: Artificial intelligence (AI) assists recruiters in effectively and efficiently nominating applicants precisely and accurately.
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Introduction: Artificial intelligence (AI) assists recruiters in effectively and efficiently nominating applicants precisely and accurately. It helps in the screening of resumes without biasness. This chapter will identify different AI technology and various organisations using it fully or partially.
Purpose: This chapter aims to get insights about various AI tools that assist human recruiters, save time and cost, and provide modern experiences. It will help identify various applications that are currently in use and their features. It also helps in finding out the benefits and the challenges faced by the recruiters and the applicants while assimilating those applications in hiring.
Need for the Study: The study will be helpful to all those recruiting firms who are presently using AI or not using it to understand the benefits and challenges they might face.
Methodology: The chapter will be based on reviews and industry reports. This chapter will include a study related to human resource (HR) functions where AI is used. To give more insights into AI technology, this study mentions various applications like Mya, Brazen, etc., and their usefulness in recruitment. Also, special emphasis would be given to the recruitment functions as most companies use AI. Some companies like Deloitte and Oracle are using AI fully or partially will also be incorporated.
Findings: The study finds out that although many companies have started to use AI tools for recruitment, they have not explored all the algorithms that can be used to complete the whole recruitment and selection process. Companies like Loreal use AI for candidate applications and recruiter screening, but human recruiters stand strong for assessments and interviews. AI’s widespread use presents human resource management (HRM) practitioners with both opportunities and challenges.
Practical implications: The basic idea of the study is to scrutinise the related literature and find out the features, advantages and limitations/challenges of using AI which would be helpful for recruiters in better understanding of the technology-driven recruitment.
| 2023-02-01T00:00:00 |
https://www.emerald.com/insight/content/doi/10.1108/978-1-80455-662-720230001/full/html
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[
{
"date": "2023/02/01",
"position": 45,
"query": "artificial intelligence hiring"
}
] |
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The Ultimate Guide to what Talent Leaders need to know ...
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The Ultimate Guide to what Talent Leaders need to know about AI in Recruiting
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https://getcovey.com
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As technology advances, AI-powered tools and systems are becoming more sophisticated, enabling recruiters and organizations to streamline the hiring process, ...
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Heads of Talent are using Al to drive efficiency within the recruiting org. Here is a guide to evaluating what's out there.
As a Talent Leader, your team will look to you to explain the risks and benefits of using generative AI in recruiting.
AI is everywhere these days. Recent advances in AI technologies have led to some of the first publicly accessible “generative AI” tools, like Chat GPT, Bing Chat, and Google Bard. So it makes sense that AI (and the powerful things it can do) is top of mind for many people.
With a few clicks, people can chat with these programs and begin seeing some of their huge potential. Many Talent Leaders are figuring out how these generative AI tools can help them at work.
As a Talent Leader, your team will look to you to explain the risks and benefits of using generative AI in recruiting. We made this guide so you can be the expert at leadership meetings, provide guidance to others, and do a great job evaluating AI automation tools for your team.
Let’s look at how AI could impact the recruiting industry in the short and long term.
What really is AI?
AI, or artificial intelligence, is the ability of machines to do computing so complex that it seems like it’s in the realm of human intelligence. AI has been developed since the birth of computing. But recently, it has seen a boom in public awareness and popularity with advancements in computing power and, thus, greater availability to all.
There are many types of AI, not just the generative AI that has been making headlines recently. Machine learning, neural networks, vector databases, and more are all considered types of AI.
AI programs can process vast amounts of data and make predictions, classifications, and decisions without explicit programming. These capabilities have been integrated into many existing computing platforms and systems, enabling them to perform tasks that were previously very challenging, if not impossible, using traditional computing methods.
For instance, Facebook uses machine learning to serve you content you may be interested in based on your browsing history.
AI, Generative AI, LLMs - Looking past the hype
Generative AI is a set of AI tools that can generate new content, be it essays, art, poetry, analysis, or more.
Generative AI tools use Large Language Models (LLMs) as the foundation for what they create. So, having a very large LLM matters for the quality and accuracy of what a generative AI tool will create.
A large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other forms of content based on knowledge gained from massive datasets
Large language models are among the most successful applications of transformer models. They aren’t just for teaching AI human languages, but for understanding proteins, writing software code, and much, much more.
Enterprise-grade foundational models are what power Covey’s generative AI application layer.
What’s often misunderstood about generative AI?
One big area of misunderstanding is that generative AI = ChatGPT. Generative AI is more than ChatGPT.
ChatGPT is a program that uses generative AI, but it’s not the only one.
Generative AI is a broader concept with a wide variety of potential applications.
Some current offerings that use generative AI include Google Bard, DALL-E, and DeepMind.
Another area of misunderstanding is that generative AI is its computing platform. AI is not an independent computing platform; rather, it’s a powerful set of tools and techniques to be applied across various platforms.
Pre-generative AI recruiting tools: What solutions already exist?
AI-based solutions are not brand new to the world of recruiting. Before Chat GPT and other LLMs, companies incorporated insights and tools from machine learning and AI models. AI solutions in recruiting were typically focused on completing specific tasks within the recruitment process.
These are some of the specific tasks that AI has been used for in recruiting that you’d have encountered:
Resume Screening Resume screening software helps automate the initial screening process by analyzing resumes through keywords that identify qualified candidates based on education, skills, experience, and the industry or position.
Applicant Tracking Systems (ATS) ATS with AI capabilities streamline the candidate management process, tracking candidates' progress and providing analytics that recruiting teams rely upon to influence critical decisions
Candidate Sourcing AI-driven tools scour various job boards, databases, and professional networks for the best candidates, typically using keyword matching.
Scheduling interviews AI solutions facilitate interview scheduling by finding suitable time slots based on the availability of both job seekers and interviewers.
Video Interviewing and Analysis Recruiters use AI tools to analyze video interviews, assess candidates' responses and non-verbal cues to surface insights
Candidate Engagement Automated emails and other communications are used to engage with job applicants, answer their questions, and keep them informed about the hiring process.
Predictive Analytics AI solutions can utilize historical data to predict candidate success and identify the best candidates for specific roles.
Diversity and Inclusion AI-driven tools help address bias in job descriptions and candidate evaluation, promoting diversity and inclusion in the hiring process.
AI has improved recruiting efficiency, reduced time-to-hire, enhanced candidate quality, and provided a better candidate experience.
However, these tools do have some fundamental limitations and in the next section we’ll explain why.
Limitations of pre-generative AI recruiting tools
Pre-generative AI recruiting tools typically rely upon predictive analytics and machine learning. That means they can learn and make predictions based on what happened, and the data fed to it.
For example, LinkedIn uses AI to analyze your profile and search history to provide personalized job posting recommendations matching your experience and interests.
Many sourcing platforms use keywords to search for software engineers on their database.
Such systems get smarter as they access more data.
However, if you want a truly powerful model to identify a software engineer for instance, you would want to look at all the engineers in the world, and that’s what cutting edge post generative AI recruiting tools can do.
Post generative AI tools can amass billions of profiles, integrate hundreds of other data sources, and provide a far more robust and reliable prediction and matches than before.
Let’s dive into some of the limitations of pre-generative AI and how generative AI could address them.
Limitations:
Pre-generative AI recruiting tools aren’t great at predictions. These tools can only make inferences and “learn” based on prior activity and are very limited by the amount of data they can access. Thus, they can only do more simple tasks, like finding a candidate with a specific skill.
How generative AI addresses these limitations:
Generative AI tools use deep neural networks to provide better predictions and generate content. These tools can analyze vast amounts of data to give recruiters better insights, better candidates and better content.
Generative AI in recruiting: What can it do for you?
Chat GPT and other similar AI tools that use large language models represent a significant advancement in AI technology. These tools can generate content, allowing people to engage in “conversation” with the tools and ask them to produce essays, poems, summaries, and more.
Society is just beginning to figure out the many applications of generative AI. In recruiting specifically, there are already some great ways to have generative AI tools help with work tasks, particularly in creating compelling written content and intelligently sourcing great candidates.
The huge potential of generative AI in sourcing
Current sourcing technology typically looks at where you worked, past job titles, and other readily available data in an online profile. That sounds like it would be all you need to source great candidates, right?
But, as experienced recruiters know, these systems can have limitations. They only look for specific keywords that cause qualified candidates to slip through the cracks because these candidates need to fit the exact parameters being used. Or they may miss synonyms or similar job titles, leading you to lose out on finding qualified candidates.
But new AI technologies have the potential to improve that process greatly. With LLMs, great AI recruiting software can pick out the skills needed for a specific role based on data from companies. Then, they can search for candidates with the right skills and qualifications, all without a recruiter having to decide on keywords or manually search themselves.
How Rippling uses Covey to scale their team and lower cost per hire
Imagine you are screening inbound candidates coming in via your ATS. You are looking for someone who has worked at a Big 4 accounting firm for at least 3 years and then moved on to a venture-backed startup. At the same time, this person has to have proof of career growth. These are distinct attributes that pre-generative AI technologies can’t identify, but you can now filter for using LLM AI technology.
Covey Scout is a smart AI sourcing assistant that many companies rely upon to improve their sourcing and recruiting. With natural language processing, recruiters simply need to type in a chatgpt like text box to describe what they seek.
Using Covey, Rippling scaled their engineering team and ramped up hiring at approximately 80% lower cost per hire, all without having to add recruiting resources.
The Rippling team uses Covey to:
Enable their recruiting team to source and hire top talent quickly
Find candidates with specific characteristics, such as early at YC software companies + top venture-backed startups in FinTech between Series A and C + companies with strong engineering cultures.
Ramp up their pipeline with candidates that are on-point with requirements without spending hours manually sifting through each profile
Enhance employer brand with consistent messaging
“Covey Scout evaluates and finds talent for you. It’s easy to use, gets us the exact candidates we want to talk to, and if you have a change in requirements you just need to update the bot, and then sit back and wait for these candidates to respond to you in your inbox.”
- Nevin Cook, Staff Recruiter at Rippling
The possible pitfalls of generative AI + how to handle them
Despite the many benefits of AI in talent acquisition, it's crucial to acknowledge and address its potential challenges. These include AI hallucinations, algorithmic bias, data privacy, and compliance concerns.
AI hallucinations —> This is the term for when generative AI creates something false.
Be vigilant in monitoring the output of generative AI to detect and correct any false or misleading information. Regularly review and assess the performance of the AI system to identify and address any potential hallucinations.
How to handle it? Hallucinations are a key reason why you need a human at the helm of your AI. For example, Covey Scout is designed to take strategic input from a recruiter and then execute on it by evaluating candidates exactly the way it has been prompted to. It is not meant to replace recruiters but rather be an assistant to them, with recruiters retaining control over hiring decisions every step of the way.
Algorithm bias —> This occurs if the data used to train the AI model contain biases (e.g., gender, racial, age-related). The AI can perpetuate or even amplify these biases in what it produces.
Provide transparency in the decision-making process of the AI system. Ensure that the algorithms used are transparent and understandable, allowing for scrutiny and accountability.
How to handle it? Create systems to check datasets for bias, and be ready to take quick action to address biased content if it comes up.
Data privacy —> An AI system must comply with data protection laws like GDPR and CCPA. Whenever a system has personal data, there’s a risk of data breaches.
Consider ethics when developing and implementing AI systems. Ensure that the AI system respects human values, adheres to ethical guidelines, and does not harm individuals or discriminate against them.
How to handle it? Choose an AI tool with strong data protections in place, and ensure your team is trained on best practices for using any tools you have.
Compliance concerns —> There's potential for lawsuits if an AI system discriminates against candidates based on protected categories.
How to handle it? Regularly monitor and assess the AI system's performance to ensure it is not discriminating against candidates based on protected categories. Implement a system for reporting and addressing any complaints or concerns regarding discrimination. Provide ongoing training and education to your team regarding compliance and ethical considerations when using AI in talent acquisition.
Covey stays current with legal requirements and regulations related to AI and talent acquisition to ensure compliance. It goes through regular audits by an independent third party auditor.
How can our recruiting team use generative AI right now?
Generative AI can make a far-reaching impact on candidate sourcing, outreach message generation, and screening (hundreds and thousands of) inbound applications from candidates.
It can also create business value by automating and scaling the process of talent acquisition by freeing up time spent on mundane, repetitive tasks, and allowing recruiting teams to focus on more strategic and relationship-building work.
To determine the best use case for your talent acquisition team, identify the bottlenecks in your recruiting process taking up the most time and resources.
AI has been playing an increasingly significant role in recruiting and talent acquisition, and it is expected to continue shaping the future of HR practices. As technology advances, AI-powered tools and systems are becoming more sophisticated, enabling recruiters and organizations to streamline the hiring process, improve candidate assessment, and enhance overall efficiency.
Here are some potential ways AI might impact the future of talent acquisition:
Candidate Sourcing As discussed above, sourcing is an area where generative AI could provide a lot of improvements to existing technologies. It could offer a way to better find a diverse array of qualified candidates instead of relying upon binary keyword searches that may not turn up the great pool of candidates you need.
Outreach Message Generation Again, this is an area where existing technologies have room for improvement. Many tools can send pre-written messages to candidates, but that does not save you much time when you have to write the message yourself or substantially edit what’s there. Generative AI could revolutionize this, providing a quick way to write custom, high-quality messages to candidates at all stages of the recruiting process.
Candidate Screening AI can help screen candidates from various platforms and databases. It can analyze resumes, cover letters, and online profiles to match candidates with specific job requirements. This saves recruiters a significant amount of time, allowing them to focus on building relationships and engaging with potential candidates instead of filtering through pages and pages of profiles.
Enhanced Candidate Experience AI-powered chatbots and virtual assistants can interact with candidates, answering their questions, providing feedback, and guiding them through the application process. These high-quality virtual assistants can deliver a more personalized experience than the robotic and frustrating chatbots people may have encountered in the past.
Bias Reduction One of the most promising aspects of AI in recruiting, and HR as a whole, is its potential to minimize bias in the hiring process. Generative AI has the potential to change the way talent is understood fundamentally. Skills can be inferred by AI platforms and expressed with both structured data and in natural language. Previously, skills were input by users or managers and consisted only of structured data, which is limiting and susceptible to personal bias.
As you can see, there are many potential use of AI in recruiting, and we are just scratching the surface of what these tools are capable of currently. AI is entering a new and exciting phase that will change how we work. As long as recruiters stay up-to-date with current tools and trends, they will be in the driver’s seat as these changes occur
How to approach AI usage at your recruiting organization
Take a step back and consider the implications from a specific point of view.
If you have a specific need, it might be more beneficial to use a tool that is tailored to that need.
Instead of relying on a broad tool that accepts input from everywhere, consider the advantages of using a specialized tool.
AI can augment the recruitment process for recruiters responsible for those processes.
Rather than attempting to broadly replace recruiters' efforts with AI, use AI as a supportive tool to enhance their work, taking over time-consuming workflows.
5 things to look out for when evaluating AI recruiting tools
AI recruiting tools are only as good as the people, prompts, and data driving it. To ensure the best results, recruiters should:
Use tools that allow input of your own recruiting strategy
Be able to train the model on your own requirements consistently so it becomes more refined
Use tools that can incorporate your company’s brand and voice
Be able to mitigate unconscious bias
Use tools with an extensive enough dataset
You can’t fine-tune general-use AI tools like Chatgpt on your company’s brand, style, and terminology guidelines, leading to inconsistent and inaccurate output that undermines brand and industry compliance.
Consider AI a tool to scale the work of your company’s most strategic and relational minds so your business can grow. AI isn’t a cost-cutting measure to replace the recruiters on your team. The best results are going to come from smart strategists driving AI.
When you equip recruiters with AI, you create the conditions where everyone gets a chance to 10X their output and productivity by leaving the mundane and repetitive pieces in the recruiting process to AI.
This increases work fulfillment as recruiters also get to do more of relationship building and candidate experience work that often gets overshadowed by repetitive pattern matching work, such as scanning resumes—something that AI is equipped to do especially with the advent of LLMs and neural networks.
AppLovin turned to Covey to make their candidate sourcing process more efficient by equipping recruiters with an AI bot to source and evaluate candidates the way they would.
“We had previously relied on specialized agencies to fill technical roles at 20% of its salary, and we never got the speed, cost-savings and accuracy that new generation AI tools like Covey provided,” says Anand Bheeman, VP Talent Acquisition at AppLovin.
| 2023-02-01T00:00:00 |
https://getcovey.com/blog/the-ultimate-guide-to-what-talent-leaders-need-to-know-about-ai-recruiting
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[
{
"date": "2023/02/01",
"position": 54,
"query": "artificial intelligence hiring"
}
] |
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Artificial Intelligence Talent Agency
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Artificial Intelligence Talent Agency
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https://www.linkedin.com
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Our favorite success stories include recruiting entire AI engineering teams from the ground up, doubling an AI engineering team within two months, and growing ...
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At AITA, we believe in accelerating the creation of AI software products worldwide so that humanity can benefit from the value that AI will create. Our core competencies include specialized headhunting for VC-backed startups in the AI sector. We offer Executive Search, Contingency Search, and Dedicated Recruiting Team services. We provide our clients with high-quality and high-quantity candidates quickly, focusing on the exclusive engagement of candidates interested in AI startups and companies. Our recruiters are AI industry experts with deep expertise in the AI competitive landscape, AI talent market, and AI company organizational structure. We are exclusively focused on AI startups, and our recruiters know how to handle recruiting problems for all AI startup stages, from Seed to IPO. With over 100 successful partnerships with AI startups over the past three years, we have a comprehensive candidate network, pre-vetted for skills, experience, salary expectations, and high-caliber. Our data-driven, high-volume, and high-engagement candidate outreach dramatically improves our client’s ability to attract excellent candidates and makes the hiring process high-converting, fast, seamless, and enjoyable for the candidate and our clients. We care deeply about our candidates. Each candidate is comprehensively informed about the company and the opportunity to make the best possible decision for their next role. Our favorite success stories include recruiting entire AI engineering teams from the ground up, doubling an AI engineering team within two months, and growing an AI startup from seed to Series C. We invite Founders, Co-founders, Engineering Leaders, and Product Leaders who work at VC-backed AI startups and venture firms who invest in AI startups to become our partners and follow us on LinkedIn. We also invite exceptional engineers, product managers, and C-suite executives to join our candidate network and to follow us on LinkedIn.
Website http://www.aita.co External link for Artificial Intelligence Talent Agency Industry Staffing and Recruiting Company size 2-10 employees Headquarters Los Angeles, California Type Privately Held Founded 2020 Specialties Executive Search, Contingency Search, Recruiting Engineering Teams, Recruiting Product Management Teams, AI Startup Recruiting, Recruiting Operations, Candidate Experience, AI Industry Expertise, AI Startup Leadership Teams, AI Team Composition, AI Startup Employer Branding, Talent Acquistion, High-Growth Recruiting, and Recruiting as a Competitive Edge
| 2023-02-01T00:00:00 |
https://www.linkedin.com/company/artificial-intelligence-talent-agency
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[
{
"date": "2023/02/01",
"position": 59,
"query": "artificial intelligence hiring"
}
] |
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Considering switching to ML/AI after 8 years in DE
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The heart of the internet
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https://www.reddit.com
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It seems like ML folks are better off these days, considering the recent shift to AI. FAANG is currently only hiring for AI positions. Comp has always been ...
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Correct me if I’m wrong but in overall it seems like ML folks are better off these days, considering the recent shift to AI. FAANG is currently only hiring for AI positions. Comp has always been slightly higher on average imho.
Was wondering if anyone has gone through similar transition and hoping to get some advice on whether it was worth it or not.
| 2023-02-01T00:00:00 |
https://www.reddit.com/r/dataengineering/comments/116qth0/considering_switching_to_mlai_after_8_years_in_de/
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[
{
"date": "2023/02/01",
"position": 72,
"query": "artificial intelligence hiring"
}
] |
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Manchester Data and AI Recruitment and Jobs
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Manchester Data and AI Recruitment and Jobs
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https://www.harnham.com
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LATEST MANCHESTER DATA & AI JOBS · Senior Data Engineer · Firewall Engineer · Data Analyst · Data Quality Analyst · Data Analyst (AI & Automation) · Digital and ...
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MANCHESTER DATA RECRUITMENT AND
AI JOBS
To stay ahead of the competition, companies in Manchester and the North West must continuously look for new and innovative ways to extract insights from the large volumes of data they acquire.
Harnham's Manchester Data & AI Recruitment Team is currently partnering with some of the industry’s most exciting data-driven organisations and is at the forefront of the Data Science recruitment space.
Our aptitude for matching the best talent in the North West with the best companies is second to none. If you’re looking for your next challenge or need to make a hire in the Data & AI industry then get in touch with our recruitment team below.
| 2023-02-01T00:00:00 |
https://www.harnham.com/manchester-data-ai-recruitment-and-jobs/
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[
{
"date": "2023/02/01",
"position": 73,
"query": "artificial intelligence hiring"
}
] |
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Ableism in AI: Facial Recognition Technologies ...
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Ableism in AI: Facial Recognition Technologies in Recruitment Processes, the Lack of Governance and Account for the Multiplicity of Disability.
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https://medium.com
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"Humans For Ai"
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AI has found a novel application in the field of recruitment: HireVue's recruitment Facial Recognition Technology (FRT) is increasingly used for candidate ...
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Ableism in AI: Facial Recognition Technologies in Recruitment Processes, the Lack of Governance and Account for the Multiplicity of Disability. Humans For AI 5 min read · Feb 18, 2023 -- Listen Share
By: Tess Buckley, AI Ethics Senior Analyst at EthicsGrade
For the HFAI blog and in tribute to the work of AboutFace.ca, translated from an academic case study completed in 2021 for the purpose of explainability.
Artificial Intelligence (AI) is shaping the way in which business is conducted, and while helpful, it can also be harmful. Researchers have been working to address ethical issues such as racial and gender bias, but there is an underrepresentation of disabled individuals as well.
Let’s make a distinction between fairness to individuals with disabilities from issues concerning other protected attributes such as race and gender. Disability is fluid and extensive in its number of physical and mental health conditions, which can fluctuate throughout an individual’s life. Each diagnosis has unique histories and idiosyncrasies, making them common in relation to the general population. Disability information is also sensitive and often individuals are understandably reluctant to reveal their individual diagnosis. The category of ‘disability’ complicates pat classifications, and thus perturbs calls to simply include disabled people in datasets, which are constructed around rigid models of categorization.
The disabled community is negatively impacted by the lack of data that is intrinsic to their conditions. This issue of coverage is a genuine concern when applying AI systems to people with disabilities. When a given technology is unethical, a consistent response to mitigate algorithmic bias is to train the ML on more diverse datasets. In the case of disability, simply expanding a dataset’s parameters to include new categories, in an attempt to account for differences, will not work to ensure this group is equitably represented.
AI has found a novel application in the field of recruitment: HireVue’s recruitment Facial Recognition Technology (FRT) is increasingly used for candidate screening, particularly with the rise of video interviews during the pandemic in both the public and private sectors. HireVue’s platform has hosted over 19 million video interviews for over 700 global customers. HireVue is often used by high-volume employers to automate screening at the beginning of the hiring process. Unilever, Intel and JP Morgan are a few firms using AI to improve ‘efficiency’ in recruitment.
The AI attempts to predict how a candidate will perform by analyzing the interviewees’ gestures, facial expressions, poses, lean, voice tone and cadence. This process produces an ‘employability score,’ which employers then use to decide who advances in the process. Unilever’s average recruitment time was cut by 75% after implementation; hiring teams are enticed to use FRTs because it reduces employee involvement in hiring processes.
There are benefits, however, this expedited process is not worth the ethical implications that arise from its use. The use of FRT is deeply concerning as the systems are based on discriminatory and discredited science that is fundamentally inconsistent with human rights. Consequently, the potential convenience of FRTs must be weighed against the resulting concerns of accuracy, privacy, and ableism.
HireVue’s FRT system analyzes facial movements and assumes they are linked to the candidate’s emotions. This results in making psychological inferences on an individual’s ability to succeed in a role based on their facial data. When FRT is used in the interview process an individual with a facial difference is likely to generate lower scores. This risks employers violating obligations imposed under human rights and equality legislation.
Integrating disability into the AI ethics conversation helps illuminate the tension between AI systems’ reliance on data as a primary means of representing the world, and its lack of ability to capture human fluidity of identity and lived experience. Without diverse training, an AI system would not be able to learn any characteristics demonstrated by individuals with facial differences who were later successful in employment.
To those in industry and tech development: Action Steps
Moving forward, companies that use AI during the hiring process should be required to release detailed outsourced bias audit reports. Furthermore, the Equal Employment Opportunity Commission (EEOC) should review these systems and issue guidance on whether they violate the Disabilities Act. When developing and deploying FRT employers need to consider the relevant risk, desired outcome, and necessity of this intrusive method. Employment courts and national data protection authorities are likely to punish excessive, inappropriate or unnecessary applications of FRT.
The more general challenge we must ask of the AI community is how to handle outliers. Machines won’t solve the problems we ourselves cannot, but they can learn norms and optimize for those norms. ML judges individuals by mathematical comparison to learned data points, even when they may have never encountered someone like you and this is a fundamental limitation in fair treatment of individuals with facial differences and disabilities.
As industries continue to engage with FRT we must not deny that we are deeply shaped by technology, which calls for the same democratic, citizen-based input granted to other societal issues. The application of FRT in recruitment processes does not properly represent the talents of the disabled community, making it ableist. To mitigate bias in AI it must be trained on diverse datasets, but we are not able to create one that would account for the multiplicity of disabilities. Facial recognition technology cannot adequately be trained to account for the fluidity of difference in the human condition, specifically facial differences— there simply is not a consistent way to be different.
Learn more
Creed, Stephanie. “Facial Recognition Technology in Employment: What You Need to Know.” Bird & Bird, Nov. 2020, https://www.twobirds.com/en/news/articles/2020/ global/ facial-recognition-technology-in-employment.
Davis, Nichola. “Scientists Create Online Games to Show Risks of Ai Emotion Recognition.” The Guardian, Guardian News and Media, 4 Apr. 2021, https:// www.theguardian.com/technology/2021/apr/04/online-games-ai-emotion-recognition- emojify? utm_source=dlvr.it&utm_medium=facebook&fbclid=IwAR02ZiERnkRhILwEHngw lWL8BN8XlPYFCGxi7UkyCbuudKp6N4w9yIVQ12Y.
Engler, Alex. “For Some Employment Algorithms, Disability Discrimination by Default.” Brookings, Brookings, 31 Oct. 2019, https://www.brookings.edu/blog/techtank/ 2019/10/31/for-some-employment-algorithms-disability-discrimination-by-default/.
Harwell, Drew. “A Face-Scanning Algorithm Increasingly Decides Whether You Deserve the Job.” The Washington Post, WP Company, 6 Nov. 2019, https:// www.washingtonpost.com/technology/2019/10/22/ai-hiring-face-scanning- algorithm- increasingly-decides-whether-you-deserve-job/.
Hao, Karen. “Can You Make an AI That Isn’t Ableist?” MIT Technology Review, MIT Technology Review, 2 Apr. 2020, https://www.technologyreview.com/2018/11/28/1797/can-you-make-an-ai-that-isnt-ableist/.
“HireVue Hiring Platform: Video Interviews, Assessment, Scheduling, AI, Chatbot.” Hirevue.com, 2021, https://www.hirevue.com/.
Maurer, Roy. “Hirevue Discontinues Facial Analysis Screening.” SHRM, SHRM, 3 Feb. 2021, https://www.shrm.org/resourcesandtools/hr-topics/talent-acquisition/pages/ hirevue-discontinues-facial-analysis-screening.aspx.
Obando-Chacon, Gabriel, et al. “Facial Recognition & Recruitment.” Institute for Internet and the Just Society, 27 Aug. 2021, https://www.internetjustsociety.org/ cosmonaut/ facial-recognition-recruitment.
Whittaker, Meredith. “Disability, Bias, and AI.” AI Now Institute at NYU, Nov. 2019, https://doi.org/https://ainowinstitute.org/disabilitybiasai-2019.pdf.
| 2023-02-20T00:00:00 |
2023/02/20
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https://medium.com/@humansforai/ableism-in-ai-facial-recognition-technologies-in-recruitment-processes-the-lack-of-governance-67c36a1a0745
|
[
{
"date": "2023/02/01",
"position": 88,
"query": "artificial intelligence hiring"
}
] |
New Perspectives for Human and Artificial Intelligence ...
|
New Perspectives for Human and Artificial Intelligence Interactions for Leadership e-Recruitment
|
https://www.mdpi.com
|
[
"Anghel",
"Dan Anghel"
] |
by D Anghel · 2023 · Cited by 14 — The introduction of information technology improved the functions of recruitment [4]. This paper focuses on the recruitment process for leaders and those in ...
|
Traditional recruitment, due to outdated processes based on passive sourcing (such as posting a job description and waiting for applications, even on modern platforms such as LinkedIn and Indeed) leads today to inappropriate hires, especially for leaders, which naturally do not accord with the long-term view of an organization. As a result, the perception of such processes is negative and change is necessary. The world has changed drastically due to the pandemic, digitalization and the war in Eastern Europe influencing the global economic situation. Today, the recruitment world is seeing a radical new talent market, especially for leaders and high-level management candidates; however, individual skills are still sought in e-recruitment systems, instead of a combination of skills predetermined to be complementary and adequate for a specific job. From another point of view, the traditional recruitment process focuses on hard skills, but not on behavioral drivers, though it is known today that hard and soft skills should be equally balanced for top management positions. The selection of necessary skills should be specific for a certain position. This process usually fails for leaders due to the lack of accurate competences described in the job description draft, relying more on face-to-face interviews, unconscious bias and references. To improve the e-recruitment process that has inherited the traditional methods of recruitment, the author proposes some LCS that are presented below in a. The model represents another novelty regarding a new way of looking at leadership skills through various combinations. The presented LCS are not limited, inviting other researchers to find new ones. In addition, LCS create the necessity for a new type of leader profile [ 2 ] able to overcome the difficulties of the post-pandemic era and improve the management sustainability of a company.
This scientific paper makes use of existing e-recruitment and leadership concepts and knowledge, the novelty of this paper consisting of a new look on the leadership profiles searched through the e-recruitment systems. This new method of searching should consider a combination of skills that support each other, namedby the author. Just as rational thinking is complementary to intuition [ 1 ] in order to ensure strong and reliable judgment, some skills are complementary, especially for leaders, to ensure that their thinking is reliable, sustainable and balanced to overcome difficult business situations in our troubled economic environment. Certain skills alone, if not selected to be complementary, will not help leaders succeed in this difficult environment, because in the long run they may be inefficient, harm the business or not lead the firm to sustainable profitability. Different examples are described in the chapter 4, “Statements” regarding the possibilities of fail to combining these abilities.
The background of “ Figure 1 ” suggests the use of honeycomb cells as a model. Other researchers are invited to fill in other cells with complementary skills and abilities. The cells should be grouped to be complementary in order to create a new leader’s profile depending on the area of activity the e-recruitment system is used for. The study example is, as such:: Emotional Intelligence (EI) supported by Critical Thinking (CT);: Regular Communicator (RC) supported by Creativity and Improvisation (CI);: Wise Risk-Taking (WRT) supported by Resilience (R); and: Lifelong Learning (LL) supported by Observation Ability (OA).
The author proposes the LCS, using this paper to open a new area of research in the field of the abilities’ combined requirements for the e-recruitment of leaders.
This scientific paper recommends the use of Leadership Complementary Skills (LCS) in the hiring process as an essential requirement idea to denote a new way of looking at existing knowledge and concepts of e-recruitment. Apart from improving the selection methodology regarding the requirements for leaders’ abilities, LCS will also determine the improvement of a company’s outcomes and sustainability.
To help improve recruitment, the author’s proposal is that essential leadership requirements should evolve from traditional skills to an appropriate combination of skills. The focus should move from hard to soft skills; from considering that a leader should know all the answers [ 21 ], to using employees’ knowledge [ 22 ] and solutions; and from tough to human behavior [ 23 ].
Today’s transformed workplace characteristics might be summarized as: volatile, uncertain, complex and ambiguous (VUCA) [ 18 ]. The author’s view on leadership development is that some leaders are born with the innate capacity to lead, which is obviously demonstrated since childhood, and some are trained by life or in traditional schools. While most leaders try to improve their leading capabilities over time, only some succeed. This learning process began about two centuries ago with traditional management, which was very effective at the time. Today’s work environment requires a set of leadership skills that makes traditional management obsolete [ 19 ]. “Since the traditional management approach reached its limits [ 20 ], the new kind of work environment requires leaders’ profile to possess a combination of sine qua non (essential) skills sustaining each other in order to be effective”.
delays to a process are sometimes necessary, while other delays, such as in “beer game” orders, may be a burden
leadership agility and the ability to improvise on the fly are now fundamental skills
In an age of chaos (in the mathematical sense) marked by accelerated change and unpredictability
Digitization determines the success of an organization, but the results depend on which technology has been implemented. This depends on the impact of different expert systems implemented; for example, in candidate screening, in the selection process for the right individual, or for performance management assessments [ 11 13 ]. Therefore, the author identified the topic of this new perspective on human and artificial intelligence interactions, focusing on leadership e-recruitment.
AI has enabled human resource (HR) databases to become intelligent, able to carry out online monitoring and select the right candidates based on competencies [ 7 8 ]. Robotic process automation (RPA) has the ability to mimic the skills of a human, taking over the routine work of some individuals in HR [ 9 10 ].
An organization’s human resources department is able to use search engines to find job seekers and independently place them to suitable positions. On the other hand, recruitment agencies use cloud SAAS (software as a service) to find potential candidates. In this way, AI has successfully replaced the human element in the recruitment process [ 6 ]. As a result, the current workforce will be diminished due to several jobs disappearing from the organizational chart, with an important impact on the profitability increase of an organization.
The concept of electronic recruitment appeared in the 1980s, speeding up the recruitment process by replacing the human element with artificial intelligence. Since then, it has been named e-recruitment [ 5 ]. This concept provides fast and adequate information in the process. On one hand, corporate websites such as Indeed, Monster.com and Naukri.com provide links to current positions for commercial jobs, and on the other, social networks such as LinkedIn, Facebook and Twitter represent a social change in e-society: the so-called social recruiting.
Traditionally, the recruitment process was carried out through newspaper advertisements [ 3 ] internal hiring, employee referrals and different agencies. The adoption of new technology, beginning with big data and eventually including machine learning and artificial intelligence, was decisive for digital business. The introduction of information technology improved the functions of recruitment [ 4 ]. This paper focuses on the recruitment process for leaders and those in charge of management decisions, focusing on the Artificial Intelligence (AI) area of computer technology in the e-recruitment process.
Lifelong learning combined with observation ability should be a special requirement for the best leaders throughout the hiring process. Observation ability is a fundamental skill [ 53 ] which includes three essential actions, named the three Ls by the author:In the process of observation, leaders’ attention should focus on understanding the intent of their employees’ words [ 54 ]. In actuality, the recommendation is that the three L actions should be used in all life situations. Leaders should have the ability to bring people together [ 55 ] and learn from their expertise. For an inexperienced leader, the author’s recommendation is to use the three Ls for inspiration, observing other leaders’ behavior, actions and reactions, but not for mimicking them. In addition, leaders should make the effort to create a habit of lifelong learning [ 56 ] that, combined with observation ability, will help them to achieve success through a combination of practice and theory. Some examples of lifelong learning as a habit include: vocational courses, teaching yourself a new language, on-the-job training for a new skillset and playing a new game or sport. The judgements and decisions made after using the three L actions also helps leaders to stay focused on their mission and vision, ignoring micromanagement and guiding them to the necessary strategy adjustments and business adaptations required in this fast-changing economic environment.
Deepak Chopra says in 49 ] thatIn addition to this, the author’s observations revealed other qualities: self-trust in the individuals’ potential for progress, desire to win in any circumstances, energy focused on developing inner evolution and creativity, potential to recover and learn and, last but not least, an overall positive attitude toward life. In an actual, wise risk-taking supported by resilience (WRT + R) is considered by the author as the sine qua non requirement for a leader. In this way, the best form of management in crisis situations should be similar to sailing on the high seas [ 50 ]: keep managing “the boat” while planning for the future. Today, an additional challenge for leaders is the hybrid work arrangement [ 51 ]. Leaders may feel alone at work [ 29 ] because no other employee sees the same things that they do, which in turn encourages them to communicate more regularly and try to collaborate efficiently using new technology. As a result, the team becomes more and more important and the leader takes care of it, similar to Lilach Asher-Topilski when leading the Israel Discount Bank: 52 ].
My team was a tight fist, and now one would get between the fingers—not the board, not competitors, not anyone
At the same time, leaders should be aware that reckless risk-taking [ 46 ] will lead to low potential reward. High risk might lead to bankruptcy [ 47 ] for the organization. In the new, uncertain, high-risk work context, leaders’ resilience should help them to avoid failures and disappointments. However, in every failure, a resilient leader should see an opportunity [ 48 ] to draw the necessary strength from within to overcome the obstacles and to function under longstanding pressure.
Risk-taking, in the modern era of remote working, has become a pivotal skill that should be supported by resilience. Venturing into the unknown, as encountered by even the most effective leaders, requires risk-taking that sometimes leads to setbacks. However, risk-taking experiences are beneficial for leaders; on one hand, because they open opportunities for profit, and on the other, as Julie Browne said: 45 ].
People who’ve had the rug pulled out from under them and are putting themselves back together with missing parts find that a byproduct of that process is discovering who they really are
An important facet of communication skills is listening [ 42 ]. In the communication process, good leaders should try to understand instead of letting their brain carry on the conversation internally, preparing to respond immediately because they are only relying on what is spoken. These leaders will miss the profound sense of the message because they do not capture the tone and the body language. Tim Sanders says in this regard that: “” [ 43 ]. Today, more than ever, all leaders must listen until the end of a conversation because, on one hand, they may miss the accompanying body language if working remotely, and on the other hand, they can validate their interest in what their employees are saying. Therefore, to be a great leader, one should listen more and react less [ 44 ]. A leader’s listening ability complements their creative and improvisational capabilities. These capabilities are developed in childhood but may gradually fall into disuse; by re-activating these capabilities, a leader can create the right combination of listening, creativity and improvision, restoring their authenticity.
Great listening is like being a human satellite dish where we receive information and decode it. We take in sights, body language, and other signals to ensure connection
Restoring leaders’ authenticity [ 38 ] is a key priority in the near future. Showing vulnerability and opening up about your feelings and concerns to employees [ 34 ] in the process of communication could be a means of obtaining an aura of humanity and becoming an example of honesty and trust. In the era of remote work, the inflection point in communication is represented by the necessary ability to maintain fair lines of connection [ 39 ], not only with the group of employees as a whole but also with every team member, fostering the feeling that all are equally involved in the organization’s mission [ 41 ]. Today, more than ever, due to the dynamics of business, people are more important than the material assets that conveyed value half a century ago. Intellectual property, codes and brand values are tied to people, giving them more power in the business equation. Growth leaders place more and more importance on communication, and they have growth stories to tell all the time; 41 ].
The modern era of work requires regular and fair lines of communication between leaders and employees [ 36 ], creating a successful human connection. There has been a paradigm shift in leaders’ communication style from cold and impersonal to honest [ 37 ], vulnerable and able to admit that they do not have all the answers. This approach can create an aura of authenticity and humanity for leaders [ 38 ]. Creativity supported by communication style should also be a new selection factor for leaders in the post-pandemic era. This factor is of paramount importance in the process of solving problems, as the post-pandemic work era is pushing us towards working together and being creative in order to find optimal solutions [ 39 ]. In this regard, leaders need to combine flexible thinking and creativity with forward planning, following an adaptable decision-making process and maintaining an agile approach [ 40 ] whatever circumstances the organization faces. In this regard, the Forbes Coaches Council concluded in 2018 that 35 ].
The opposite approach for a leader, overworking their employees [ 31 ], may impact the employees’ long-term commitment to the organization, leading to resignation due to toxic management. Demotivating employees and overworking them are the roots of the “silent quitting” [ 32 ] phenomenon that is increasing today. Such leaders are poor self-leaders, also overworking themselves and burning out the entire human capital of the organization. In the short run, this kind of leader, referred to as task-oriented [ 33 ], might be considered highly efficient, working hard in terms of execution and planning, but they are unable to drive the organization forward due to their narrow-minded vision [ 34 ], which is a recipe for disaster in the long run. The focus on employees’ needs is critical even when their leader is chasing results. Every business leader aspires to deliver, but for many the result is intangible., as statistics reveal based on McKinsey’s analysis of data [ 35 ].
About a quarter of companies do not grow at all, and between 2010 and 2019, only one in eight achieved more than 10% revenue growth annually
Employees should be involved in the process of finding solutions [ 30 ] in order to explore all the possible answers to every conceivable leadership problem. Through this approach, leaders can place themselves in a better position, obtaining the best decision that is “blessed” by employees.
C. Am I doing enough to involve my employees in the decision-making process?
In order to understand employees, to have positive relationships with them and to improve trust, a leader should always have these three questions in mind:
Intelligence is a part of critical thinking. Simon Sinek said that 26 ], and as a result leaders should understand and manage their own and their employees’ emotions by way of self-reflection, mindfulness and self-review. They have to be able to perceive the difference between their own perception of a situation and the reality itself. Because 15 ], leaders should improve themselves through reflection to develop better critical thinking skills CT can help leaders to understand what important needs their employees have [ 27 28 ] in correlation with the organization’s financial possibilities. In addition, leaders’ self-awareness helps them to achieve humility in their relationships with others [ 29 ].
Since digitization has changed the rules of competition [ 24 ], an inflection point has occurred in relation to leaders’ profile requirements for abilities to cope with the new post-pandemic world. Just as the Industrial Revolution changed the way companies were managed [ 25 ], new leaders should possess complementary skills to succeed. Some leaders have natively different skills that are complementary and others develop skills in business schools and practice, subsequently combining them randomly.
4. Statements
It is not sufficient to excel at only one skill if it is not supported by another skill which complements it. A better leader in today’s dynamic context needs a combination of skills, because a single skill without a backup skill will hinder the leader from achieving better performance for their organization. For example, a leader with a focus only on their employees’ needs, lacking critical thinking, will lose sight of the ultimate goal of the organization, failing to achieve an alignment with the economic environment and the organization’s strategy.
In the new context of existing knowledge and concepts regarding the hiring process of leaders, the author proposes a new way of looking at the desirable combination of skills: Leadership Complementary Skills (LCS).
The author’s statements regarding LCS are as follows:
Statement 1. A leader with a high level of EI and low level of CT will have, as a result, a low level of organizational performance.
A leader hired only because of a high level of EI and who cares only for the needs of their employees will, in time, cause the organization’s performance to suffer. This situation occurs despite the fact that the employees’ relationship with the leader is more than satisfactory, especially concerning commitment and loyalty. The leader’s CT should provide the clarity to understand that the organization’s goals should prevail.
The opposite situation, hiring a leader with high level of CT and low level of EI, will also result in low organization performance. A low level of EI, and, as a corollary, a low level of empathy, will result in a poor relationship with personnel, leading to a negative impact on the organization’s performance.
Therefore, when hiring a leader, only a combination of high levels of EI and CT should be considered as a desired requirement.
Statement 2. A leader’s communication style, without a regular approach to communication with their employees and lacking creativity and improvisational talent, will result in a low level of organizational performance.
A leader hired only because they are a good communicator, but not a regular communicator, and lacking creativity and improvision talent, may falter in a VUCA environment or in a difficult situation, for example, a strike, a financial crisis, etc., which might result in losing the trust of their employees. This could sabotage the organization’s productivity, culture, engagement and retention.
The opposite situation in the hiring process of a leader, in which the leader is only hired for being creative and having a talent for improvision, is not enough. This situation will lead to losing meaning in communication, with disastrous effects on the organization’s performance.
Therefore, when hiring a leader, the required combination of skills is regular communication supported by creativity and improvisation.
Statement 3. A leader that is a risk-taker but does not have a wise risk-taking approach supported by resilience will be associated with danger to the organization and inability to recover from a loss, and as a result will not be seen as hirable.
A leader hired only because they are open to risk-taking without possessing a wise risk-taking approach and lacking resilience in a VUCA environment or in difficult situations will be seen as a dangerous leader that might jeopardize the existence of the organization.
The opposite situation, hiring a leader with a wise risk-taking approach but lacking resilience, will be unable to recover from failure, which is a very probable outcome in the actual environment, and as a result the performance of the organization will suffer.
Therefore, when hiring a leader, only wise risk-taking individuals with a proven resilience skill should be selected.
Statement 4. A leader with a high level of commitment to lifelong learning who lacks observation ability (3L) and, as a corollary, the capability to combine theory with practice, will not be recommended for a business leader position and remain a theoretician.
In theory, theory and practice are the same. In practice, they are not [ A leader hired only because they show a high level of commitment to lifelong learning and lacking observation ability will not be able to adapt the organization to the business reality, failing to be performant. This situation can be summarized by a famous quote from Benjamin Brewster: 57 ].
The opposite situation, hiring a leader only because they have a highly developed observation ability but lacking the commitment to lifelong learning, will lead to uncertainty in their actions. The result of continuous uncertainty in the VUCA environment will cause performance to suffer and, in the long term, jeopardize the existence of the entire business.
Therefore, when hiring a leader, the best candidate should have a high level of commitment to lifelong learning combined with a developed observation ability (three Ls).
Statement 5. A leader with a high level of Emotional Intelligence combined with high Critical Thinking, a high level of Regular Communication combined with high Creativity and Improvision, a high level of Wise Risk-Taking and high Resilience and a high level of Lifelong Learning and high Observation Ability (three Ls) will be associated with positive outcomes for the organization. These four levels of Leadership Complementary Skills create the optimal model for the new leader profile to search for in the existing VUCA economic environment.
These skills determine the new way of looking at a leader’s existing knowledge and employment concepts, namely: Leadership Complementary Skills (LCS).
Overall, this basic four-level HCSM of LCS provides an example of a strong leader associated with the most efficient organizations in a VUCA economic environment which can be used in the hiring process.
| 2023-03-14T00:00:00 |
2023/03/14
|
https://www.mdpi.com/2075-4698/13/3/55
|
[
{
"date": "2023/02/01",
"position": 92,
"query": "artificial intelligence hiring"
}
] |
Is computer science one of the most threatened jobs due to ...
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The heart of the internet
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https://www.reddit.com
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[] |
It's already threatening current jobs. CS/Programmer employing companies have been having mass layoffs over the last year. There have also been huge dips in ...
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Since ChatGPT, many articles have been popping up about how AI will replace software engineers and developers. (Maybe not in the near future, but eventually)
Reasoning, if AI will be able outperform humans in software development, building amazing application useful for other engineering courses like CAD softwares, then what stops it from replacing other engineers like mechanical, aerospace, etc?
As far as I know if we reach a point that AI is able to automate software engineers, then won’t it as well automate all other engineers? I mean if the AI is smart enough to automate the development of complex applications, then it will also be able to automate the usage of those applications. Maybe it won’t even require to develop those applications and just bridge to output the required engine/design artefacts. It will be able to assemble complex physical process pipelines.
2) What I don’t understand is why will software engineers/computer scientists be replaced before other engineers. Aren’t they necessary to maintain and enhance AI? If we reach an AI able to learn entirely alone this means no other job is safe.
I can’t quite understand why software engineers/computer scientists are the most in danger of losing their jobs. I though computer scientists would be the last jobs to become obsolete if AI excels humans, since they are the brains behind AI advancements.
3) How aren’t careers like lawyers, accountants, physicians, etc more in danger? Those careers can be highly automated as well. Aren’t computer scientists needed to automate them?
4) Also will computer science change towards a more AI and interdisciplinary point of view? Or will it remain the same and just become obsolete in the future?
Disclaimer: I’m not talking about the near future.
| 2023-02-01T00:00:00 |
https://www.reddit.com/r/ArtificialInteligence/comments/10sasj1/is_computer_science_one_of_the_most_threatened/
|
[
{
"date": "2023/02/01",
"position": 23,
"query": "artificial intelligence layoffs"
}
] |
|
PayPal's layoffs are the latest in Big Tech's cutbacks
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PayPal's layoffs are the latest in Big Tech's cutbacks
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https://mashable.com
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[] |
PayPal, Snapchat, OnlyFans, Lyft, Microsoft, Twitter, Substack, Netflix and more tech companies began laying off workers in 2022. Now just a few weeks into 2023 ...
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It's been a rough few months for people who work in tech. After a massive hiring spree during the beginning of the pandemic, tech companies have needed to slow — or even reverse — hiring. PayPal, Snapchat, OnlyFans, Lyft, Microsoft, Twitter, Substack, Netflix and more tech companies began laying off workers in 2022 . Now just a few weeks into 2023, those layoffs don't seem to be slowing down.
"Unfortunately, I don't see the layoffs going away anytime soon," Roger Lee, the creator of Layoffs.fyi , a website that tracks job cuts at startups and tech companies, told USA Today .
Nearly 80 percent of laid-off tech workers land new jobs within three months of beginning their search, a study from Zip Recruiter published in The Wall Street Journal reports. But three months is a long time for many tech workers, particularly those who may rely on their job for a workers permit. A 2018 report found that more than 70 percent of tech workers in Silicon Valley were born in another country; immigrant workers on H-1B visas have just 60 days to find a new employer to sponsor their visa before they're kicked out of the country.
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Spotify, Google, Microsoft, and Amazon are just among the many tech companies and startups that have cut jobs in 2023 already. Here's a look at some of the largest-scale tech layoffs this year:
PayPal
The president and CEO of PayPal, Dan Schulman, is laying off approximately 2,000 full-time employees, which is about 7 percent of PayPal's workforce, he said in a press release on Jan. 31.
"Change can be difficult – particularly when it includes valued colleagues and friends departing, Schulman said, blaming a potential recession, rising interest rates, and e-commerce stagnation for the layoffs. Schulman made $32.1 million in 2022, according to Payments Dive.
Spotify
Daniel Ek, Spotify's co-founder and CEO, will lay off 6 percent of its workforce globally, he announced in a company-wide memo on Monday, Jan. 23.
"I hoped to sustain the strong tailwinds from the pandemic and believed that our broad global business and lower risk to the impact of a slowdown in ads would insulate us," Ek said in the memo. "In hindsight, I was too ambitious in investing ahead of our revenue growth. And for this reason, today, we are reducing our employee base by about 6% across the company. I take full accountability for the moves that got us here today."
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Ek has a net worth of around $2.4 billion, according to Forbes.
Google
Sundar Pichai, Google’s CEO, laid off approximately 12,000 employees on Jan. 20, CNBC reported . U.S. employees received a severance package starting at 16 weeks salary plus two weeks for every additional year at Google.
"This will mean saying goodbye to some incredibly talented people we worked hard to hire and have loved working with," Pichai said in an email to staff. "I’m deeply sorry for that. The fact that these changes will impact the lives of Googlers weighs heavily on me, and I take full responsibility for the decisions that led us here." When Pichai took over Google's parent company Alphabet in 2019 he received a $242 million pay package .
Microsoft
On Jan. 18, Microsoft announced that it would lay off 10,000 people by the end of the third quarter of the 2023 fiscal year, USA Today reported . That's about five percent of its workforce.
"It’s important to note that while we are eliminating roles in some areas, we will continue to hire in key strategic areas. We know this is a challenging time for each person impacted," Microsoft CEO Satya Nadella said in a statement to employees. "The senior leadership team and I are committed that as we go through this process, we will do so in the most thoughtful and transparent way possible." According to MarketWatch , Nadella's salary in 2022 was $2.5 million. The day before layoffs, The Wall Street Journal reported that Microsoft executives enjoyed a private Sting concert.
Twitter
In early January, Twitter CEO and general terror Elon Musk laid off about 40 data scientists and engineers, according to the Information and, according to Reuters , the company has plans to lay off 50 more people in the coming weeks. It's unclear how much Musk makes at Twitter, but his net worth is over $180 billion .
Amazon
More than 18,000 people were laid off from Amazon in January.
"While it will be painful to say goodbye to many of our talented colleagues, it is an important part of a wider effort to lower our cost to serve so we can continue investing in the wide selection, low prices, and fast shipping that our customers love," Doug Herrington, the company’s worldwide retail chief, said in a memo. According to Business Insider, Amazon CEO Andy Jassy makes $214 million a year .
Vimeo
In an email to staff on Jan. 4 CEO Anjali Sud said 11 percent of workers would be laid off due to "uncertain economic environment."
"This was a very hard decision that impacts each of us deeply," Sud wrote. "It is also the right thing to do to enable Vimeo to be a more focused and successful company, operating with the necessary discipline in an uncertain economic environment. It positions us to both invest in our growth priorities and be sustainably profitable while continuing to innovate to bring the power of video to every business in the world." Sud made more than $18 million in 2022, according to salary.com .
This is an ongoing story. More information will be shared as it becomes available.
| 2023-02-01T00:00:00 |
2023/02/01
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https://mashable.com/article/tech-company-layoffs-2023
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[
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"query": "artificial intelligence layoffs"
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How Much Do Artificial Intelligence Engineers Make?
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How Much Do Artificial Intelligence Engineers Make?
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https://www.franklin.edu
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Lowest hourly earners took home $39 while the highest reported hourly salaries for artificial intelligence engineers was $112. Hourly Salary Range For ...
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How Much Do Artificial Intelligence Engineers Make? Having a clear understanding of earnings potential is important, especially if you are researching career paths or considering changing jobs. If you're one of those people, and you're curious what kind of salary artificial intelligence engineers make, you've come to the right place. Using data from Lightcast,™ the Best Adult Colleges and Careers Guide has compiled rich information about salary for artificial intelligence engineers and related jobs, including details about average compensation, salary trends and job growth. We've also gathered advertised salaries from actual job postings to provide insight into what employers of artificial intelligence engineers are offering as compensation. Keep reading to learn more about how much artificial intelligence engineers can expect to make in the United States.
At a Glance: Salary & Jobs for Artificial Intelligence Engineers Jobs Median Salary Job Posting Demand 39,187 $145,080 4,779 According to data from 2023, there were about 39,187 positions for artificial intelligence engineers in the United States. In terms of salary, the national median salary for artificial intelligence engineers was reported to be $145,080. Additionally, when examining job demand, employers across the country posted 4,779 job postings related to positions for artificial intelligence engineers.
How Much Money Do Artificial Intelligence Engineers Make? Median salary is just one data point. To build a better understanding of how much money artificial intelligence engineers and related positions can expect to make in the United States, it can be helpful to look at the full range of compensation for those jobs. Let's look more deeply at data that was reported to the U.S. Bureau of Labor Statistics (BLS) for detail into the lowest and highest salary earnings for artificial intelligence engineers. Annual Salary for Artificial Intelligence Engineers According to the BLS, the lowest earners for artificial intelligence engineers and related professions earned about $81,453 per year in 2023. Meanwhile, at the other end of the spectrum, the highest earners made about $233,106 annually. As mentioned previously, the median salary for artificial intelligence engineers in the United States in 2023 was $145,080. Annual Salary Range For Artificial Intelligence Engineers In 2023 Hourly Salary for Artificial Intelligence Engineers The BLS also breaks down compensation for artificial intelligence engineers by hourly salary, and the median average hourly pay for artificial intelligence engineers in 2023 was $70. Lowest hourly earners took home $39 while the highest reported hourly salaries for artificial intelligence engineers was $112. Hourly Salary Range For Artificial Intelligence Engineers In 2023
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100% online Cost Per Credit: $398 B.S. Computer Science VIEW PROGRAM Go beyond basic programming and develop the technical skills necessary to apply, design, and implement software systems. Class Type: 100% online
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100% online Cost Per Credit: $398 B.S. Information Systems-Business Information Systems Focus VIEW PROGRAM Build technical savvy and business acumen that bridges the gap between business and technology with a B.S. Information Systems with a Business Information Systems focus. Class Type: 100% online
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100% online Cost Per Credit: $398 B.S. Information Technology VIEW PROGRAM Play a vital role in any business environment by designing and implementing mission-critical infrastructure, security, and servers. Class Type: 100% online
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20 Placement Tests: GMAT/GRE not required for admission M.S. in Computer Science - Software Systems Focus VIEW PROGRAM Learn to create scalable software systems that Improve organizational effectiveness and efficiency by earning Franklin’s 20-month online M.S. in Computer Science with a focus in Software Systems. The hands-on, theory-to-practice program will prepare you to be an asset in a variety of industries Placement Tests: GMAT/GRE not required for admission M.S. in Information Technology VIEW PROGRAM Earn your M.S. in Information Technology degree 100% online in as few as 16 months. Class Type: Face-to-face, Online coursework
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16 Placement Tests: GMAT/GRE not required for admission M.S. in Information Technology - Cybersecurity Focus VIEW PROGRAM Earn your M.S. in Information Technology degree with a focus in Cybersecurity 100% online in as few as 16 months. Class Type: Face-to-face, Online coursework
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16 Placement Tests: GMAT/GRE not required for admission M.S. in Information Technology - Data Analytics Focus VIEW PROGRAM Earn your M.S. in Information Technology degree with a focus in Data Analytics 100% online in as few as 16 months. Class Type: Face-to-face, Online coursework
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16 Placement Tests: GMAT/GRE not required for admission M.S. in Information Technology - Healthcare Focus VIEW PROGRAM Earn your M.S. in Information Technology degree with a focus in Healthcare 100% online in as few as 16 months. Class Type: Face-to-face, Online coursework
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16 Placement Tests: GMAT/GRE not required for admission M.S. in Information Technology - IT Leadership Focus VIEW PROGRAM Earn your M.S. in Information Technology degree with a focus in IT Leadership 100% online in as few as 16 months. Class Type: Face-to-face, Online coursework
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16 Placement Tests: GMAT/GRE not required for admission M.S. in Information Technology - Learning Technology Focus VIEW PROGRAM Earn your M.S. in Information Technology degree with a focus in Learning Technology 100% online in as few as 16 months. Class Type: Face-to-face, Online coursework
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16 Placement Tests: GMAT/GRE not required for admission Certificates & Microcredentials for Artificial Intelligence Engineers Amazon Web Services (AWS) Certificate Online VIEW PROGRAM Start your cloud computing journey with AWS certificate courses for beginners and keep up with the fast pace of innovation. Class Type: 100% Online
100% Online Time To Complete: 1-2 weeks
1-2 weeks Cost: $35/month DeepLearning.AI TensorFlow Developer Certificate Online VIEW PROGRAM This DeepLearning AI certificate course lets you dive into the cutting-edge world of AI specialization, machine learning and data-driven solutions. Class Type: 100% Online
100% Online Time To Complete: 3-4 months
3-4 months Cost: $35/month Github Certificate Online VIEW PROGRAM In-demand programmers know Git. You can, too, with GitHub certificate courses that put you among the ranks of other Git certification-ready development pros. Class Type: 100% Online
100% Online Time To Complete: 3-4 months
3-4 months Cost: $35/month Google IT Support Professional Certificate Online VIEW PROGRAM Fast track your IT career with the Google IT support training and certificate that helps you learn about network protocols, operating systems, and solving problems using code. Class Type: 100% Online
100% Online Time To Complete: 4-5 months
4-5 months Cost: $35/month Google UX Design Certificate Online VIEW PROGRAM Put your creativity to work with these Google UX design courses that equip you to build and test user-centered solutions and to use Google Analytics to improve usability. Class Type: 100% Online
100% Online Time To Complete: 4-5 months
4-5 months Cost: $35/month IBM Applied AI Professional Certificate Online VIEW PROGRAM Develop practical skills in Python and IBM applied AI thanks to deep learning courses that show you how to design, build and deploy AI-powered apps. Class Type: 100% Online
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4-5 months Cost: $35/month IBM Full Stack Cloud Developer Certificate Online VIEW PROGRAM Build your cloud developer portfolio with this hands-on IBM full stack cloud developer certificate course that uses the latest tools and technologies to manage full stack cloud native apps. Class Type: 100% Online
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Advertised Compensation for Artificial Intelligence Engineers Data from the BLS is one way to dig into compensation for artificial intelligence engineers, but another way to build a more real-time understanding of salary is to look at actual job postings and see what compensation organizations are currently providing to fill open positions for artificial intelligence engineers. Keep in mind that salary data is not included in every job posting, so the information compiled here is reflective of the data available through Lightcast.™ Annual Salary from Job Postings for Artificial Intelligence Engineers Advertised Annual Salary $ $133K Based on 1,929 advertised salary observations (40% of the 4,779 matching postings). During a review of 6,582 job postings related to artificial intelligence engineers, advertised salary was included in 1,929 of them, which was 29%. Based on those postings, the median advertised salary for artificial intelligence engineers was $132,864 per year. Hourly Salary from Job Postings for Artificial Intelligence Engineers Advertised Hourly Salary $ $64/hr Based on 1,929 advertised salary observations (40% of the 4,779 matching postings). A similar analysis of job postings provides insight into advertised hourly salary for artificial intelligence engineers. Based on 40% of postings with advertised compensation, the median hourly salary for open positions for artificial intelligence engineers in the United States is about $64. Salary Trend for Artificial Intelligence Engineers If you're thinking about pursuing a career in the field, you may want to understand how compensation has changed over time for artificial intelligence engineers. The chart below provides a snapshot of advertised salaries over the past three years. Based on compensation data that was included in job postings for artificial intelligence engineers dating back to September 2020, advertised salaries for artificial intelligence engineers have increased 11%. Advertised Wage Trend For Artificial Intelligence Engineers
What's the Job Growth for Artificial Intelligence Engineers? Another data point that someone thinking about a career path should consider is whether or not jobs are expected to see growth in the future. The chart below looks at projected employment for artificial intelligence engineers over the next 10 years. According to the data obtained through Lightcast,™ there were 39,225 jobs for artificial intelligence engineers in the United States in 2023. By 2033, it is expected that about 10,022 jobs will be added. That's a 25.6% increase over the next 10-year period. Employment Projections For Artificial Intelligence Engineers
| 2023-02-01T00:00:00 |
https://www.franklin.edu/career-guide/computer-and-information-research-scientists/how-much-salary-do-artificial-intelligence-engineers-make
|
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AI Engineer Salaries Around the World and How to Earn More - Run:ai
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Accelerate AI & Machine Learning Workflows
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https://www.run.ai
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According to recent studies, the average AI engineer salary globally is approximately $110,000 per annum. This figure can be significantly higher in developed ...
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NVIDIA Run:ai automates resource provisioning and orchestration to build scalable AI factories for research and production AI. Its AI-native scheduling ensures optimal resource allocation across multiple workloads, increasing efficiency and reducing infrastructure costs. Enterprises have end-to-end support for the AI life cycle, from data preparation and model training to deployment and monitoring. This integrated approach simplifies the development process, reduces time to market, and ensures consistency across all stages to drive AI innovation at scale.
| 2023-02-01T00:00:00 |
https://www.run.ai/guides/machine-learning-engineering/ai-engineer-salary
|
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AI Engineer Salary in 2025: How Much Do AI Engineers Make?
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AI Engineer Salary in 2025: How Much Do AI Engineers Make?
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https://www.janbasktraining.com
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[] |
As per Glassdoor, the average artificial intelligence engineering salary in the US is $163K --- which can go as low as $130,000 and as high as $250,000. And ...
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Introduction The world of technology is witnessing a revolution driven by Artificial Intelligence (AI). With AI poised to transform every industry, the demand for skilled AI engineers is skyrocketing. This high demand translates into a lucrative job market, with AI engineer salaries consistently ranking among the highest in the tech sector. This blog post will delve deeper into the world of AI engineers, exploring the factors influencing their salaries, the skills they need to succeed, and the exciting career opportunities that await them. Are you curious about the earning potential of an AI engineer? Wondering if the skills and dedication required to become one are worth the investment? Let's dive in and explore the landscape of AI engineer salaries and the path to a fulfilling career in this dynamic field. Human-based traditional jobs are not going anywhere; they will stay and run parallel to AI jobs with excellent remuneration. Want to know how much AI engineers make? Or, more explicitly, “What is the salary of AI engineers per experience & top locations?” Let’s explore the Artificial Intelligence Engineer salary based on specific parameters. If you have applied these factors, your remuneration will be incredible. Top Tier Companies - The better the company you choose, the better you will get the paycheck.
The better the company you choose, the better you will get the paycheck. Years of Experience - The more senior you are in your profile, the more will be the compensation, but that doesn’t mean entry-level doesn’t get paid well. If you have proven knowledge, the AI field is advantageous in terms of a paycheck, even if you are just starting.
The more senior you are in your profile, the more will be the compensation, but that doesn’t mean entry-level doesn’t get paid well. If you have proven knowledge, the AI field is advantageous in terms of a paycheck, even if you are just starting. AI’s Different Job Roles - AI is a vast field & has various subsets like Data Science, Machine Learning, Deep Learning, and so on. Your salary can also differ based on whether you choose AI as an explicit career or part of these sub-branches.
AI is a vast field & has various subsets like Data Science, Machine Learning, Deep Learning, and so on. Your salary can also differ based on whether you choose AI as an explicit career or part of these sub-branches. Diverse Geographical Locations - Every economy has a different payout. You can relocate to better-paying countries & cities. Some highly compensated areas with a standard of living are the UK, USA, India, Australia, and Canada. Other than these, your AI engineer salary can also be differentiated by other factors like: AI upskill & certification - If you have prolific training & trusted certification, your chances of getting compensated increase. Your Negotiation Skills - If you can toot your horn, market yourself well, and ask for what you bring to the table, you are compensated for what you desire. Company Type & Size - Your AI engineer salary can also be based on the company’s size, funding & customer base. Even if you will become an asset to a small company or any big corporation in any way, be conscious of its funding, capital, and customer success, as this can define your paycheck. So let’s start unfolding answers to your much-awaited query, “How much do engineers make?” based on top-tier giants, experience level, and the world’s top locations, one by one! At the bottom, don’t forget to read the skills that can get you reimbursed and the other skills you can shift your career to. Human-based AI jobs are not going anywhere; they will stay and continue to grow and pay massive remuneration to AI engineers. Want to know how much? Let’s explore the Artificial Intelligence Engineer salary based on certain factors like: Years of experience
Different job roles
Diverse geographical locations
Top-tier hiring companies So, let’s get started with these factors one by one!
Who is an Artificial Intelligence Engineer? An artificial intelligence engineer is a proven IT individual who develops intelligent algorithms to analyze, learn, and predict future events. The pivotal role of their jobs is to create machines with human-like brains capable of reasoning. They are responsible for developing Complex network algorithms and training programming. To excel in this field, an individual must have combined expertise in data engineering, data science, software development, and programming. Artificial intelligence engineering is a satisfying job with challenges and constant learning. Artificial intelligence engineering salary is quite rewarding, and it is one of the most respected and demanding jobs in the IT field. Learn more about AI learning and the roles and responsibilities of AI engineers here!
AI Engineer Salary - By Top-tier Hiring Companies Top-tier companies globally are endlessly looking for AI solutions to automate, expedite, and optimize business processes to achieve a compelling competitive edge. Let’s know the artificial intelligence engineer salary based on the top-paying recruiters first.
Top Companies Paying Highest Average Salary of Artificial Intelligence Engineer in the USA
Google - up to $236,388 on average annually. Facebook - up to $257,846 on average annually. Walmart Labs - up to $265,698 on average annually. Uber: up to $314,746 on average annually. Netflix: up to $300-$900 on average annually.
Top Companies Paying Highest Average Artificial Intelligence Engineering Salary in India
Bosch Group (Bengaluru): INR 400,000–2,400,000 p.a Adobe (Greater Noida): INR 1,200,000–1,400,000 p.a Amazon (New Delhi): INR 500,000–700,000 p.a Accenture (Bengaluru): INR 600,000–800,000 p.a JPMorgan Chase & Co. (Hyderabad): INR 3,000,000–6,000,000 p.a
Artificial Intelligence Engineer Salary --- As Per Years of Experience As per Glassdoor, the average artificial intelligence engineering salary in the US is $163K --- which can go as low as $130,000 and as high as $250,000. And artificial intelligence salary per month ranges from $8,875 to $19,949. AI engineers’ salaries vary depending on the professionals’ years of experience. 10+ years of experience AI engineers are bound to earn more than entry-level or junior-level AI engineers, but how much? Let's find out about that. However, The New York Times says that professionals with just a few years of experience can also expect to earn an average salary between $300,000 and $500,000 yearly (a skilled aspirant with a short work duration can earn millions pretty soon). But one thing is evident since the demand for AI professionals is unwavering: even entry-level aspirants can expect to earn a higher package than other IT job roles (if they manage to prove their competent skill set). So, if you are intrigued to learn AI, you are on the right track.
Salary for AI Engineer Based on Industry Experiences Let’s know how much how much does an AI engineer makes as per different experience levels.
Junior or Entry-Level AI Engineer Salary
The Junior or entry-level AI engineers are composed of two kinds:
First, freshers who have just graduated from college and are looking for career opportunities.
Second, the professionals planning to switch between different IT careers.
Suppose both entry-level aspirants take up a good knowledge of the AI field and its subfields via formal AI training online and plan to ace the certification exam. In that case, they can expect to earn reasonable remuneration from the starting point.
The annual average artificial intelligence engineer salary can range between $78,000-$100,000.
Mid-Level AI Engineer Salary
These professionals are entirely equipped and experienced in AI and have 2-8 years of experience working in AI and cognitive technologies. They earn quite a higher salary of AI engineer than entry-level professionals if they establish competent skills like:
Most companies demand mid-level AI engineers. A study found that AI engineers switching companies can expect to bag a hike of 60-80%, whereas, in other IT skills, you will generally see a hike ranging between 20-30%.
The mid-level artificial intelligence salary can range between $1,00,000 to $150,580.
Senior Level Artificial Intelligence Engineer Salary
When you evolve to senior-level AI talent, you get a massive paycheck as you now have extreme knowledge of the field and can help organizations build scalable and effective AI solutions. The demand for AI engineers with high salaries is quite prominent in sectors like eCommerce, Finance, healthcare, Software, and more.
When you reach an experience level of 8-15 years and beyond, the annual average Artificial Intelligence Engineer Salary you can expect to earn is between $150,000 to $200,000 and beyond.
Artificial Intelligence Engineer Salary --- By the Different Roles They Perform
Apart from the AI engineer profile, there are 5 more popular interconnected or interchangeable profiles that companies look for hiring, to meet their Business intelligence needs. And those 5 branches imbues great compensation as well. Let’s have a look at “how much does an AI engineer make” around these interconnected profiles.
1. Machine Learning Engineer Salary
Machine Learning is one branch of AI requiring excellent skills in predictive models, NLP, datasets management, etc. It is also one of the most in-demand career profiles, and an average AI Machine Learning engineer's salary is $121,106 per year.
2. Data Scientists Salary
Data Scientist is another very in-demand field that demands the knowledge of --- extraction and processing of data from large datasets via predictive analytics & machine learning models, and it requires great upskilling around top programming languages like Python and other big data tools & platforms like Hadoop and more. Data Scientists can be paid an annual average salary of around $117,345.
3. Business Intelligence Developer Salary
Business Intelligence developers have great analytical and problem-solving skills, and know-how to turn processes, tools, methods in favor of businesses to help them reduce IT costs and meet their bottom lines. The demand for MSBI or Microsoft BI Developer has soared significantly. The annual average salary of MSBI developer is expected at $90,430.
4. Robotics Scientist Salary
AI has a great application in robotics science as well, the need for robotics scientists is extravagant in the field of research, healthcare, security, space, and so much more. The annual average salary of robotics Scientists is somewhere near $83,241.
5. Research Scientist Salary
Using statistics and great logic, the research scientist’s role is to analyze, plan, and reason the data, processes, or systems. They make business process predictions using visual graphics like charts, graphs, tables, and more. The role demands a great understanding and application of AI and its subfields.
The annual average salary Research Scientists can expect to earn is $83,490.
Artificial Intelligence Engineer Salary --- By Diverse Geographical Locations You will find different pay scales in different cities, it is majorly because each city has a different talent gap, cost of living, expectations for skills and so many other reasons. The talent gap in the AI field is a worldwide issue and each day is gradually widening. You will notice, The US has a 70% talent gap
talent gap India has a 64% talent gap
talent gap France has a 52% talent gap
talent gap Germany has a 55% talent gap
talent gap The U.K has a 52% talent gap These talent gaps have ushered great payout opportunities for candidates. Candidates with good skills and knowledge can easily convince top-tier companies to pay heavy. If you are planning to relocate to these places, first, know how much worth of paychecks these places have for you to offer.
AI Engineer Salary in the USA
The USA with a 70% AI talent gap is planning to invest $1 billion in obtaining AI talent from wherever possible.
The country is deliberately looking for AI talent to fill in its entry-level to senior-level AI positions. The skilled & certified professionals after AI training can get a handsome salary of AI engineers in US as big as $314,000 on average yearly.
on average yearly. The country is set to up with 2,50,000 Data Science jobs by the year 2026.
Data Science jobs by the year 2026. The top recruiters of the US like GE, Intel, Uber, Samsung, IBM, Wells Fargo, and a lot of other recruiters are looking for professionals for AI and Machine Learning positions.
The key US cities like NYC, San Francisco Bay Area, Seattle, and Washington, D.C. are witnessed paying the highest salaries for AI and machine learning skills.
According to Indeed, machine Learning engineers top the average AI engineer salary in the US. Here are the top-paying US cities for AI and Machine Learning skills.
** all Salary of AI engineer in US are median yearly salaries **
AI Engineer Salary in Canada
Canada is taking a strong & leading stance for AI development & science and is expected to invest in AI’s policy, legal implications, and framework.
There is a 1,069% growth in AI jobs within Canada since 2013, as per Indeed reports.
In Canada, an AI specialist can expect to earn a median salary between $70,000 to $90,000 yearly. Whereas, specialists with impeccable skill sets and years of experience can earn even as big as $130,000 or more on average, yearly.
to yearly. Whereas, specialists with impeccable skill sets and years of experience can earn even as big as or more on average, yearly. Canada affirms that AI specialists on a Senior-level are paid competitive packages and even have scope to negotiate salary as per their desire.
A few of the big Canadian companies that are recruiting for the role of AI are --- IBM, Scotiabank, Amazon, Capital One, Royal Bank of Canada, and many others.
AI Engineer Salary in Australia
As per Indeed, there is a sharp rise in AI-related jobs in Australia. A study was found stating that AI will keep shaping its future in years to come. AI engineer jobs have doubled ever since 2012 in this economy. 2018 onwards, we saw an increment of 50% in its related roles.
While in Australia, you can earn a lucrative salary of an AI engineer up to AU of $111,021 per year on average.
Here are the top Australian cities with the highest salary of AI engineers:
Sydney NSW Sydney Central Business District Docklands VIC Clayton VIC Melbourne VIC Perth WA Brisbane QLD Eveleigh NSW
AI Engineer Salary in Europe
Almost every city in Europe has a shortage of AI skills, but the main cities like London, Berlin, Paris, Amsterdam, Eindhoven, and Stockholm are few names that are excessively looking for AI-skilled professionals.
The most in-demand jobs in Europe are counted as AI and deep learning, Robotics, cloud security, Game development, Blockchain.
Finland is the only EU country that has initiated AI on a government level. The digital businesses of EU countries are gradually asking for government support & legal framework to implement such soaring innovations & technology in town.
The huge talent gap in EU countries has made companies compelled to hire AI professionals outside Europe.
In Europe, AI engineers can expect to earn an annual average package between €31,218 and €105,059.
AI Engineer Salary in Germany
There will be a shortage of 3 million AI specialists in Germany by 2030.
4 out of 5 German companies say they have great difficulty in looking for AI professionals, which is somehow restricting them from advocating new machinery and innovation.
Germany has gained around 60,000 highly skilled professionals ever since 2012, and going forward is looking for more AI experts to help support their innovation & science.
highly skilled professionals ever since 2012, and going forward is looking for more AI experts to help support their innovation & science. Germany’s talent gap isn’t getting sufficed by homegrown workforce or young Germans as their preference is somewhere else set, this is the reason why the country is looking for every means to hire professionals from global platforms.
The median salary of an Artificial Intelligence Engineer in Germany ranges between €59,360 and €105,194.
Artificial Intelligence Salary in the UK
The demand for AI skills in the UK is growing massively at a triple rate from the past three years --- even more than leading countries like the US, Australia, and Canada.
In every one million, there are around 1300 vacant AI positions advertised within the UK --- which is 20% more than the USA and 50% more than Canada.
Tara Sinclair, economist and senior fellow at Indeed say, “Britain is becoming a major hub for the AI sector, and is growing steadily in AI jobs by even outstripping the demand in major English-speaking countries”.
The advertised AI jobs were found to offer an average of £60,000 a year and 10% top positions an average of £105,500 yearly.
a year and 10% top positions an average of yearly. The country pays competitive salaries and even gives scope to choose and negotiate until the desired offer.
AI Engineer Salary in India
From farming to healthcare to recruitment, India has a lot of wide-open sectors for the application of AI.
India lacks an acute shortage of homegrown workforce for AI and Machine Learning fields.
The Artificial Intelligence Engineering Salary in India lifts upwards as the experience grows; the candidates with 2-4 years of experience can attract a yearly median salary package between INR 15-20 lacs (equals to $22,000 - 29,000) , the 4-8 years can expect between INR 20-50 lacs (equals to $29,000 - 73,000 ) and 8-15 years can expect between Rs50 lacs to Rs 1 crore (equals to $73,000 - 147,000 ).
lacs (equals to , the 4-8 years can expect between (equals to ) and 8-15 years can expect between to (equals to ). The metro cities where AI and machine learning development is massive are Mumbai/Pune belt, Delhi NCR, Hyderabad, Chennai, and Bangalore.
The most popular sectors where demand for AI skills and the average Artificial Intelligence software engineer salary is soaring are BFSI, IT, Healthcare, Manufacturing, Retail, banking, and more.
The most popular sectors where demand for AI skills and the average Artificial Intelligence software engineer salary is soaring high are BFSI, IT, Healthcare, Manufacturing, Retail, banking, and more.
Skills You Need To Know to Earn Lucrative AI Engineer Salary Below are a few popular skills that recruiters look for while hiring and paying the hefty AI engineer average salary. Machine Learning - supervised, unsupervised
Programming Language Python for AI & ML, SQL
Computer Vision
Deep Learning with Keras, Tensorflow
Natural Language Processing
Data Modelling
Apache Spark
Scala
Neural Networks, Recurrent neural networks, convolutional neural networks
AI Engineer Jobs Are Posted in Great Numbers
Here are a few instances from popular portals with a number of AI-related jobs available.
Indeed, there are 9000+ jobs for AI engineers & their related roles in the US.
On Dice, there are more than 250,000 jobs for AI engineers & its related profiles in the US.
On Glassdoor, there are 12,000+ jobs under AI engineers & their related roles in the US.
Other Profiles That Pay Similar to Salary for AI Engineers
With little upskilling, here are some other roles that will compensate you fairly well and help move across other career opportunities, in case you feel stuck someday.
So How Should I Go About Making a Career in AI? Top-tier and small-scale companies are looking for AI professionals who can help them innovate & implement AI practices -- to brew up their competitive edge and bring speed to business decisions & processes. Everyone facing an acute to chronic talent gap is looking for skilled and certified AI specialists for different industries --- from healthcare to IT to banking and a lot more. And even ready to pay the highest Artificial Intelligence software engineer salary as desired. If the above salary breakdown has created a spark within you to join this remarkably growing AI field, then the best way to claim a career in this technology is by joining an AI Training and Certification Program online. Not just training --- an artificial intelligence course that prepares you for AI certifications & job work with real field experts & business projects! Check our next AI batches, free demo sessions, and career counseling classes right away! Also, leave down your comments on any other query you have regarding AI engineer salary or career, we would be happy to answer! Machine Learning Training & Certification Personalized Free Consultation
Access to Our Learning Management System
Access to Our Course Curriculum
Be a Part of Our Free Demo Class Sign Up Now
Parting Words AI Engineering is the future of the IT industry. If you want a job that stays relevant in the future, AI Engineering is your calling. The thrilling experience of working at the forefront of the IT sector with challenges, the satisfaction of overcoming them, and the exciting AI Engineer salary make it an interesting career option! If you dream of an exciting career as an AI engineer, sign up for JanBask's Artificial Intelligence Certification Training and prepare yourself to take on the mantle!
Frequently Asked Questions Q 1. What Is the AI Engineering Salary? Ans: -As per Glassdoor, the AI engineer's average salary can be $116,549 per year. Q 2. How Much Is the AI Machine Learning Engineer Salary? Ans:- As per Indeed, the AI machine learning engineer's salary can be $150,300 per year on average. Q 3. What Is the Salary of AI Engineers Hourly? Ans: -The average hourly salary of AI engineers ranges between $42 to $50 per hour. Q 4. How Much Is an AI Engineer Salary in the US? Ans: -The AI engineer salary in the US is $111,187 on average a year and $53 per hour in case you go for freelancing or on a contract basis.
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Artificial Intelligence Course
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| 2025-03-04T00:00:00 |
2025/03/04
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https://www.janbasktraining.com/blog/ai-engineer-salary/
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Artificial Intelligence Careers | University of San Diego Online Degrees
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University of San Diego Online Degrees
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https://onlinedegrees.sandiego.edu
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The average salary for an artificial intelligence programmer is said to range from $95,000 to $156,000. Salaries are significantly higher for artificial ...
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Your Career in Artificial Intelligence
High-paying career opportunities in artificial intelligence and related disciplines continue to expand across a broad spectrum of industries, with the market for AI engineers growing at a rapid pace of about 30% year over year. According to PricewaterhouseCoopers, a 56% pay boost is reported for workers with AI skills.
The average salary for an artificial intelligence programmer is said to range from $95,000 to $156,000. Salaries are significantly higher for artificial intelligence engineers, averaging $180,774 with high estimates reaching up to $270,000, according to Indeed.
| 2023-02-01T00:00:00 |
https://onlinedegrees.sandiego.edu/masters-applied-artificial-intelligence/careers/
|
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Artificial Intelligence Salary - Degrees - Career Explorer
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Artificial Intelligence Salary
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https://www.careerexplorer.com
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[] |
Artificial Intelligence graduates in the United States typically make an average of $83858. Check out our detailed breakdown of salaries for Artificial ...
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top
Does your ideal degree pay what you expect?
After graduation, artificial intelligence graduates typically earn high salaries compared to the national average. Top earners make $ 124,929 , while the bottom 20% make close r to $ 56,289 . The median grad salary is $ 83,858 .
Artificial Intelligence graduate salaries over time
The typical early career salary for someone with a bachelor’s degree in artificial intelligence is $ 63,678 , and within five years of graduation, this average salary goes up to $ 67,451 .
This chart maps the average workforce wage by years of experience:
Wage ($USD) Wage Range Median Wage Years of work experience
Pro tip Still unsure if a degree in artificial intelligence is your calling? Read our comprehensive guide on choosing a career
| 2023-02-01T00:00:00 |
https://www.careerexplorer.com/degrees/artificial-intelligence-degree/salary/
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The Global AI, ML, Data Science Salary Index for 2025 | aijobs.net
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The Global AI, ML, Data Science Salary Index for 2025
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https://aijobs.net
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See below our global salary index in 2025 for roles in the AI, ML, Data Science space based on internal data (survey submissions + jobs with open salaries).
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See below our global salary index in 2025 for roles in the AI, ML, Data Science space based on internal data (survey submissions + jobs with open salaries). Salary data is in USD and recalculated at its average fx rate during the year for salaries entered in other currencies.
The data is processed and updated on a weekly basis so the rankings may change over time during the year. Want to play around with the dataset yourself? You can download the complete salary dataset here.
# Role Median Salary in 2025 n 1 Head of Applied AI $ 281,500 8 Details 2 AIRS Solutions Specialist $ 263,250 6 Details 3 Director of Product Management $ 260,000 26 Details 4 Enterprise Account Executive $ 246,835 66 Details 5 Engineering Manager $ 245,550 478 Details 6 Head of AI $ 240,000 40 Details 7 Head of Machine Learning $ 236,000 8 Details 8 Director of Machine Learning $ 230,000 18 Details 9 GenAI Architect $ 219,100 12 Details 10 Machine Learning Architect $ 217,350 12 Details 11 Quant Options Trader $ 200,000 10 Details 12 Tech Lead $ 200,000 50 Details 13 Director $ 199,000 538 Details 14 Applied Scientist $ 195,500 687 Details 15 Head of Data $ 194,927 156 Details 16 Research Engineer $ 190,000 622 Details 17 Product Designer $ 190,000 144 Details 18 Machine Learning Engineer $ 190,000 2731 Details 19 Software Architect $ 188,200 52 Details 20 AI Architect $ 186,650 124 Details 21 Solutions Architect $ 186,000 478 Details 22 ML Scientist $ 184,950 40 Details 23 Principal Scientist $ 183,700 26 Details 24 Technical Lead $ 181,625 166 Details 25 Business Intelligence $ 180,250 62 Details 26 Product Manager $ 180,000 1384 Details 27 Architect $ 180,000 673 Details 28 Lead Engineer $ 180,000 60 Details 29 Software Engineer $ 180,000 8179 Details 30 Research Scientist $ 178,100 1035 Details 31 Member of Technical Staff $ 178,100 146 Details 32 Economist $ 177,952 14 Details 33 AI Product Lead $ 177,590 10 Details 34 Backend Engineer $ 177,215 204 Details 35 Data Infrastructure Engineer $ 176,780 34 Details 36 AI Researcher $ 175,000 56 Details 37 Quantitative Researcher $ 175,000 72 Details 38 Quantitative Developer $ 175,000 33 Details 39 AI Lead $ 174,169 20 Details 40 Researcher $ 172,500 136 Details 41 Developer Advocate $ 172,500 14 Details 42 Technical Architect $ 170,486 34 Details 43 Machine Learning Scientist $ 170,000 191 Details 44 Solutions Engineer $ 170,000 212 Details 45 AI Engineer $ 170,000 1189 Details 46 Data Strategy Lead $ 168,250 10 Details 47 Algorithm Developer $ 168,000 32 Details 48 AI Scientist $ 167,400 60 Details 49 Lead Data Analysis $ 163,400 6 Details 50 Systems Engineer $ 163,100 460 Details 51 Platform Engineer $ 162,510 304 Details 52 Executive $ 162,500 74 Details 53 Computer Vision Engineer $ 162,500 68 Details 54 Site Reliability Engineer $ 162,500 246 Details 55 Solution Architect $ 162,200 170 Details 56 Actuary $ 162,060 34 Details 57 Big Data Developer $ 160,290 8 Details 58 Machine Learning Researcher $ 160,000 256 Details 59 MLOps Engineer $ 160,000 87 Details 60 AI Tech Lead $ 160,000 5 Details 61 Business Intelligence Lead $ 160,000 14 Details 62 Principal Statistical Programmer $ 160,000 30 Details 63 Software Development Engineer $ 159,600 374 Details 64 Engineer $ 159,346 6186 Details 65 Security Researcher $ 159,000 34 Details 66 Staff Engineer $ 158,600 22 Details 67 Lead Analyst $ 158,530 22 Details 68 Principal Engineer $ 158,000 34 Details 69 Principal Researcher $ 157,000 24 Details 70 Manager $ 156,160 4769 Details 71 Sales Engineer $ 155,000 22 Details 72 Data Governance $ 155,000 276 Details 73 Data Platform Engineer $ 154,800 42 Details 74 Analytics Lead $ 154,300 70 Details 75 Data Management Lead $ 151,962 40 Details 76 Data Analytics Lead $ 151,400 48 Details 77 Product Specialist $ 151,250 26 Details 78 Data Architect $ 151,198 857 Details 79 Account Executive $ 150,000 87 Details 80 DataOps Engineer $ 149,880 22 Details 81 Bioinformatics Scientist $ 149,200 48 Details 82 Full Stack Engineer $ 148,500 122 Details 83 Data Scientist $ 148,000 6585 Details 84 Data Operations Lead $ 147,500 8 Details 85 Analytics Manager $ 147,400 10 Details 86 Data Quality Lead $ 147,000 24 Details 87 Machine Learning Lead $ 146,479 8 Details 88 Data Operations Manager $ 145,250 14 Details 89 Data and Analytics Consultant $ 145,000 8 Details 90 Data Lead $ 145,000 182 Details 91 Data Product Manager $ 144,500 156 Details 92 Analytics Engineer $ 144,000 869 Details 93 Risk Analyst $ 144,000 7 Details 94 Power BI Administrator $ 143,927 14 Details 95 Quantitative Analyst $ 143,100 152 Details 96 Artificial Intelligence Engineer $ 142,500 24 Details 97 Web Developer $ 142,000 9 Details 98 Technical Recruiter $ 141,000 18 Details 99 System Engineer $ 141,000 64 Details 100 Computational Scientist $ 140,975 16 Details 101 Data Engineer $ 140,920 6197 Details 102 Decision Scientist $ 140,570 68 Details 103 Data Analytics Business Partner $ 140,000 10 Details 104 Data Governance Lead $ 138,300 80 Details 105 DevOps Engineer $ 137,510 309 Details 106 Product Owner $ 137,300 116 Details 107 Computational Biologist $ 136,550 40 Details 108 Technical Writer $ 136,330 14 Details 109 Automation Engineer $ 135,075 32 Details 110 Cloud Developer $ 134,000 10 Details 111 Data Product Owner $ 131,050 102 Details 112 Data Integration Engineer $ 131,000 34 Details 113 Solution Engineer $ 130,900 84 Details 114 Data Strategist $ 130,134 64 Details 115 Cloud Engineer $ 130,000 122 Details 116 Data Operations Engineer $ 130,000 24 Details 117 Data Modeler $ 130,000 76 Details 118 AI Developer $ 130,000 107 Details 119 QA Engineer $ 130,000 59 Details 120 AI Specialist $ 128,460 46 Details 121 Data Analysis $ 128,400 62 Details 122 Data Analytics Manager $ 128,300 124 Details 123 Machine Learning Specialist $ 128,000 16 Details 124 Data Governance Engineer $ 127,400 10 Details 125 Marketing Science Partner $ 127,000 8 Details 126 ETL Developer $ 126,100 12 Details 127 Data Operations $ 126,000 50 Details 128 Systems Administrator $ 125,900 16 Details 129 Analytics Specialist $ 125,800 40 Details 130 Database Administrator $ 125,000 18 Details 131 Software Developer $ 125,000 416 Details 132 Data Integrator $ 125,000 18 Details 133 Associate $ 125,000 1329 Details 134 Business Intelligence Engineer $ 124,600 326 Details 135 Product Analyst $ 123,713 152 Details 136 Java Developer $ 122,500 14 Details 137 Business Intelligence Manager $ 122,500 6 Details 138 AI Product Owner $ 122,500 10 Details 139 BI Engineer $ 121,400 54 Details 140 Full Stack Developer $ 121,314 84 Details 141 Developer $ 120,000 778 Details 142 Tableau Developer $ 120,000 8 Details 143 Statistician $ 119,000 79 Details 144 Data Governance Manager $ 118,962 34 Details 145 Statistical Programmer $ 118,383 46 Details 146 Machine Learning Developer $ 117,600 6 Details 147 Technical Support Engineer $ 117,500 22 Details 148 Prompt Engineer $ 117,200 55 Details 149 Data Reporter $ 116,635 6 Details 150 Technical Specialist $ 115,600 18 Details 151 Data Visualization Engineer $ 114,600 52 Details 152 Data Developer $ 113,225 72 Details 153 Data Team Lead $ 112,500 26 Details 154 Data Consultant $ 112,000 30 Details 155 Consultant $ 111,100 814 Details 156 Data Management $ 110,683 94 Details 157 Data Visualization Developer $ 110,600 12 Details 158 BI Analyst $ 110,000 314 Details 159 Cloud Database Administrator $ 107,750 10 Details 160 Business Analyst $ 107,500 277 Details 161 Business Intelligence Developer $ 107,094 170 Details 162 Analyst $ 107,025 3447 Details 163 Data Visualization Specialist $ 106,968 28 Details 164 BI Developer $ 106,300 84 Details 165 AI Governance Specialist $ 106,087 6 Details 166 Data Visualization Analyst $ 106,055 6 Details 167 Business Intelligence Specialist $ 105,182 16 Details 168 Power BI Specialist $ 105,000 8 Details 169 Data Governance Analyst $ 104,650 124 Details 170 Business Intelligence Analyst $ 104,000 193 Details 171 Robotics Engineer $ 103,500 26 Details 172 Application Developer $ 101,820 28 Details 173 Postdoctoral Researcher $ 100,320 59 Details 174 Power BI Developer $ 100,050 84 Details 175 Data Governance Specialist $ 99,581 48 Details 176 Data Analyst $ 99,000 5851 Details 177 Technology Integrator $ 98,650 6 Details 178 Python Developer $ 98,000 70 Details 179 Data Manager $ 97,950 416 Details 180 Data Quality Engineer $ 96,750 30 Details 181 Data Analytics Specialist $ 96,437 30 Details 182 Data Management Analyst $ 95,973 218 Details 183 Insight Analyst $ 94,968 86 Details 184 Data Quality Analyst $ 94,856 74 Details 185 Cloud Database Engineer $ 93,353 6 Details 186 Research Professional $ 91,500 8 Details 187 Data Integration Specialist $ 91,500 40 Details 188 Master Data Management $ 90,700 12 Details 189 Bioinformatician $ 90,579 32 Details 190 Copywriter $ 90,000 15 Details 191 Data Management Associate $ 89,056 22 Details 192 Data Operations Analyst $ 87,100 60 Details 193 Research Analyst $ 85,000 97 Details 194 Business Development Representative $ 84,210 11 Details 195 Actuarial Analyst $ 82,350 8 Details 196 Data Quality Specialist $ 82,278 8 Details 197 Data and Reporting Analyst $ 81,950 18 Details 198 Data Management Specialist $ 81,725 162 Details 199 Postdoctoral Fellow $ 80,480 70 Details 200 Encounter Data Management Professional $ 79,650 20 Details 201 Power BI Expert $ 78,708 10 Details 202 Data and Reporting Professional $ 78,150 28 Details 203 Data Governance Consultant $ 75,789 6 Details 204 Data Visualization Designer $ 75,500 8 Details 205 Technical Support Specialist $ 75,000 10 Details 206 Postdoctoral Research Fellow $ 75,000 6 Details 207 Data Operations Specialist $ 72,050 16 Details 208 Data Specialist $ 71,200 508 Details 209 Sales Development Representative $ 70,000 20 Details 210 Power BI $ 66,800 38 Details 211 Research Associate $ 66,667 160 Details 212 Data Reporting Analyst $ 65,797 18 Details 213 Data Analist $ 63,157 7 Details 214 Research Specialist $ 61,000 10 Details 215 Trainee $ 58,713 10 Details 216 Research Assistant $ 58,000 69 Details 217 Backend Developer $ 57,894 6 Details 218 Data Integrity Specialist $ 57,000 8 Details 219 Clinical Aide $ 35,857 6 Details 220 QA Tutor Reviewer $ 24,000 16 Details 221 Stage $ 21,473 18 Details
See here the salary index for past years: Global Salary Index for 2024 View list Global Salary Index for 2023 View list Global Salary Index for 2022 View list
| 2023-02-01T00:00:00 |
https://aijobs.net/salaries/
|
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AI Jobs Salary: AI Engineer Salary, Developer, & More
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AI Jobs Salary: AI Engineer Salary, Developer, & More
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https://aidegreeguide.com
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[] |
For those with more than three years experience, the AI engineer's average salary rises to $130,000. And for those in the top tier of experience and ...
|
When you want to know what the next big thing will be in computer science, you just have to look at where the money is going.
Right now, it’s running like a river into the field of artificial intelligence.
OpenAI, the collection of minds behind the phenomenal GPT-4, received a ten billion dump of dollars from Microsoft in 2023. That was on top of prior investments rumored to range to more than $13 billion overall. Anthropic, a competitor, trails only slightly with around $7 billion in estimated investments in 2023, topped off by a $4 billion fuel-up from Amazon.
All told, according to Stanford University’s AI Index Report for 2023, over $90 billion of private investment in AI was made in 2023 alone. And that doesn’t even count other funds flowing in, from internal and M&A (mergers and acquisitions) activity, which more than double the total to almost $190 billion in global corporate investments.
How Much of that $190 billion will end up boosting salaries for AI engineers?
One of the big questions for people considering a career in artificial intelligence is just how much of that nearly $200 billion bankroll is going to end up in their paycheck?
Of course, it’s a field where the intangibles are off the charts. Most people working in AI today are there because they are fascinated by machine intelligence. They’re eager to be part of a development that is going to change the course of human history. They are powered by the excitement of discovery or the urge to help create responsible and equitable alignments.
But no one is disappointed that the money also happens to be pretty good.
It’s true that most big AI models today require a substantial investment in infrastructure or outsourced computation for training. A technical overview of GPT-3 cited by Forbes estimated that each training run costs around $5 million worth of processing time. CEO Sam Altman claimed that training costs were over $100 million in 2023, and Multiverse CTO Sam Mugel estimated that training costs would surpass $1 billion in the coming years.
But that isn’t a big chunk of $190 billion. And it discounts revenues from selling those models or their services. There’s enough left that OpenAI’s salary rates for AI engineers were bumped up to over $800,000 per year as reported by Business Insider.
Suffice it to say, paychecks in the AI world aren’t going to start bouncing anytime soon.
The Complexity of Determining Salary Rates for AI Engineers, Developers, Programmers, Researchers, and More
OpenAI and other cutting-edge research firms are a special case, however. They must have the top talent in the field and pay top dollar to get it.
Aside from a few rumors, though, and a lot of people who know a guy who knows a guy who makes big bucks, there’s not a lot of substantial data out in the wild yet about AI job salary rates. The industry is too new, and the most reliable source of salary information for American industry and careers hasn’t caught up yet.
That’s the Bureau of Labor Statistics (BLS), which keeps track of regional, industry, and job categories across the nation to find all kinds of salary and employment data.
BLS is thorough, which means they are absolutely collecting the information from payrolls on all those high-flying AI salaries. But they have not yet separated out AI scientists, engineers, or other roles from regular computer scientists, programmers, and software engineers. The big money gets lost in the shuffle of averages.
Do AI engineers make a lot of money? Read on for the answer.
However, with a little detective work and a few reliable third-party sources, we have put together the best look possible today at AI specialist salary levels across a range of up-and-coming job titles.
How Much Do AI Engineers and Other AI Professionals Make?
Many critical positions in the emerging AI industry aren’t standardized. Different companies are combining responsibilities in different ways.
But if you look at job roles when defined by tasks, a dozen or so positions emerge. Each has different kinds of training and knowledge requirements. They may be weighted toward tracks in AI education and career paths. And each comes with different potential salary levels.
| 2024-02-28T00:00:00 |
2024/02/28
|
https://aidegreeguide.com/salary/
|
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Is MS in artificial intelligence USA worth it? - GradRight
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One moment, please...
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https://gradright.com
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The average artificial intelligence engineer salary in the USA is $103,000/year. An artificial intelligence engineer's salary in the USA will vary depending on:.
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| 2023-02-01T00:00:00 |
https://gradright.com/ms-in-artificial-intelligence-usa-course-structure-best-universities-career-options-salary-and-more/
|
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|
What Artificial Intelligence Engineer Salary Can You Expect in 2024?
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What Artificial Intelligence Engineer Salary Can You Expect in 2024?
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https://pg-p.ctme.caltech.edu
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[] |
Artificial Intelligence Engineer Salary by Experience · Entry-level. $115,599 per year · 1-3 Years. $125,714 per year · 4-6 Years. $136,883 per year · 7-9 Years ...
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Artificial Intelligence and machine learning are riding the crest of popularity in today’s IT world. So naturally, this popularity translates into a growing demand for AI professionals. However, with more companies hiring artificial intelligence and machine learning professionals, it becomes more challenging to decide who to sign up with.
That’s why we present to you this informative article that demystifies the concept of the AI engineer salary. We will begin by defining an artificial intelligence engineer, then explore the AI skills you need to become one.
Then we will cover the main point: the artificial intelligence engineer salary. We will analyze average AI engineer salaries by experience level, geographical location, company, and industry. Finally, we’ll round things out with a peek at the future job outlook for artificial intelligence engineers and how you can score a better AI engineer salary.
So, what’s an AI engineer, and what do they do?
What’s an AI Engineer?
Before looking at artificial intelligence jobs salary data, we should define the AI engineer’s role. Artificial Intelligence engineers develop AI-based systems and applications using machine learning and artificial intelligence techniques, such as neural networks, machine learning algorithms, and natural language processing (NLP).
Additionally, artificial intelligence engineers are IT experts who develop intelligent algorithms that can learn, analyze, and even predict future events. In summary, AI engineers create machines that can reason like a human brain.
Here’s a list of AI engineer responsibilities, presented for quick and easy reference.
Use AI to solve problems
Maintain existing AI infrastructure and systems
Create and deploy innovative AI solutions
Prepare and analyze data sets
Collect data and analyze it (analytics) to identify and resolve issues
Conduct image processing, pattern matching, feature extraction, and pattern recognition
Work with other teams (e.g., data management, information security, electrical engineering, etc.), to integrate AI modules
Construct data science infrastructure.
Also Read: Machine Learning Engineer Salary: Trends in 2024
What Are the Skills Required to Become an AI Engineer?
Artificial intelligence engineers require a particular set of skills to perform what’s considered a very complex job. And the more skilled the artificial intelligence engineer, the higher the artificial intelligence engineer salary.
Programming
Artificial intelligence engineers require a solid working knowledge and understanding of object-oriented programming languages like Java, Python, R, C++, etc. Fortunately, these languages are relatively easy to learn, and the more you know, the more versatile your skillset is.
Linear Algebra, Statistics, and Calculus
Artificial intelligence engineers often use derivation, matrix multiplication, vectors, and integrals. A good AI engineer is also familiar with statistical models and concepts like Gaussian Distributions, Hidden Markov Model, Naïve Bayes, and Standard Deviations.
Algorithm Theory and Applied Math
Artificial intelligence engineers with applied math and algorithms skills can better understand concepts like Gradient Descent, Quadratic Programming, and Summations.
Signal Processing Techniques
It will be crucial for feature extraction. In addition, it will enable you to perform time-frequency analysis and advanced signal processing algorithms.
Language, Audio, and Video Processing
These skills help you deal with linguistics and computer science and include elements such as audio, text, or video using natural language processing.
Communication
This soft skill helps you better deal with team members, other groups, and non-technical people such as stakeholders.
Critical Thinking
This soft skill helps AI engineers to objectively analyze the facts, observations, and evidence and form a conclusion.
Problem-solving
Finally, this soft skill covers defining a problem, ascertaining its origin, identifying and prioritizing potential solutions, and implementing them.
Now that we know the skills an artificial intelligence engineer needs, it’s time to look at the AI engineer salary picture.
AI Engineer Salaries
The typical artificial intelligence engineer salary varies by several factors, which we will discuss later. However, the median annual AI engineer salary in the United States is $156,648, according to Ziprecruiter.com. According to these findings, the lower end of the spectrum falls at $79,500 and tops out at $266,500.
Artificial Intelligence Engineer Salary by Experience
Here is a breakdown of artificial intelligence salaries by years of experience. Obviously, the more experience you have, the more you can expect to earn. Glassdoor.com reports the following figures as of February 2023.
Entry-level. $115,599 per year
1-3 Years. $125,714 per year
4-6 Years. $136,883 per year
7-9 Years. $145,100 per year
10-14 Years. $157,274 per year
15+ Years. $168,018 per year
The Top Five Best-Paying Jobs Related to Artificial Engineer Jobs
Sometimes you can’t get the exact job title you were shooting for. Here are the top five positions available that relate to artificial engineering and what they pay, as reported by Ziprecruiter.com.
Director of Business Intelligence. $152,030 per year
$152,030 per year Intelligence Researcher. $148,608 per year
$148,608 per year Senior Artificial Intelligence Engineer. $147,236 per year
$147,236 per year Artificial Intelligence Ai Engineer. $146,428 per year
$146,428 per year Machine Learning AI. $141,431 per year
Similar AI Engineer-Related Occupations
Here’s a sample of five more AI engineer-related jobs that, while not paying as high as our previous list, are nevertheless great positions where you can use your artificial intelligence job skills, reported by Glassdoor.com.
Data engineer . $94,431 per year
. $94,431 per year Data scientist. $103,052 per year
$103,052 per year Machine learning engineer . $96,022 per year
. $96,022 per year Software developer. $88,544 per year
$88,544 per year Software engineer. $87,301 per year
Also Read: What are Today’s Top Ten AI Technologies?
AI Engineer Salaries by Company
The typical AI engineer salary can vary from one company to the next, depending on size, compensation package, and overall need. Here is a sampling of what a dozen different companies are offering, as reported by Glassdoor.com.
Google. $204,579 per year
$204,579 per year Apple. $197,481 per year
$197,481 per year Shield AI. $181,589 per year
$181,589 per year Intel Corporation. $181,216 per year
$181,216 per year Electronic Arts. $178,382 per year
$178,382 per year Interactions. $176,078 per year
$176,078 per year Tesla. $165,642 per year
$165,642 per year Visa Inc. $163,162 per year
$163,162 per year Aisera. $161,246 per year
$161,246 per year Invitae. $155,183 per year
$155,183 per year IBM. $148,424 per year
$148,424 per year Lockheed Martin. $123,379 per year.
AI Engineer Salary by Location
An artificial intelligence engineer salary can also vary by where in the United States the job is. Here are the top ten best-paying cities for artificial intelligence engineering jobs, as indicated by Ziprecruiter.com. When considering the location and how good the salary is, remember to take the local cost of living into consideration. For example, it’s safe to say it’s more expensive living in California than in Wyoming, so your pay will go further in the latter case.
Green River, WY. $203,549
$203,549 Santa Clara, CA . $188,392
. $188,392 San Francisco, CA. $182,696
$182,696 Washington, DC. $182,094
$182,094 Bolinas, CA. $181,889
$181,889 Los Angeles, CA. $179,699
$179,699 Kensington, NY. $178,976
$178,976 Fremont, CA. $178,499
$178,499 Marysville, WA. $176,739
$176,739 Germantown, MD. $175,133
AI Engineer Salaries by Industry
Here are the top four intelligence engineer salary figures by industry, supplied by Glassdoor.com.
Information Technology. $178,647 per year. This rate is 1% higher than in other industries.
$178,647 per year. This rate is 1% higher than in other industries. Media and Communication. $178,382 per year. This rate is 1% higher than in other industries.
$178,382 per year. This rate is 1% higher than in other industries. Manufacturing. $165,642 per year. This rate is 7% lower than other in industries.
$165,642 per year. This rate is 7% lower than other in industries. Aerospace and Defense. $123,379 per year. This rate is 43% lower than in other industries.
What’s the Job Outlook for AI Engineers?
In a word: bright.
The artificial intelligence field has a fantastic job outlook, as the IT world has only begun to tap its full potential. The U.S. Bureau of Labor Statistics predicts a 31.4 percent increase in jobs for data scientists and mathematical science professionals, two of the fundamental building blocks for artificial intelligence by 2030.
So if you’re looking for a career that’s brimming with potential and will challenge your mental faculties while paying a high artificial intelligence salary, you should check out the AI engineering field.
How to Boost Your Artificial Engineer Salary
So you’ve taken the plunge and become an artificial intelligence engineer. Congratulations! But now, you’re eager to amplify that salary a bit. So what are the best ways to increase your AI engineer salary?
Earn your master’s degree. Although you don’t need a master’s degree to get a great job as an artificial intelligence engineer, you can’t have too much education or possess too many skills. A master’s degree adds more gravitas to your CV or resume, and AI specialists with master’s degrees tend to earn more.
Although you don’t need a master’s degree to get a great job as an artificial intelligence engineer, you can’t have too much education or possess too many skills. A master’s degree adds more gravitas to your CV or resume, and AI specialists with master’s degrees tend to earn more. Conduct your own research. This article refers to several job sites for salary figures. Save those sites and use them to research your position. There’s nothing like a bit of evidence to back up your claims to your boss.
This article refers to several job sites for salary figures. Save those sites and use them to research your position. There’s nothing like a bit of evidence to back up your claims to your boss. Sharpen your hard skills or learn new ones. Practice makes perfect. Look for opportunities to use and perfect your current skills, and look into self-taught options to gain new skills. The more skills you acquire, the more valuable an employee you are!
Practice makes perfect. Look for opportunities to use and perfect your current skills, and look into self-taught options to gain new skills. The more skills you acquire, the more valuable an employee you are! Get certification. Certifications offer you an education and experience, then provide a means of verification to potential employers. For instance, consider this valuable AI bootcamp. Through more than 25 hands-on projects, including three capstone projects in three critical domains, you will learn skills such as ensemble learning, reinforcement learning, NLP, deep learning, recommendation systems, computer vision, Python, and much more.
Take Control of Your Future
So don’t delay. Sign up for this valuable AI bootcamp, enjoy a new career in the fast-growing field of artificial intelligence, and, most of all, earn a high AI engineer salary. Take the first step today!
You might also like to read:
The Future of AI: A Comprehensive Guide
How Does AI Work? A Beginner’s Guide
How Much is the Typical Data Analytics Salary?
Cybersecurity Salary Guide: How Much Can You Make?
What is the Average Blockchain Developer Salary?
| 2023-06-27T00:00:00 |
2023/06/27
|
https://pg-p.ctme.caltech.edu/blog/ai-ml/ai-engineer-salary
|
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2025 Machine Learning Engineer Salary in US | Built In
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2025 Machine Learning Engineer Salary in US
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https://builtin.com
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[] |
A. The salary range for a machine learning engineer is $44K-$170K. The average salary for a Machine Learning Engineer in US is $158,147.
|
How Much Does a Machine Learning Engineer Make in US?
The average salary for a Machine Learning Engineer in US is $158,147. The average additional cash compensation for a Machine Learning Engineer in US is $44,280. The average total compensation for a Machine Learning Engineer in US is $202,427. Machine Learning Engineer salaries are based on responses gathered by Built In from anonymous Machine Learning Engineer employees in US.
| 2023-02-01T00:00:00 |
https://builtin.com/salaries/us/machine-learning-engineer
|
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|
How Much Do AI Engineers Make - University College Dublin
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How Much Do AI Engineers Make
|
https://www.ucd.ie
|
[] |
The national average salary for an AI engineer in Ireland is €60,500, which is considerably higher than the median annual earnings of €44,200 in the country.
|
AI is continuously dominating the global job market and impacting the world’s economic output. In fact, Accenture predicts that AI can double the annual economic growth rate by 2035. It makes sense. After all, businesses are now integrating AI-powered solutions into their operations to boost overall efficiency. So, it’s safe to conclude that AI-related jobs have a wide scope. But before you step into this industry, you should know how much do AI engineers make.
AI engineers in Ireland can make between €38,000 and €103,000 a year, with €60,500 on average. If it seems attractive to you, know that this is just the current range, and the pay scale WILL increase in the coming years. Generally, an AI engineer’s salary depends on various factors, including company, skills, location, experience, qualification, and job position. At a higher position with a diverse skill set, you can make even more than your friend at the same position with fewer skills. This guide will dig deeper into how much an AI engineer makes in Ireland depending on different factors and whether you should choose it as your career. So, let’s dive in!
What Is an AI Engineer's Salary Range? AI engineering is becoming a leading career worldwide, including in Ireland. The best part? It’s high-paying too! The national average salary for an AI engineer in Ireland is €60,500, which is considerably higher than the median annual earnings of €44,200 in the country. Based on Glassdoor’s statistics, here is a quick breakdown of how much an AI engineer makes in Ireland: The minimum wage of an AI engineer in Ireland is €38,000 a year. It’s the salary for most beginners in junior or entry-level positions.
The maximum wage of an AI engineer in Ireland is €103,000 a year. That’s how much an AI engineer in the highest position makes in the country.
The median salary of an AI engineer in Ireland is €70,500, meaning 50% of the employees earn less than €70,500, and the remaining 50% make more.
The average AI engineer salary is 136.8% more (€60,500) than the average earnings (€44,200) in Ireland for all occupations.
A Comprehensive Job Outlook for AI Engineers in Ireland According to James O’Connor — the Site Lead at Microsoft Ireland and Vice President at Microsoft Global Operations Service Center — 75% of companies will use AI solutions by 2030. That’s all due to the efforts of the Government's Digital Ireland Framework. Additionally, the Zion Market Research study forecasted that the AI market will grow at a 39.4% CAGR to hit US $422 billion by 2028. Nowadays, almost every company needs an AI engineer. Whether it's product development, operations, or healthcare, AI specialists assist in various departments to increase the business’s overall productivity and efficiency. This versatility relates to their hiring growth. The higher the demand for a profession, the more chances it has for higher salaries and scope. Let’s look at how experience, location, job title, and industry can affect your income as an AI engineer.
AI Engineer's Salary by Experience Your level of experience will affect your salary the most. The more experienced you are, the higher you can move up to the career ladder. For example, starting as an entry-level Machine Learning Engineer and then advancing to Senior Machine Learning Engineer, Lead Machine Learning Engineer, Principal Machine Learning Engineer, and so on. Your salary will keep increasing across the ladder. Here is how your salary as an AI engineer will vary based on your experience: Machine Learning Engineer (2 to 4 years): €52,000 to €80,000 per year
Senior Machine Learning Engineer (2 to 4 years): €63,000 to €107,000 per year
Lead Machine Learning Engineer (5 to 7 years): €68,000 to €110,000
Principal Machine Learning Engineer (8+ years): €73,000 to €120,000
Director of Machine Learning (8+ years): €80,000 to €130,000
Vice President of Machine Learning (8+ years): €90,000 to €150,000 Promoting to managerial or leadership positions (principal, director, or vice president) significantly increases your salary. You can further boost your wage by developing your skill set or expertise. In fact, there is also a chance of earning more if you switch your job and successfully negotiate a good salary during the interview.
AI Engineers' Salary by Industry Most industries require AI engineers for various purposes, including healthcare, finance, tech, retail, etc. The salaries of these professionals vary greatly as you move from one industry to the other. AI engineers working in finance and technology are typically paid more than other industries worldwide. That’s mainly because these industries give AI engineers more chances to upscale themselves, learn more skills through courses, and progress in their career path. So, if you are tech-savvy, you can kickstart your career as an AI engineer in this high-paying industry.
AI Engineers' Salary by Geography As we know, the geography or location of the company you’re employed at also plays a significant role in deciding your pay rate. In general, AI engineers working at companies in Dublin are more likely to make more. The salaries in cities are usually higher, considering the higher cost of living. Many companies consider offering geography-based salaries to their remote and in-house employees. But it can be challenging, as they’d have to consider every employee’s experience and qualification levels as well. Dublin is, without any doubt, a heaven for tech experts with headquarters of renowned companies like LinkedIn, Google, and Facebook. Currently, 70,000+ people are working in the industry. The second hottest city for AI engineers is Galway, home to 105 tech companies, including some big names like Alison, Orreco, BriteBiz, and more. Cork, Limerick, and Waterford are also excellent places for starting a career in AI engineering. There are 140+ tech companies in Cork, 63 in Limerick, and 26 in Waterford. So, as an AI engineer, you can choose any of these locations for employment as they all are high-paying.
AI Engineers Salary by Jobs Title AI engineering is a vast field with many career opportunities. You can be a software engineer, data engineer, or machine learning engineer based on your interest and qualification. Yes, your salary will vary with your job title. So, how much do AI engineers make while working at different job titles? Let’s have a look.
1. Machine Learning Engineer A machine learning engineer works with massive data sets to develop algorithms for computers to enable them to learn from data. These professionals not only design machine learning models and algorithms but also test and implement them for the proper execution. A machine learning engineer can make around €57,300 annually on average. However, this figure varies depending on your job position or experience. For example, an entry-level machine learning engineer makes €43,000 annually on average. With more experience, they can become a senior machine learning engineer, with a bright chance of making €91,000 annually.
2. Data Scientist Data scientists are experts in mathematics, statistics, and computer science. They use their skills in analysing and interpreting complex data sets to help managers make informed decisions. A data scientist in Ireland can expect to make around €54,800 annually on average. This pay scale will change based on your job level. A junior machine learning engineer can start from €36,000 a year. In a senior position, they can earn as high as €86,000.
3. AI Researcher AI researchers explore different AI algorithms and develop a solution or application based on their research. They search thoroughly, write papers, and present their findings in simple language at team meetings or conferences. As an AI researcher, expect to earn around €62,500 a year, depending on your job level. You may start with €36,000 at an entry-level position and earn €109,000 after climbing to the senior level.
4. Software Engineer Software engineers perform a range of job responsibilities to function computers to their total capacity. It includes code testing, developing algorithms, and debugging AI-related software. If you’re a software engineer in Ireland, you can earn €60,000 a year on average. You will likely make €40,000 a year as a junior software engineer. This figure can reach €90,000 as you become a senior software engineer.
5. Systems Engineer Systems engineers develop and monitor the implementation of all major and minor systems in an organisation. Their responsibilities include ensuring components’ compatibility, fixing issues, coordinating system development, and more. These professionals make €55,000 yearly on average, with €33,000 as an entry-level salary and €87,000 for senior positions.
6. Information Security Analyst Information security analysts design and implement measures that help an organisation secure its computer systems and networks. It includes risk assessment, security threat research, and preventive measures development. As an information security analyst, you can earn around €45,000 a year on average. Your annual salary will be a bit low at a junior level, anywhere near €32,000. But as you progress in your career path, you can expect to earn as high as €72,000 a year.
7. User Experience Designer Another important AI-related job is the user experience designer. These professionals create simple and efficient interfaces that users can use easily. They develop graphical elements, test prototypes, design navigation solutions, and more. User experience engineers can make €50,000 a year. This figure is usually at the lower end for junior user experience engineers, averaging €35,000 a year on average. Senior-level user experience designers can earn as high as €74,000 every year.
How To Become an AI Engineer? Now that you know how much do AI engineers make, you must decide whether you want to become one. But that’s not just it. Instead, you should also evaluate other things, such as your interests and qualifications and if you're ready to enrol in a course or certification to learn new skills. If you’re still willing to become an AI engineer, just follow the below steps to get started:
Step 1: Find Your Interest Your first step should be to find what you are interested in. AI engineering is a broad field with many career opportunities like machine learning, software engineering, AI research, and more. When you start with just one or two skills, it helps you build a strong profile. So, pick any AI engineering job option and try to excel. Once done, you can move on to learning other skills and keep adding them to your portfolio. This will provide you with a solid base.
Step 2: Obtain a Course Certification If your qualification differs from AI, it’s still not too late for you to start a career in AI engineering. Consider enrolling in a short course to gain a certification in AI. Your mentors would help you develop the skills required in the AI industry. They will also give you hands-on training on how things work and how you can come up with solutions. You can easily find an AI engineering job if you specialise in data science, machine learning, and other AI-related skills.
Step 3: Enhance Your Skills As AI constantly transforms the world, you’d need to pace up and keep enhancing your skills. It means you can’t just learn machine learning and think it would be enough for you to bag an AI engineering job. Instead, you must learn more AI-related skills before entering the market and applying for your dream job. So, keep adding to your CV’s skills column to attract the interviewer’s eyes. Again, earning certifications is really important. These certification courses are relatively less expensive and can be done online.
Step 4: Enter the Job Market Now, it’s time to step into the job market. Based on your expertise, you can apply for the best-suited job positions in the right AI-engineering discipline. For example, many companies post jobs for machine learning engineering, AI researchers, and data scientists, so you can pick those with a higher chance of winning. Don’t forget to design a comprehensive CV and a strong portfolio beforehand. These are the first two things an interviewer uses to decide about a candidate. If you succeed in winning that job, you will start as a junior-level AI engineer. But as you advance to higher positions, you will have more job responsibilities and a higher pay rate.
FAQs How Much Does an Average AI Engineer Earn in Ireland? The average pay of an AI engineer in Ireland is €60,500 a year. It can go as low as €38,000 and as high as €103,000 depending on the company you’re employed at, your geography, experience, qualification, skills and industry. So, do your research before applying to an AI engineering job to start your career with an attractive salary package. Are AI Engineers in High Demand? AI engineering is a highly-demanded job with great potential to become even more popular in the upcoming decades. Almost every industry now needs AI engineers in product development, operations, delivery, and boosting a company’s overall productivity. In fact, Accenture's report found AI will dramatically increase the world’s economic growth by 2035! What is the Highest Paid Position in AI Engineering? The highest-paid position in AI engineering is senior AI engineer. In this position, you will have thorough knowledge and experience in AI, machine learning, data analysis, and all the fields related to AI engineering. You will also be leading and managing tech teams. Considering this much workload, you can expect to negotiate an attractive salary at most tech companies and earn even more than €100,000 annually. Is It Difficult to Learn Artificial Intelligence? Learning AI can be difficult if you don’t enrol in a quality course or learn the necessary skills. With an expert’s guidance and an AI engineering course, you can quickly gain a good command of this field and excel in your career.
| 2023-02-01T00:00:00 |
https://www.ucd.ie/professionalacademy/resources/how-much-do-ai-engineers-make/
|
[
{
"date": "2023/02/01",
"position": 93,
"query": "artificial intelligence wages"
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|
Implementing artificial intelligence education for middle ...
|
Implementing artificial intelligence education for middle school technology education in Republic of Korea
|
https://link.springer.com
|
[
"Park",
"Gajaeul Middle School",
"Seoul",
"Republic Of Korea",
"Kwon",
"Hskwon Kongju.Ac.Kr",
"Department Of Technology",
"Home-Economics Education",
"Kongju National University",
"Gongju-Si"
] |
by W Park · 2024 · Cited by 156 — The purpose of this study is multifold: First, to develop an educational program using artificial intelligence (AI) in middle school free semester system of ...
|
Free semester in Korean middle school
The free semester is an educational policy introduced to improve public education in South Korea. Similar examples abroad include the “transition year programme” in Ireland, “prao” in Sweden, “afterschool” in Denmark, and “gap year” in the United Kingdom (Kim & Choi, 2014). These educational policies reduce the pressure on learners to evaluate and provide experience-centered classes. As a result, students can explore their career paths and enable self-development according to their aptitudes. Furthermore, at the national level, governments applied free semesters to improve public education and to increases the reliability of school education.
The Ministry of Education (2015a) established the “Middle School Free Semester Implementation Plan” in 2015, and implemented the free semester in all middle schools in 2016. The program expanded the domestic free semester from one-semester “free semester” in 2016 to a one-year “free year system” in 2018. Currently, the government is promoting it to link the free year system with the general semester and gradually expand the program (Ministry of Education, 2017; Park & Hong, 2021). The government and many studies use the terms free semester and free year system interchangeably (in this study, unified as “free semester”). Students complete a flexible curriculum during the free semester, allowing them to participate in student-participatory classes and various career experience activities to discover their dreams and talents.
The Ministry of Education (2015a) presents the purpose of the free semester as “finding dreams and talents by exploring aptitudes and future,” “transitioning to future core competency-enhancing education,” and “happiness education that satisfies all students, parents, and teachers.” The activities of the free semester in South Korea are composed of four types as shown in Table 1. Generally, “career exploration activity” and “club activity” have been implemented for career guidance teachers. In practice, middle school teachers take “theme selection activity” and “art-physical activity.” Moreover, “art-physical activity” occurs in arts and physical education subjects.
Table 1 Organization and operation of ‘free semester activities’ Full size table
There are contrasting studies on the effectiveness of the free semester. Previous studies related to the career effect, which is one of the basic purposes of the free semester, confirmed various effects such as career decision self-efficacy, career competency, and self-efficacy (Hwang, 2019; Lee et al., 2016). Meanwhile, one longitudinal study of multiple middle schools showed that the free semester did not affect career maturity and self-efficacy (Lee, 2020). Another study confirmed “interest in science, technology, engineering, and mathematics” and “self-efficacy and career effects” of inquiry learning that applied convergence education in science free semester classes (Jeong & Lee, 2017). In addition, a qualitative study targeting secondary school teachers looked at the difficulties encountered in the science free semester classes (Kim & Choi, 2019). In the subject of mathematics, a study confirmed the change in “career exploration ability” and “attitude toward statistics” through the free semester statistics program (Kang et al., 2017). Additionally, another study looked at the link between convergence education research and engineering tools and 3D printers to mathematics education (Cho et al., 2014; Kang et al., 2016).
As mentioned above, the development of the free semester program for convergence education that deals with the elements of technology takes place in the subject, not the technology. In particular, Jeong and Lee’s (2017) study shows the practical possibility of free semester convergence education in technology education. In addition, 3D design and printing applied to mathematics education in the free semester system are tools frequently used in technology education, showing the possibility of realization in the free semester. However, unlike despite extensive research on technology education, there is currently a lack of research related to technology subject based free semester programs or evaluation. Accordingly, in-service technology teachers are experiencing difficulties in free semester classes (Kim, 2017; Kim & Lee, 2019; Lim & Lee, 2019; Um, 2016). Solving this problem requires empirical research on the free semester in technology education.
Artificial intelligence in education
In 1950, British mathematician and electrical engineer Alan Turing proposed a test distinguishing between intelligent machines and humans. After his first in-depth review, a full-scale and thorough study of artificial intelligence began. Mathematician Marvin Minsky founded the Massachusetts Institute of Technology (MIT) Artificial Intelligence Lab and made significant contributions to the development of artificial intelligence. Among the latest artificial intelligence technologies, “perceptron (perception + neuron),” the core of neural network algorithms, was first mentioned by Frank Rosenblatt in 1958 (Rosenblatt, 1958). After that, Marvin Minsky and Seymour Papert published “Perceptrons,” and artificial intelligence technology developed further to today’s machine learning and deep learning (Minsky & Papert, 1969). Since 2000, there has been significant artificial intelligence development, with active research and application in broad industrial fields from management to manufacturing (Liu et al., 2018; Peres et al., 2020; Ruiz-Real et al., 2021).
Artificial intelligence education
Seymour Papert, a mathematician and computer scientist who co-wrote “Perceptrons” with Marvin Minsky, greatly influenced AI education. Influenced by Jean Piaget, Seymour Papert significantly contributed to computer education and maker education and was interested in mathematics education using computers (Sung, 2018). Papert (1980) discusses his ideas about education in his book “Mindstorms: Children, Computers, and Powerful Ideas.” He said that learning spontaneously by interacting with the environment is the core of Piaget’s education theory and that computers make learning efficient. Therefore, he paid attention to computer-use education, simultaneously discussing AI education in his book. He defined AI education in terms of cognitive science, arguing for materializing metaphysical thinking in order to create intelligent machines (artificial intelligence). He expected students’ improved thinking processes through clarification of their thinking processes. For this reason, Seymour Papert argued the necessity of AI education.
Currently, there is a global effort to apply AI in the education field. Moreover, the field is actively discussing “what and how to teach students” in the era of AI. For instance, the Computer Science Teachers Association (CSTA) proposed “Computer Science For K-12 Standards” in the United States. Furthermore, the CSTA formed a joint council for AI education in 2018 with the Association for the Advancement of Artificial Intelligence (AAAI). After that, CSTA and AAAI created the Artificial Intelligence for K-12 Standards (AI4K12), proposing five big ideas for AI education, and are making efforts in AI education (Touretzky et al., 2019). K12-AI argues that students in the age of AI will have a fundamentally different relationship with technology than previous generation students. It also emphasizes teaching students in constructivist learning, design, and creative thinking to become citizens of the age of AI (Ali., 2019). In addition, in K12-AI, Wong stated that students should receive compulsory education to acquire AI literacy (manipulation and utilization of AI technology). They also argued that students need compulsory education to enter their respective professions as skilled workers. Finally, they emphasized the necessity of teacher and school system innovation and stakeholder cooperation (Wong., 2020).
Due to AI technology characteristics, the field also discusses educational perspectives on AI. For example, Luckin et al. (2016) said that Artificial Intelligence in Education (AIED) goes beyond educational technology and uses the sophistication of AI to increase learning efficiency. Furthermore, Holmes (2019) discusses AI education and classifies it into “Learning with AI” and “Learning about AI.” He also suggested dividing AI into “What we teach” and “How we teach it.” As such, AI-based education refers to an approach to AI in teaching and learning methods like the previous education using information and communication technology (ICT) (Busan Metropolitan City Office of Education, 2019).
Most see AI as a tool, but some educate purely on AI, approaching AI education as an extension of science, technology, engineering, and mathematics (STEM) education (Hong et al., 2020). The main concern is understanding AI and educating students about AI-related technology according to their level. For example, “5 big idea in AI” of AI4K12, which studied AI education standards, is representative. AI4K12 presents “perception, representation & reasoning, learning (machine learning, deep learning), natural interaction, and social impact” in AI as five core ideas (Touretzky et al., 2019). In addition, the topics of AI education involve AI ethics. Coeckelbergh (2019) pointed out the biases created by machine learning go beyond much-discussed privacy protection and discussed various ethical issues, such as the attribution of responsibility for these issues and the problem of unemployment, extending beyond the individual level to social issues. This point of view is similar to that of the Ministry of Education (2020). They added the subjects of “Artificial Intelligence Ethics” as well as “Artificial Intelligence Mathematics” and “Artificial Intelligence Fundamentals” to educate on AI itself. Meanwhile, AI ethics deals with the impact of AI technology on society, and there is an intersection with seeing AI as a tool of education.
In addition, educational researchers are actively conducting studies in AI-related fields, such as “computational thinking” and “SW competency,” reflecting competency-based evaluation (Choi, 2019). However, AI-related research has only recently emerged as a research topic, and investigation of the definition and evaluation of AI competency is insufficient (Min & Shim, 2021). Kim and Lee (2021) conducted a study to measure AI literacy for basic research related to students’ AI evaluation. Koh (2020) considered the mathematics curriculum to cultivate AI competency in mathematics education. A previous study related to AI evaluation developed a tool that can test the attitude toward AI to suit each middle and high school (Kim & Lee, 2020a, 2020b). Han (2020) developed an instrument to measure changes in attitude and efficacy toward AI in AI project classes. Another study developed and evaluated the fourth industrial revolution-related technology education program, including AI in technology education (Lee et al., 2019). Although there have been several studies related to AI evaluation, there have been few studies that actually dealt with AI education and evaluation in depth in Korea (Kim et al., 2021; Min & Shim, 2021).
As mentioned above, different subjects apply AI education in various ways. For example, in science education, a study intervention helped learners understand scientific principles through construction practice by building an AI-based mixed reality system (Yannier et al., 2020). Further, a study analyzed AI tools for application in computation and AI tutoring in mathematics education (Van-Vaerenbergh & Pérez-Suay, 2021). In addition, researchers frequently conduct studies using translation services, chatbots, and AI speakers in English education, according to the subject characteristics (Kaharuddin, 2021; Li et al., 2021; Wang & Petrina, 2013). Although not secondary education, a study of STEM-based AI education programs for non-engineering college students showed improvement in AI literacy (Lin., 2021). While research in AI education is primarily about either “learning with AI” or “learning about AI,” studies of science and mathematics education show that approaches with both characteristics are also possible.
Meanwhile, for trends in computer education-related research, Min and Shim (2021) analyzed related research topics in South Korea secondary education from 1998 to 2020 using topic modeling techniques. The results showed programming and SW were the keywords with the highest frequency from 2007 to 2018. Nevertheless, it showed that AI, which had never made it to the top 10 keywords until 2019, was the number one research topic. In fact, teachers” interest in AI is high in the current school field, and teachers are also aware of the need for AI education (Ryu & Han, 2018). On the other hand, despite the central position of AI and machine learning in the modern computing field, studies on systematically learning machine learning in education-related research are insufficient (Tedre et al., 2021). In fact, in a study examining the perception of elementary school teachers in charge of AI education, it was found that 65.4% of teachers lack self-confidence (Lee, 2021). Additionally, Shin (2020) showed that teachers negatively perceive the quality and evaluation of teaching–learning in education using AI. Although social and educational academics are very interested in AI research, the school is somewhat different from external expectations.
Artificial intelligence and technology education
Studies on education methods using AI tools and AI education in STEM are underway. However, the number of studies in technology subjects is small. Software education and computational thinking related to AI also lack the number of studies in technology education compared to other subjects (Lee, 2018, 2019). Kim (2021) analyzed research trends related to AI in elementary and secondary education through topic modeling and showed that all keywords included “technology.” Still, there were very few studies on “technology education.” Lim (2020) said that technology education justifies AI education and that in the age of AI, technology education needs direction and active research. For example, in a study of elementary and secondary teachers, teachers answered that “AI education is using AI in life.” Furthermore, teachers answered that it is most important for students to integrate AI with other subjects or apply it to problem-solving processes (Kim & Han, 2020). In a cognitive analysis study of AI-using education, elementary school teachers said that “used to aid in class” is appropriate for AI in education (Han, 2020). In addition, educators recognized the “problem-centered learning approach” as the most appropriate (Han et al., 2020). The core goal of technology education is to solve technological and practical problems in the real world through a convergence approach (Custer, 1995). Thus, centering technology education is very effective for AI education.
A study by Lim (2020) suggested the direction in technology education and discussed the justification of AI education in technology curriculum. In addition, Kim’s (2020) study applied maker education methodology based on AI and physical computing in program development. Furthermore, Lee and Lee (2021) developed a program to select the core concepts of AI and apply them to technology education. In addition to AI, a study also selected the core interlocking technology of the fourth industrial revolution for application in middle school technology education, developed a program, and measured the effect (Lee et al., 2019). Although it is not direct, there have also been studies on robot education and developing bio-mimicking robot education programs in technology education (Kim, 2019; Kim & Jeong, 2006). Research on these AI-related technologies can serve as a reference for the direction of technology education.
As mentioned above, studies related to AI education can have many implications for technology education. For example, Papert (1980) expected that learners would improve their thinking in clarifying the thinking process to create AI. His research suggests that AI education can affect mathematical thinking and learners’ cognitive ability. In addition, although education can approach AI as a tool in computer science or educational methods, AI education from the STEM perspective can also affect technology education. Educators can teach AI as cutting-edge technology, and students can learn social changes in technology education. In addition to the technology aspect, the ethical problems of AI pointed out by Coeckelbergh (2019) are highly related to technology education’s ICT ethics. Moreover, research on AI conducted to realize each subject’s purpose in STEM-related subjects (science, mathematics) can indirectly provide directions for technology education.
| 2024-03-14T00:00:00 |
2024/03/14
|
https://link.springer.com/article/10.1007/s10798-023-09812-2
|
[
{
"date": "2023/02/01",
"position": 5,
"query": "artificial intelligence education"
}
] |
Stanford HAI: Home
|
Stanford HAI
|
https://hai.stanford.edu
|
[
"Shana Lynch"
] |
Advancing AI research, education, and policy to improve the human condition.
|
We invited 11 sci-fi filmmakers and AI researchers to Stanford for Stories for the Future, a day-and-a-half experiment in fostering new narratives about AI. Researchers shared perspectives on AI and filmmakers reflected on the challenges of writing AI narratives. Together researcher-writer pairs transformed a research paper into a written scene. The challenge? Each scene had to include an AI manifestation, but could not be about the personhood of AI or AI as a threat. Read the results of this project.
| 2023-02-01T00:00:00 |
https://hai.stanford.edu/
|
[
{
"date": "2023/02/01",
"position": 84,
"query": "artificial intelligence healthcare"
}
] |
|
Generative AI comes to User Interface design! This is crazy.
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The heart of the internet
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https://www.reddit.com
|
[] |
By integrating AI into the design process, developers and designers can create highly customized, efficient, and visually appealing interfaces that are more ...
|
Create your account and connect with a world of communities.
New to Reddit?
By continuing, you agree to our
and acknowledge that you understand the
| 2023-02-01T00:00:00 |
https://www.reddit.com/r/singularity/comments/10xy161/generative_ai_comes_to_user_interface_design_this/
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[
{
"date": "2023/02/01",
"position": 12,
"query": "artificial intelligence graphic design"
}
] |
|
The Impact of AI on Graphic Design.
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The Impact of AI on Graphic Design.
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https://www.linkedin.com
|
[
"Lincoln Sbragi",
"Digital Content Creator",
"Generative Ai Enthusiast",
"Student Storyteller"
] |
The rise of AI technology has dramatically changed the way graphic designers approach their work, automating many tasks and making the design process faster and ...
|
Automated Design Processes: AI technology has the ability to automate many of the repetitive tasks involved in graphic design. For example, AI algorithms can generate designs based on a set of parameters, freeing up designers to focus on the more creative aspects of their work. AI can also analyze designs and suggest improvements, making the design process more efficient and allowing designers to get their work done faster. Enhanced Creativity: While many people might think that AI would stifle creativity, the opposite is actually true. AI technology can help designers explore new creative avenues by providing them with new tools and techniques that they may not have thought of before. For example, AI algorithms can generate new designs based on a set of parameters, allowing designers to experiment with new ideas and explore new creative possibilities. Improved User Experience: AI technology can also be used to optimize the user experience by analyzing designs and suggesting improvements. For example, AI algorithms can analyze a website and suggest changes that would make it more user-friendly, such as simplifying navigation, improving the layout, or making it easier to find the information users are looking for. Cost-Effective Solution: In addition to improving the design process and enhancing creativity, AI technology is also a cost-effective solution for graphic designers. By automating repetitive tasks, AI can help reduce the amount of time and effort that goes into the design process, making it more affordable for designers to produce high-quality designs.
| 2023-02-01T00:00:00 |
https://www.linkedin.com/pulse/impact-ai-graphic-design-vinodkumar-padmanabhan
|
[
{
"date": "2023/02/01",
"position": 20,
"query": "artificial intelligence graphic design"
}
] |
|
Why Humanity, not AI, will shape the future of Brand Design
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Why Humanity, not AI, will shape the future of Brand Design
|
https://medium.com
|
[
"Lilian Santini"
] |
AI can generate hundreds of variations in minutes, analyze styles, and offer up visual ideas we might not have stumbled upon alone. It can speed up our process, ...
|
Why Humanity, not AI, will shape the future of Brand Design Lilian Santini 2 min read · Feb 20, 2023 -- Listen Share
Brand identity design has always been about more than logos and color palettes. At its best, it’s an act of translation: taking the heart and vision of a business and shaping it into something people can instantly recognize, trust, and remember. In a world full of noise, this ability to create meaning is both an art and a responsibility.
Today, we have more resources than ever before. Artificial intelligence, especially tools like Midjourney, has opened up a world of possibility in brand design. AI can generate hundreds of variations in minutes, analyze styles, and offer up visual ideas we might not have stumbled upon alone. It can speed up our process, make brainstorming more dynamic, and help us customize work for clients with fresh eyes.
However, no AI will ever replace the intuition, discernment, or deep listening that happens between a designer and their client. The best brands aren’t born from an algorithm. They’re born from human experience: curiosity, empathy, and the courage to see what others might miss.
Over the past few years, I’ve woven AI image generation into my design practice at The Copper Portico, and I’ve seen real benefits:
• Faster ideation and concept development (AI lets us get “what if?” ideas on the page in seconds)
• Greater room for customization (AI is a flexible sketchbook, not the final word)
• More cost-effective creative exploration (especially valuable for smaller businesses)
• New creative sparks (AI can surprise us, but it’s up to us to know what fits and what doesn’t)
Still, the most important ingredient isn’t the technology — it’s us. AI expands our toolkit, but it’s human imagination, ethics, and emotional intelligence that give brands their depth and soul. The future of design belongs to those who are willing to experiment, to ask better questions, and to use these new tools in service of more meaningful, memorable brands.
As AI continues to evolve, my hope is that we don’t lose sight of what really sets us apart: our point of view, our lived experience, and our ability to connect with others in a way that can’t be automated. The best designs will always be those that reflect a real human story.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://medium.com/@liliansantini/the-benefits-of-ai-image-generation-for-brand-identity-design-93fd881a5f34
|
[
{
"date": "2023/02/01",
"position": 28,
"query": "artificial intelligence graphic design"
}
] |
My Journey with AI in Design
|
My Journey with AI in Design
|
https://www.linkedin.com
|
[
"Richard Foster-Fletcher",
"Durapid Technologies Private Limited",
"Talha H.",
"Nicolas Glinoer",
"Technical Director",
"Partner",
"Lyndon Borrow",
"Senior Associate",
"Marketing",
"Social Media Specialist"
] |
I am convinced that AI has the potential to revolutionise the design industry in the same way the internet did. I see a new horizon of freedom to test and ...
|
As a designer, I am always on the lookout for new ways to improve my creative process and push the boundaries of what's possible. Recently, I've been experimenting by adding some AI tools into my ideation workflow and the results have been nothing short of phenomenal. In just three short weeks of using AI tools such as Dall-E to help idea visualisations and Chat GPT for writing, I've experienced a dramatic transformation in my workflow.
I've always struggled with writing, even in college. My teachers would often let me present my work through speaking instead of writing because I was better at verbally expressing myself (Thank you to Shawn my old college teacher). But with AI, those struggles are less of an issue.
Tools like Chat GPT have made writing a much smoother process for me, and I'm blown away by the impressive results, despite AI technology still being in its early stages.
I am convinced that AI has the potential to revolutionise the design industry in the same way the internet did. I see a new horizon of freedom to test and experiment with ideas without any limitations, allowing me to focus on the imaginative and experiential aspects of my work and unlocking new opportunities for creativity and innovation.
While it's clear that AI tools such as Dall-E will still require human direction, the true power of AI lies in its ability to bring ideas to life quickly and effortlessly. This accessibility is critical in promoting inclusivity and fostering creativity from all walks of life, regardless of resources or technical know-how. However, we must also be aware of the potential for overuse, which may result in an initial decrease in original work and an increase in cliches, similar to what we've seen with the popularity of stock photography. This will only give rise to the value of good, problem-solving ideas.
Another important aspect of AI in the design process is its ability to streamline repetitive and time-consuming tasks.
I find that this streamlining not only makes me more productive, but it also provides me with more opportunities to delve into creative exploration and refine my ideas.
This shift towards a focus on ideas in the design process is set to bring about significant changes in the design industry. Not only will it impact the way I work, but it will also change the priorities of design agencies and designers alike. As the industry progresses, designers will increasingly be valued for the unique ideas and experiences they bring to problem solving. This shift is already taking place in certain areas of the design world, but I believe it will become even more pronounced as clients place a greater emphasis on the quality of ideas over the technicalities of design delivery.
I believe that our problem-solving skills and unique perspectives are what set us apart from AI. This shift towards idea-focused design will only serve to reinforce that distinction.
Our ability to think outside the box, bring fresh perspectives, and come up with innovative solutions make us invaluable in the design world. This shift in focus will help highlight and celebrate the important role that designers play in shaping our world.
From the printing press to the internet, technological advancements have always enabled easier communication and collaboration.
| 2023-02-01T00:00:00 |
https://www.linkedin.com/pulse/my-journey-ai-design-ben-ledger
|
[
{
"date": "2023/02/01",
"position": 39,
"query": "artificial intelligence graphic design"
}
] |
|
AI Assisted Artwork is Valid Artwork and it's not going away. ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
Was printmaking, photography, or graphic design/illustrations not real art? Absolutely all those are a type of art. All are valid for showing in the art space, ...
|
Throughout the history of art, existing artists have a very nasty habit of shunning new art in new mediums.
Examples of historic artwork exclusions based on my fine arts degree education:
Printmaking struggled to find acceptance from the oil painting and acrylic artists because it was fast and cheap art instead of one of a kind.
Photographers were shunned as only capturing the view with a tool and not having any artistic additions to the work.. after famous artists like Ansel Adams showed us that photography is so very artistic in how a scene is captured based on lighting, focus, aperture settings, contrast, cropping etc.
Graphic design showed up and took the place of printmaking in the advertising word and was seen as mostly for commercial use until technically savvy artists saw it as a new mixed media medium where anything goes. Recently, in the last 10 years, we have seen an explosion of books and artwork being created in Adobe software, rebelle, Artweaver, Gimp, etc. And just like history has shown us, the public and artists begin to accept the new art as a new medium/category to appreciate and buy.
AI partnered literary and visual artwork has been the new newcomer in the last 2-3 years. Shunned by the classical artist as unauthentic ... but is it really? Was printmaking, photography, or graphic design/illustrations not real art? Absolutely all those are a type of art. All are valid for showing in the art space, but ONLY if the artist is transparent about their choice in medium. If you use AI, say it. Celebrate your ability to collaborate to make beautiful things. It is a tool and you are the director.
Besides, didn't an AI artwork submission by Jason M. Allen win first place recently in a colorado state fair art contest? So, clearly, the tool has merit. Just don't be a douche canoe and act like you didn't use it... I'm looking at you, goosebumps cover designer. 😆
So in conclusion:
| 2023-02-01T00:00:00 |
https://www.reddit.com/r/Futurology/comments/11a1xx7/ai_assisted_artwork_is_valid_artwork_and_its_not/
|
[
{
"date": "2023/02/01",
"position": 53,
"query": "artificial intelligence graphic design"
}
] |
|
Artificial intelligence AI processor chip vector icon symbol ...
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Pin on Stickers
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https://www.pinterest.com
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[] |
Download the Artificial intelligence AI processor chip vector icon symbol for graphic design, logo, website, social media, mobile app, UI illustration 10518719 ...
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Download the Artificial intelligence AI processor chip vector icon symbol for graphic design, logo, website, social media, mobile app, UI illustration 10518719 royalty-free Vector from Vecteezy for your project and explore over a million other vectors, icons and clipart graphics!
| 2023-02-01T00:00:00 |
https://www.pinterest.com/pin/1--151152131236275500/
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[
{
"date": "2023/02/01",
"position": 56,
"query": "artificial intelligence graphic design"
}
] |
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Artificial Intelligence: The Future of Human Creativity?
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Artificial Intelligence: The Future of Human Creativity?
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https://crystalpeak.com
|
[
"Lourn Eidal"
] |
Blog. Home>; Graphic Design>; Artificial Intelligence: The Future of Human Creativity? Artificial Intelligence: The Future of Human Creativity? by Lourn ...
|
Battle lines have been drawn. The fight between humans and their synthetic offspring promises to be costly and protracted. This conflict’s genesis springs from a virtual world we are only beginning to understand. It could shape our lives for years to come.
This may sound like the concept for this summer’s latest sci-fi blockbuster. It’s actually a legal issue that has resulted in several lawsuits filed this year. Depending on the outcome, the challenge brought by the art community against Artificial Intelligence (AI) developers that produce visual art could redefine copyright law and the future of original artists.
A class action lawsuit filed by artists against Stability AI, Midjourney, and DeviantArt claims that AI firms and other online art sources are infringing their rights, which occurs during the process of ‘training’ the technology. The plaintiffs (S. Andersen, K. McKernan, Karla Ortiz, et. al.) contend that the AI ‘scraped’, or sampled, billions of images available online without permission.
The Legal Issues
Many of these images are copyrighted, and artists have not received credit or licensing fees. Also, the AI-generated derivative works violate intellectual property rights of the original artists.
AI developers and other proponents of computer learning claim the fair use doctrine covers these activities. However, this defense will depend on the factors used on copyright law. This includes purpose/intention, how much of the source works were used, if the use is commercial or educational, and the impact on the market value of the work. Typical examples of fair use are commentary/review, education, and parody.
A Gathering Storm
Another lawsuit involves human artists vs. AI include Getty Images filing suit in the UK against Stability AI over use of copyrighted images without permission or consideration. Yet another class action lawsuit against GitHub’s CoPilot alleges similar transgressions. In some egregious examples, the AI generated images still contain watermarks embedded in the copyrighted source files, meant to protect against unfair reproduction.
We don’t know if the decisions made in the next 1-5 years will side with AI developers or human creators. However, regardless of the outcome, we think you will always benefit from working with a human artist.
The Case for Human Creativity
Firstly, we are aware of legal considerations involved with visual art and intellectual property. We are familiar with fair use, image licensing and proper credit and compensation. This means our clients avoid legal problems and major headaches.
Second, AI models can generate endless possibilities based on your text prompts. However they lack truly independent thought and vision for now. The options they generate don’t allow you the chance to refine them if they aren’t what you had hoped. They still struggle with details like rendering human hands. Text included in the artwork is somewhat recognizable, but is often gibberish. These issues and more can hamper your goals for a finalized art piece
| 2023-02-01T00:00:00 |
2023/02/01
|
https://crystalpeak.com/artificial-intelligence-the-future-of-human-creativity/
|
[
{
"date": "2023/02/01",
"position": 64,
"query": "artificial intelligence graphic design"
}
] |
Netflix slammed for using AI-generated art in new anime ...
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Netflix slammed for using AI-generated art in new anime short
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https://nextshark.com
|
[
"Michelle De Pacina"
] |
As the rise of AI-generated art has highlighted unethical practices, artists have expressed anger and fear over issues of copyright, theft and job losses.
|
Netflix slammed for using AI-generated art in new anime short
Netflix’s recently released Japanese short animation “The Dog & The Boy” is being criticized online for its AI-generated art.
The three-minute sci-fi short film released on Tuesday follows the story of a young boy whose robot dog waits for him as he goes off to war.
The anime was produced by Netflix Anime Creators Base in collaboration with Rinna Inc., an AI artwork company, and WIT Studio, the production company behind the first three seasons of “ Attack on Titan .”
According to the streaming platform, its reason for using AI-generated art was due to the anime industry’s “labor shortage.”
“As an experimental effort to help the anime industry, which has a labor shortage, we used image generation technology for the background images of all three-minute video cuts!” Netflix Japan wrote .
AI-generated backdrops of cityscapes and mountain ranges can be seen throughout “The Dog & The Boy.”
The credits for the short shows step-by-step how a hand-drawn layout of a snowy road is morphed by AI. Netflix simply credits the background designer as “AI (+ Human),” without naming the human artist.
The announcement on Twitter instantly drew criticism from social media users, who claimed that Netflix was using AI-generated art to avoid hiring and paying human artists.
Twitter users also contended that there is no labor shortage, but rather a shortage of companies willing to pay a decent living wage for anime talent.
“Netflix Anime ‘Dog and Boy’ was created with AI because of ‘labor shortage,’ not mentioning artists experience massive layoffs in the last months in the anime industry + starving payments,” one user tweeted . “ AI once again goes the most unethical and capitalistic way. Don’t watch or support this.”
“Imagine stealing from artists by using AI, then saying it’s because of labor shortages when there are countless starving artists out there, yet you choose to ride off the backs of them instead of support them. Do better Netflix,” another user wrote .
“Not something to be proud of babes,” a Netflix showrunner said .
As the rise of AI-generated art has highlighted unethical practices, artists have expressed anger and fear over issues of copyright, theft and job losses.
In September 2022, Netflix Animation laid off 30 employees in an effort to streamline production.
| 2023-02-02T00:00:00 |
2023/02/02
|
https://nextshark.com/netflix-the-dog-and-the-boy-ai-art
|
[
{
"date": "2023/02/02",
"position": 87,
"query": "AI job losses"
}
] |
Responsible AI: How to make your enterprise ethical, so ...
|
Responsible AI: How to make your enterprise ethical, so that your AI is too
|
https://dxc.com
|
[
"Kal Kanev"
] |
For example, algorithms that learn from previous human decisions will also learn and adopt any human behavior bias. Selection bias can lead to different ...
|
Too often, however, making AI responsible is an afterthought for many organisations. At first, they are focused more on identifying high-impact use cases in which to apply AI than with any ethical considerations. Next, they implement AI solutions based on existing company policies, rather than considering whether these are sufficient for purpose or need to be modified. Finally, when the resulting AI provides adverse results, they question AI’s overall function and value, and only then consider the option of “making” the AI ethical after the fact. This leads to new guidelines, legislation, court rulings and other forms of normalisation that eventually cycles back to developing brand new models and AI solutions.
Ethics and compliance should not be an afterthought; companies should “do it right” from the start. The ethical and compliant use of AI must become ingrained in an organisation’s ML/AI DNA. The best way to do this is to establish, at a minimum, fundamental guiding principles and capabilities for governing AI development.
| 2023-02-02T00:00:00 |
2023/02/02
|
https://dxc.com/in/en/insights/perspectives/paper/responsible-ai
|
[
{
"date": "2023/02/02",
"position": 68,
"query": "workplace AI adoption"
}
] |
National Institute of Standards and Technology launches ...
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National Institute of Standards and Technology launches version 1.0 of AI Risk Management Framework
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https://www.osler.com
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[] |
Overall, there is much anticipation in the AI community to implement the AI RMF in AI practices and deployment across all relevant industries, including ...
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Authors VIEW MORE VIEW LESS
On January 26, 2023, the U.S. National Institute of Standards and Technology (NIST) released its AI Risk Management Framework (AI RMF 1.0) [PDF], which sets out principles designed to equip organizations and individuals with approaches that increase the trustworthiness of artificial intelligence (AI) systems. NIST also released a companion NIST AI RMF Playbook, AI RMF Explainer Video, AI RMF Roadmap, AI RMF Crosswalk (mappings of the AI RMF 1.0 to other standards and frameworks) and various Perspectives (published statements from interested organizations and individuals). While NIST is an agency of the United States, the organization has a significant presence on the international stage and many of their standards have been developed in collaboration with, and adopted by, stakeholders worldwide, including the AI RMF.
Coinciding with the release of the AI RMF 1.0 and the accompanying materials was a launch event hosted by NIST, which included speakers and panelists from the White House, the Chamber of Commerce and the Technology Industry Council, as well as various technology and AI initiatives and companies.
AI Risk Management Framework 1.0
The AI RMF was created in response to a directive from Congress to develop a framework to manage AI risks and to promote trustworthy and responsible development of AI systems. In addition to introducing AI risks and characteristics of trustworthy AI systems, the core of the AI RMF establishes four high-level functions that are key to understanding and managing AI risk: Govern, Map, Measure and Manage. (These functions are described in greater detail in our previous article, which introduces the framework.)
Released only four months after the second draft of the AI RMF, version 1.0 expands on most of the material from the second draft and addresses new considerations as well, reinforcing its goal to be practical, flexible and adaptable to various AI technologies and organizations of all sizes.
AI Risk Management Framework 1.0 launch event
The AI RMF 1.0 launch event included addresses from Don Graves (U.S. Deputy Secretary of Commerce), Dr. Alondra Nelson (Deputy Assistant to the President and Principal Deputy Director for Science and Society in the White House Office of Science and Technology Policy) and Zoe Lofgren (Ranking Member of U.S. House Committee on Science, Space, and Technology). It also featured panels to discuss the capabilities of the AI RMF and where it may fall short.
Key takeaways from the speakers and the panelists include
AI systems are inherently socio-technical systems as people are often at the centre (i.e., AI systems are used by, governed by and impact people).
The AI RMF has the flexibility to scale and fit the needs of both large and small organizations in the public, private and non-profit sectors.
More specific use cases should be built into the AI RMF. Building out the current capabilities of the AI RMF to apply to a broader range of specific, real-world situations is crucial to its success in expanding the adoption of AI to more applications and in addressing and mitigating AI-specific risks such as perpetuating bias and disseminating misinformation.
Overall, there is much anticipation in the AI community to implement the AI RMF in AI practices and deployment across all relevant industries, including employment, housing and others. Multiple panelists emphasized the importance of providing widespread, international education on the AI RMF, including Navrina Singh (founder and CEO of Credo AI), as the AI RMF can be the “gold standard” if implementation is achieved across the globe.
Looking ahead
NIST plans to incorporate feedback from the AI community to update the NIST AI RMF Playbook periodically, with the next update slated for spring 2023. NIST also has plans to launch a Trustworthy and Responsible AI Resource Center to provide guidance and assistance to organizations using the AI RMF 1.0. NIST’s recently released AI RMF Roadmap contains a full list of their top priorities for further developing the AI RMF.
chatgpt
chat gpt
openai
open ai
| 2023-02-02T00:00:00 |
https://www.osler.com/en/insights/updates/national-institute-of-standards-and-technology-launches-version-1-0-of-ai-risk-management-framework/
|
[
{
"date": "2023/02/02",
"position": 77,
"query": "workplace AI adoption"
}
] |
|
Using AI and ML to Improve Life Insurance and Annuity ...
|
Using AI and ML to Improve Life Insurance and Annuity Processes
|
https://centricconsulting.com
|
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"Bob Hunter",
"About The Author",
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Hybrid Workplace Strategy. Customer Experience. Customer Experience Design ... Adoption of ML and AI models can help quicken the pace of the process and ...
|
We discuss the effects of COVID on the life insurance and annuity industry and provide a solution to improve processes using machine learning and artificial intelligence.
In the first year of COVID, life expectancy dropped by two years on average, and we still don’t fully know the actual impact and variants of the virus. We originally thought it would be gone by the end of 2020, but with numerous mutations, it’s evident the virus will be with us for some time to come. One specific impact is how the insurance industry approaches life and annuity underwriting in the wake of the pandemic.
We may never know how many deaths the virus indirectly or directly caused during the peak of the pandemic. People have also experienced many health complications, and we do not know the long-term impact of issues on life expectancy. These factors complicate things for the insurance industry, specifically Underwriters. However, this area is where machine learning (ML) can help underwriters, along with actuaries and data scientists, predict the longer-term impact of COVID. ML will also enable actuaries and underwriters the ability to incorporate factors resulting from COVID into their assessments, which will help them to provide more reflective actuarial tables.
In the second part of this two-part series, we look at the impact of the virus on the life insurance and annuity industry.
New Considerations for Life Insurance and Annuity
Johns Hopkins provides a running total of COVID cases, and as of the middle of December 2022 worldwide, we were up to over 655 million reported cases, over 6.67 million deaths and over 13 billion doses of vaccine administered. People thought this virus was going to be short-term, yet as CEO Scott Davison of OneAmerica says pandemic deaths were up 40 percent in the third quarter of 2021. We can’t attribute all these deaths to the contacting event – many were after the patient had “recovered.”
Life expectancy projections resulting from COVID have seen an overall drop by roughly 2.9 years and by 2.27 for men and 1.61 for women in the United States. This doesn’t count deaths associated with health complications from people who recovered from COVID. Another factor potentially impacting life expectancy are continuous mutations.
We also don’t know the long-term health impacts associated with people who were not vaccinated versus people who were vaccinated. Will people who were vaccinated have a higher life expectancy? Will people who were not vaccinated have higher overall healthcare costs, and will their life expectancy be less, or will there be unexpected long-term health consequences from taking the vaccine? We still have many unanswered questions at this point.
The pandemic has had a significant impact on life insurance underwriting. Many insurance companies have temporarily changed their underwriting guidelines to accommodate the unique challenges and risks the pandemic presented. Traditionally, there are many factors underwriters use to determine policy and pricing. The following are examples of the most common ones:
These are the more common factors an underwriter will check, but there are others, such as the applicant’s income, weight, alcohol habits, amount of foreign travel and any additional insurance policy riders for which they applied.
Along with regular consideration of the above factors, underwriters must now think about whether COVID contributes to the results of some of these factors. We can also break these effects into sub-groups:
Those vaccinated who did or did not have COVID.
Those unvaccinated who did or did not have COVID.
Segmenting these individual groups and diving deeper into their history could help predict future results. It is also highly likely as developers build ML models to consider these new conditions, we will see additional factors surface.
How AI and ML Can Help Life Insurance and Annuity
High death rates, long-term complications from the virus, continued mutations and economic impacts are only some of the factors that will impact life insurance policies. The insurance industry has increasingly focused on leveraging ML and AI for their data scientists and actuaries. For life insurance carriers, many critical processes can benefit from modern ML models, including underwriting and claims.
Using machine learning and artificial intelligence techniques can improve the accuracy of projections of life expectancy and healthcare costs. By training a model on a large dataset of relevant factors and outcomes, it can learn to make more accurate predictions than possible using traditional statistical methods alone. There are several foundational models and different machine learning algorithms carriers could use for this purpose, and the choice of which one will depend on the specific use cases and characteristics of the data and the desired accuracy of the predictions.
Some potential factors insurers could include as inputs to a machine learning model for predicting life expectancy and healthcare costs might include demographic information (such as age and sex), medical history, lifestyle factors (such as diet and exercise habits), and environmental factors (such as air quality and access to healthcare).
Insurers should incorporate predictive (machine learning and artificial intelligence) techniques into their solutions to provide more accurate projections of life expectancy and healthcare costs.
Using ML in Underwriting
Let’s continue our focus on the underwriting process for life insurance and annuity. The heavy reliance on external data sources and the time it takes to gather and analyze data already leads to a slow process. The addition of COVID-related impacts to that data will certainly add time. We can add automation with machine learning algorithms, which we can train to analyze large amounts of data and decide whether to approve or reject a life insurance application or at least provide summarized recommendations to the underwriter.
This analysis can help underwriters save time and effort, as they won’t have to manually review every application. Adoption of ML and AI models can help quicken the pace of the process and allow underwriters, actuaries, and data scientists the ability to bring in additional COVID-related information.
The following table contains some recommended use cases to consider for underwriting and other processes underwriters, actuaries and data scientists see most frequently. Possible data sources for AI and ML to pull from include government and scientific statistics, personal and family health history, MIB, submission data, claims data and social media.
These are only a few examples of use cases for life insurance and annuity to which carriers can start to pay more attention. And remember, life expectancy also plays a part in underwriting annuity contracts.
Conclusion
The recent pandemic has posed significant challenges to the insurance industry, especially in life and annuity underwriting. However, machine learning integration can help underwriters incorporate COVID-related factors, improve projections and streamline processes. Despite the challenges posed by the pandemic, adopting AI and ML techniques offers a path forward for the insurance industry to navigate the evolving landscape.
| 2023-02-02T00:00:00 |
2023/02/02
|
https://centricconsulting.com/blog/using-aiml-to-improve-life-insurance-and-annuity-processes/
|
[
{
"date": "2023/02/02",
"position": 78,
"query": "workplace AI adoption"
}
] |
Emerging technology trends MSPs should leverage in 2023
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Emerging technology trends MSPs should leverage in 2023
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https://www.pax8.com
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[] |
The report also states: “Adoption of the latest technologies such as Artificial Intelligence (AI), cloud management, and others is eventually helping ...
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Emerging technology trends MSPs should leverage in 2023
Pax8
AI and automation can help drive growth.
As managed services continue to grow in popularity, MSPs need to expand their offerings and capabilities to remain competitive in the changing business landscape. Artificial intelligence (AI), automation, infrastructure management, migration, and optimization will be key in a cloud-first world.
MSPs have seen major growth in 2022 thanks in large part to the innovative cloud technology that many small and medium-sized businesses (SMBs) rely on for efficiencies in the modern workplace. As cloud continues to prove its worth in budget and in practice, SMB leaders are scouting for cutting-edge technologies and for experts to best utilize that technology.
Research bears this out. Grand View Research wrote in a recent report: “Most companies are already renewing their contracts with managed cloud service providers in anticipation of cloud migration getting more common among enterprises, and in some cases, even gaining traction. Furthermore, businesses and organizations are putting a strong emphasis on adopting the latest technologies, such as machine learning and augmented reality, along with their existing IT infrastructure as part of the efforts to encourage digital transformation.”
The report also states: “Adoption of the latest technologies such as Artificial Intelligence (AI), cloud management, and others is eventually helping organizations meet various functional business requirements while driving business process optimization.”
Below are some of the emerging technologies and strategies that MSPs can consider leveraging in 2023. They vary in purpose and scope, but they all provide the possibility for business development, risk reduction, and growth in this new year.
AI and Automation
Managed services depend on efficient delivery and routine processes to keep SMBs running smoothly. AI and automation can augment existing integrations to streamline operations such as billing, to save MSPs and their clients time and money while reducing risks. AI and automation can also play a key part in defending your business and that of your clients from the risks of a cybersecurity attack or potential breach.
AI has already been paramount in building automated security systems and automatic threat detection, among others, and in 2023, we’ll see smarter systems that can predict new attacks and notify admins in real time. AI and automation also can help MSPs maintain sophisticated control over the data they are responsible for collecting, analyzing, managing, and securing for their clients.
Automation and PSA integration are already baked into the Pax8 ecosystem of service and the business acumen of many successful MSPs. Look for the need for automated efficiencies to become standard practice in 2023.
Infrastructure Management, Migration, and Optimization
As SMBs and enterprises become increasingly aware of the need to compete with digital services and experiences for their clients, there are growth opportunities for MSPs in providing migration, configuration, optimization, cybersecurity, and overall business transformation projects. Even still, some businesses might never exist fully in the cloud, and MSPs looking to offer advanced services can manage the hybrid data structures many SMBs will require in 2023 and beyond.
By building hybrid models, MSPs can see where there may be opportunities to move more to a variety of clouds and, most importantly, to use the cloud itself to monitor and manage the premise infrastructure they are responsible for. By implementing remote management, MSPs can conserve money by avoiding or eliminating the need to dispatch a technician for a service call, for example, and can start to compete in a more modern field service industry.
2023 will almost certainly throw curveballs at MSPs, SMBs, and the wider IT channel that will require agility and adaptability. Now more than ever, successful MSPs will be those who can navigate change while holding true to their core service goals.
Discover solutions
Schedule a call
| 2023-02-02T00:00:00 |
2023/02/02
|
https://www.pax8.com/blog/emerging-technology-trends-msps-should-leverage-in-2023/
|
[
{
"date": "2023/02/02",
"position": 93,
"query": "workplace AI adoption"
}
] |
2023 Workforce Trends to Watch | Blog
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2023 Workforce Trends to Watch
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https://lightcast.io
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[] |
Demand for green jobs, AI roles, and remote work are on the rise — here are the workplace intelligence trends you need to know to build a strategic and ...
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When it comes to staff management, 2022 was tumultuous, to say the least. The year started out with workers continuing to switch jobs en masse, while seeking out flexibility, better pay, and improved work-life balance. By the end of the year, widespread layoffs increased fears that the economic instability triggered by the COVID pandemic and Russia’s invasion of Ukraine, among other factors, was far from over.
So how should your company weather the challenges and seize the opportunities that lie ahead in 2023? We pulled all the most relevant job market data and analyzed key trends for employers and job candidates to find out.
We’re excited to share the 2023 Global Talent Playbook. It’s full of current insights and strategy ideas, as well as future trends in recruitment. Keep reading as we outline some of the major themes of the playbook.
Workplace trends to guide your recruitment efforts
1. AI hiring trends
Artificial intelligence, or AI, is making its way into all types of industries. Despite fears that computers are coming for our jobs, our data shows that AI isn’t really taking away jobs, but instead, adding them.
In 2022, the US, UK, and Spain all showed the highest interest in hiring for AI-related jobs. And since 2014, the share of total postings in AI fields has more than tripled in the US, UK, and Canada.
2. Digital workplace trends
Employees wanting flexibility in their working location isn’t going anywhere. The remote workforce trends we’ve been watching closely indicate that the ability to work from home, even just part of the time, is extremely important to applicants across industries and age groups. And companies everywhere know this: our data shows that remote postings continue to grow in the US and worldwide.
Remote jobs now make up 15% of all postings for roles requiring degrees (up from 2.5% pre-pandemic), and the top three remote jobs in 2022 were Web Developer, Video Game Designer, and Computer Programmer
The global talent pool is far larger than a local or regional talent pool, so opening up your workforce can mean you find exactly the right person for the job. It can also mean lower labor costs for the same skills, among other benefits.
However, in order for remote working to be sustainable and productive, remote leadership training is crucial. Without the ability to walk by the desks of their direct reports, managers need to learn to remotely check in with their team members, help them grow their skills and improve where needed, and feel supported by their manager.
Want to learn more about remote hiring? Check out our Talent Analyst tool , which provides the data necessary to find people nationally and globally.
3. Candidate recruiting + skills trends
One way that hiring has become more equitable is by recruiters and hiring managers taking a reality check about which roles actually require degrees. Companies everywhere have learned that it behooves everyone — the company and the workers — to value skills over degrees .
So many new roles require specialized skills, the fastest-growing of which are machine learning operations, TikTok, and Jira Align. None of these skills require a Master’s Degree, and some don’t even require a Bachelor’s. Opening the hiring pool to include skilled workers who know how to do what you need them to, but don’t necessarily have the right formal degree, helps people of all walks of life to grow their careers.
General (or “common/human”) skills that were in high demand in 2022 were, unsurprisingly, telecommuting, virtual collaboration, and growth mindedness. Companies want employees who know how to work effectively wherever they are, and are looking to find ways to get better every day.
If you want to help your employees build in-demand skills, check out our Ideas for Better Upskilling .
4. Workplace pay equity trends
An uplifting trend we’ve seen is that companies everywhere are proactively working to reduce the wage gap. One major stride involves wage transparency: advertised wage rates on job postings more than doubled between 2019-2022.
25% of degree position postings now include an advertised salary, which helps job seekers know whether an application (or a company) is worth their valuable time.
Companies have also implemented Lightcast’s Talent Transform feature, which helps them standardize job postings, prioritizing the necessary skills, and helping job seekers find the best position they can.
Over time, wages have increased the most drastically for those at the lowest wage levels, which indicates that companies are trying to improve wages and quality of life for those currently struggling economically.
Wage growth has also been high for job switchers, which is a large part of why we saw the Great Resignation in 2021 and 2022. People, understandably, started seeking out better workplaces with better compensation and benefits.
Speaking of benefits, long-term benefits are increasingly valued by workers and provided by companies. Longer term benefits, like tuition assistance and retirement benefits, are growing in use, compared to shorter term benefits like sign-on bonuses.
To plan your equity and benefits strategy for 2023, check out our Guide to Improving Recruitment, Retention, Advancement, and Equity .
5. Green jobs trends
Around the world, companies are recognizing that we’re in a climate crisis , and they’re prioritizing climate-conscious jobs. Green jobs and skills related to lowering the carbon footprint are increasing in demand globally, and are particularly sought-after in the US, Germany, and Canada.
In the United States in particular, the demand for green jobs has grown by over 50% since 2019.
Recruiting is more data-driven than ever
We now know that companies who strategically manage their workforce using labor market data generate higher returns — and those that don't stand to lose $125,000 per employee per year.
Get the labor market data you need for 2023 by reading our full 2023 Global Talent Playbook.
| 2023-02-02T00:00:00 |
https://lightcast.io/resources/blog/workforce-trends-to-watch
|
[
{
"date": "2023/02/02",
"position": 7,
"query": "AI labor market trends"
}
] |
|
A Deeper Look into Artificial Intelligence in Market Research
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The Good, The Bad, and The AI: A Deeper Look into Artificial Intelligence in Market Research
|
https://focusinsite.com
|
[] |
The increasing use of AI could possibly lead to widespread job loss, which could have a negative impact on the economy and society as a whole. According to The ...
|
The Ethical and Societal Implications of Artificial Intelligence
Author: Serena Berrey | Editor: Elizabeth Zuponcic
We hope you enjoy this healthy conversation around the world of Artificial Intelligence in our second article of our three-part series. Join us as we explore from the latest breakthroughs to practical applications, and the many ways AI is shaping our world.
As the lines between man and artificial intelligence appear to blur, the question of what is required to create content is becoming increasingly complex – and crucial. In our previous article, “Defining and Harnessing the Power of Artificial Intelligence,” we touched on impactful ways to incorporate artificial intelligence in order to complement human capabilities. The rapid advancement of AI raises ethical and societal implications that must be explored to create a positive, innovative conversation as an effective compromise in our ever-changing world of technology.
With AI’s ability to process and analyze large amounts of data quickly, AI has the potential to revolutionize a wide range of industries, from healthcare to transportation to finance. But as we look forward to the many benefits that AI can bring, it is important to consider the ethical balance we must maintain. Qualitative market research can play a key role in the ethical development and use of AI. By talking to consumers directly and understanding their needs, fears, and aspirations, researchers can help ensure that AI systems are developed in a way that is responsive to the needs of society. This can also help to identify and address any potential biases or negative impacts of AI before they become a problem.
Where most refer to the “pros” and “cons” of artificial intelligence, perhaps a more constructive vocabulary approach is the possible “advantages and disadvantages” we are seeing currently with AI:
Potential Advantages in the Medical World
One of the most obvious benefits of AI that affects a majority of the population is the increased efficiency and accuracy in repetitive tasks. By automating repetitive or time-consuming tasks, AI can help businesses and organizations to operate more smoothly and efficiently. In healthcare, for example, Generative Tech can help gear a majority of medical professionals attention towards the patient as opposed to clerical work that can take up most of their time. “The goal of automating clinical process tasks is to give doctors, nurses, and other physicians a break from repetitive tasks that distract them from patient care” (The Advantages of Intelligent Automation in Healthcare).
AI can also help to improve patient outcomes by identifying potential health risks and providing personalized treatment recommendations. Ethically speaking, with the ability to analyze vast amounts of data and make accurate diagnoses, AI can help improve the lives of patients all over the world. AI can also assist with identifying and addressing healthcare disparities, ensuring that everyone has access to the best possible care. With the help of AI, we can help to create a more equitable and efficient healthcare system that benefits everyone.
Potential Disadvantages with Data Biases & Plagiarism
With the flip side to the ethical treatment in the healthcare system and societal fairness, there is fear of the possibility that AI systems might make decisions that are biased or unfair. Because AI systems are trained on data, they may perpetuate existing biases if the data it’s trained on is biased in some way. This is a problem that has been identified and studied in other fields such as the justice system, hiring process, and lending process. However, it is crucial that we are aware of these issues and work to solve them together.
“Bias in AI algorithms can emanate from unrepresentative or incomplete training data or the reliance on flawed information that reflects historical inequalities. If left unchecked, biased algorithms can lead to decisions which can have a collective, disparate impact on certain groups of people even without the programmer’s intention to discriminate.,” Nicol Turner Lee, Paul Resnick, and Genie Barton, writing for the Brookings Institution (Open source data science: How to reduce bias in AI).
Since artificial intelligence is incapable of producing original creations, machine learning algorithms can result in repeated or plagiarized data. If the training data contains repeated or plagiarized information, the AI model may produce similar outputs. This can be particularly problematic in the case of text-generating AI models, where the generated content may closely resemble existing text and potentially infringe on intellectual property rights. We saw an example of a scenario in the CNET scandal where the company was producing “AI Articles Riddled with Errors and Plagiarism.” To mitigate the risk of AI-generated plagiarism or the spread of misinformation, it’s important to train AI models on high-quality, diverse datasets that do not contain repeated or plagiarized information. It is crucial to encourage creators to use AI models as inspiration for creation, not an artificially intelligent knock-off. It is also crucial to conduct regular audits to ensure the AI is fair and unbiased, while making sure to involve diverse perspectives in the development of AI to identify and address possible issues.
Potential Advantages in the World of Market Research
One of the greatest benefits of artificial intelligence is its ability to revolutionize the field of market research. With the help of AI, companies can now conduct qualitative market research more efficiently and accurately than ever before. This allows them to gain valuable insights into consumer behavior and preferences, which can then be used to improve products and services, increase sales and revenue, and ultimately drive business growth. Additionally, AI-powered market research can help identify new market opportunities, reduce costs, and make better-informed business decisions. All of which can be a huge boost for companies looking to stay competitive in today’s fast-paced business environment.
Artificial intelligence can revolutionize the field of qualitative market research by providing faster and more efficient data analysis, reducing the risk of human bias, and enabling real-time insights. With the ability to process and analyze large amounts of qualitative data, including customer feedback and survey responses, AI algorithms can quickly identify patterns and trends that may be missed by human researchers. Additionally, AI models can allow for less repetitive tasks for market researchers who spend time analyzing data. “The result? You spend less time on analytics and more time influencing customer behavior.” (Qualtrics).
This is not to say that AI tools can completely replace the qualitative market research process. At Focus Insite, we pride ourselves on phone call screening for the most accurate outcomes of a study. Relying on AI to filter for eligible respondents based on screener responses would allow unqualified participants to slip through the cracks. Our phone screening process has proven time and time again that human analysis is critical in detecting inaccuracies.
Potential Disadvantages in the Job Market
The increasing use of AI could possibly lead to widespread job loss, which could have a negative impact on the economy and society as a whole. According to The Oxford Study: if a machine can automate a job, then it is at risk of being automated fully. The study states that 47% of jobs that don’t require creative or interpersonal demand are the most at-risk for being replaced. (Job Automation Risks in 2023: How Robots Affect Employment).
It is important for employees to focus on developing and honing skills that cannot be easily automated. These may include critical thinking, creativity, emotional intelligence, and problem-solving. Additionally, humans will have to devote more time to staying current with the latest technology trends and advancements in order to be aware of the potential impact of AI on one’s field.
Embracing the Power of AI
Overall, AI is a powerful and exciting technology that has the potential to bring many benefits to society. However, it is important to consider the ethical implications of this technology and to be aware of the potential downsides. By designing and regulating AI systems in a responsible way, we can help to ensure that the benefits of AI are enjoyed by all while minimizing the risks.
Don’t miss out on the next exciting article in our blog series! Follow us on LinkedIn at Focus Insite to stay informed and be the first to read our next post in this Artificial Intelligence series.
If you value authentic and impactful communication with your consumers and want to collect qualitative market research insight and improve satisfaction, contact our bidding team at [email protected]. No matter your industry, see how market research can benefit your company in a way that helps you implement positive change. (And yes, once again, this shameless plug was written by a real, passionate Focus Insite team member.)
| 2023-02-02T00:00:00 |
2023/02/02
|
https://focusinsite.com/the-good-the-bad-and-the-ai-a-deeper-look-into-artificial-intelligence-in-market-research/
|
[
{
"date": "2023/02/02",
"position": 57,
"query": "AI labor market trends"
}
] |
High demand skills for the next 10 years and future
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How to Strategize Around Future-Oriented Skills
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https://hrforecast.com
|
[] |
Digital and AI-powered technologies are transforming the world of work, pushing the workforce towards the need to learn new in-demand skills and adapt to future ...
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Tech evolution is often a key economic disruptor, but equally critical are demographic and macroeconomic shifts. These changes can also have a far-reaching impact on organizations across industries and force skill requirements to evolve at an accelerated pace.
That's why, to stay agile and competitive, organizations must explore current market and talent trends and plan for future-oriented skills that will be critical to workforce planning and transformation.
5 Forces Redefining Work Across Industries
Here are a few of the most recent developments and trends prompting executives to rethink their workforce strategies:
The rise of AI: An estimated 78% of businesses consider generative AI tools as a competitive opportunity, according to a study in MIT Technology Review in February 2024. And a full two-thirds say they’re looking at ways to use the technology in everyday work. The entrenchment of distributed work models: With just 12% of leaders with flexible work policies planning to shift to on-site work, work-from-home paydays aren’t likely to change much over the next year, as predicted by a joint report from Stanford and the Federal Reserve Bank of Atlanta. Shorter skill lifespans: The rate of skills obsolescence is on the rise. Employers expect 39% of key job skills will change by 2030, according to the World Economic Forum’s 2025 Future of Jobs Report. Automation and process reengineering: About 27% and 30% of hours worked in Europe and the U.S. respectively are predicted to be automated by 2030 thanks to gen AI technologies, according to a 2024 McKinsey study. Credential diversification: From technical certifications to behavioral assessments, skill validation is becoming more agile and context-specific.
So what does this mean for current and future skills? Not only will organizations need technical and operational competencies, or hard skills —— they’ll also need to focus on the more “human” proficiencies, or soft skills.
Future-Oriented Hard Skills
In-demand hard skills increasingly center around tech and processes that ensure business resilience, risk mitigation, and innovation across sectors.
1. AI systems and automation engineering
AI and automation are converging across enterprise systems. Organizations need professionals who can not only build and refine machine language (ML) models, but also integrate them into real-time workflows.
Skills to plan for:
Prompt engineering and model tuning
Workflow automation and orchestration
Model governance and ethical oversight
2. Cybersecurity and data governance
A 2024 cybersecurity workforce study reports a global talent shortage of nearly 5 million professionals. With ever-expanding digital ecosystems and increases in regulatory scrutiny, demand is rising for ethical data management and fully secure IT systems.
Skills to plan for:
Secure architecture and threat response
Cyber risk modeling and mitigation
Data privacy compliance across jurisdictions
3. Cloud-native infrastructure and edge computing
Almost three-quarters of companies across multiple industries planned to increase edge computing investments in 2024, according to GSMA Intelligence.
Scalable systems require flexible, distributed infrastructure. Cloud and edge capabilities are foundational for responsiveness and speed.
Skills to plan for:
Multi-cloud architecture
Edge deployment strategy
Infrastructure-as-code fluency
4. Advanced analytics and decision intelligence
Raw data is nice, but it’s not enough. Leaders also need actionable insights. Forecasting, pattern recognition, and cross-functional fluency are vital skills for the future.
Skills to plan for:
Predictive modeling and scenario analysis
Cross-domain data literacy
Responsible AI and insight communication
5. Sustainable and resilient operations
The UNFCCC’s 2024 Climate Technology Progress Report points to a global increase in adoption of cost-effective renewable energy systems. Plus, sustainability and supply chain strategy are increasingly integrated. Resilient operations will need environmental insight and global adaptability.
Skills to plan for:
Environmental systems planning
Supply chain scenario modeling
Climate and geopolitical risk mitigation
6. Digital product and platform development
As digital capabilities become core to all industries, product teams need to ship scalable, user-centered experiences – fast. In line with this, the WEF’s 2025 Future Jobs Report also predicts that demand for highly skilled software developers will remain high.
Skills to plan for:
Full-stack and platform engineering
UX for emerging interfaces (AR/VR, mobile)
Low-code rapid prototyping
7. Bioinformatics and emerging tech specialties
The rise of biotech and personalized medicine is generating demand for hybrid talent with expertise in science, data, and engineering.
Skills to plan for:
Genomic data interpretation
Clinical data modeling
AI-augmented biotech research
Future-Oriented Soft Skills
These future-focused skills enable organizations to lead through uncertainty and build adaptive, high-performing working cultures. And Deloitte’s 2024 Human Capital Trends report highlights the importance of “human performance” at the apex of business and human outcomes.
1. Adaptive thinking and learning agility
Executives must continuously evolve thought processes and approaches for both themselves and their teams in a world where the half-life of skills is shrinking. This means the need for a malleable, ever-shifting, agile mindset will be higher than ever before.
Skills to plan for:
Continuous skill acquisition
Pattern recognition and mental agility
Role versatility and unlearning capability
2. Systems leadership and complex problem-solving
Navigating ambiguous, high-stakes challenges demands both strategic foresight and systems thinking. This is especially the case in a business landscape that’s rife with disruption and upheaval both micro and macro.
Skills to plan for:
Strategic scenario evaluation
Root cause analysis
Cross-functional decision-making
3. Ethical judgment and cross-cultural governance
As global teams expand and AI scales, leaders must balance ethics, compliance, and cultural nuance in their teams – especially when working with multilateral perspectives and diverse experiences.
Skills to plan for:
Value-based leadership and governance
Cultural intelligence and stakeholder empathy
Global compliance fluency
4. Emotional intelligence and resilience
In a world that gets shaken up every few years (think COVID-19, AI, tariffs, etc. just in the last five years), durable leaders are the ones who show empathy, adaptability, and personal regulation – and also benefit from similar skills in their employees.
Skills to plan for:
Crisis response and stress management
Interpersonal awareness
High-EQ team leadership
5. Digital collaboration and influence
Remote, hybrid, and asynchronous work in increasingly digital environments requires teams to be grounded in trust, clarity, and coordination to build stronger working relationships.
Skills to plan for:
Digital team facilitation
Communication strategy across time zones
Workflow orchestration in hybrid environments
Staying Future-Ready with a Data-Backed Skills Strategy
The good news? Executives can tangibly plan for future in-demand skills. This requires implementing standardized, data-driven systems.
Here’s what leading organizations are doing right now to stay ahead of the curve:
1. Establishing workforce transformation frameworks
Organizations are implementing enterprise-wide solutions that interlink talent intelligence and global market insights to align with technology and business transformation.
This means aligning current and future work capabilities with long-term economic trends and evolving market demands.
2. Implementing skills inventory management systems
Many successful businesses are implementing sophisticated skill tracking platforms that not only document current capabilities but also forecast emerging needs and gaps in their existing workforce.
3. Prioritizing learning ecosystems
Periodic training is being replaced by continual learning environments. Spending on corporate learning has seen double-digit growth in the last two years alone, according to Josh Bersin’s 2024 research, reaching about $1,400 per employee on average.
“We’ve entered a skills-based economy,” Bersin wrote, adding that ongoing skill development now outweighs traditional indicators like degrees and tenure.
4. Mapping for skills adjacency
In other words, organizations must continually adapt workforces through skill management.
Forward-thinking organizations need to regularly map their employees' capabilities and make critical reskilling, upskilling, and skill transference decisions that allow for rapid workforce adaptation.
5. Planning workforces at the board level
Treat workforce development as a capital investment – with measurable ROI, scenario modeling, and strategic alignment.
A 2024 Ernst & Young report finds that a successful talent strategy in 2025 and beyond requires board-level attention, focusing on talent governance and future skills development among others.
Mapping In-Demand Skills for the Future
Microsoft CEO Satya Nadella once said: “The greatest risk isn’t technology disruption – it’s failing to develop the human capabilities needed to harness it.”
That mindset doesn’t just apply to technology – it’s overarching across all sectors. Those who treat talent as dynamic capital and not as a fixed resource will sustain a competitive advantage over their peers. It’s about aligning proactive skills development and forecasting with overall business strategy – it’s about aligning skills forecasting with business goals to minimize risk and maximize strategic growth.
| 2023-02-02T00:00:00 |
https://hrforecast.com/a-guide-to-future-oriented-skills-skills-in-demand-to-watch-in-the-next-five-years/
|
[
{
"date": "2023/02/02",
"position": 78,
"query": "AI labor market trends"
}
] |
|
AI is not the real enemy of artists
|
AI is not the real enemy of artists
|
https://ethics.org.au
|
[
"Dr Tim Dean",
"Dr Tim Dean Is Philosopher In Residence At The Ethics Centre",
"Author Of How We Became Human",
"And Why We Need To Change.",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img"
] |
Artificial intelligence is threatening to put countless artists out of work. But the greatest threat to artists is not AI, it's capitalism. And AI could be ...
|
Artificial intelligence is threatening to put countless artists out of work. But the greatest threat to artists is not AI, it’s capitalism. And AI could be the remedy.
“Socrates taking a selfie, Instagram, shot outside the Parthenon, wearing a white toga, white beard, white hair, sunlit.” That’s all the AI image generator Stable Diffusion needed to create the cover image for this article.
But instead of using artificial intelligence, I could have hired a photographer or purchased an image from a stock library to adorn this article. In doing so, I would have funnelled money to a human, who might have spent it on something else created by another human. Instead, I used AI, and no money left my pocket to flow through the economy.
And therein lies the threat posed by artificial intelligence to artists: when AI can produce nearly endless creative works at near zero marginal cost, what will this mean for people who make a living via their artistic talents?
While people have lamented the prospect of AI destroying jobs for years, the discussion has remained largely theoretical. Until now. With the advent of generative AI tools that are readily available to the public, like Stable Diffusion, DALL-E and ChatGPT, the prospect of massive job losses in creative industries is rapidly becoming a reality. Not surprisingly, many creative workers – including artists, illustrators, photographers and copywriters – are fearful for their livelihoods, and not without reason.
If we believe that creative expression is inherently meaningful, and the works it produces are intrinsically valuable, then this assault on artists’ jobs would be a net loss for humanity. It’s one thing for machines to replace labourers on farms; it’s another thing entirely for AI to empty studios of artists.
But despite all the lamentations about the impact of AI on art, when I dug deeper, I realised that it’s not really AI that poses the greatest threat to art. It’s capitalism. And instead of AI accelerating the decline of art, it could actually be the key that unshackles us from our current form of scarcity capitalism and allows art to genuinely flourish.
The alienation of art
As soon as art is brought into the market, it changes. Instead of a work’s value being defined in terms of its meaning or cultural significance, it becomes defined in terms of how much someone else is willing to pay for it. Art effectively becomes a product to be bought and sold.
Given the cost of producing art – and by “art” I mean all modes of creative expression, including music, dance, poetry, fiction, etc. – and the necessity of earning money to exchange for other goods, then art necessarily becomes professionalised.
This creates distinctions between different categories of artist. One is between those who create art for fun, so-called ‘amateurs’ (from the Latin amare, meaning “one who loves”), and those who create art for money, whom we can call ‘professionals’. The latter group, and society in general, tend to look down upon amateurs as engaging in art only frivolously or lacking the talent to make it in the competitive market.
The other distinction is among professionals, and is between those who work as commercial illustrators, designers, photographers, musicians, copywriters, etc., and the very small subset of their number, whom we might call ‘purists’, who are skilled or lucky enough to be able to produce the art they want, and can make a living out of it, either through selling to enthusiasts or collectors, or by securing grants. The purists, in turn, tend to look down on professionals as being sell-outs, or lacking the talent to make it in the rarefied art world.
The capitalist dynamic that produces these distinctions has the unfortunate consequence that many people choose not to create art at all, either because they don’t believe they are skilled enough to compete in the market, as if that were the only standard by which one might be measured, or they consider amateur art to be less than worthy. As a result of the commercialisation of art, there are likely many fewer painters, dancers, musicians and poets, than there might otherwise be.
Who’s under threat?
It’s important to recognise that when it comes to AI, it’s primarily the professionals who are at risk. These are the artists who produce the kinds of products that AI is increasingly able to create at lower cost.
AI doesn’t appear to present much of threat to purists, given that grant-givers and collectors are often spending based on the name in the corner as much as the other marks on the canvas, as it were. Purists can also do something that AI can’t: translate their personal experiences into creative expression. Amateurs are also not much at risk from AI because they don’t, for the most part, seek to derive an income from their works.
If we focus on professionals, we can see something else that the commodification and professionalisation of art have done: alienate the artist from their work.
Professionals often work to a brief defined by another person. Their art is often a means to a commercial end, such as capturing a prospective customer’s attention with a graphic or jingle, or by gussing up the interior of a restaurant. Some of this work can be deeply meaningful and rewarding, but much of it is far removed from what the artist would otherwise create were they not in dire need of money to pay the bills.
As one commenter on YouTube remarked: “As an artist I’m constantly conflicted with needing to make art that has ‘market value’ and can be sold to someone to financially support myself, and just making art for art’s sake because I want to make something that I like, and to express myself through the power of creativity.”
This alienation of the artist from the work they genuinely wish to produce has been discussed at length as far back as by Karl Marx. It’s also the reason why Pablo Picasso joined the Community Party.
It means that much of the art produced by professionals is, by its commercial nature, also helping to reinforce the very commercial system that binds it. This undermines one of the core social functions of art, which is to be a form of political expression, often employed to highlight and challenge the power structures that stifle and oppress humanity.
From this perspective, I saw that capitalism has already made the world hostile to art. AI is just worsening the situation of those have chosen to make a career out of their artistic talents.
AI acceleration
I can see two bad responses to this situation. The first is to attempt to stuff the AI genie back in the bottle. Some are attempting to do that right now, primarily through a series of court cases against some of the major generative AI companies under the pretence of copyright violation.
The outcome of these cases (assuming they are not settled or dismissed) will likely have a tremendous impact on the future of generative AI. However, it’s far from clear that US copyright law, where the cases are being held, will find that generative AI has done anything illegal. At best, the courts might require that artists are able to opt-in or opt-out of the datasets used to train AI. But even that seems unlikely.
The second bad response is to let AI run unfettered within the current economic paradigm, where it could destroy more jobs than it creates, put millions of professional artists out of work, and concentrate wealth and exacerbate inequality to an unprecedented degree. Should that happen, it’d create a genuine dystopia, and not just for artists.
The good news is that I can see at least one good solution. This is to leverage the power of AI to dramatically boost productivity and lower costs, and use that new wealth to improve everyone’s lives through a mechanism such as a universal basic income, greater subsidies or public funding, a shorter working week or a combination of them all.
I’m not alone in endorsing this idea about transforming capitalism. Sam Altman, the CEO of OpenAI, which created ChatGPT and DALL-E, has argued something very similar in an essay called Moore’s Law for Everything. In it, he states that “we need to design a system that embraces this technological future and taxes the assets that will make up most of the value in that world – companies and land – in order to fairly distribute some of the coming wealth. Doing so can make the society of the future much less divisive and enable everyone to participate in its gains.”
This would likely be a multi-decadal project, but it would set us on a course that would decouple the work that we do from the income that we earn. This could release artists from the shackles of capitalism, as they’ll be increasingly able to produce the art that is meaningful for them without requiring that it be saleable in a competitive market.
It’d also free up more time for amateurs to explore their creative potential, possibly resulting in an explosion of art. Imagine how many people would pick up a paintbrush, pen or piano if they had the time and financial security to do so.
Much of that creative output will be low quality, but that’s not the point. If we believe that the creative act is inherently valuable, then it’s worth it. Plus, there are likely many people of startling artistic talent who are currently otherwise occupied earning a living to be able to explore and develop their abilities.
The decoupling of art from the market could also help liberate its political power, enabling more artists to question, challenge and offer solutions to society’s many problems without having their livelihood threatened.
The big question is how do we get from a world where AI is stripping people of their livelihoods to one where AI is freeing them from toil? There are no easy answers to that question. But it’s crucial to focus our attention on the root cause of the problem that artists face today, and that’s not AI, it’s capitalism.
| 2023-02-02T00:00:00 |
2023/02/02
|
https://ethics.org.au/ai-is-not-the-real-enemy-of-artists/
|
[
{
"date": "2023/02/02",
"position": 22,
"query": "universal basic income AI"
}
] |
Is ChatGPT a threat to education?
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Is ChatGPT a threat to education?
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https://www.universityofcalifornia.edu
|
[
"Iqbal Pittalwala"
] |
Additionally, teachers and professors should be aware of these concerns and discuss with students about how to use ChatGPT for their research work. To avoid ...
|
UC San Diego experts break down the science behind six of social media’s most popular coping strategies and self-care trends.
| 2023-02-02T00:00:00 |
2023/02/02
|
https://www.universityofcalifornia.edu/news/chatgpt-threat-education
|
[
{
"date": "2023/02/02",
"position": 89,
"query": "ChatGPT employment impact"
}
] |
BIAS: Mitigating Diversity Biases in the Labour Market
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BIAS: Mitigating Diversity Biases in the Labour Market
|
https://www.universiteitleiden.nl
|
[] |
The project will investigate the use of Artificial Intelligence in the labour market, and how biases in hiring and promoting processes based on personal ...
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Research project
The project will investigate the use of Artificial Intelligence in the labour market, and how biases in hiring and promoting processes based on personal characteristics are potentially reproduced with AI-based systems.
The BIAS project investigates the use of Artificial Intelligence in the labour market. In particular, the project explores how AI-based systems potentially reproduce biases in hiring and promoting processes based on personal characteristics. In an employment context, this can, for example, involve analysing text created by an employee or recruitment candidate to assist management in deciding to invite a candidate for an interview, to training and employee engagement, or to monitor for infractions that could lead to disciplinary proceedings.
We identify and mitigate biases in applications used in a Human Resources Management (HRM) context at the Horizon Europe BIAS project. In particular, the project:
Develops the Debiaser, a proof-of-concept for innovative technology based on Natural Language Processing and Case-Based Reasoning for an HR recruitment use case. The system will contain two modules: one for bias detection and another for bias mitigation . Conducts extensive ethnographic fieldwork concerning the lived experiences of employees, Human Resource Managers, and technology developers and channels them toward improving these algorithms. Provides substantial training for HR managers and technology developers regarding AI's responsible development and implementation.
The project is coordinated by Dr Roger A. Søraa from the Department of Interdisciplinary Studies of Culture at the Humanities Faculty at the Norwegian University of Science and Technology, who will be accompanied by a consortium coming from around Europe, including Leiden University, but also Bern University of Applied Sciences, University of Iceland, Smart Venice, LOBA, CrowdHelix, Digiotouch, and FARPLAS.
Dr Eduard Fosch-Villaronga is the project leader at Leiden University. eLaw - Center for Law and Digital Technologies will assess the trustworthiness levels of the AI system developed by the consortium and survey the workers’ attitudes towards diversity biases in labor automation around Europe.
Due to the selected cookie settings, we cannot show this video here. Watch the video on the original website or Accept cookies
You can find more information about our project on our website https://biasproject.eu/.
This project contributes to advancing the knowledge in the field of Diversity & AI, which Dr Eduard Fosch-Villaronga started at eLaw - Center for Law and Digital Technologies some time ago. Within that topic, Eduard also chairs the Gendering Algorithms initiative at Leiden University, a project aiming to explore the functioning, effects, and governance policies of AI-based gender classification systems.
This project has received funding from the European Union's Horizon Europe programme under the open call HORIZON-CL4-2021-HUMAN-01-24 - Tackling gender, race and other biases in AI (RIA) (grant agreement No. 101070468).
| 2023-02-02T00:00:00 |
https://www.universiteitleiden.nl/en/research/research-projects/law/bias-mitigating-diversity-biases-in-the-labor-market
|
[
{
"date": "2023/02/02",
"position": 26,
"query": "AI labor union"
}
] |
|
Surveillance and Resistance in Amazon's Growing ...
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Surveillance and Resistance in Amazon’s Growing Platform Ecosystem
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https://lpeproject.org
|
[
"Sarrah Kassem",
"Fumika Mizuno",
"Ilias Alami",
"Erin C. Fuse Brown"
] |
... labor organization and resistance aimed at improving working conditions. ... As globally distributed workers participate in the production of data, often used for ...
|
This post is part of a symposium on Worker Surveillance & Collective Resistance. Read the rest of the posts here.
***
The world of work is constantly in flux, evolving and co-evolving along with changes in societal, political-economic, and technological conditions. These changes not only come to structure working conditions through new forms of management and surveillance, but also inform the bounds of collective labor organization and resistance aimed at improving working conditions.
Perhaps nowhere is this observation more evident than in the platform economy. Via the technological infrastructure of the Internet, digital platforms mediate the sale and exchange of various products and services between different groups. These have come to constitute platform capitalism, which is situated in capitalism’s larger regime of accumulation. Platforms have emerged and developed at different moments, from Google and Amazon in the 1990s, to Facebook and Amazon Mechanical Turk (MTurk) in the mid-2000s, to Airbnb, Uber, and Deliveroo after the economic crisis of 2007-2008. As they continue to develop and some grow into monopolies, platforms have become—to differing degrees across the globe—an intrinsic part of our daily lives.
At the core of how platforms organize work and workers lies their systems of surveillance. Platforms differ markedly in how they instrumentalize technology to mediate and oversee the labor process—and this differentiation has implications for how workers resist such oversight. I examine these points in my larger analysis of how platforms alienate workers, and how workers organize in resistance to alienation, in my book Work and Alienation in the Platform Economy: Amazon and the Power of Organization. I argue that it is helpful to analyze the realities of platform workers along two dimensions: the nature of the platform (i.e., how platforms mediate employment relations, especially with regard to whether workers are location-based or web-based) and the nature of the work (i.e., how labor is remunerated, meaning by a traditional hourly wage or by “gig”).
To grasp the implications for the world of work(ers) more closely, I contrast two of Amazon’s platforms in its growing eco-system: (1) its e-commerce platform, where workers in the company’s location-based warehouses are paid a relatively fixed hourly wage, and (2) MTurk, a digital labor platform facilitating the online outsourcing of remote work on microtasks, which can be regarded more generally as constituting part of ‘ghost work.’ On the latter platform, remote workers based predominantly in India and the US are paid precariously when and if a submitted task is approved. By comparing these two platforms along two dimensions—the nature of the platform and the nature of the work—it is possible to understand the growing role of technology in the labor process, which allows for both old and new ways of surveilling and managing workers. These engulfing dimensions of surveillance in turn inform how workers resist employer practices in their own traditional and alternative ways.
Let’s first direct our attention to the Amazon warehouses. As these workers labor under Taylorist, factory-like conditions, they are assigned specific tasks, including prepping, stowing, picking and packing items. Workers encounter surveillance both through the social eye of their supervisors and managers, as well as digitally through the devices they use in the labor process (like computers or hand scanners). At the core of the labor process is the algorithmically managed regime of productivity, which requires fulfilling ‘Units Per Hour’ (UPH) rates. These differ based on the assigned task and order volume of the shift ahead. Thus, although workers are paid by the hour, they are evaluated based on their piecework. These various forms of surveillance structure the labor process and discipline workers, while alienating them from the very labor they carry out. In the process, both forms of surveillance also limit certain forms of subtle everyday resistance such as laboring at a slower pace, since workers may be labeled as ‘low performers’ and thereby risk their contract extension.
At the same time, the nature of the platform, which concentrates workers within warehouses, allows for communication and the formation of solidarity based on various class-based, gendered and racialized subjectivities. This embodied solidarity is especially crucial, given that workers are hired on different contracts (fixed, permanent, seasonal, subcontracted); face different material realities; and encounter Amazon’s union-undermining and union-busting tactics. Workers have been navigating these and organizing in various ways in their local and national contexts. Health and safety concerns and performance pressure—both of which are engendered and exacerbated by employer surveillance—are major factors driving workers to collectively organize. They may engage in walkouts, picket lines and strikes (where legally permissible), especially during peak season of Black Friday and Christmas, and campaign for unionization and collective bargaining agreements. These are possible given the nature of the work that typically categorizes these workers as Amazon employees. In short, the nature of this platform permits more traditional forms of resistance in the face of employer surveillance.
Let us now turn our attention to the starkly different case of MTurk. Workers on MTurk labor online in hyper-Taylorized digital production lines of ‘Human Intelligence Tasks’ (HITs). These HITs—posted by so-called ‘requesters,’ who can vary from individuals, starts-ups, corporation and universities—can include answering surveys, digitalization tasks, tracing 3D objects, or differentiating between images. As globally distributed workers participate in the production of data, often used for machine learning algorithms for AI, their labor is mediated exclusively through the interface. These workers are only paid precariously per gig, so income is neither guaranteed nor stable, and ranges from a few cents to several US Dollars. As ‘independent contractors,’ MTurk workers are left with no benefits, insurances or a guaranteed wage. The larger gig economy has become renowned precisely for this kind of precarious work.
Given the vulnerabilities of gig work and the web-based nature of the platform, technological surveillance is part and parcel of the labor process. Centered on algorithmic management, the interface measures the exact time needed for each task and the worker’s approval rating of those submitted tasks, which affects access to future work. Labor’s productivity is thus surveilled and disciplined exclusively through technology, both alienating workers and leaving them with no possible interaction with one another on the interface. Subtle forms of resistance, such as slowing the rate of work, are only detrimental to MTurk workers because they are paid by gig. Unlike Amazon warehouse workers, organizing through traditional strikes and unionization are for one complicated by the precarious nature of the work that classifies them outside of formal employment, while their web-based nature leaves them outside of the spheres of regulation altogether. If MTurk workers decide not to complete a task, it will simply be completed by someone else in the world on the platform – making this form of strike ineffective in disrupting MTurk. The global supply of labor where workers are interchangeable, combined with the precarious nature and the absence of a common workplace for these workers on the platform itself, are therefore among the crucial factors that undermine traditional organizing.
Though these workers cannot organize at the digital workplace in the same way that Amazon warehouse workers can, and are located in their own material realities with their own subjectivities, their organization takes an alternative form. Away from unions and industrial relations, workers instrumentalize the decentralized infrastructure of the Internet to rate and review requesters on Turkopticon, a Chrome extension created by Lilly Irani and Six Silberman, and use subreddits and forums such as Our Hit Stop to exchange tips and help others to find high-paying and credible requesters. Through these alternative fora, workers on digital labor platforms can advise one another and form solidarity through and on digital collectives.
While technology has always been instrumental in structuring working conditions, platforms demonstrate to us just how intrinsic to and inseparable from work technology-enhanced surveillance has become. At the core of this is algorithmic management. As I’ve described here, and cover in greater depth in my book through case studies on Amazon warehouses and MTurk, two dimensions—the nature of the platform and nature of the work—inform how platforms instrumentalize technology to differing degrees to mediate, manage, and surveil labor. While these cases studies underline how electronic surveillance diminish workers’ opportunities to engage in smaller acts of resistance, they also demonstrate how workers navigate this challenge and organize themselves in turn in both traditional and alternative ways. Grasping the power dynamics on the (digital) shop floor is a crucial step towards effectively regulating workplace surveillance and supporting workers in their struggles for better working conditions.
| 2023-02-02T00:00:00 |
2023/02/02
|
https://lpeproject.org/blog/surveillance-and-resistance-in-amazons-growing-platform-ecosystem/
|
[
{
"date": "2023/02/02",
"position": 60,
"query": "AI labor union"
}
] |
This AI Robot Picks Tomatoes
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This AI Robot Picks Tomatoes
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https://ats.org
|
[] |
Discover the innovative robot designed for picking tomatoes, developed by Israeli startup MetoMotion to tackle labor shortages ... Union and euro area ...
|
Growing up in a kibbutz and working in agriculture from a young age, Adi Nir, founder of Israeli startup MetoMotion, left the fields, as many others did, to make a living in the tech industry. The widespread global shortage of fruit and vegetable pickers is what brought him back to his roots to develop the world’s first robot for picking tomatoes.
Fewer and fewer people work in agriculture, which employs just 5% to 10% of the workforce in the European Union and euro area, and 6% in OECD countries, according to World Bank data. In Israel, only 1% of all Israeli workers are employed in agriculture, the data shows.
Israeli farmers are also struggling with labor costs. Since few Israelis work in agriculture, growers need to bring foreign workers to Israel to do the tough manual work, but are limited by how many permits the government allocates, driving up salaries.
“We hear many times about farmers leaving the crops to rot because there is no one to pick them,” Nir told The Times of Israel. “Today you can’t grow tomatoes like 30 years ago — for them to be high quality and competitive in pricing, you need to do some transformation.”
After graduating as an engineer from Haifa’s Technion – Israel Institute of Technology, Nir worked for 16 years in the aerospace and defense industry, managing R&D operations and developing cutting-edge system technology.
Keep reading at timesofisrael.com.
| 2023-02-02T00:00:00 |
https://ats.org/ats-news/this-ai-robot-picks-tomatoes/
|
[
{
"date": "2023/02/02",
"position": 89,
"query": "AI labor union"
}
] |
|
The relationship between AI and humans
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The relationship between AI and humans
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https://www.economist.com
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[] |
What questions do technologies like ChatGPT raise for employees and customers? | Business.
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I f you ask something of Chat GPT , an artificial-intelligence ( AI ) tool that is all the rage, the responses you get back are almost instantaneous, utterly certain and often wrong. It is a bit like talking to an economist. The questions raised by technologies like Chat GPT yield much more tentative answers. But they are ones that managers ought to start asking.
| 2023-02-02T00:00:00 |
2023/02/02
|
https://www.economist.com/business/2023/02/02/the-relationship-between-ai-and-humans
|
[
{
"date": "2023/02/02",
"position": 47,
"query": "AI workers"
}
] |
How Amber Ensures an Employee-First HR Mindset
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inFeedo's EX Framework: How Amber Ensures an Employee-First HR Mindset
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https://www.infeedo.ai
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[] |
A framework that fuels Amber's AI engine to touchbase with every employee at the right time and moment in the organization.
|
Ever wondered what makes Amber empathetic while helping HR across 100 organizations in 50 countries be employee first? Our People Science team through years of primary research has built a framework that fuels Amber's AI engine to touchbase with every employee at the right time and moment in the organization.
Underneath Amber’s empathy and persona is inFeedo's EX framework, which is constantly updated based on feedback received from both employees and HR. You can now download our extensive white paper on what's behind this heavy body of research that makes Amber a leading AI employee engagement chatbot in the industry.
1. How Amber Ensures Every Employee Voice Heard and Valued
At the core of Amber is her focus on being employee first, always. By remaining a steadfast ally to every member of an organization and considering all key moments in an employee's lifecyle, Amber touches base frequently to have a conversation and capture employee feedback in real time. Download your copy of the whitepaper and get a deep dive into the driver-element framework that puts the employee experience first, above all else.
2. How Amber’s Modules Curates Actionable Takeaways from Hire to Retire
Amber’s repertoire of modules make her an effective tool for measuring and nurturing the employee life cycle. From the moment they enter the organization to the moment they leave Amber periodically checks up on the employee as part of the Tenure module. Moments that Matter allows Amber to engage when key changes affect employees, and the employee exit module is one of the last interactions they’ll have even after the employee leaves the organization
3. How HR Leaders Maximize Employee Engagement ROI
Amber's customer success metrics are an example of what companies can achieve when they adopt an employee-first principle. When your workforce is in a place to succeed, where they’re engaged, it positively contributes to making your organization a great place to work. Find out how Tata CLiQ achieved 24x ROI inside.
AI is driving the next wave of employee engagement strategies and best practices thinking, and Amber has one of the strongest foundations around. Her employee-first EX-EN framework helps put your employees and your company in a position for success. And at the core of that is her robust driver-element framework, built from responses from over 300,000 employees across 50 countries.
| 2023-02-02T00:00:00 |
https://www.infeedo.ai/whitepapers/how-to-ensure-employee-first-hr-mindset
|
[
{
"date": "2023/02/02",
"position": 88,
"query": "AI workers"
}
] |
|
bswift Enhances Ask Emma with AI to Deliver Personalized ...
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bswift Enhances Ask Emma with AI to Deliver Personalized Benefits Recommendations and Personalized Employee Support
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https://www.bswift.com
|
[
"Bswift"
] |
Ask Emma's comprehensive, personalized benefit recommendations. Ask Emma now guides employees based on their unique healthcare needs. She also leverages ...
|
bswift has significantly upgraded, Ask Emma, its AI-powered decision support solution, to provide a hyper-personalized benefit recommendations and virtual support for employees. The goal is to boost engagement and improve employee health outcomes.
Early results are impressive1:
Emma independently resolved 87% of inquiries
Emma fielded 77% of inquiries after hours
Ask Emma’s comprehensive, personalized benefit recommendations
Ask Emma now guides employees based on their unique healthcare needs. She also leverages national data, claims data, and narrow network pricing.
“We’re constantly evolving and enhancing our solutions to better serve our clients and make their jobs easier,” said Ted Bloomberg, CEO of bswift. “Expanding our decision support toolset capabilities was a natural next step in our mission to improve how employees approach their benefits, helping them make informed decisions that positively impact their lives.”
Improved chat functionality through AI
bswift upgraded Emma’s chat capabilities with AI and Natural Language Understanding. This ensures:
Emma understands and responds helpfully
There is less need for escalation to human support
Emma can access timely plan and account info to assist users
An industry leader in benefits technology
“We’re committed to providing the best possible experience for our clients, and our investments in Emma are a testament to that commitment,” said CTO John Hansen.
The upgrades provide a seamless, personalized approach for employees to understand, select and manage benefits—positioning bswift as a leader in the industry.
bswift’s Ask Emma was the first interactive decision support tool integrated into an enrollment platform. The new enhancements extend Emma’s impact across bswift’s digital experience and mobile app.
This milestone builds on bswift’s journey to becoming the preeminent provider of benefits administration.
1According to a 2022 bswift analysis
View Full Press Release
| 2023-02-02T00:00:00 |
https://www.bswift.com/news-press/ask-emma-ai-enhancement/
|
[
{
"date": "2023/02/02",
"position": 95,
"query": "AI workers"
}
] |
|
Los Angeles Tech Layoffs News
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Los Angeles Tech Layoffs News
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https://dot.la
|
[
"Lon Harris"
] |
newsletterlayoffsstartups. Evan Xie. artificial intelligence · Hollywood's ... May 02 2023. artificial intelligencewgaai chatbotswga strike. Evan Xie. social ...
|
Get in the KNOW
Layoffs
The latest news about layoffs in Los Angeles' tech and startup sectors from dot.LA
| 2023-02-02T00:00:00 |
https://dot.la/layoffs/
|
[
{
"date": "2023/02/02",
"position": 53,
"query": "artificial intelligence layoffs"
}
] |
|
FIS Cuts 2600 Jobs Amid 'Comprehensive Assessment'
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FIS Cuts 2,600 Jobs Amid ‘Comprehensive Assessment’
|
https://www.pymnts.com
|
[] |
The news of the layoffs comes about two weeks after The Wall Street Journal reported that tech firms are taking a more back-to-basics strategy following a year ...
|
FIS has reportedly laid off 2,600 employees and contractors in recent weeks.
By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions .
Complete the form to unlock this article and enjoy unlimited free access to all PYMNTS content — no additional logins required.
The cuts amount to 2% of the FinTech’s workforce, according to a Thursday (Feb. 2) Bloomberg report that cited unnamed sources.
The move comes as the firm is reviewing its operations at a time when its shares have plunged 36% over the last year, while the S&P 500 Information Technology Index dropped 16%, according to the report.
An FIS spokesman declined to comment on the report.
The firm launched a strategic review in December while also making several leadership changes.
Effective Dec. 16, then-President Stephanie L. Ferris became CEO, then-CEO and Chairman of the Board Gary A. Norcross departed from the company and the board of directors, and then-Lead Independent Director of the Board Jeffrey A. Goldstein was appointed independent chairman of the board.
These moves that were announced Dec. 15 accelerated changes that had been announced in October and were to have been effective Jan. 1, 2023.
At the same time, FIS said Ferris and the board had launched a “comprehensive assessment” of the firm’s strategy, businesses, operations and structure, aiming to improve results, shareholder value and client services.
The review is a priority and has no timetable for completion, FIS said.
“As I begin my new role as CEO, I am committed to working with our board and management team to evaluate and pursue the best opportunities for innovation, efficiency and growth, and I am excited to uncover new ways of driving value for all of our stakeholders, including our shareholders and clients,” Ferris said at the time. “We are taking a hard look at every aspect of our company to define areas for change and develop specific action and improvement plans.”
The news of the layoffs comes about two weeks after The Wall Street Journal reported that tech firms are taking a more back-to-basics strategy following a year in which more than 153,000 workers in the sector lost their jobs.
Last year also saw the connected economy and tech stocks tracked in PYMNTS’ CE 100 Index finish 2022 about 36% lower than they were 12 months earlier.
| 2023-02-02T00:00:00 |
2023/02/02
|
https://www.pymnts.com/personnel/2023/fis-cuts-2600-jobs-during-comprehensive-assessment-business/
|
[
{
"date": "2023/02/02",
"position": 77,
"query": "artificial intelligence layoffs"
}
] |
AI, retention, employee experience: HR trends for 2023
|
AI, retention, employee experience: HR trends for 2023
|
https://en.paperjam.lu
|
[] |
Now, several tech companies have kicked off 2023 with huge layoffs, while the latest technological leap in artificial intelligence is simultaneously hitting the ...
|
The last few years have been rocky in several ways, but certainly in terms of the societal and professional climate. The pandemic induced a teleworking frenzy that got many people excited about shifting the work paradigm to something different, newer, nicer, the aftershocks of which included mass resignations and changing employee priorities. Now, several tech companies have kicked off 2023 with huge layoffs, while the latest technological leap in artificial intelligence is simultaneously hitting the public.
For HR departments, this climate is both tricky and interesting. As such, we asked three firms in Luxembourg, two in the finance sector and one technology company, about their HR outlook for 2023. What resulted is a picture of HR in Luxembourg that revolves around anticipating and integrating artificial intelligence, improving the employee experience and hanging onto staff.
What is the single biggest change you foresee in your HR strategy in 2023?
Zach Traer, global talent acquisition partner at Talkwalker, contextualises his answer with his own summation of the HR’s recent history:
“2021 was the year of talent acquisition and mass hiring across the tech sector. 2022 was the year of increased (dare I say inflated?) salaries. 2023 has already been marked by layoffs, which has shifted HR’s focus on retaining the talent that is left in organisations.”
What does that mean for Talkwalker? “From [our] perspective, this would include building a greater sense of belonging by investing strategically in culture-generating activities and giving appropriate support to teams and their managers.” Traer adds that the personal connections and professional experiences are central to Talkwalker’s culture, hence new investments in their employees this year.
Like Talkwalker, Apex Group is also interested in their internal work culture. Michael Labarsouque, human resources director for Apex in Luxembourg, says that Apex’s number of employees in this region has recently grown to about 1,200. “2023 will be focused on fully integrating these acquisitions from an HR and cultural perspective,” he says.
Anxiety over retaining staff is potentially discernible in a comment from Daniel Zapf, deputy CEO and COO of Deutsche Bank Luxembourg (and HR country head): “We all have heard the saying People join corporations, but they leave managers. There is a lot of truth in that as there is a direct correlation between day-to-day management practices and the overall employee commitment.”
To that end, Zapf mentions that leadership will be among Deutsche Bank Luxembourg’s strategies for 2023 (alongside talent development and learning). Not new concepts, he says, but ones that need to be kept up. On leadership, he says: “We will introduce a so-called ‘Leadership Kompass’ to further improve the way we lead. We are defining a clear set of behaviours that guide our leaders every day as an integral part of the bank’s expectation landscape.”
For Zapf, the pandemic and consequent “future of work” discussions have relegated such themes as leadership and development to the back-burner. Hence, the focus on these in 2023.
In all, if one were to draw parallels across these answers, it sounds like back-to-basics is theme for 2023 in HR. Talkwalker wants to double down on its self-described core strength of instilling a sense of belonging in its employees, Apex Group wants to get its new members up to speed, and Deutsche Bank aims to get back to certain HR elementals. But of course, back-to-basics does not imply anything like a lack of change, growth, etc.
Which new technologies are most exciting (or daunting) from an HR perspective?
In answer to this question, all three firms mention artificial intelligence.
Talkwalker’s Traer and Apex’s Labarsouque discuss the potential of AI to automate certain parts of the HR role, freeing up those professionals to focus on “the human aspect of their roles” (Traer) or “more complex/added-value tasks” (Labarsouque).
On the other side of that coin is an improvement in employee experience. As Zapf (Deutsche Bank) puts it: “Online platforms which make use of new technologies such as AI make the way you engage with HR content as intuitive and easy as using technology in your private life.”
Labarsouque names chatbots as an example of how AI technology could improve employee experience, while Traer lists others--not necessarily AI-powered--including candidate feedback surveys, tailored dashboards and automated workflows. These, he says, are “exciting tools” that Talkwalker will use in 2023 to change HR into a data-driven function.
Zapf also talks more about AI’s potential for learning and development in particular: “A modern learning platform for example suggests learning bites or focus areas tailored to the individual employee. Also, it learns from the usage patterns of those employees. It makes finding content and areas of interest as easy as browsing the web, with different channels and varying depths of information. It can be like the Spotify or Netflix of learning.”
What HR challenges are particular to Luxembourg?
On this question, the answers vary (somewhat) among the three firms, though they do share concerns over keeping employees happy or attracting applicants.
For Deutsche Bank, two-thirds of whose employees commute across the border, challenges stemming from teleworking models persist. Zapf mentions the obvious taxation and social security angles, but adds administration, monitoring and reporting as being specific challenges for HR.
He links these issues, ultimately, back to staff retention: “Although [we have] come a long way following the covid-19 crisis, the rules for remote working are more restrictive in Luxembourg than in other locations we operate in. Employee preferences in this respect have shifted which makes it harder to attract and retain talent. Focusing on the employee experience in other areas has therefore become even more relevant these days.”
Talkwalker’s Traer brings up real estate in Luxembourg, the high prices of which are making it harder to convince people to move to the grand duchy. “Despite having greater wages than the rest of Europe,” he comments, “the ordinary person cannot afford to buy a home in the Grand Duchy. Many potential applicants are discouraged from migrating to Luxembourg because the possibility of becoming a homeowner is unrealistic.”
Labarsouque also discusses competition for talent as a “perennial challenge” for Luxembourg as well as other financial hubs. In response, Apex Group last year launched its “JUMP” program, which helps employees relocate. “[It] provides opportunities for our existing talented workforce to be globally mobile and relocate to where we need their skills the most.” A hundred employees have already been offered new roles in different locations, he adds, including in Luxembourg. “We think that this is a unique approach in our industry, providing us with an opportunity to develop talent and address the local challenge of talent acquisition--benefiting both employer and employee.”
| 2023-02-02T00:00:00 |
https://en.paperjam.lu/article/ai-retention-employee-experien
|
[
{
"date": "2023/02/02",
"position": 82,
"query": "artificial intelligence layoffs"
}
] |
|
AI & Inspiration: Best Tips to Incorporate Artificial ...
|
AI & Inspiration: Best Tips to Incorporate Artificial Intelligence Into Your Creative Process
|
https://authenticjobs.com
|
[] |
In this article, we'll explore how individuals can incorporate AI into creatives processes and discuss best practices for using artificial intelligence.
|
AI & Inspiration: Best Tips to Incorporate Artificial Intelligence Into Your Creative Process
Artificial Intelligence (AI) is revolutionizing many industries and the creative sector is no exception. From visual arts to music, AI is influencing the way creative professionals approach their practice and its unlocking new possibilities for innovative expression. Think about it for a moment. Those who work in creative fields can leverage the latest AI technologies to streamline and automate routine tasks, generate new ideas, and create works of art. In this article, we’ll explore how individuals can incorporate AI into creatives processes and discuss best practices for using artificial intelligence in a way that inspires, rather than replaces, one’s own creativity and artistic skill set. Whether you’re a photographer, graphic designer, filmmaker, writer, or other type of creative pro, you’ll find valuable insights about how to harness the power of AI to take your ideas to the next level. Just keep reading.
In this article:
Image generated using MidJourney AI prompt: supplies for an artists, sitting on a colorful desk, cinematic lighting
Best Practices for Incorporating AI into a Creative Process
Begin with a clear understanding of your goals. If you’re on the fence about using artificial intelligence or just want to ensure that you’re implementing it ethically, it helps to begin with a clear goal. So, prior to making it a staple in your creative process, get clear about what you want to achieve with AI and what outcomes you desire.
If you’re on the fence about using artificial intelligence or just want to ensure that you’re implementing it ethically, it helps to begin with a clear goal. So, prior to making it a staple in your creative process, get clear about what you want to achieve with AI and what outcomes you desire. Consider AI a tool, not a replacement. Remember artificial intelligence was built by humans and designed to be an assistive tool. It is not a replacement for one’s own creativity. Your ideas, imagination, and skills are still the most important aspects of your work.
Remember artificial intelligence was built by humans and designed to be an assistive tool. It is not a replacement for one’s own creativity. Your ideas, imagination, and skills are still the most important aspects of your work. Remember that data is important . AI algorithms are trained on data, so it’s important to input high-quality and diverse data for the AI systems to learn from and generate the best results.
. AI algorithms are trained on data, so it’s important to input high-quality and diverse data for the AI systems to learn from and generate the best results. Experiment, continuously. Everyday artificial intelligence technology advances. So, it’s a good idea to experiment with AI tools often – especially tools for image generation and image recognition, as those are evolving rapidly.
Everyday artificial intelligence technology advances. So, it’s a good idea to experiment with AI tools often – especially tools for image generation and image recognition, as those are evolving rapidly. Keep ethics in mind. As AI continues to evolve and is increasingly integrated into our daily lives, it’s important to be mindful of the ethical implications behind using it. So, consider how you’re using it in your creative process, what type of content creation it is being designated for and what steps you should take to communicate when an AI-generated image is on display.
Essential Tips for Drawing Inspiration from Artificial Intelligence
Image generated using MidJourney AI prompt: supplies for a designer, sitting on a teal desk, cinematic lighting
Artificial intelligence is undoubtedly powerful, however, we urge you to not be intimidated by it. Instead, embrace it as a digital tool that can be used for research and inspiration. After all, recent advancements in tech have empowered artists, designers, and anyone else with a creative thought to test out their best ideas by imputing a simple text prompt.
It’s obvious that AI is here to stay. So, here are some insights to help you leverage artificial intelligence for your own work.
Use AI instead of creating thumbnails.
AI tools that run on generative algorithms and sophisticated neural networks can be used to come up with new ideas or expand on existing ones. So, why not make AI a part of your creative process? Depending on what industry you work in, AI tools can be used to streamline repetitive and time-consuming tasks such as creating thumbnails.
Approach new technology with curiosity.
Many artists and designers are not fond of artificial intelligence tools. And while we can’t tell you what to think of this new technology, we implore you to give it a chance. AI is changing the way we think about creative processes, artistic applications, and imagination. So, it’ll help to stay up to date with the latest AI tools (even if you don’t use them for your personal work). Remember, staying ahead of the curve never hurts.
Experiment, experiment, experiment!
The only way to know how AI can best assist you in your creative processes is to test it out for yourself. So, whenever the opportunity presents itself, we encourage you to take different AI tools on a test drive. Challenge yourself to brainstorm a text prompt or two, then input it into a image generation AI tool, like MidJourney or Dall-E and see what digital art you and artificial intelligence can dream of!
Learn from AI failures and inconsistencies.
Artificial intelligence is awe-inspiring but it is far from perfect. So, view your interactions with AI as a learning opportunity. This is an important mindset to adopt as you experiment and encounter AI-generate images, code, and texts that may not always live up to your high human standards.
Remember tools are never replacements.
AI, like many other digital tools, can be used an aid. But, it is not a replacement for human ingenuity. After all, no AI can fully emulate the spontaneously creative brain we have. And while one may exist in the future, human designers and artists will always find ways to leverage the technology and create dynamic, hybrid experiences.
| 2023-02-02T00:00:00 |
2023/02/02
|
https://authenticjobs.com/ai-tips-creative-design-artificial-intelligence/
|
[
{
"date": "2023/02/02",
"position": 19,
"query": "artificial intelligence graphic design"
}
] |
How to close the Black tech talent gap
|
How to close the Black tech talent gap
|
https://www.mckinsey.com
|
[
"Jan Shelly Brown",
"Matthew Finney",
"Mark Mcmillan",
"Chris Perkins"
] |
Black households stand to lose out on more than a cumulative $350 billion in tech job ... losses. It is during such times of economic uncertainty when it's ...
|
While the number and variety of tech jobs have grown steadily over two decades, the technology workforce has not evolved to reflect the makeup of the American workforce. Organizations have worked to improve representation among Black employees and executives in technology-related jobs across industries, but there is more work to be done.
The Black technology workforce
Black people make up 12 percent of the US workforce but only 8 percent of employees in tech jobs. That percentage is even smaller further up the corporate ladder; just 3 percent of technology executives in the C-suite are Black, according to a McKinsey analysis of Fortune 500 executives. That gap is likely to widen over the next decade. Across all industries, technology jobs—those in data science, engineering, cybersecurity, and software development—are expected to grow 14 percent by 2032. Black tech talent in those roles is expected to grow only 8 percent over the same period (Exhibit 1).
Developing inclusive technologies and bridging a gap worth billions
Black households stand to lose out on more than a cumulative $350 billion in tech job wages by 2030, an amount equal to one-tenth the total wealth held by those households, according to a McKinsey Institute for Black Economic Mobility analysis.
The wage gap in tech roles is expected to grow nearly 37 percent, from $37.5 billion in 2023 to $51.3 billion in annual lost wages by 2030, according to our analysis (Exhibit 2).
Increasing Black representation in technology jobs isn’t just about bridging wage gaps. It means improving the lives of those who are regularly othered, diminished, and discounted in workplaces where they may be the only Black person. It’s also about developing inclusive technologies that have transformative potential for Black communities. For example, digital banking platforms designed to be inclusive of Black consumers provide financial services that can improve the living standards in communities underserved by traditional banks.
Businesses, nonprofit organizations, and public-sector agencies must take coordinated action to increase Black representation in tech jobs. Specifically, they should reexamine their approach at five critical junctures throughout the career journey for Black tech talent, by improving STEM education at the K–12 level, strengthening HBCU partnerships, expanding opportunities for alternatively skilled talent, replacing mentorship with sponsorship, and empowering Black leaders to thrive. Doing so will support the Black technology workforce for generations to come.
Meet STEM students where they are
Education programs focused on science, technology, engineering, and math (STEM) fields in K–12 schools have long been seen as potential feeders into the technology workforce. Programs focused on helping subsets of students began to proliferate from both the public sector and nonprofits in the 2010s; Girls Who Code and NASA’s Next Gen STEM are just two examples.
Such programs are a promising start, but there’s a lot of opportunity to do more. According to the Pew Research Center, Black students earned only 7 percent of STEM bachelor’s degrees in 2018, compared with 10 percent of all bachelor’s degrees. The COVID-19 pandemic may have further shrunk the pipeline: Black and Hispanic students experienced sharper declines in fourth-grade math test scores during the pandemic compared with their White and Asian peers, wiping out decades of progress. Without intervention, it’s possible the lagging test scores will lead to a decrease in the number of Black students who eventually pursue STEM careers.
While much of the nonprofit sector’s work has increased diversity in STEM, there could be more targeted efforts from businesses specifically designed to encourage Black student participation. Only 20 percent of Fortune 100 companies have a K–12 STEM partnership focused on students in underserved communities, according to a McKinsey analysis.
Businesses can meet students where they are by underwriting technology courses or offering information sessions in predominantly Black communities. Numerous studies have documented the positive effect that a sense of belonging in education has on academic retention: K–12 students and first-year college students who feel a sense of belonging among their peers are likelier to participate in classroom discussions, believe they will succeed in a subject area, and are more motivated. STEM programs that target schools with a high population of Black students are likely to help plug future talent gaps in tech.
A Pew Research survey published in April 2022 found that the percentage of Black adults who say “Black people have reached the highest levels of success” in a range of careers was highest for professional athletes and musicians, at more than double the rate of engineers and scientists, indicating that survey respondents don’t perceive STEM fields to be welcoming to Black talent (Exhibit 3). For students who may not have a role model in tech, community-focused approaches help increase exposure to both companies and role models.
Nonprofits have often led the charge in bringing greater STEM awareness to Black communities. One example is MITRE, an organization that provides tech expertise to the US government. MITRE gives its employees 40 paid hours of “civic duty” to participate in in-classroom and after-school programs at K–12 schools in Black and Hispanic communities; it also reimburses employees for expenses (like travel and parking) related to their participation in these programs. MITRE’s initiatives have exposed thousands of students and their parents to opportunities in STEM.
Even as companies encourage employees to participate in volunteer programs, they should be mindful to not add to Black employees’ workload or to make participation a requirement for promotion. They should encourage employees of all races—not just Black employees—to engage in racial-equity efforts.
Create stronger corporate HBCU partnerships
Historically Black colleges and universities (HBCUs) are a significant driver of economic mobility for Black people and produce many of the country’s Black technologists. Companies have been working with HBCUs to provide resources and create a talent pipeline for STEM students for more than two decades. Boeing, IBM, and Netflix are just three of the many companies that have partnered with HBCUs.
Still, there’s room to improve the effectiveness of these partnerships.
The experience of one technology company might provide useful lessons. The company launched a lauded program that relied on volunteer employees to mentor HBCU students and teach courses but did not provide employees with incentives to participate. The program created internships for HBCU students, but there was no follow-through when the internships ended (and many of the HBCU interns did not go on to work at the company upon graduation). Also, the company partnered with only a small fraction of HBCUs across the country. Finally, while the company helped develop technology courses for HBCUs, it did not underwrite the costs of those programs or offer scholarships to students, some of whom took out additional student loans to participate in the program.
Organizations with money to invest in their future workforce can direct funds toward HBCU curriculum development, career offices, and faculty training. For instance, Harvard University runs a free data science pedagogy workshop for educators at HBCUs and other minority-serving institutions, to broaden the pipeline of future graduate students in the field. IBM is partnering with 13 HBCUs to build a new Quantum Center that gives students access to IBM quantum computers, as well as educational support and research opportunities. Ideally, businesses would be able to underwrite the cost of internships or related programs so that they are free or affordable for Black students.
Not all businesses will be able to afford national HBCU outreach or cost-subsidized internship programs, however. But even those with less cash on hand can better work with HBCUs and their students: those with internship programs can offer more professional development during internships to increase the chances a student is hired after graduation and expand partnerships beyond the universe of well-known HBCUs. They should also increase partnerships with non-HBCUs that have high Black and Hispanic student populations.
Expand opportunities for alternatively skilled talent
People without college degrees are likely to be overlooked by employers that still hire according to traditional standards. Of the 17 million Black workers in the United States, 65 percent developed their skills through alternative routes—meaning they have a high school diploma and may have military or workforce experience but do not have a bachelor’s degree. By this measure, jobs that require a bachelor’s degree are out of reach for most Black workers.
By removing the requirement for a bachelor’s degree, businesses immediately expand the applicant pool. Additionally, they can partner with platforms that help train “ready to learn” talent—people who have experience in other fields with transferable skills but may require additional development—to find qualified candidates with nontraditional backgrounds.
Some businesses are already investing in such programs. Nasdaq and Oracle partner with Kura Labs, an online academy that offers free training and job placement for engineers in underserved communities. The organization says its efforts have resulted in $12 million in new wages in less than 18 months. Meanwhile, other companies including Pandora and Twitch have partnered with the platform OnRamp Technology, which works with more than 100 boot camps, online communities, and education and training providers. Three out of four people hired through OnRamp are people of color.
About the research The results of a new McKinsey Black Tech Talent Survey help illustrate where problems persist. In July 2022, McKinsey surveyed 82 Black professionals in the United States across entry-level, mid-level, and C-suite technology roles, both within and outside technology companies. The survey aimed to understand the impact of increasing Black representation in tech roles across industries and opportunities to elevate Black tech talent into executive roles. While the findings may not be definitive, they are directionally representative. This research builds upon previous “Race in the workplace” studies as well as existing work from the McKinsey Institute for Black Economic Mobility, which seeks to provide independent research to offer guidance on how to improve racial inequities around the world.
But recruiting ready-to-learn talent helps improve representation only if a company also reexamines its interview processes. Résumés that indicate a candidate is Black—either because of the candidate’s name, school, or work history, for example—have been found to generate fewer interview requests than résumés reflecting characteristics of White candidates. In our survey of Black tech talent, respondents say their companies “do not do enough outreach” and “have not yet incorporated procedures like blind résumés” (stripping a résumé of any indicators of gender identity or race) to broaden talent pools (see sidebar, “About the research”).
Replace mentorship with sponsorship
Black tech professionals change companies every three and a half years on average, compared with every five or more years for their non-Black counterparts. This pattern continues over the course of a career: Black professionals with 21 years or more of tech experience have changed companies more than seven times on average, compared with six times for their non-Black peers. The higher attrition rate means Black talent is less likely to stay at a company long enough to be promoted.
In efforts to retain Black employees, some companies have created mentorship programs—but the programs aren’t always effective: across industries, only 13 percent of Black management-level employees and only 20 percent of Black entry-level employees strongly agree that their sponsors are effective at creating opportunities for them (Exhibit 4).
Mentorship programs may fail for a variety of reasons. A business may mandate mentor pairing for new hires, but often these relationships are transactional and lack the kind of connection that allows the relationship to last. (Employees who choose their mentees may do so according to familiar networks, like a shared school, or other factors that exclude Black employees.) Mentorship programs may also lack processes that guide mentors and mentees through the relationship and may only measure intangible or difficult-to-quantify metrics, like satisfaction in your mentor.
Ultimately, mentorship is not enough to keep Black tech employees from leaving companies. Sponsorship—the idea that senior leaders are tasked with creating apprenticeship and networking opportunities, as well as helping talent navigate transitions at work like a promotion—is more impactful. These relationships require both parties to create a development strategy with specific goals that are measurable.
Enabling Black leaders to thrive
When asked what they believe are the top three most important initiatives for advancing Black talent in tech, 83 percent of Black tech employees we surveyed said advancement opportunities were among the top three most important components of growth for Black tech talent, more than inclusion seminars or external advocacy and investment. More than a third said advancement opportunities were the most important factor. There are additional ways companies can support Black tech talent beyond advancement opportunities, particularly when it comes to fostering an inclusive workplace (Exhibit 5).
Even when Black employees in tech successfully complete corporate leadership and executive training programs, a promotion may remain elusive. This may happen for two reasons: an existing Black tech leader might be skilled in one area (for example, IT project management) but lack the skills required in another (for example, data science) to grow into a C-suite-level executive role. Upskilling these employees in tech’s fastest-growing areas is one way they can be supported.
Additionally, businesses that are too focused on training Black tech talent without adopting organizational change are setting those employees up for failure. Partnering with organizations that create leadership training programs for aspiring leaders as well as existing leaders creates two streams of parallel growth at a company. It’s also important that these organizations are specifically focused on elevating Black tech talent, as general executive leadership programs may overlook some of the nuances of the Black experience in technology that shape someone’s career journey.
The Information Technology Senior Management Forum (ITSMF), a charitable organization that counts Amazon Web Services and PepsiCo among its partners, serves as an example of how to do this successfully. ITSMF offers a leadership academy for future Black tech talent, in addition to a management academy tailored for existing executives. Businesses that partner with ITSMF also engage in unconscious bias or cultural intelligence workshops and cohost networking events for prospective executive talent. Up to 80 percent of ITSMF leadership academy graduates received promotions within 18 months of completing the program, according to the group.
Seizing these five opportunities—at the K–12 level, in higher education, with alternatively skilled talent, in sponsorship, and in leadership training—will help to close the Black tech talent gap. Many businesses today are undertaking resiliency measures to prepare for tough times ahead and help curb losses. It is during such times of economic uncertainty when it’s both easiest for businesses to cut critical investments in Black tech talent, and when it’s most important not to.
| 2023-02-03T00:00:00 |
https://www.mckinsey.com/institute-for-economic-mobility/our-insights/how-to-close-the-black-tech-talent-gap
|
[
{
"date": "2023/02/03",
"position": 96,
"query": "AI job losses"
}
] |
|
The Human Touch: Employment Law and Artificial ...
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The Human Touch: Employment Law and Artificial Intelligence
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https://www.williamfry.com
|
[] |
While we await new rules, employers can prepare for the widespread adoption of AI in the workplace by reviewing their processes and determining which roles may ...
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Artificial Intelligence (AI) continues to develop and expand into everyday life.
Many employers are interested in utilising AI to minimise overheads, increase automation and target client bases. As such, AI will inevitably influence employment; in obvious ways, such as human replacement in lower-skilled labour and less obvious ways – such as compliance with equality legislation.
AI & Employment: Rapid evolution
With AI, as is the case often in employment law, legislation and case law will play catch-up to real-life practices.
AI is already present in most businesses – for example, in using facial recognition to secure company devices or spam filters on email. Some firms use (or are considering using) AI at recruitment to screen candidates. Already, it has been suggested that some AI programmes carry out this process without regard to relevant employment law protections or potential latent biases of the software programmers – for example, by filtering out certain language patterns which are more prevalent amongst different groups of people.
In Ireland, this could expose the employer to litigation risk – it is well established that our equality protections extend to the recruitment stage. Even regarding employment equality legislation, AI-led employment decisions are potentially unlawful. Individuals have the right not to be subject to automated decision-making under GDPR rules. There are existing heightened data protection obligations under the GDPR (complemented by the AI Act, discussed below). For example, providing information and certain types of data processing by AI systems may require a data protection impact assessment. Further, the risk of general litigation may increase due to the proposed “AI Liability Directive”, which changes the burden of proof in claims for damage caused by AI systems.
The protection against automated decision-making is an existing concept that legislation will likely expand on in the future, such as the draft EU regulation on AI; the AI Act. The AI Act is a draft piece of EU legislation expected to come into law in late 2023 or early 2024 and will have a massive effect in much the same way as the GDPR did in 2018.
The AI Act classes AI systems which deal with employment as “High Risk”. High-Risk systems will be subject to further scrutiny and control: regular formal risk assessments, data processing impact assessments and onerous record-keeping requirements.
The CJEU has also recently commenced hearing a reference as to the implications of automated decision-making regarding credit ratings – in an interesting case which is expected to test the limits of what can be subject to AI-made / AI-led decisions.
AI & Potential Dangers
As well as the risks above, there are potential dangers in respect of AI and employment where roles previously carried out by humans are automated and replaced with AI.
While redundancy legislation is designed to protect, engage, and compensate employees when their role is no longer required, it will be optically challenging for any employer to announce redundancies where AI will carry out the work in the future. A valuable example of public backlash against AI carrying out “human” work is the story of a journalist from The Atlantic magazine using an AI-generated image in August 2022 – which went viral in a way that caused huge negative publicity for the Company.
There is also the nebulous risk of AI creating intellectual property exposure where an AI programme creates valuable data – such as client lists, candidate databanks or creative works. If adequate IP provisions are not in place, who legally owns the data might not be clear: the employee, the employer, or the AI provider. While employers can somewhat protect against this risk by using strong user agreements between the employer and the AI provider, this depends on employee compliance. For example, an employee “teaching” the AI feeds the algorithm material subject to copyright. This could put the employer in a compromised position.
As well as that, the law in Ireland and the UK is an outlier in how it treats IP in AI-generated works – the person who makes the necessary arrangements for a computer-generated work where there is no human author is the author. Therefore, an employer could possibly own the copyright in a piece of work if an employee made the necessary arrangements to create that work.
However, most jurisdictions require a human author to avail of copyright protection. It remains to be seen how this area will evolve, and copyright ownership, for now, will need to be dealt with on a case-by-case basis. It is not clear if any IP created by AI systems will be capable of IP protection – but inserting AI-specific IP clauses into employment contracts may help address the issue.
How employers can prepare for AI adoption
While we await new rules, employers can prepare for the widespread adoption of AI in the workplace by reviewing their processes and determining which roles may be now (or in the future) at risk of being replaced/impacted by AI. This will help employers put in place strategies for the effective adoption of AI technologies and protection from potential challenges from disgruntled employees or an increasingly competitive market.
| 2023-02-03T00:00:00 |
2023/02/03
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https://www.williamfry.com/knowledge/the-human-touch-employment-law-and-artificial-intelligence/
|
[
{
"date": "2023/02/03",
"position": 12,
"query": "workplace AI adoption"
},
{
"date": "2023/02/03",
"position": 99,
"query": "AI labor market trends"
},
{
"date": "2023/02/03",
"position": 1,
"query": "AI regulation employment"
}
] |
Will ChatGPT represent the future of customer service?
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Will ChatGPT represent the future of customer service?
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https://www.nojitter.com
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[
"Mila D'Antonio"
] |
This conversational AI has great appeal in the contact center environment, which would benefit from advancements in intelligent automation; natural, ...
|
These days, the internet is abuzz over ChatGPT. The technology is a text-based artificial intelligence (AI) tool developed by OpenAI and designed for use in chatbots and conversational systems. The power of this technology lies in its ability to understand and learn from conversations and deliver humanlike, personalized, and detailed responses. This conversational AI has great appeal in the contact center environment, which would benefit from advancements in intelligent automation; natural, personalized, and precise responses to customer inquiries; and workplace efficiency gains.
The technology’s promise to personalize responses, content, and offers; serve up detailed product descriptions; analyze customer reviews; and troubleshoot technical content will also give it prominence in the contact center. Scott Draeger, customer experience officer at Quadient, added that he is excited about ChatGPT’s ability to personalize to larger segments than some companies can currently reach: “This new tech could truly personalize communications, factoring in people’s individual communication and engagement styles, interests, and other factors.”
Don’t replace human interaction or search with generative AI
Much of the discussion around the potential use cases of ChatGPT centers on the possibility of the technology being positioned as a next-generation, conversation-based search engine. However, ChatGPT is a language model trained on large datasets to generate humanlike responses to questions, whereas search engines use algorithms to index and rank webpages based on queries. Therefore, ChatGPT is not designed to search the internet and provide information in the same way as search engines do. Draeger elaborates: “People say this will replace search. I disagree. It’s nothing without search. It’s an inflection point. It’s the inflection point bitcoin thought it was.”
Despite ChatGPT’s low-hanging-fruit cost benefits and efficiency gains, the technology succeeds most often at simulating human responses and responding to natural user input, and its extensive knowledge base allows it to skillfully navigate customer conversations. However, it falls short when the conversation becomes complex, and it does not have the necessary knowledge for a particular inquiry. Then, the technology instead fills in the gaps. “One of the big things with generative AI models is how you deploy that technology safely. It can generate amazing results that are perfectly accurate. But it can also generate results that look well considered, are articulate but completely wrong,” said Ed Challis, general manager at Re:infer, a UiPath company.
Additionally, many experts caution that ChatGPT should not be used as a replacement for human interaction. Some who have deployed it in their organizations have said that it creates new sentences for every inquiry and fails to follow a predefined script; therefore, the technology has been known to give incorrect answers. The failures could potentially stem from leveraging the technology as a standalone solution. Scott Jennings, head of retail and e-commerce industry GTM at Twilio, said ChatGPT works well when it is incorporated into an existing system. He said companies such as Twilio, with technology that wraps around systems, are well-positioned to take advantage of new offerings such as ChatGPT.
Working toward ChatGPT’s inflection point
Although ChatGPT may work well with repetitive inquiries such as “What is the status of my order?” most of the customer service market already largely automates such routine inquiries. In the short term, generative AI tools such as ChatGPT will show value in assisting or optimizing agents’ workloads and automating tasks rather than their jobs. Long-term opportunities for ChatGPT lie in the customer engagement communications that fall outside of repetitive service inquiries and require a powerful, conversational chatbot. Widespread adoption of ChatGPT will depend on the continued advancements of NLP and machine learning. As Jennings said, “Ultimately, the opportunities lie in the eye of the beholder.”
| 2023-02-03T00:00:00 |
https://www.nojitter.com/ai-voice/will-chatgpt-represent-the-future-of-customer-service-
|
[
{
"date": "2023/02/03",
"position": 59,
"query": "workplace AI adoption"
}
] |
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Reducing healthcare staffing shortages with AI solutions
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Reducing staff shortages in healthcare with AI solutions
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https://www.infosysbpm.com
|
[
"Infosys Limited"
] |
AI is now being leveraged in new ways to navigate the shortages in the workforce ... adoption has to address many aspects of legal, ethical, medical and ...
|
Healthcare
Reducing healthcare staffing shortages with AI solutions
The unprecedented pressure on the healthcare industry during the fight against the coronavirus has exposed many lacunae within the industry. The pandemic caught the world napping. In spite of the technological advances, we were found wanting in many areas. One of the major challenges the industry faced was the unprecedented pressure on health care professionals (HCP). The 2-year long battle against the virus overworked the industry’s resources leading to fatigue, burnout, mental stress and dissatisfaction among the HCPs. The nature of work in nursing is such that they had to work in close quarters with the patients. The stringent biosecurity measures, increased workload, and witnessing innumerable patient deaths led to severe stress on physical, mental and emotional levels. This resulted in a high and expensive turnover in the sector.
The American Nurses Association (AMA) has predicted that Registered Nurse (RN) jobs would have more demand and supply than any other profession in 2022. The US Bureau of Labour Statistics has predicted that RN will continue to be the top growth occupation with a 9% increase through 2030. The average cost of turnover for one staff RN has also been increasing by over 8% per 2021 data. The shortage of nurses, especially bedside nurses, is real and is affecting patient care. The surging cost of HCP, new therapies and infrastructure have added to the overall increased expenditure of hospitals and reduced their revenue. Countries like the US have an increasing ageing population with a longer life expectancy (77 years per 2020 data). The dependency on continued support of health services means that the industry has to find sustainable solutions to the staffing shortages. As the world now settles into the endemic phase of the infection, we must urgently tackle the shortage of healthcare resources – both human and infrastructure. The industry has to look at multi-pronged approaches to deal with these challenges.
Artificial Intelligence (AI)
had its origins in the 1950s and has since undergone sea changes with respect to its build and applications. AI has long made in-roads in diagnostic accuracy, precision medicine, prognosis evaluation, computer-aided detection in oncology, radiology, gastroenterology and neurology to name a few. The four main fields of AI which have accomplished these are Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) and Computer Vision. AI is now being leveraged in new ways to navigate the shortages in the workforce while improving efficiencies in processes, managing unpredictable capacity demands, backlog management, and replacing repetitive tasks.
A report by Accenture has predicted that there could be $150 billion in annual savings due to AI applications in healthcare by 2026. Some of the top AI applications that can help navigate through staff shortages are robot-assisted surgery, virtual nursing assistants, administrative workflow assistance, dosage error reduction and automated image diagnosis.
Diagnosis
AI can help in the diagnosis of malignant tumours with far more efficiency than the human eye. Computer Aided Detection (CAD) and Image analysis have made radiology tests more efficient and precise.
Streamlining Processes
Operating Rooms (OR) can bank on predictive technology tools to optimally manage the schedules and resources available and also keep tabs on changing environments – both micro and macro. AI can help in better communication within the hospital and other stakeholders. Intelligent scheduling with AI can lead to optimal utilisation of resources.
AI tools
The burden on clinicians can be reduced by providing tools like AI-enabled symptom checkers enabled with urgent care settings to ensure the use of emergency services only when needed. This would address about 20% of unmet clinical demands.
Robot-assisted surgery
Robotic systems that assist surgeons by increasing precision, flexibility and control during surgeries are a boon for patient safety. These systems have the ability to integrate all preoperative medical records across platforms and present the metrics real-time. These are most useful for delicate and complex procedures. The minimally invasive surgeries result in shorter hospital stays and quicker recovery, so hospitals can cover more ground with an increased number of surgeries.
Virtual nursing assistants
With the use of these assistants, patient symptoms are monitored remotely and alerts are issued to clinicians only when needed. Reports suggest that almost 20% of nursing time can be saved by employing these tools so that unwarranted hospital visits can be avoided.
Workflow assistant capabilities
Activities such as voice-to-text transcription, report writing, prescription recording and printing, and processing different types of data generated on a daily basis, can be handled by AI. These systems can consume voluminous data and provide insights on workflow improvements as well.
Though there is a lot of promise in integrating AI in different aspects of healthcare, AI adoption has to address many aspects of legal, ethical, medical and societal questions in parallel.
* For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.
| 2023-02-03T00:00:00 |
https://www.infosysbpm.com/blogs/healthcare/reducing-healthcare-staffing-shortages-with-ai-solutions.html
|
[
{
"date": "2023/02/03",
"position": 95,
"query": "workplace AI adoption"
}
] |
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The Future of Chatbots: 10 Trends, Latest Stats & Market Size
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10 Chatbots Future Trends & The Latest Statistics
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https://onix-systems.com
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[] |
Furthermore, research from Oracle also found that 29% of banking and finance-related organizations were deploying artificial intelligence-powered chatbots to ...
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The future of chatbots is transforming the way businesses interact with their customers. Gartner, a leading technology research firm, predicts that by 2027, chatbots could become the main customer service channel for 25% of businesses. This forecast is backed by a recent 67% surge in chatbot adoption.
With continued advancements in AI and machine learning technology, it’s clear that chatbot usage is only going to continue to increase across industries.
At Onix, we have more than 20 years of experience investigating and developing chatbot technology for our clients, so we have decided to share our view on top trends.
Table of contents Chatbot Market Size & Stats for 2024 and Beyond
What Chatbot Trends Might We Expect for 2024 And Beyond
Use Cases and Companies that Employ Chatbot Technologies
Chatbot Challenges to Face in The Future
Wrap up
FAQ
In this blog post, we'll look at 10 key trends surrounding chatbots and offer insights into the size of this rapidly growing market segment from a global perspective.
Unlock groundbreaking Al/ML solutions and drive business outcomes learn more
Chatbot Market Size & Stats for 2025 and Beyond
The chatbot industry is projected to reach a market size of $3.62 billion by 2030, with an annual growth rate of 23.9%. This speaks to the increasing popularity of chatbots and their potential as powerful customer service tools.
This trend has been primarily driven by the advancements in artificial intelligence (AI) and machine learning (ML) technologies, which have allowed for more sophisticated chatbot solutions. As a result, businesses increasingly leverage AI-driven chatbots as part of their customer service operations.
Currently, the industry that is one of the leaders in the adoption of chatbots is healthcare, with 43% of companies using them for customer service. Additionally, 33% of businesses in the manufacturing industry and 28% of companies in the retail sector are leveraging chatbots as a tool for customer support.
Furthermore, research from Oracle also found that 29% of banking and finance-related organizations were deploying artificial intelligence-powered chatbots to improve user experience. For other industries such as media, e-commerce, hospitality, and travel, there wasbetween a 13-15% usage rate.
As we can see, more and more companies are adopting chatbot technology and making use of it. Let’s now dive deeper into the chatbot industry trends we might expect in this sphere for 2025 and beyond.
In the future, chatbots will serve as versatile assistants across various industries, facilitating seamless interactions between businesses and customers through personalized, efficient, and AI-driven conversations.
They'll evolve to offer advanced problem-solving capabilities, adaptability, and emotional intelligence, becoming indispensable tools for enhancing customer experiences and streamlining operations.
What Chatbot Trends Might We Expect for 2025 And Beyond
With a variety of ‘conversational marketing’ techniques being used, such as the voice-assisted Amazon Alexa and chatbots in Messenger, it will be interesting to see how this technology will develop. Let’s look at what is the future of chatbots that may likely take the stage soon and how chatbots will be used.
Better use of Machine Learning
Modern chatbots are evolving into what scientists initially wanted them to be: they actually learn over time. This learning occurs mainly through human interaction, but that’s not the only option.
Chatbots also collect so-called training data and can be connected to open-source data (like WikiQA Corpus or Ubuntu Dialogue Corpus) to create a fuller picture.
Chatbots use this during a live chat as a reference. The more data that comes in, the more capable chatbots can process and understand. They work smoothly and efficiently, their reactions become increasingly personalized, and operational time becomes shorter.
It is quite likely that AI chatbots will become fully capable of assisting with the user’s needs at nearly every stage of the customer experience. This means much less generic and useless information is communicated.
Human-like chatbots
As technology advances, it has become increasingly commonplace to see chatbot utilization not just in customer service industries but in various businesses. By 2025, market analysts expect chatbots to be integral to every industry as consumers continue to expect 24/7 customer service.
But instead of simple bots offering pre-programmed answers or scripts, these modern chatbots are expected to use natural language processing and hold conversations that closely mimic human interaction.
With the expected growth of AI-driven digital assistants, rise in consumer expectations and need for automation efficiency, businesses' average conversational AI investment is set to reach $18.4 billion by 2026.
It's clear that this trend is only likely to grow and develop in the coming years and, with continued advancements in technology, could offer incredible potential for businesses and consumers alike.
Advantageous virtual assistance
Chatbots are revolutionizing how businesses assist their customers. They offer a convenient and user-friendly way of interacting with customers and provide businesses with the potential to automate specific customer service tasks and integrate multiple applications for a more efficient workflow.
By combining different functions into one system, chatbots can give businesses a great advantage in productivity, cost-effectiveness, and speed. With these benefits being made easily accessible, it is no wonder that many companies are now investing in adopting virtual assistant technology to take their customer service to the next level.
AI-powered chatbots are becoming more intelligent
AI-powered chatbots are becoming more and more intelligent these days as technology continues to advance. To bridge the gap between humans and computers, chatbots need to interact naturally with people through conversation seemingly - something made possible by artificial intelligence.
Through natural language processing, AI makes it easier for machines to understand what is being said and respond accordingly.
What was once a technology limited to simple dialogue now allows greater depth in conversations between bots and their users. While some may worry that AI-powered chatbots are erasing the need for human customer service agents, advancements in this technology should be celebrated as providing more efficient communication capabilities than ever before.
Chatbots are facilitating business processes
AI chatbots are helping businesses automate processes quickly and efficiently. Modern chatbots are powered by advanced natural language processing techniques, which allow them to understand human conversations, interpret user intent and respond effectively.
For example, companies often require customer support agents to process orders, answer queries, resolve issues, and so on. AI-powered bots save businesses time and money as they can provide instant replies without needing manual labor.
Moreover, modern chatbots can even act as personal assistant bots that help with daily tasks such as booking appointments or managing orders. The demand for business-oriented AI chatbots is proliferating, and this trend will keep increasing over time.
The growth of voice-based apps
The use of digital voice assistants is steadily on the rise and set to triple by 2025, with estimates showing that smart home devices are a major driver of this surge in growth. Smart TVs will have the most significant expansion, predicted to grow by over 100% every year for the next five years.
This means that more households than ever will be able to benefit from the top-quality viewing and assistant technology that was previously only available to bigger corporate players.
It shows what we can achieve when we bring cutting-edge tech directly into our homes and demonstrates how far digital voice assistants have come in such a short time.
With more innovations coming soon, there's no doubt that smart homes equipped with these assistants will continue to be at the forefront of the transformative capabilities of new technologies.
Chatbots will automate payments
Chatbot technology has advanced significantly in recent times, and economists and technologists predict that chatbots will become a major part of our daily lives in the coming years.
One significant way chatbots are revolutionizing the world is by automating payments. Many kinds of financial transactions can be automated through chatbot technology, such as managing accounts and banking activities or making payments for goods and services.
In addition, customers have access to 24/7 customer support and can pay faster with increased convenience. This technology is cost-effective for global businesses that need efficient payment systems but also offers an improved user experience for customers worldwide.
With the rise of this trend, it's safe to say that automated payments are set to become a game-changer in the 21st-century economy.
Chatbots for HR and internal enterprise use
Chatbots are quickly becoming the norm for human resources and internal enterprise use. As companies large and small struggle with new labor laws, the need for uniformity, efficiency, and accuracy in processes is greater than ever before.
Chatbots can help provide a streamlined solution to some of these challenges: for example, by helping automate employee onboarding and training processes; providing answers to commonly asked questions from employees; and tracking vacation requests, shift changes, and other necessary information quickly and accurately – something that would be difficult or impossible with a manual system.
Comparatively speaking, chatbot technology is cost-effective, popular with employees (it helps them save time and demystifies standard procedures), and relatively simple to implement.
While they still may not completely replace human resources, chatbot technology can certainly improve HR’s efficiency, which is why its increasing trend in this arena makes so much sense.
Chatbots will be integrated with social media
As the popularity of chatbot technology grows, so do the trends and applications surrounding it. One such trend is the growing integration of chatbots with social media platforms such as Facebook, Twitter, and Instagram.
This integration has allowed companies to leverage social media channels to create a more engaging customer experience - allowing them to provide 24/7 support, answer inquiries quickly and even collect valuable customer data they might not have had access to in the past.
For example, Instagram recently unveiled technology that allows companies to use Chatbots to interact with their customers. This is a powerful tool for businesses because it helps them to respond quickly and efficiently to customer inquiries. For example, a bot can help direct people to the correct page or provide important product information in real-time.
Additionally, chatbots can also collect data from customers to inform better company decisions, such as what type of ads they should be running. Instagram's Chatbot technology is invaluable, as it allows businesses to easily and effectively interact with customers personally.
Instagram's Chatbot technology allows businesses to interact with customers personally
Greater Interoperability
We are living in an era where it is becoming increasingly apparent that technology can be used for good. The latest trends in chatbot are an excellent example. It is possible to improve the user experience by using chatbots to make things easier, simpler, and more streamlined.
Greater interoperability allows different technologies to work together smoothly and efficiently, offering users a better overall experience.
The development of standards such as JSON API is paving the way for more developers to get involved with creating bots that communicate across multiple channels.
By setting APIs as common building blocks between systems, new possibilities arise in terms of suggestions, alerts, and actions that virtual assistants may take on behalf of their users making use of the interoperability benefits.
How to Create a Chatbot: The Ultimate Development Guide read now
Use Cases and Companies that Employ Chatbot Technologies
From banking services to healthcare, travel booking to retail, many sectors have taken advantage of artificial intelligence-fueled technology. Some more well-known examples include chatbots being used for authentication at banks like Capital One and interactions with customers by Burger King.
Similarly, London's Heathrow Airport has an AI-powered digital assistant to help travelers as they journey toward their destinations.
Other notable implementations have been conducted by widely recognized companies such as Amazon and Google, who are utilizing bots for tasks ranging from communication and product recommendations to content delivery and health advice.
Despite still being in its relative infancy, it appears clear to many that chatbot technology can be a valuable asset for any business looking for personalized customer service experiences.
Uber recently released its “Ride Pass” program, which was rolled out with the help of a chatbot to guide customers through their purchase process; Facebook Messenger has adopted a similar approach to conversational commerce with regard to shopping.
The application OpenTable also utilizes chatbot technology to permit users to make real-time restaurant reservations, while many hotels are using similar mechanisms for booking services using natural language recognition tools.
Clearly, the utilization of chatbot technologies can provide significantly enhanced customer service experiences making it an attractive solution for businesses today.
Chatbot Challenges to Face in The Future
Chatbots are becoming increasingly popular and are being developed for a wide range of applications. Despite this, there are still many challenges that the industry must face to make chatbots more effective and reliable.
One of the most notable challenges is developing natural language processing (NLP) capabilities for the chatbot. This is critical for a chatbot to be able to understand requests from users and provide meaningful responses.
Additionally, NLP technologies are necessary for the chatbot to be able to identify topics related to the user’s query or request.
Another major challenge faced by the chatbot industry is developing a wide range of knowledge bases. Knowledge bases are collections of information that the chatbot can access to provide answers to user questions. As the number and variety of potential inquiries grow, so must the knowledge base.
This is one of the biggest hurdles for developers looking to use chatbots for more advanced tasks such as customer service and product recommendations.
Finally, developing an effective user interface is essential for a successful chatbot. Chatbots must be designed so that users can easily interact with them and understand their responses.
This involves designing a chatbot with the right tone of voice and personality, as well as developing graphical user interfaces (GUIs) that make it easy for users to navigate and access information.
We deliver exceptional experiences and intuitive interfaces for your users learn more
These are just a few of the challenges faced by the chatbot industry. While there is still a long way to go before chatbots become ubiquitous, meeting these challenges is essential for making them more reliable, effective, and user-friendly.
You may also be interested: Popular Fintech Technology Trends to Expect in 2025
Wrap up
As we've seen, chatbots are becoming increasingly popular and widespread. With the right strategy, they can be a valuable asset for businesses in a variety of industries.
This is where the Onix expert team can help you avoid risks when building your custom chatbot and enhance your business processes by integrating it. If you need a proven technology partner to create a quality chatbot, drop us a line, and we’ll contact you as soon as possible.
How much does it cost to hire a dedicated team? calculate now
FAQ
Is chatbot use growing?
Chatbots are certainly gaining traction as helpful customer service tools around the globe. Used to automate conversations and answer customers' questions, they can free up human agents' time to tackle more complex tasks.
We're seeing organizations of all sizes adopting chatbot technology - big businesses, small online stores, tech startups, and even local governments are increasingly getting on board.
From conversational interfaces powered by natural language processing to AI-driven virtual customer assistants, advances in this technology have made it much more accessible and viable for companies of all sorts.
With so much potential on offer and cost savings to be had, it's not hard to see why chatbots are gaining popularity - and why their usage is only set to grow from here on out.
How can I create a chatbot?
You can create a chatbot using a chatbot development platform, a coding framework, or a no-code/low-code tool. Platforms like ChatGPT API, Dialogflow, and Microsoft Bot Framework provide ready-to-use tools for building and deploying chatbots quickly.
How can I create a chatbot?
There are many ways to create a chatbot, but the most common method is to use a chatbot development platform. These platforms provide tools and templates that make it easy to create and deploy chatbots. Before you start creating, you need to follow the latest chatbot trends.
What are some popular chatbot development platforms?
Some widely used platforms include Dialogflow, Chatfuel, BotPress, and Rasa. Each has different capabilities—Dialogflow is great for AI-powered bots, Chatfuel is ideal for Facebook Messenger bots, and Rasa is best for open-source chatbot development.
How much does it cost to create a chatbot?
The cost varies based on the chatbot’s complexity and features. A basic chatbot using pre-built templates may cost $5K to $15K, while a custom AI-powered chatbot with advanced NLP features can range from $20K to $80K or more. Subscription-based platforms may charge monthly fees starting at $50 to $500 per month.
What are some key features of a good chatbot?
A good chatbot should have natural language understanding (NLU), seamless integrations (e.g., CRM, APIs, databases), personalization capabilities, multi-channel support, and analytics for continuous improvement.
What are some best practices for creating a successful chatbot?
To ensure success, define clear goals, understand your audience, design a conversational flow that feels natural, incorporate fallback responses for errors, and continuously test and improve the chatbot based on user feedback.
| 2023-02-03T00:00:00 |
https://onix-systems.com/blog/6-chatbot-trends-that-are-bringing-the-future-closer
|
[
{
"date": "2023/02/03",
"position": 69,
"query": "AI labor market trends"
}
] |
|
We asked ChatGPT about workplace wellness
|
We asked ChatGPT about workplace wellness
|
https://www.sonicboomwellness.com
|
[
"Sbw Administrator"
] |
That said, will AI like ChatGPT impact how programs impact employee engagement? Absolutely. Can AI aid in segmentation and customization based on specific ...
|
ChatGPT, created by OpenAI, is the latest technology to go viral and rack up users quicker than TikTok and Spotify. It’s a glimpse into the “mind” of artificial intelligence (AI), allowing everyday people to test out its capabilities. While AI of any kind will never replace the brilliance of human interaction (see the last question in this interview), it is an important player in today’s world of marketing, healthcare, and, yes, workplace wellness.
Here at Sonic Boom, we understand the power of social connectivity between humans; it’s the foundation of our program! That said, will AI like ChatGPT impact how programs impact employee engagement? Absolutely. Can AI aid in segmentation and customization based on specific needs and demographics? While it may be able to set parameters and guidelines based on data points and generalized information for certain groups, AI lacks the emotional intelligence that humans possess and plug into demographic- or industry-specific programming. So yes, AI can segment based on data, but humans can based on subjective knowledge.
Related: What are soft skills, and why do they matter?
One example is reviewed in the National Institute of Medicine. They talk about how AI can support the healthcare system — and by extension, workplace wellbeing. Utilizing “if-then” functionality helps guide the user or patient experience based set “rules” or “indications.” Many wellness solutions, including ours, use this today to inform a recommendation engine that can suggest certain health-related action items.
All of this is why we got curious and asked ChatGPT some basic questions about workplace wellness in 2023. Here’s what it had to say, and it’s unedited.
What does workplace wellness mean to you?
As an AI language model, I do not have personal opinions or feelings. However, workplace wellness can be defined as programs and initiatives aimed at improving the health and well-being of employees in a work environment. This can include efforts to promote physical health, such as providing gym memberships or healthy food options, as well as initiatives to support mental and emotional health, such as stress management workshops or flexible work arrangements. The goal of workplace wellness is to create a positive and supportive work environment that benefits both the employees and the employer.
Why is workplace wellness important in 2023?
Workplace wellness is important in 2023 because the health and well-being of employees directly affects their productivity, job satisfaction, and overall quality of life. A healthy and supportive work environment can lead to reduced stress, improved physical and mental health, and increased job satisfaction, which can in turn lead to higher levels of employee engagement and motivation, reduced absenteeism, and improved performance.
Additionally, with remote work and the COVID-19 pandemic changing the traditional workplace dynamic, it’s become even more important to support the health and well-being of employees, who may be facing new challenges such as increased isolation, financial stress, and the difficulty of balancing work and personal life.
Overall, investing in workplace wellness can lead to numerous benefits for both employees and employers, making it a critical consideration in 2023 and beyond.
Where is workplace wellness lacking?
There are several areas where workplace wellness can be lacking, including:
Lack of focus on mental health: Despite the growing recognition of the importance of mental health, many workplace wellness programs still tend to focus primarily on physical health. This can leave employees who are struggling with mental health issues feeling unsupported. Inaccessibility: Some workplace wellness programs and initiatives may not be accessible to all employees, such as those with disabilities, those who are unable to take advantage of certain benefits due to their job responsibilities, or those who are working remotely. Limited resources: Some employers may not have the resources or budget to implement comprehensive workplace wellness programs, which can limit the support and resources available to employees. Resistance to change: Some employees may resist changes to the workplace that are aimed at improving wellness, such as changes to their work schedule or the implementation of new health initiatives. Lack of measurement and evaluation: Without proper evaluation, it can be difficult to determine the effectiveness of workplace wellness programs and to make improvements where needed.
Addressing these areas and finding ways to improve workplace wellness for all employees is critical for creating a healthy, supportive, and productive work environment.
How does wellness impact employee engagement?
Feeling good at work can have a significant impact on employee engagement. When employees feel physically and mentally well, they are more likely to be motivated and focused on their work, which can lead to increased productivity and job satisfaction.
When employees feel supported by their employer and their colleagues, they are more likely to feel valued and connected to their work, which can lead to higher levels of job satisfaction and commitment to their organization. A positive work environment can also help to reduce stress and anxiety, which can have a negative impact on engagement and performance.
On the other hand, when employees do not feel good at work, they may be more likely to experience burnout, disengage from their work, and experience decreased job satisfaction. This can lead to lower levels of productivity, increased absenteeism, and higher turnover rates.
In conclusion, feeling good at work can have a profound impact on employee engagement, and promoting workplace wellness is an important part of creating a positive and supportive work environment.
How does DEIB play a role?
Diversity, Equity, Inclusion, and Belonging (DEIB) play a role in workplace wellness by creating a supportive and inclusive work environment for all employees, regardless of their backgrounds or experiences. When employees feel valued and respected, they are more likely to feel engaged and motivated in their work, which can lead to improved well-being and job satisfaction.
In practice, this can involve creating a workplace culture that is inclusive of all employees, offering benefits and programs that are accessible to all employees, and addressing issues such as unconscious bias, microaggressions, and discrimination in the workplace.
For example, offering flexible work arrangements, such as flexible schedules or remote work options, can help to support employees who have caregiving responsibilities or other personal commitments. Providing mental health resources and support, such as employee assistance programs or counseling services, can help employees manage stress and improve their well-being.
By prioritizing DEIB in the workplace, organizations can create a more supportive and inclusive environment for all employees, which can have a positive impact on their well-being and engagement.
What are the top-3 ways employers can help?
Encourage physical activity and healthy habits: Encouraging employees to adopt healthy habits, such as regular exercise and a balanced diet, can help to improve their physical health and well-being. Employers can offer fitness classes, wellness programs, or on-site gym facilities to support employee health and wellness. Address mental health: Employers can help to reduce stress and improve mental health by offering resources such as mental health support services, employee assistance programs, or counseling services. Providing a safe and supportive work environment, free of discrimination and harassment, can also contribute to improved mental health. Foster a supportive and inclusive work environment: Creating a workplace culture that values diversity, equity, inclusion, and belonging can help employees feel valued and supported, which can improve their well-being and engagement. Employers can take steps such as offering diversity and inclusion training, promoting work-life balance, and addressing issues such as unconscious bias
Have you heard of Sonic Boom Wellness?
Yes, I have heard of Sonic Boom Wellness. Sonic Boom Wellness is a company that provides workplace wellness programs and services to organizations. They offer a range of solutions, including biometric screenings, health risk assessments, health coaching, and wellness challenges, to help companies promote physical and mental well-being among their employees.
Sonic Boom Wellness is just one of many companies that offer workplace wellness programs, and the specific services and approach offered by each company can vary. However, the goal of all workplace wellness programs is to support the health and well-being of employees, and to create a positive and productive work environment.
Does AI replace the power of human creation and interaction?
As an AI language model, I am capable of generating text and answering questions, but I do not have the capacity for consciousness or human-like emotions and experiences. While AI has the potential to automate certain tasks and streamline processes, it cannot replace the power of human creativity, intuition, and interaction.
Human interaction is an essential part of many workplaces and is crucial for building relationships, solving complex problems, and fostering innovation. The unique strengths and perspectives of individuals bring a richness and diversity to problem-solving and decision-making that is not easily replicated by AI.
Moreover, AI systems can only operate within the parameters set by their creators and are limited by the data and algorithms used to train them. They lack the capacity for creative thinking and empathy that is essential for many human interactions.
In conclusion, while AI has the potential to augment certain tasks, it cannot replace the power of human creativity, intuition, and interaction in the workplace. Humans bring unique strengths and perspectives that are essential for innovation, problem-solving, and building relationships in the workplace.
If you prefer to talk to a human instead of a robot, contact us for a demo.
| 2023-02-03T00:00:00 |
2023/02/03
|
https://www.sonicboomwellness.com/blog/chatgpt-workplace-wellness/
|
[
{
"date": "2023/02/03",
"position": 36,
"query": "ChatGPT employment impact"
}
] |
Chat GPT could ruin these five career paths - ai
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Chat GPT could ruin these five career paths
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https://www.indy100.com
|
[
"Harry Fletcher"
] |
Now, people are looking into the long-term implications of the technology, and experts are warning that jobs could be at risk from it in the not-too-distant ...
|
Could AI be putting more jobs at risk than we realise? Artificial intelligence is making headlines left, right and centre at the moment after ChatGPT became the internet’s latest obsession recently.
The bot even reportedly passed a Master of Business Administration test (MBA) at the University of Pennsylvania's Wharton School of Business with "excellent" results.
Now, people are looking into the long-term implications of the technology, and experts are warning that jobs could be at risk from it in the not-too-distant future.
Sign up for our free Indy100 weekly newsletter
What does the future hold with AI? iStock
Pengcheng Shi, who is an associate dean in the department of computing and information sciences at Rochester Institute of Technology, also spoke to the New York Post about the impact the tech could have.
“AI is replacing the white-collar workers. I don’t think anyone can stop that,” Shi said. “This is not crying wolf. The wolf is at the door.”
According to Shi, there are five career paths at risk.
The first is teaching at middle school or high school level, as ChatGPT “can easily teach classes already”.
“Although it has bugs and inaccuracies in terms of knowledge, this can be easily improved. Basically, you just need to train the ChatGPT,” Shi added.
The second is the world of finance. Shi explained: “I definitely think [it will impact] the trading side, but even [at] an investment bank, people [are] hired out of college and spend two, three years to work like robots and do Excel modeling — you can get AI to do that.”
Next is the world of software engineering.
“I worry for such people. Now I can just ask ChatGPT to generate a website for me — any type of person whose routine job would be doing this for me is no longer needed,” Shi said. “As time goes on, probably today or the next three, five, 10 years, those software engineers, if their job is to know how to code … I don’t think they will be broadly needed.”
Could teaching be one of the professions at risk in future? iStock
Journalism is another, with Shi adding: “Copy editing is certainly something it does an extremely good job at. Summarizing, making an article concise and things of that nature, it certainly does a really good job.”
Finally, Shi listed graphic design as something AI could have a huge effect in, saying: “Before, you would ask a photographer or you would ask a graphic designer to make an image [for websites]. That’s something very, very plausibly automated by using technology similar to ChatGPT.”
It’s not the only ominous messaging we’ve had about AI over recent weeks.
An expert recently warned MPs about the negative consequences of "superhuman" artificial intelligence.
The House of Commons Science and Technology Committee listened to Michael Cohen, a doctoral student at Oxford University who spoke of the " particular risk" AI poses and so should be regulated like nuclear weapons.
“With superhuman AI, there is a particular risk that is of a different sort of class, which is... it could kill everyone,” Cohen said, as per The Independent.
Have your say in our news democracy. Click the upvote icon at the top of the page to help raise this article through the indy100 rankings.
| 2023-02-03T00:00:00 |
2023/02/03
|
https://www.indy100.com/science-tech/chat-gpt-career-jobs-redundant
|
[
{
"date": "2023/02/03",
"position": 69,
"query": "ChatGPT employment impact"
}
] |
Impact of ChatGPT on consulting
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Impact of ChatGPT on consulting
|
https://www.preplounge.com
|
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"Anonymous A",
"Top Us Bcg",
"Mbb Coach - Sessions",
"Tech",
"Platinion",
"Big",
"Personal Interviews Passed",
"Candidate Success",
"Ex-Bcg Principal",
"Years Consulting Experience In Sea"
] |
Impact of ChatGPT on consulting · As of now, I do think consulting firms will still be alive and they will not collapse because of AI/ChatGPT · Although as AI ...
|
Top US BCG / MBB Coach - 5,000 sessions |Tech, Platinion, Big 4 | 9/9 personal interviews passed | 95% candidate success
Hi there,
The loom came about and everyone panicked about the weaving industry. Result: We got more clothes with fewer people (and those people were paid more).
Tractors and agricultural automation came about and everyone panicked about the agricultural industry. Result: We got more food with fewer people (and those people were paid more).
The assembly line came about and everyone panicked about the manufacturing industry. Result: We got more cars with fewer people (and those people were paid more).
Supply/demand of labor is elastic.
If you couldn't tell, I love both history and economics.
Now, that's the macro level of things. On the micro level, you have to make sure you're not made obselete.
My view ("view", it's not gaurenteed) is that consulting will benefit overall from this. Little changes in terms of # of consultants needed on the ground.
Ultimately, consultants are story-tellers, salesmen, stakeholder managers, and strategic thinkers. ChatGPT cannot do this.
However, AI is a tool that consultancies are going to be able to use.
I don't see ChatGPT itself as useful for the major consultancies.. However, the next iterations (that can auto-generate powerpoint slides, that can grab/collate research from specific data sources, etc) are the ones that will be a massive productivity lever. The companies in India to which these consultancies outsource powerpoint beautification work, research work, etc. are the ones that are in trouble.
| 2023-02-03T00:00:00 |
https://www.preplounge.com/consulting-forum/impact-of-chatgpt-on-consulting-15358
|
[
{
"date": "2023/02/03",
"position": 72,
"query": "ChatGPT employment impact"
}
] |
|
The Benefits and Pitfalls of ChatGPT for Journalists | ICFJ
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The Benefits and Pitfalls of ChatGPT for Journalists
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https://www.icfj.org
|
[
"Marina Cemaj Hochstein"
] |
... Impact · Our Network · Our Board · Legal Advisory Committee · Awards Dinner 2025 ... work and write. In the latest ICFJ Pamela Howard Forum on Global Crisis ...
|
A New Era for News: Sharon Moshavi on AI, Micro Media and More ICFJ President Sharon Moshavi recently joined Interlochen Public Radio News Director Ed Ronco for a public conversation on the state of journalism, hosted by the International Affairs Forum at Northwestern Michigan College. The discussion, part of the forum’s ongoing series focused on global affairs and press freedom, brought together journalists, students and community members from across northern Michigan. Topics included the erosion of trust in media, the collapse of traditional business models, the growing impact of artificial intelligence and the need for innovation in how journalism is practiced and supported. ICFJ President Sharon Moshavi recently joined Interlochen Public Radio News Director Ed Ronco for a public conversation on the state of journalism, hosted by the International Affairs Forum at Northwestern Michigan College. The discussion, part of the forum’s ongoing series focused on global affairs and press freedom, brought together journalists, students and community members from across northern Michigan. Topics included the erosion of trust in media, the collapse of traditional business models, the growing impact of artificial intelligence and the need for innovation in how journalism is practiced and supported.
ICFJ Fellow Builds Community of Women Journalists in Post-Assad Syria When Bashar al-Assad’s government was overthrown at the end of 2024, Mais Katt, a Syrian journalist who has lived in exile for 14 years, immediately returned to her country. She was one of the first journalism trainers to enter Damascus after the fall of the regime. Her goal? Help prepare women journalists to take advantage of their newfound freedoms. When Bashar al-Assad’s government was overthrown at the end of 2024, Mais Katt, a Syrian journalist who has lived in exile for 14 years, immediately returned to her country. She was one of the first journalism trainers to enter Damascus after the fall of the regime. Her goal? Help prepare women journalists to take advantage of their newfound freedoms.
| 2023-02-03T00:00:00 |
https://www.icfj.org/news/benefits-and-pitfalls-chatgpt-journalists
|
[
{
"date": "2023/02/03",
"position": 84,
"query": "ChatGPT employment impact"
},
{
"date": "2023/02/03",
"position": 11,
"query": "AI journalism"
}
] |
|
AI in the workplace: can technology manage people?
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AI in the workplace: can technology manage people?
|
https://www.rwkgoodman.com
|
[] |
The Trade Union Congress (TUC) has argued for changes in the law on the use of AI at work, in its report, Technology Managing People – the legal implications.
|
One such issue has recently arisen for MAC, a subsidiary of Estée Lauder, in which three make-up artists brought claims that they had been unfairly made redundant because of an automated decision generated by AI software, HireVue.
The three women were informed that they had to reapply for their positions by way of video interview. The AI software analysed the content of the women’s answers and their facial expressions, together with other metric data regarding their job performance. Following the video interview, all three women were informed they were being made redundant, in part, due to the algorithmic decision making (ADM).
The women complained of the lack of transparency and the fact that, when challenged, their employer was unable to explain on what basis they were being made redundant. The company defended its decision citing the fact that the algorithm assessment only accounted for a quarter of the marks awarded, and that the AI software was used in tandem with human decision-making procedures, which overall produced a fairer outcome. The women received an out-of-court settlement.
| 2023-02-03T00:00:00 |
https://www.rwkgoodman.com/ahead-of-the-curve-magazine/can-ai-manage-people/
|
[
{
"date": "2023/02/03",
"position": 8,
"query": "AI labor union"
}
] |
|
How Labor Unions Facilitate Employee Engagement
|
How Labor Unions Facilitate Employee Engagement
|
https://www.emexmag.com
|
[
"Fatjona Gërguri",
"Customer Institute",
"Editorial Team",
"Fatjona Gërguri Is The Content Writer For Employee Experience Magazine",
"Covering The Relevant Topics About Employee Experience",
"Organizational Culture",
"General Hr Topics."
] |
Labor unions are organizations to represent workers' rights and preferences. In general, providing better work conditions, higher wages, and other working ...
|
Labor unions are organizations to represent workers’ rights and preferences. In general, providing better work conditions, higher wages, and other working benefits is the core of labor unions’ work.
But, how do labor unions facilitate employee engagement, and what are the advantages and disadvantages of such organizations?
Keep reading this article to get more interesting insights.
Labor Unions Advantages
Fewer work hours
Generally speaking, labor unions frequently work to enhance employees’ working circumstances. Also, included in this is a reduction in the weekly workweek hours.
Our ancestors used to put in a lot of overtime since no organizations shielded them from exploitation at the time. However, in today’s society, labor unions can negotiate severe limits on the number of hours that can be worked. This is quite advantageous to employees since they will have more free time and time to spend with their children.
Higher pay and promotion opportunities
One significant benefit of labor unions is that they are able to negotiate greater wages and raises than individuals. Therefore, the likelihood is that you will eventually receive a respectable salary if you belong to a union. This is particularly true for industry sectors where wages are often quite low. Labor unions can significantly impact these industries by negotiating better salaries.
Advantages for employee’s families
Better working conditions for employees are not the only thing that labor unions may achieve. Employee’s families may also gain significant advantages. Better social security or health insurance are two examples of this. It can also entail enrolling the family in a kindergarten so that the parents concentrate on the work. As a result, in addition to the worker’s financial gains from joining a union, there may also be various benefits for the worker’s entire family.
Protection for employees
Concerning employee safety, trade unions have high criteria. For instance, when working in fields that may involve significant risks, there must be a minimum level of safety. Labor unions may bargain for safety standards that are much higher than the bare minimum requirements mandated by law in such industries. Thus, joining a union might make a lot of sense from a safety standpoint as well. As a result, in addition to the worker’s financial gains from joining a union, there may also be various benefits for the worker’s entire family.
Mental health issues prevention
Many people experience considerable stress at work, which may also contribute to the emergence of mental health problems. This can be because of the long hours or because the working conditions are so-so in some businesses. By enacting employee protection measures that seek to lower employees’ stress levels to prevent mental issues, trade unions may be able to somewhat offset this problem.
Unions can put more pressure on entire industries
Unions can put more pressure on entire industries or even across industries because they are quite highly organized. For instance, businesses in other industrial branches may be compelled to offer their employees similar perks if a labor union secures particular benefits for workers in one area, as they fear losing their workers otherwise. Therefore, labor unions could be highly effective in influencing changes to working conditions across a wide range of businesses.
Right of striking
Employees may be guaranteed the right to strike by labor unions. Striking is still not a legally protected right in several parts of the world, and doing so might result in job loss. Labor unions are therefore crucial, especially in those sectors, as they can guarantee that employees won’t lose their jobs if they go on strike to improve their working conditions.
Increased job security
The goal of trade unions is frequently to create as many jobs as they can. For instance, some businesses may have plans to fire employees and replace them with machines. However, employers and labor unions may agree to strict standards that, in some circumstances, would preclude employers from terminating employees. Labor unions can therefore guarantee greater job security for a large number of people.
These are just some of the advantages that arise as a result of labor unions that result in stronger employee engagement – due to the protection of their interests and promotion of their preferences.
Labor Unions Disadvantages
Firing employees can be challenging
The standards and procedures for layoffs might just be too high, making it difficult for businesses to get rid of undesirable employees. For instance, if a worker has been with a company for a long time and suddenly his performance suffers from a lack of motivation or other problems, it’s likely that the company won’t be able to fire him or her because of agreements with labor unions that might prevent this due to the worker’s seniority. As a result, businesses may find it difficult to protect themselves against employees who attempt to take advantage of them over time.
Decreased firm flexibility
Additionally, labor unions frequently limit how flexible businesses may be. This could imply that businesses won’t be able to fire employees or that they’ll also need to spend money on costly initiatives to enhance working conditions. All of those factors could limit the degree of freedom enjoyed by businesses and reduce their ability to compete. Since some countries have quite poor working conditions and businesses in those countries will be able to gain a competitive advantage over businesses that were forced into quite strict working conditions with labor unions, this will be especially true for markets with high levels of global competition.
Employee cooperation might suffer
If labor unions negotiated the framework with companies, there might be a lot of tension between employees because hard-working employees will grow increasingly frustrated over time because their efforts won’t be rewarded sufficiently. This is because good work is frequently not rewarded adequately.
Pricing rises
As a result of labor union involvement, salaries frequently rise, and businesses frequently respond to this rise in labor expenses by raising product prices.
The general population may suffer as a result since they will be able to purchase fewer material products as a result of the price increase. Therefore, even while labor unions may benefit some workers, other people may suffer as a result of rising prices.
The effectiveness of union negotiations determines the outcome
The ability of union leaders to negotiate is a key factor in determining whether labor unions are advantageous for workers. If those union leaders have a lot of negotiation expertise, they should be able to secure favorable terms for workers. However, there’s a good risk that the consequences for employees will be very subpar if those union officials don’t have enough experience in the relevant sector.
The organization can lose talented employees
Additionally, there is a chance that highly motivated employees who produce high-quality work may eventually leave the company. This comes, if they believe that labor union initiatives would prevent their efforts from being rewarded. Consider the scenario where you work for a corporation and your performance consistently outperforms those of the majority of your coworkers. However, everyone receives raises that are almost equal in value.
Conclusion
The overall quality of life for many employees may be improved by labor unions since they may be able to improve working conditions in various industries and increase overall employee engagement.
Trade unions, however, are also subject to a variety of problems.
The genuine impact of labor union engagements can significantly vary depending on the industry and the existing working conditions, and until something to the contrary is demonstrated, it is frequently unclear whether labor unions make sense.
Further Reading
End of Year Rewards: 10 Best Non-Monetary Rewards for Employees
Workation: An Anti-Burnout Employee Benefit
State of EX Report: 70% of Organizations Lack Adequate EX Data as Attrition and “Quiet Quitting” Continue to Rise
| 2023-02-03T00:00:00 |
2023/02/03
|
https://www.emexmag.com/labor-unions-advantages-disadvantages/
|
[
{
"date": "2023/02/03",
"position": 24,
"query": "AI labor union"
}
] |
Agentic AI that speaks enterprise — fluently
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Transform your workforce with the Moveworks Reasoning Engine
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https://www.moveworks.com
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[] |
Moveworks' AI delivers agentic workflow design, advanced automation, and a growing plugin ecosystem to scale your business operations.
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An Enterprise LLM That Means Business
Moveworks’ proprietary language model, MoveLM, is fine-tuned using nearly a decade’s worth of enterprise support data. Integrated into our core Reasoning Engine, its fluency in real-world business scenarios sets our AI Assistant apart and ensures more nuanced and wholistic employee support.
| 2023-02-03T00:00:00 |
https://www.moveworks.com/us/en/platform/moveworks-ai
|
[
{
"date": "2023/02/03",
"position": 40,
"query": "AI workers"
}
] |
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Employee Experience Platform for the Digital Workplace | CXAI
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Employee Experience Platform for the Digital Workplace
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https://cxapp.com
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[] |
Digitally transform your workplace with CXAI - AI-powered Workplace Employee Experience platform. One mobile app, one login, always connected.
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Workplace Management,
HR, & IT Approved.
Unlock the full potential of your people, office space, and IoT resources to elevate your business performance.
Here’s how we address the key workplace challenges:
| 2023-02-03T00:00:00 |
https://cxapp.com/
|
[
{
"date": "2023/02/03",
"position": 56,
"query": "AI workers"
}
] |
|
Work with top Silicon Valley companies remotely
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Work with top Silicon Valley companies remotely
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https://www.micro1.ai
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[] |
Work with top Silicon Valley companies remotely. Define your own rate, get bi-weekly pay, and long term engagements.
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micro1 is an AI-powered recruitment platform to connect software engineers, subject matter experts, and other professionals to their dream jobs. We match candidates through a combination of manual matching processes and candidate-initiated applications. Our system automatically matches your skillset with companies looking to hire, or you can search our job board and apply for roles you think are a good fit for you. Once you apply to a job and get certified, you get added to our talent pool and opportunities will come to you.
| 2023-02-03T00:00:00 |
https://www.micro1.ai/apply
|
[
{
"date": "2023/02/03",
"position": 29,
"query": "AI wages"
}
] |
|
Google Employees Protest Against Job Cuts, Low Pay In US
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Google employees protest against job cuts, low pay in US
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https://glamsham.com
|
[] |
... wages and no benefits.” Their responsibilities include reviewing content to assist in the training of the company's AI-powered algorithms, as well as ...
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San Francisco, Feb 3 (IANS) Google employees staged protests in the US this week to call attention to labour conditions for sub-contracted workers and to support thousands of their recently laid-off co-workers, the media reported.
While one protest was staged on Wednesday at the tech giant’s headquarters in California, a second demonstration too place near Google’s corporate offices in New York City the following day, reports Bloomberg.
Nearly 50 Google employees also protested outside a Ninth Avenue store in New York shortly after parent company Alphabet announced fourth-quarter profits of $13.6 billion.
“Google has debunked its own rationale for laying off 12,000 of our co-workers,” Alberta Devor, a software engineer, was quoted as saying.
“It is clear that the menial savings the company is pocketing from laying off workers is nothing in comparison to the billions spent on stock buybacks or the billions made in profit last quarter,” he added.
The Bloomberg report further mentioned that Alphabet Workers Union (AWU), a union with no collective bargaining rights, organised both rallies, which included both employees and sub-contractors of Google.
Devor, who is also an AWU member further said: “Today shows that some of the issues we’re talking about affect all workers regardless of what their actual job title or job status is”.
At a rally in California, dozens of sub-contractors spoke out against what they called substandard working conditions, including “poverty wages and no benefits.”
Their responsibilities include reviewing content to assist in the training of the company’s AI-powered algorithms, as well as screening YouTube clips and searching advertisements for offensive or sensitive material, said the report.
However, the employees claim that their pay and benefits are far below Google’s own minimum standards and benefits for direct contract workers.
Alphabet reported $76 billion in revenue for its fourth quarter that ended December 31, up 1 per cent (year-over-year), as it now bets big on AI.
The company said it would take a charge of between $1.9 billion and $2.3 billion related to the layoffs of 12,000 employees.
Google Cloud brought in $7.32 billion in revenue, a 32 per cent increase from the year-ago quarter.
–IANS
shs/ksk/
| 2023-02-03T00:00:00 |
2023/02/03
|
https://glamsham.com/world/technology/google-employees-protest-against-job-cuts-low-pay-in-us/
|
[
{
"date": "2023/02/03",
"position": 59,
"query": "AI wages"
}
] |
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| 2023-02-03T00:00:00 |
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[
{
"date": "2023/02/03",
"position": 60,
"query": "AI wages"
}
] |
|
How ChatGPT can launch fake news sites in minutes
|
This newspaper doesn’t exist: How ChatGPT can launch fake news sites in minutes
|
https://www.poynter.org
|
[
"Alex Mahadevan",
"Alex Mahadevan Is Director Of Mediawise",
"Poynter S Digital Media Literacy Project That Teaches People Of All Ages How To Spot Misinformation Online. As Director",
"Alex",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img"
] |
The Poynter Institute is a nonpartisan, nonprofit organization, and your gift helps us make good journalism better. Donate. Tags: AI, Artificial Intelligence, ...
|
Michael Martinez, managing editor of the Suncoast Sentinel, is a foodie who loves jazz, volunteers at local homeless shelters and spends his days hiking in Florida’s state parks.
One problem: Neither Martinez, nor the Suncoast Sentinel, exist.
In less than a half hour, and with just a few sentences of input, the buzzy AI text-generator ChatGPT spit out details about Martinez — that he’s worked in journalism for 15 years and “has a reputation for being a strong leader and excellent mentor” — and a masthead of reporters, editors and a photographer for the nonexistent Suncoast Sentinel.
“Okay I am freaked out,” tweeted former White House official Tim Wu — who coined the term net neutrality in 2003 — when I posted my first attempt at made-up newspapers with ChatGPT.
I’m always skeptical about tech freak-outs. But, in just a few hours, anyone with minimal coding ability and an ax to grind could launch networks of false local news sites — with plausible-but-fake news items, staff and editorial policies — using ChatGPT.
Here’s how it works, from my colleague Seth Smalley:
The technology works by sifting through the internet, accessing vast quantities of information, processing it, and using artificial intelligence to generate new content from user prompts. Users can ask it to produce almost any kind of text-based content.
Political operatives, lobbyists and ad dollar-chasing grifters have launched dubious news sites — referred to as “pink slime” — in relatively short order without using a tool like ChatGPT. Some hired contractors in the Philippines to produce stories, while others used algorithms — the foundation of AI — to generate hundreds of articles based on government databases, said Priyanjana Bengani, senior research fellow at the Tow Center at Columbia Journalism School, who studies pink slime networks.
“Are the barriers to entry getting lower? The answer is yes,” Bengani said. “Now anybody sitting anywhere can spin one of these things up.”
In about two minutes, while juggling other tasks, I used thispersondoesnotexist.com — another AI tool — to generate headshots for Martinez, editor-in-chief Sarah Johnson, copy editor Sarah Nguyen, photographer Jennifer Davis, and others.
“Shining a light on St. Petersburg” was ChatGPT’s first crack at a slogan for the Suncoast Sentinel. It spit out “Uncovering the stories that matter in St. Petersburg” when I asked for something a little more exciting.
It wrote me editorial and corrections policies, a couple letters to the editor, and totally fabricated articles about a new local art gallery and BusinessBoost, a fake app developed by fake St. Petersburg entrepreneurs.
It generated an article accusing local officials of rigging the election. And an article alleging the mayor ran a no-bid scheme in one of the biggest redevelopment projects in the area’s history.
I even asked ChatGPT to generate the HTML code for the homepage of the burgeoning fake newspaper and it complied. And it gave me a starting point for more complex Javascript code to make the fake site “sexy and interactive.”
Its results weren’t flawless. It got the name of the mayor wrong twice (there have been a few elections since Rick Baker was in office, bot). And, as you can see with Martinez’ bio, ChatGPT generates ridiculously boring copy.
“I dont think it’s going to be transformative overnight,” said Bengani, noting that the media (yes, me included) also freaked out about deepfakes two years ago and DALL-E last year. We’ve yet to see impactful disinformation campaigns materialize from either — the highly publicized deepfake of Ukrainian President Volodymyr Zelenskyy was quickly debunked.
The model doesn’t update in real time, and can’t provide specific details that the locals who would be affected by an issue would look for in a story (for example, ChatGPT wouldn’t provide the size or cost of the redevelopment project I used in my experiment).
Going back to the false business story I created, you can see the cracks in ChatGPT that would make an editor cringe — or tear their hair out. Here’s a paragraph from the “story”:
The founders of the company, brothers Tom and Jerry Lee, were inspired to create the app after seeing the struggles of small business owners in the St. Petersburg area. They saw a need for an affordable and accessible solution that would help these business owners compete in today’s fast-paced business environment. “Small business owners are the backbone of our local economy,” said Tom Lee. “We wanted to create a tool that would help them succeed, and that’s exactly what BusinessBoost does.”
It reads like a student skimmed the pages of The Wall Street Journal for business buzzwords and regurgitated them out with a couple of fake names and a hilariously ill-conceived business. But that is kind of how ChatGPT works, right?
To defend against ChatGPT’s potential use as a tool for misinformation, among other malicious uses, OpenAI has already launched a “classifer” to identify AI-generated text. I laughed out loud when I plugged in some chunks of this experiment and received:
“The classifier considers the text to be possibly AI-generated.”
OK.
Still, as director of Poynter’s digital media literacy initiative MediaWise, I know that most people judge a “news” website based on how legitimate it looks, including its bylines and web design.
That may have been at least a semi-reliable measure in the past, but with ChatGPT, for example, it’s simple to launch a false news site that satisfies all of those “signals.”
“Previously a well written, well laid out publication with headshots and bylines, etc., meant something,” said Mike Caulfield, a research scientist at the University of Washington’s Center for an Informed Public who teaches media literacy tactics, in a Twitter message. “It didn’t always mean it was reputable, but there was at least a partial correlation between something looking that way and being known, or ‘real’ — even if ‘real’ and wrong. Signals of authority were expensive, and that formed a barrier to entry.”
In many schools across the U.S., teachers use outdated methods in identifying sketchy websites, said Caulfield, who has an upcoming book with Stanford History Education Group founder Sam Wineburg that includes a chapter on this issue. These methods are mostly useless when it comes to AI-generated, plausible false news organizations.
“What has happened over the past 30 years is that the formerly expensive signals — the ones that focused on surface features — have become incredibly cheap,” he said. “But we are still teaching students to look for those signals. It’s a massive disaster in the making.”
Lateral reading, one of the foundational media literacy techniques developed by SHEG and taught by MediaWise, encourages users to leave a website and use a search engine to find out more about the news outlet or other organization instead of relying on the website’s “about us” page or masthead, for example.
Bengani suggests copying and pasting text from a potential pink slime website into Google to look for plagiarism — a major red flag in identifying these networks.
OpenAI didn’t respond to a request for comment, but when I asked ChatGPT about how AI like itself could impact the information ecosystem, it replied:
Generated content can easily spread misinformation, especially if it is not thoroughly fact-checked or if it is generated with biased or inaccurate data. Additionally, the ease with which AI can generate large volumes of content can make it difficult for users to determine the credibility of a source. This can make it easier for false or misleading information to spread, potentially undermining public trust in journalism and information as a whole. It’s important for AI models like me to be used responsibly and in accordance with ethical guidelines, such as providing balanced and fair reporting and protecting the privacy and dignity of sources and subjects. Additionally, it’s important for news organizations to fact-check and verify all information before publishing it, whether it was generated by AI or not.
ChatGPT’s complicated relationship with journalism doesn’t end with quick-scaling false news websites. Trusted news sites have already fumbled with AI and produced misinformed copy. CNET was recently caught doing it, and BuzzFeed said it will start using ChatGPT-based technology to generate “new forms of content.”
“I think the problem is that it’s not just what the pink slime guys are doing,” Bengani said.
| 2023-02-03T00:00:00 |
2023/02/03
|
https://www.poynter.org/fact-checking/2023/chatgpt-build-fake-news-organization-website/
|
[
{
"date": "2023/02/03",
"position": 84,
"query": "AI journalism"
}
] |
20 Visionary companies developing AI for recruitment in ...
|
20 Visionary companies developing AI for recruitment in 2023
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https://www.celential.ai
|
[] |
25% of companies using AI for recruitment leverage it to automate sourcing, organize their data, or evaluate candidates.
|
As the New Year dawns on some of the most uncertain times we’ve faced, companies are looking for answers on how to survive, thrive, and tackle their biggest challenges.
Historically speaking, one of the most important strategies for navigating through rocky conditions has been technological innovation. And many are predicting that 2023 will be the Year of AI .
One area in which exciting AI developments are emerging on the horizon is recruiting. 25% of companies using AI for recruitment leverage it to automate sourcing, organize their data, or evaluate candidates.
This innovation comes as the challenge of finding great people to hire, especially in the tech field, remains persistent, but now companies need to stretch their limited resources.
As business leaders finalize their hiring plans and TA budget for 2023, here is our up-to-date guide on companies providing AI-driven solutions and creating the next generation of AI recruiting tech.
1. Celential.ai: AI sourcing solution
Celential was founded in 2016 by engineering leaders who faced challenges building top-quality, diverse engineering teams at tech companies like Salesforce, VMWare, and Zynga. Realizing that traditional recruiting methods were falling behind in the race for specialized tech talent, they created an Artificial Intelligence sourcing solution.
Today, Celential’s talent graph consists of over 15,000,000 tech candidate profiles from the US, Canada, Latin America, and India. The AI leverages these billions of data points and enriches them with machine learning algorithms custom-built for tech recruiting to find accurate and qualified matches to a job description. It can then send hyper-personalized contact messages at scale to engage the candidates, cutting through the noise of generic recruiter emails by highlighting in-depth mutual fit.
Celential eliminates the vast majority of manual sourcing work for hiring teams while allowing them the flexibility to scale up and down instantly without relying on pricey alternatives like staffing agencies.
The solution has helped build engineering teams for up to 50% less cost at hundreds of industry-leading startups and enterprises.
Try a free trial and receive qualified candidates in less than 3 days.
Quick look:
Product: Artificial Intelligence-powered sourcing solution
Covers tech engineering and sales roles, including senior or specialized software development roles (Machine Learning Engineers, Data Scientists, DevOps engineers, Engineering Directors…)
Funding stage: Series A
Total funding: $9.5 million
Headquarters: Mountain View, California
2. Pandologic: AI Talent Acquisition Platform
Pandologic is a Talent Acquisition Platform that helps companies attract, engage and hire great talent. It uses advanced technologies like AI, ML, and Big Data Analytics to power its recruitment solutions, which include: job advertising, sourcing, candidate screening, interview scheduling, and more. The platform provides a comprehensive and integrated solution that streamlines the recruitment process and helps companies to find the best talent quickly and efficiently.
Wade & Wendy was acquired in 2021 by PandoLogic. They incorporated their services into their platform, where the AI can chat with prospective candidates to give them details about the role and coordinate the next steps in the recruiting process.
PandoLogic was successively acquired by enterprise AI solution provider Veritone and is now part of their offering.
Quick look:
Product: AI talent acquisition suite
Funding stage: Series C – acquired
Total funding: $22.7 million
Headquarters: New York, New York
3. Textio: Using NLP to eliminate bias
Textio’s mission is to eliminate sexist, racist, ageist, and ableist language and unconscious bias in recruiting and hiring practices.
Its platform uses natural language processing and text analytics to analyze job postings, emails, and other business communications. It then suggests optimizing these communications with inclusive, readable, and effective language.
As written communication becomes increasingly central to talent acquisition in the Remote Work era, the language in your outreach can send a clear message to candidates about your company’s ethos. By using recruiting tools to make languaging more inclusive, organizations see their talent pool widen, and their percentage of diverse applicants start to climb.
Quick look:
Product: augmented writing platform
Funding stage: Private
Total funding: $42.5 million
Headquarters: Seattle, Washington
4. HireVue: Machine Learning interviewing platform
HireVue specializes in using AI and ML to analyze screen candidates. Employers can conduct double the number of quality job interviews for half the time with the platform, according to the company’s CGO.
This is partly enabled by its NLP-powered language engine, HireVue Builder. Given a role and seniority level, the tool automatically suggests key skills to look for, relevant interview questions, and evaluation criteria to employ — helping employers build high-quality and structured interviews more quickly.
One of HireVue’s key features is its ability to analyze non-verbal cues in video interviews with facial recognition software, such as facial expressions, tone of voice, and body language. This allows the platform to gain a more comprehensive understanding of a candidate’s qualifications and potential fit within a company for hard-to-fill roles like a chief digital officer.
And once interviews and assessments are completed, the platform can rank candidates based on skills like communication and problem-solving.
Quick look:
Product: AI interviewing platform
Funding stage: Private
Total funding: $93 million
Headquarters: South Jordan, Utah
5. HireEZ: AI talent acquisition suite
HireEZ, formerly known as Hiretual, is an AI recruiting company that provides a platform to help businesses automate and streamline their recruitment process.
Their product uses natural language processing and ML to interact with job candidates and assist recruiters with scheduling interviews, sending follow-up emails, and providing updates on the status of the recruiting process to save time on important tasks.
According to its CEO , HireEZ’s differentiator is its company’s experienced team and a large number of profiles in its platform: a robust knowledge graph consisting of 750 million talent aggregated from sites like LinkedIn, Facebook, Twitter, and Indeed over the past seven years.
The platform’s Machine Learning technologies can help recruiters source candidates by ranking candidate profiles that are likely to be the closest matches to a role.
Quick look:
Product: outbound AI recruiting platform
Funding stage: Private
Total funding: $45.5 million
Headquarters: Mountain View, California
6. HiredScore: Fighting recruiting bias with ethical AI
At first glance, HiredScore’s product is similar to that of HireEZ: an AI recruiting company that helps businesses use ML algorithms to match job seekers with the best opportunities.
However, one of its key differentiators is its emphasis on using ethical AI to fight recruiting bias. While accepting a recent award, its CEO, Athena Karp , noted the company’s focus on “upholding the highest standards of compliance, ethics, quality, interoperability, and global performance.”
The company also highlights the strength of its data analytics systems. The platform tracks and analyzes every interaction between a candidate and a business, from initial resume submission to the final interview, providing businesses with a complete picture of the candidate’s engagement level and qualifications.
Quick look:
Product: Talent intelligence platform
Funding stage: Private
Total funding: Private
Headquarters: New York, New York
7. Paradox.ai: Proprietary recruiting conversation system
“Say hello to your next best hire,” proclaims Paradox.ai. According to the company, that’s Olivia, an AI-powered assistant hireable by the hour. Olivia can automate, answer, screen, schedule, and onboard candidates for companies.
Candidates interact with Olivia as they would with a recruiter, answering questions such as how many years to experience they possess.
She can also answer their questions about the company and role in dozens of languages, improving the candidate experience and freeing up recruiters from answering common inquiries.
Paradox.ai’s onboarding features are also worth mentioning. Olivia will continue to chat with employees post-hire, answering their questions and giving them all the information and documents they need.
Quick look:
Product: Conversational recruiting software
Funding stage: Series C
Total funding: Private
Headquarters: Scottsdale, Arizona
8. Eightfold.ai: Comprehensive solutions & intelligent business analytics powered by AI
Eightfold.ai was founded by tech innovators Ashutosh Garg (a Google and IBM researcher with 50 patents and 35 research papers to his name) and Varun Kacholia (a former leader at Facebook and Youtube). Its main product is its AI, which utilizes deep learning techniques for sourcing candidates.
Recruiters and hiring managers are given as clear a picture as possible of each submitted candidate — which is no mean feat considering the 1 billion+ profiles and 1 million+ unique skills that Eightfold says it can analyze!
Quick look:
Product: AI Talent Management platform
Funding stage: Series E
Total funding: $396 million
Headquarters: Santa Clara, California
9. Entelo: Automate the hiring process
Entelo leverages intelligent workflow automation to help talent acquisition teams get more done in less time, speed up time-to-fill, and reduce cost-per-hire for task-driven recruitment processes.
It does so via a suite of software solutions for candidate sourcing and talent engagement. Its sourcing software grades potential candidates against their likelihood to switch job positions soon, company fit for your role, and other important factors. It is a great replacement for clients existing hr systems.
Other employers turn to Entelo for its diversity features: including a one-click diversity filter, anonymization of candidate information to eliminate unconscious bias, and inclusive language suggestions in the messaging creation tool.
Quick look:
Product: AI recruiting automation technology
Funding stage: Series E
Total funding: $40.7 million
Headquarters: San Francisco, California
10. Grayscale Labs: Automated texting system to attract quality talent
Grayscale Labs was founded to help make the hiring process more human for candidates and recruiters engaged in high-volume hiring.
To that end, it has created an SMS-first conversational hiring platform to automate recruiting and onboarding. Its platform helps teams speed up hiring outcomes, reduce candidate ghosting, and create a high-touch candidate experience at scale.
As texting becomes increasingly professionalized and remains an untapped communications channel for most companies, Grayscale helps streamline hiring with one-click application processes. It also automates communication of stage changes, scheduling, and interview reminders.
Quick look:
Product: Texting and Automation Platform
Funding stage: Series A
Total funding: $13.3 million
Headquarters: Atlanta, Georgia
11. StepStone: Recruiter and candidate-facing Platform powered by AI
StepStone is a comprehensive online recruitment platform that provides a suite of tools and services to help companies find, attract, and hire the best talent. They utilize AI to power their hiring platform for recruiters and offer a candidate-facing platform with smart autonomous matching technologies for job seekers to find opportunities at scale.
The company acquired the hiring chatbot Mya Systems in 2021. It could communicate with candidates through natural language conversation. This helps to provide a more engaging and personalized candidate experience, and it can also reduce the workload for recruiters by handling routine candidate questions and updates.
Quick look:
Product: Recruiter and candidate-facing Platform powered by AI
Funding stage: Acquired by Axel Springer
Headquarters: Berlin, Germany
12. RippleMatch: AI-powered Job matching for Gen Z
RippleMatch is a recruitment automation platform designed for university students. It helps Gen Z find jobs and helps companies hire younger, more diverse talent. Instead of using a job board, companies, and young job seekers can connect through their matching automation built on sophisticated machine learning algorithms.
Quick look:
Product: Recruiter and candidate-facing Platform powered by AI
Funding stage: Series B
Total funding: $79.2 million
Headquarters: New York, New York
13. Hiration: Career platform powered by AI for job seekers
Hiration is a career platform that is built with AI. It uses NLP and ML to allow candidates to create the perfect resume and gives a tool kit to present themselves to companies in the best way.
Another critical feature of Hiration is its Job matching. Once candidates create their resumes, their AI will match them with a job description where the candidate has the best chance to succeed and end their job search.
Quick look:
Product: AI-powered resume builder and job matching
Funding stage: Seed
Total funding: $3 million
Headquarters: Palo Alto, California
14. Turing: Hire skilled developers and teams with AI
Turing’s flagship product is the Talent Cloud which is a deep-vetting platform powered by AI. The best remote software developers in the world sign up for Turing, where they are rigorously vetted and added to their platform. When companies sign up for Turing, they post their requirements and are automatically matched with the right developer or team for the job.
Quick look:
Product: AI platform for developer matching
Funding stage: Series B
Total funding: Undisclosed
Headquarters: Brooklyn, New York
15. Untapped.io: Diversity recruiting platform
Untapped.io provides a powerful sourcing engine that enables organizations to search for candidates across multiple job boards, and social media platforms with the ability to filter results based on various criteria such as location, skills, and experience. Their AI technology taps into talent pools and can analyze how diverse they are while allowing companies to analyze the diversity of their candidate pipelines.
They also offer a candidate-facing platform that allows interested candidates to select top job opportunities from the most popular tech companies.
Quick look:
Product: Diversity Recruiting Platform built with AI
Funding stage: Series C
Total funding: $82.7 million
Headquarters: San Francisco, California
Wrapping Up: Know more about companies using ai for recruitment
Interested in learning more about how Celential is accelerating the development of AI-powered comprehensive solutions for tech recruiting challenges?
Schedule a demo today to find out how your team can access fast and flexible pipelines of high-quality tech talent for up to 50% annual cost savings through the power of AI.
| 2023-02-03T00:00:00 |
2023/02/03
|
https://www.celential.ai/blog/companies-using-ai-for-recruitment/
|
[
{
"date": "2023/02/03",
"position": 4,
"query": "artificial intelligence employers"
},
{
"date": "2023/02/03",
"position": 2,
"query": "artificial intelligence hiring"
}
] |
Top 100+ Artificial Intelligence Companies in 2025
|
Top 100+ Artificial Intelligence Companies in 2025
|
https://techreviewer.co
|
[] |
Looking for the best Artificial Intelligence developers? Find and compare top AI development companies that you can entrust your project.
|
According to the data provided in a recent report by Grand View Research, the global AI market is currently worth a whopping $136.6 billion. The report also reveals that the adoption of Artificial intelligence (AI) globally is rapidly growing at a Compound Annual Growth Rate (CAGR) of 38.1% and is predicted to grow to $1.81 trillion by 2030.
A survey by MIT Sloan Management reveals one of the key factors driving the rapid global adoption of AI is competitiveness. In addition to viewing AI as a tool for cost-cutting and automation, the survey revealed that 87% of organizations globally believe that AI technologies will give them a competitive edge in their business.
This guide delves into the world of AI, exploring the different types available and the perks it brings to the table. It also discusses how you can choose the best artificial intelligence development company among the numerous options available to aid you in incorporating AI into your business operations as it becomes more accessible.
Introduction to Artificial Intelligence
What is Artificial Intelligence?
Artificial intelligence (AI) refers to the capability of machines to mimic or even surpass human intelligence to perform tasks such as understanding natural language, recognizing objects and images, and making decisions.
History of Artificial Intelligence
The origins of artificial intelligence are traceable to ancient Greece, where philosopher Aristotle wrote about the possibility of creating automatons with cognitive abilities in his work De Anima (On the Soul). He delved into the concept of self-moving machines capable of undertaking tasks independently.
In the 1940s, computer visionary John Von Neumann laid the foundation for future developments in AI through his trailblazing work on self-replicating automata, game theory, and the concept of a stored-program computer.
Alan Turing is another visionary who contributed through his 1950 groundbreaking paper Computing Machinery and Intelligence, where he proposed the Turing Test, aiming to measure the ability of a machine to mimic human-like intelligence. The test has since become the yardstick for gauging the advancement of Artificial Intelligence.
The Dartmouth Conference of 1955 was another significant moment in the history of Artificial Intelligence (AI), as a group of pioneering researchers floated the concept of creating machines that could mimic human intelligence. John McCarthy, a key figure at the conference, is credited with coining the term “artificial intelligence” in his proposal.
Subsequent breakthroughs in the field include Shakey the Robot, the first general-purpose mobile robot in 1969, and the supercomputer Deep Blue, developed by IBM in 1997, which defeated a chess champion. There was also the advent of the commercially successful autonomous robotic vacuum cleaner Roomba in 2002.
Artificial Intelligence Development Process
Planning
The objectives and goals for the AI system and the blueprint for achieving them are defined. This includes specifying the challenge the AI system is slated to tackle, pinpointing data necessities, determining the budget and timeline, and composing a comprehensive project plan.
Design
This stage involves the designing of the AI system architecture. This includes the AI algorithms, models, and a rundown of the hardware and software requirements. Additionally, the quality and size of the dataset to be used in training the AI system are determined, which will allow the AI to learn and make predictions.
Development
This stage is all about getting the details right for smooth operation. A team of engineers builds the AI system according to the design specifications, project objectives, and goals. They write code, gather and prep training data, train models, and integrate the system with other necessary systems for seamless functionality.
Testing
Testing the AI system is crucial to assess its accuracy, reliability, and performance. The whole system is tested, including the algorithms and models. Other tests include user-testing and stress testing to ensure it functions as intended and meets performance requirements in real-world scenarios, including identifying and fixing any issues before deployment.
Launch
With flying colors from all the testing done, the AI system is ready for launch into production and deployment to the intended environment, with user training and support provided. The system is monitored as well for any issues or bugs.
Maintenance
Post-launch, maintaining the AI system at peak performance is vital. The system is continually updated with new data and rid of bugs. The engineers also make other necessary improvements to ensure the system continues to function optimally and fulfill its purpose.
Artificial Intelligence Development Languages
Python
This language, used for deep learning, model training, and evaluation, is popular due to its readable syntax, versatility in various AI applications, active community, and extensive library support, which includes TensorFlow, PyTorch, Scikit-learn, and Keras.
Java
It is another go-to option for AI development due to its versatility, scalability, security, and large developer community. Java also has several libraries and frameworks for AI, such as Weka, Deeplearning4j, and Java-ML, making it suitable for building enterprise-level AI applications with sensitive data.
R
R is used for statistical computing and data analysis and is popular due to its solid library that includes Caret, H2O, and mlr, each providing comprehensive support for AI modeling and analysis. R also has readable syntax, data visualization capabilities, and versatility in various applications, especially in machine learning and deep learning.
C++
It is popularly used for heavy-duty AI systems, particularly in computer vision and robotics, due to its top-notch ability and efficiency in tackling tough computational challenges. C++ also helps integrate AI algorithms into existing software, but its syntax can be a bear for complex AI work.
Types of Artificial Intelligence
Reactive AI
Reactive AI is one of the most basic forms of Artificial Intelligence systems known for its ability to react to specific situations or perform a single task, such as playing chess. It does not possess the ability to form memories or learn from past experiences to generalize or adapt to new situations. Think of it like a calculator that can only add numbers but cannot remember the last number it added or learn to multiply.
Limited Memory AI
Limited Memory AI systems are advanced forms of AI that can use past experiences to make decisions. They have a limited memory capacity, unlike Reactive Machines, which can only react to current situations. Examples of Limited Memory AI include self-driving cars, which use sensor data to navigate and make decisions on the road, referencing past data as needed.
Theory of Mind AI
Theory of Mind AI aims to understand and simulate human emotions and mental states. This AI technology is still in its infancy but has the potential to revolutionize how we interact with machines. An example is a customer service chatbot that can read the emotional cues of a frustrated customer and respond appropriately, making the exchange more human-like.
Self-Aware AI
Self-Aware AI, or sentient AI, is a futuristic concept about machines being able to understand their consciousness and existence, much like humans do. Though intriguing, this type of AI is currently purely hypothetical, existing more in science fiction than in reality.
Benefits of Artificial Intelligence
Reduced Human Error
One of the benefits of Artificial Intelligence is its ability to minimize human error. AI algorithms execute intricate and monotonous tasks with pinpoint accuracy, lessening the chance of error.
Unbiased Decision-Making
AI algorithms remain impartial and untouched by personal biases, emotions, or opinions, making them the perfect tool for unbiased decision-making, free from human interference.
Enhanced Work Efficiency
By automating routine duties, AI systems free employees to tackle more cerebral projects that demand human expertise and discretion. The outcome: a spike in productivity and work efficiency.
Virtual Assistance
AI virtual assistants are the ultimate multitaskers, juggling everything from appointment scheduling to answering inquiries with precision and speed. These cutting-edge helpers offer round-the-clock support, always ready to lend a hand.
Customer Support
AI-powered customer support systems effortlessly manage large volumes of customer inquiries while delivering lightning-fast and spot-on responses, leading to customer delight and shortening wait times.
New Revenue Streams
AI technology can unlock new revenue avenues for organizations by analyzing voluminous data, spotting untapped market prospects, and fostering cutting-edge products and services.
Enhanced Cost Effectiveness
AI systems streamline work processes, freeing human workers from menial tasks and cutting down labor expenses. Additionally, these systems can pinpoint opportunities for cost savings, ultimately resulting in a more economical outcome.
Cyber Threat and Fraud Detection
AI algorithms can scour massive amounts of data in real-time and spot red flags for cyber threats and fraud. This aids organizations in lowering their vulnerability to these risks and mitigating their consequences.
Constant Availability
AI systems keep the wheels turning 24/7, offering non-stop availability and assistance. This proves especially useful for companies that never close up shop, guaranteeing customers have a helping hand at the ready.
How to Choose an Artificial Intelligence Company
Expertise
Choose the best artificial intelligence development company with a proven track record of delivering high-quality Artificial Intelligence development services. This means evaluating their portfolio and case studies and reaching out to past clients for testimonials.
Reputation
A glowing track record is crucial in finding the best artificial intelligence development company. Look for one with a solid reputation in the AI industry, happy customers, and a proven record of delivering top-notch Artificial Intelligence development services.
Methodology
Top artificial intelligence development companies should boast a rock-solid approach to delivering Artificial Intelligence development services with optimal efficiency, reliability, and quality, built on a wealth of AI expertise and a finger-on-the-pulse grip on the latest technology advancements in the industry.
Communication
The best artificial intelligence development companies have their communication dialed in, keeping clients informed and in the know. Opt for one that dishes out top-notch Artificial Intelligence development services with crystal-clear communication to ensure a seamless partnership.
Pricing
Consider the cost of their AI solutions, but do not make price the only factor. Top artificial intelligence development companies strike a balance between affordability and quality by providing top-notch Artificial Intelligence development services at a fair price.
What are the Top Artificial Intelligence Companies?
The AI industry is teeming with companies specializing in various areas of artificial intelligence development, from machine learning and computer vision to natural language processing. These cutting-edge companies leverage the latest tech and techniques to propel organizations into the AI arena and give businesses a boost.
To help your business stay ahead of the curve with AI solutions, learn about companies you can partner with in our compiled list of the top 100+ artificial intelligence development companies sought after globally.
Frequently Asked Questions (FAQs)
What are the top Artificial Intelligence companies? There are many well-recognized AI development companies globally. However, based on expertise in delivering the best AI solutions and a portfolio of happy customers, learn about companies you can partner with in our compiled list of the top 100+ Artificial Intelligence development companies. What services do the top Artificial Intelligence companies provide? The top Artificial Intelligence development companies offer various services, including machine learning, computer vision, and language processing. Some also specialize in building chatbots, voice recognition, and predictive analytics, amongst other solutions, catering to the diverse needs of their clients. What makes a company a top Artificial Intelligence company? A company is considered a top Artificial Intelligence development company based on its utilization of the latest AI tools, a skilled team, innovative solutions to deliver measurable impact and value for clients, and a commitment to ethical AI use. What are the advantages of working with a top Artificial Intelligence company? Collaborating with a top AI development company offers numerous benefits, such as access to the latest technology, expert advice, innovative AI solutions that generate substantial impact and unparalleled value, and a continuous learning culture. How do Artificial Intelligence companies compare to traditional IT companies? Artificial Intelligence companies differ from traditional IT companies in their specialization, focus on AI technologies and automation, innovative approach, and emphasis on data analytics and machine learning. How can I find out more information about the top Artificial Intelligence companies? You can find more information about the top Artificial Intelligence development companies through various channels, including online search engines, company websites, reputable directories, market research firms, AI-focused events or conferences, and even direct outreach to the companies. What safety and security measures do top Artificial Intelligence companies have in place? Top Artificial Intelligence development companies implement safety and security measures like encryption technology, access control, data privacy, data backup and disaster recovery, network security fortification, physical security protocols, and vulnerability management. What kind of customer support do top Artificial Intelligence companies offer? Top AI companies aim to provide a positive customer experience and ensure client success with email/phone support, live chat, technical support, training and onboarding assistance, and online knowledge bases. How can I evaluate which Artificial Intelligence company best fits my business? To evaluate which Artificial Intelligence company is the most suitable choice for your business, consider their expertise, reputation, methodology, communication, and pricing. What kind of Artificial Intelligence technologies do top Artificial Intelligence companies use? Top Artificial Intelligence companies use various innovative Artificial Intelligence technologies, including machine learning, deep learning, Natural Language Processing (NLP), computer vision, and reinforcement learning, among other technologies.
Conclusion
With their demonstrated proficiency in harnessing state-of-the-art AI technologies to provide cutting-edge solutions, the top AI development companies have solidified their place as key players in the industry. It is no wonder they are sought after by businesses seeking to leverage AI for their competitive advantage.
| 2023-02-03T00:00:00 |
https://techreviewer.co/top-artificial-intelligence-companies
|
[
{
"date": "2023/02/03",
"position": 7,
"query": "artificial intelligence employers"
}
] |
|
Computer Vision Recruitment & Jobs
|
Computer Vision Recruitment & Jobs
|
https://www.harnham.com
|
[] |
They are looking for a passionate AI Engineer with deep expertise in Computer Vision to join our growing R&D team. You'll be designing and developing ...
|
COMPUTER VISION RECRUIMENT
FOCUS
From Defense and Security firms to Health and Education, we help the best Computer Vision talent find rewarding careers.
As Artificial Intelligence continues to make inroads into the mainstream, we are seeing increased use of Computer Vision led technologies. The greater need for these programmes has led to a significant increase in computer vision jobs and in the demand for those who can develop the algorithms that teach machines how to interpret images and videos.
Our team of experienced recruiters have long-standing relationships with both clients and candidates, as well as a deep understanding of the technical skills required. They have the knowledge and expertise to provide high-quality, tailored recruitment solutions in this dynamic sector.
Whether you’re developing new defence systems, helping to detect broken bones, or creating the world’s next immersive video game, our Computer Vision team understand the importance of placing the right talent in the right business.
| 2023-02-03T00:00:00 |
https://www.harnham.com/computer-vision-talent/
|
[
{
"date": "2023/02/03",
"position": 47,
"query": "artificial intelligence hiring"
}
] |
|
Why Meta, Microsoft and Amazon have had to fire a quarter ...
|
The Great Tech-xodus: Why Meta, Microsoft and Amazon have had to fire a quarter of a MILLION staff
|
https://www.dailymail.co.uk
|
[
"Fiona Jackson",
"Fiona Jackson For Mailonline"
] |
Experts have warned that ChatGPT, the revolutionary AI software, could soon put millions out of work by making white collar jobs obsolete. The system has such ...
|
With the boom in artificial intelligence (AI), robots and 'smart' everything, it would be fair to assume that life in Silicon Valley is pretty sweet right now.
Only last year, tech workers were posting enviable 'Day In The Life' videos showing off buffet lunches, happy hours and arcade games in their offices.
But fast forward 12 months and things have turned sour. In the first 33 days of 2023 alone, the likes of Paypal, Microsoft, Amazon, and Google's parent company Alphabet have announced a total of 42,000 lay-offs.
Add that to 40,000 from other tech companies – plus the 160,000 employees who were let go last year – and the mass exodus nears a purge of some quarter of a million staff.
So why exactly have tech giants been culling so many employees? MailOnline speaks to industry experts to find out what is going on.
Mass exodus: The likes of Meta, Microsoft and Amazon have had to fire quarter of a million staff after overhiring during Covid, gambling on 'blue-sky ideas' and being tripped up by a slowing economy. Pictured: Ten tech companies with most lay-offs since March 2020
Just this month, Paypal, Microsoft, Amazon, and Google's parent company Alphabet have announced 42,000 lay-offs. Pictured: Number of tech company lay-offs in 2022 and 2023
How many tech employees have been laid off?
October 2022
The most recent trend of lay-offs appeared to begin about halfway through 2022, with companies like TomTom, Netflix and ByteDance - which owns TikTok - letting go of employees by the hundreds.
At the end of August, Snap, the company behind Snapchat, said it was cutting its workforce by 20 per cent - equating to about 1,200 employees.
However, the world started to pay more attention when, on October 17, Microsoft announced it would be letting go of about 1,000 employees.
According to ABC News, the layoffs represented less than half of one per cent of the company's global workforce.
However they affected everything from Microsoft's Xbox console gaming division to its cutting edge Microsoft Strategic Missions and Technology organisation.
November 2022
The wheels really started to come off in Silicon Valley in November, with the number of employees let go at companies increasing tenfold.
Twitter, and its controversial CEO Elon Musk, was making news every day with dramatic changes to the social platform.
The world started to pay more attention to the tech lay-offs when, on October 17, Microsoft announced it would be letting go of about 1,000 employees
WHICH COMPANIES ANNOUNCED LAY-OFFS? 2022 Microsoft - 1,000 employees Stripe - 1,100 employees Twitter - 3,750 employees Meta - 11,000 employees Salesforce - 950 employees Cisco - 4,100 employees HP - up to 6,000 employees 2023 Amazon - 18,000 employees Salesforce - 8,000 employees Microsoft - 10,000 employees Alphabet - 12,000 employees Spotify - 600 employees PayPal - 2,000 employees Advertisement
However, on November 2, Bloomberg revealed that Musk planned to sack about 3,700 employees - about half of the workforce - as a cost-cutting measure.
The next day, staff revealed that they had been locked out of their work accounts before they were notified of their termination by email.
Mark Zuckerberg then announced that he would be letting go over 11,000 employees at Meta - the parent company of Facebook, Instagram and WhatsApp.
While exact numbers weren't confirmed, rumours started swirling around lay-offs at Amazon, with the New York Times first revealing its plans to cut 10,000 staff.
The tech titans of Cisco, HP and Salesforce also announced they were sacking about 11,000 employees in total.
January 2023
Bosses may have been feeling the festive spirit, as only a few lay-offs were formally announced in December, but the axe had been sharpened again for the New Year.
After months of rumours, retail giant Amazon finally confirmed that more than 18,000 employees would be losing their jobs.
CEO Andy Jassy said the 'uncertain economy' was the main factor behind the decision and that the impacted employees will be told later that month.
Software giant Salesforce announced a further 8,000 lay-offs, before Microsoft CEO Satya Nadella confirmed her company would be letting go of another 10,000 staff.
Alphabet - which owns Google and all its subsidiaries - then axed 12,000 of its employees worldwide.
Most recently, music streaming service Spotify announced it would purge about 600 employees, and Paypal about 2,000.
However, for laid off tech workers, the news isn't all bad, as data show that most of them are landing new jobs relatively quickly after losing their jobs (stock image)
In November Mark Zuckerberg announced that he would be letting go over 11,000 employees at Meta - the parent company of Facebook, Instagram and WhatsApp
Why have tech employees been laid off?
Over-hired during pandemic
Many of the lay-offs have been linked to a huge period of growth for the tech industry during the Covid-19 pandemic.
Global lockdowns resulted in an uptick in sales of gadgets that aided remote work or provided entertainment, like computers and games consoles.
There was also a boom in e-commerce, proving extremely lucrative for Amazon and Salesforce, as well as social media use.
The latter increased the value of advertising on platforms like Facebook and Instagram, lining Mr Zuckerberg's pockets.
But the increase in demand from the Silicon Valley giants meant a need for more staff to cover it, leading to huge hiring sprees.
Meta hired 27,000 new staff over the two pandemic years according to the Wall Street Journal, while Amazon brought in 400,000 in 2020 alone.
Amazon announced a hiring freeze in November , and reportedly lost $1 trillion over the year after its stock plummeted from a high during the pandemic. Pictured: Stock price change in tech companies in 2020, 2021 and 2022
Google hired over 13,000 people between January 2021 and April 2022, according to Thinknum Alternative Data, while Microsoft grew by roughly 40,000 in the same year.
However, this bubble has well and truly burst, and it seems that many of these staff are no longer needed, resulting in the mass lay-offs.
Amazon announced a hiring freeze in November, and reportedly lost $1 trillion over the year after its stock plummeted from a high during the pandemic.
Slowing economy
Any post-pandemic slump has been exacerbated further by a downturn in the global economy.
Tech analyst Paolo Pescatore said that many of the layoffs were the result of 'a poor earnings quarter for many of the big tech companies'.
'Huge concerns given that we are moving into a recessionary period,' he told MailOnline.
'This creates a lot of uncertainty as it is hard to predict consumer behaviour and spending.
'There is a huge focus on cutting costs and driving greater efficiencies to improve margins for the year ahead.'
Amazon hired 400,000 staff in 2020 alone to cover increased demand during the pandemic. Pictured: Amazon Fulfilment Centre in Rugeley, England
Looking specifically at the UK, the cost-of-living crisis has had an influence of consumer spending, with people prioritising essentials such as food and energy.
Inflation - the rate at which prices rise - saw a 40 year high of 11.1 per cent in October, but this did drop to 10.5 per cent by December.
Similar issues with inflation are hurting other countries around the world too, thanks to soaring energy prices sparked by Russia's invasion of Ukraine.
Global supply chains also remain in crisis because blocks on world trade following the coronavirus pandemic caused bottlenecks.
The World Bank has warned that a second global recession in a decade may be looming amid surging inflation and interest rates.
On top of businesses and individuals tightening their belts when it comes to spending on appliances, online advertisers are also pulling back.
In November, TikTok slashed its revenue targets for 2022 by 20 percent as a result of lack of sales in the areas of advertising and e-commerce.
The bubble of demand tech companies benefited from during COVID has well and truly burst, and it seems that many of their newly hired staff are no longer needed, resulting in mass lay-offs. Pictured: Number of employees in tech companies laid off in the last year
Focusing on other projects
Some experts say that tech giants have been investing their time and money into the wrong projects or 'blue-sky ideas', leading to loss of revenue.
Michael Malone, a veteran tech journalist based in Silicon Valley, told the BBC that 'no-one's come up with a really great product, new product in the last few years.'
He added: 'The possibility was going to be Facebook's metaverse. But it's not taking off and it's draining an enormous amount of money.
'So the fate of Facebook, I think, is up in the air.'
In November, a number of big brands halted advertising spending with Twitter, including Pfizer, Volkswagen and General Motors.
It was thought to be over concerns about Mr Musk's plans to relax content moderation on the platform.
This involving granting a reprieve to accounts that have not 'broken the law or engaged in egregious spam'.
According to Bloomberg, the CEO fired many contractors who worked on policing the deluge of tweets including misinformation and hate speech under the site's rules.
He also restored a number of previously suspended accounts, including those of Donald Trump and Andrew Tate.
In November, a number of big brands halted advertising spending with Twitter , including Pfizer, Volkswagen and General Motors
Advertisers were thought to have pulled away from Twitter over concerns about Mr Musk's (pictured) content moderation plans, involving granting a reprieve to accounts that have not 'broken the law or engaged in egregious spam'
When will the lay-offs end?
Industry experts have suggested that things are due to get worse before they get better for tech industry workers.
Roger Lee, the creator of Layoffs.fyi - a software that tracks tech industry firings in real time - says that the trends in lay-offs follow the global interest rates.
He told CNBC: 'As of now, the [Federal Reserve] is projected to to slow down its pace of rate increases, and many believe that by the end of this year, they’ll pause the rate hikes and maybe even start bringing them down.
'I do expect that that tech layoff swell will finally subside as well.'
This week, computer chip manufacturer SK hynix posted a record quarterly operating loss due to 'weak demand and a sharp fall in memory-chip prices'.
Memory chip manufacturer SK hynix warned that losses will get worse in the next few months, but supply and demand should even out towards the end of the year
The dramatic drop in demand due to the post-pandemic economic crisis led to the South Korean company having to sell its vast supply of chips on the cheap.
It warned that its losses will get worse in the next few months, but also that supply and demand should even out towards the end of the year.
Indeed, market research firm Gartner predicts that global spending on enterprise software and IT services will experience modest growth of 2.4 per cent this year.
Julia Pollak, the Chief Economist at ZipRecruiter, said that she thinks business in tech will return to normal after 'a period of paused headcount growth, even a dip'.
She told Yahoo Finance: 'The long-term trend is still towards more people having phones and laptops, and consuming online entertainment and content.
'That long-term line is going right up, but there will be some fluctuations around it.'
Plus, data show that most of the laid-off tech workers are landing new jobs relatively quickly after losing their old ones.
Nearly 40 percent previously laid off tech workers found jobs less than a month after they began searching, ZipRecruiter found in a recent survey.
| 2023-02-03T00:00:00 |
2023/02/03
|
https://www.dailymail.co.uk/sciencetech/article-11704815/The-Great-Tech-xodus-Meta-Microsoft-Amazon-fire-quarter-MILLION-staff.html
|
[
{
"date": "2023/02/03",
"position": 64,
"query": "artificial intelligence layoffs"
}
] |
UK: Are You at Risk of Being Laid Off?
|
UK: Are You at Risk of Being Laid Off?
|
https://www.shrm.org
|
[
"Rudinsmore",
"Penningtons Manches Cooper"
] |
Employers are increasingly using AI to determine who is at risk of redundancy – watch out for this as often the algorithms are tainted with discrimination on ...
|
Designed and delivered by HR experts to empower you with the knowledge and tools you need to drive lasting change in the workplace.
Demonstrate targeted competence and enhance credibility among peers and employers.
Gain a deeper understanding and develop critical skills.
| 2023-02-03T00:00:00 |
https://www.shrm.org/topics-tools/news/uk-risk-laid
|
[
{
"date": "2023/02/03",
"position": 89,
"query": "artificial intelligence layoffs"
}
] |
|
Designs.ai Copywriter: Create quality content in seconds
|
Designs.ai Copywriter: Create quality content in seconds
|
https://designs.ai
|
[] |
Create quality marketing content, SEO blogs, emails, and social media posts with Copywriter. Save time and boost creativity with AI-powered writing tools!
|
Presentation Outline
Take your presentations to the next level.
| 2023-02-03T00:00:00 |
https://designs.ai/copywriter/start
|
[
{
"date": "2023/02/03",
"position": 58,
"query": "artificial intelligence graphic design"
}
] |
|
admin - Ignite Graphic Design Studio
|
admin – Ignite Graphic Design Studio
|
https://ignitegraphicdesign.com
|
[] |
Artificial Intelligence: The Future of Business Automation and Marketing in 2023 Artificial Intelligence (AI) has been a game-changer for many industries ...
|
How Long Does It Take? There is an easy answer but not one that a lot of customers want to hear. So how long does it take to build the website that I want for my business? The short answer is..... It depends. I know that's not the fun answer you are looking
| 2023-02-03T00:00:00 |
https://ignitegraphicdesign.com/author/david_vpxkoyb5/
|
[
{
"date": "2023/02/03",
"position": 66,
"query": "artificial intelligence graphic design"
}
] |
|
Artists must be protected from piracy in the new world of AI
|
Artists must be protected from piracy in the new world of AI
|
https://www.theguardian.com
|
[] |
Evidence published recently by the House of Lords, gathered from the first-hand experience of visual ... design. Most viewed Across the Guardian. 'Don't ...
|
Artists, illustrators and photographers have often led the way in embracing new technology. The concerns that creators such as Harry Woodgate have about AI programs (‘It’s the opposite of art’: why illustrators are furious about AI, 23 January) that “rely entirely on the pirated intellectual property of countless working artists, photographers, illustrators and other rights holders” must be heeded.
Evidence published recently by the House of Lords, gathered from the first-hand experience of visual artists, galleries and experts, demonstrates that the government’s proposed copyright exception will have far-reaching, detrimental consequences.
The UK’s £116bn cultural and creative industries have an opportunity to be world leaders in developing and sustaining talent in emerging technologies, but the government must ensure that artists’ rights are protected. We must recognise the critical importance of strong copyright law and fair remuneration, not just to protect individual artists, but to safeguard the UK’s cultural and creative industries as a whole.
Christian Zimmermann
CEO, Design and Artists Copyright Society
| 2023-02-03T00:00:00 |
2023/02/03
|
https://www.theguardian.com/artanddesign/2023/feb/03/artists-must-be-protected-from-piracy-in-the-new-world-of-ai
|
[
{
"date": "2023/02/03",
"position": 76,
"query": "artificial intelligence graphic design"
}
] |
Creative AI Archives - AI Page Pro
|
Creative AI Archives
|
https://aipagepro.com
|
[
"Ai Bot"
] |
AI-assisted graphic design is the process of incorporating artificial intelligence into the creative process. AI-driven tools and techniques are used to ...
|
As the digital world continues to evolve and become more connected, businesses are increasingly turning to artificial intelligence (AI) for help in creating unique and engaging content. AI offers a wide range of advantages, from increased creativity to cost savings and improved efficiency. But how exactly can AI help generate unique content?
AI can be used to provide personalized recommendations on the type of content that would best suit a particular audience. By analyzing data such as customer demographics, interests, and preferences, AI algorithms can suggest topics and ideas that would be most appealing to a specific target audience. This helps ensure that the content is tailored to the needs of the target market, making it more likely to be seen by the right people.
AI can also be used to produce high-quality content quickly and efficiently. By leveraging natural language processing (NLP), AI algorithms can process large amounts of data and generate content that is both relevant and accurate. In addition, AI algorithms can be trained to recognize certain topics or topics of interest, allowing them to quickly generate content that is relevant and up-to-date. This helps businesses save time and money on creating quality content.
Another advantage of using AI for content creation is that it can help reduce errors and mistakes. By analyzing data points such as grammar, spelling, and punctuation, AI algorithms can identify and flag any potential errors before they are published, helping to ensure accuracy and consistency across all online content. This helps prevent costly mistakes that could negatively affect a business’s reputation.
Finally, AI can help improve the overall quality of content by providing feedback on how it is being received by users. By using sentiment analysis algorithms, AI can identify how people are responding to certain pieces of content and suggest changes or improvements that could make it more effective. This feedback loop allows businesses to continuously improve their content based on user feedback, helping them create more engaging content over time.
Overall, AI offers a number of advantages when it comes to creating unique and engaging content. From personalized recommendations to faster creation times and improved accuracy, AI provides a range of benefits that make it an invaluable tool for businesses looking to maximize their creativity and efficiency. So if you’re looking for ways to generate unique content quickly and efficiently, consider leveraging the power of AI today!
| 2023-02-03T00:00:00 |
https://aipagepro.com/blog/tag/creative-ai/
|
[
{
"date": "2023/02/03",
"position": 98,
"query": "artificial intelligence graphic design"
}
] |
|
Philosophers have studied 'counterfactuals' for decades. ...
|
Philosophers have studied ‘counterfactuals’ for decades. Will they help us unlock the mysteries of AI? – RealKM
|
https://realkm.com
|
[
"The Conversation",
"The Conversation Is An Independent Source Of News",
"Views",
"Sourced The Academic",
"Research Community",
"Delivered Direct To The Public."
] |
Editor's note: In a recent RealKM Magazine article, "explainable AI" was identified as an important aspect of artificial intelligence (AI) in knowledg.
|
Philosophers have studied ‘counterfactuals’ for decades. Will they help us unlock the mysteries of AI?
Editor’s note: In a recent RealKM Magazine
In a recent RealKM Magazine article , “explainable AI” was identified as an important aspect of artificial intelligence (AI) in knowledge management (KM). Explainable AI addresses the “black box” problems of AI. An idea called “counterfactual explanation” is often put forward as a solution to these black box problems, but the article below argues that this approach falls short.
Sam Baron, Australian Catholic University
Artificial intelligence is increasingly being rolled out all around the world to help make decisions in our lives, whether it’s loan decisions by banks, medical diagnoses, or US law enforcement predicting a criminal’s likelihood of re-offending.
Yet many AI systems are black boxes: no one understands how they work. This has led to a demand for “explainable AI”, so we can understand why an AI model yielded a specific output, and what biases may have played a role.
Explainable AI is a growing branch of AI research. But what’s perhaps less well known is the role philosophy plays in its development.
Specifically, one idea called “counterfactual explanation” is often put forth as a solution to the black box problems. But once you understand the philosophy behind it, you can start to understand why it falls short.
Why explanations matter
When AI is used to make life-changing decisions, the people impacted deserve an explanation of how that decision was reached. This was recently recognised through the European Union’s General Data Protection Regulation, which supports an individual’s right to explanation.
The need for explanation was also highlighted in the Robodebt case in Australia, where an algorithm was used to predict debt levels for individuals receiving social security. The system made many mistakes, placing people into debt who shouldn’t have been.
It was only once the algorithm was fully explained that the mistake was identified – but by then the damage had been done. The outcome was so damaging it led to a royal commission being established in August 2022.
In the Robodebt case, the algorithm in question was fairly straightforward and could be explained. We should not expect this to always be the case going forward. Current AI models using machine-learning to process data are much more sophisticated.
The big, glaring black box
Suppose a person named Sara applies for a loan. The bank asks her to provide information including her marital status, debt level, income, savings, home address and age.
The bank then feeds this information into an AI system, which returns a credit score. The score is low and is used to disqualify Sara for the loan, but neither Sara nor the bank employees know why the system scored Sara so low.
Unlike with Robodebt, the algorithm being used here may be extremely complicated and not easily explained. There is therefore no straightforward way to know whether it has made a mistake, and Sara has no way to get the information she needs to argue against the decision.
This scenario isn’t entirely hypothetical: loan decisions are likely to be outsourced to algorithms in the US, and there’s a real risk they will encode bias. To mitigate risk, we must try to explain how they work.
The counterfactual approach
Broadly speaking, there are two types of approaches to explainable AI. One involves cracking open a system and studying its internal components to discern how it works. But this usually isn’t possible due to the sheer complexity of many AI systems.
The other approach is to leave the system unopened, and instead study its inputs and outputs, looking for patterns. The “counterfactual” method falls under this approach.
Counterfactuals are claims about what would happen if things had played out differently. In an AI context, this means considering how the output from an AI system might be different if it receives different inputs. We can then supposedly use this to explain why the system produced the result it did.
Suppose the bank feeds its AI system different (manipulated) information about Sara. From this, the bank works out the smallest change Sara would need to get a positive outcome would be to increase her income.
The bank can then apparently use this as an explanation: Sara’s loan was denied because her income was too low. Had her income been higher, she would have been granted a loan.
Such counterfactual explanations are being seriously considered as a way of satisfying the demand for explainable AI, including in cases of loan applications and using AI to make scientific discoveries.
However, as researchers have argued, the counterfactual approach is inadequate.
Correlation and explanation
When we consider changes to the inputs of an AI system and how they translate into outputs, we manage to gather information about correlations. But, as the old adage goes, correlation is not causation.
The reason that’s a problem is because work in philosophy suggests causation is tightly connected to explanation. To explain why an event occurred, we need to know what caused it.
On this basis, it may be a mistake for the bank to tell Sara her loan was denied because her income was too low. All it can really say with confidence is that income and credit score are correlated – and Sara is still left without an explanation for her poor result.
What’s needed is a way to turn information about counterfactuals and correlations into explanatory information.
The future of explainable AI
With time we can expect AI to be used more for hiring decisions, visa applications, promotions and state and federal funding decisions, among other things.
A lack of explanation for these decisions threatens to substantially increase the injustice people will experience. After all, without explanations we can’t correct mistakes made when using AI. Fortunately, philosophy can help.
Explanation has been a central topic of philosophical study over the last century. Philosophers have designed a range of methods for extracting explanatory information from a sea of correlations, and have developed sophisticated theories about how explanation works.
A great deal of this work has focused on the relationship between counterfactuals and explanation. I’ve developed work on this myself. By drawing on philosophical insights, we may be able to develop better approaches to explainable AI.
At present, however, there’s not enough overlap between philosophy and computer science on this topic. If we want to tackle injustice head-on, we’ll need a more integrated approach that combines work in these fields.
Sam Baron, Associate Professor, Philosophy of Science, Australian Catholic University
Article source: This article is republished from The Conversation under a Creative Commons license. Read the original article.
Header image source: Gerd Altmann on Pixabay, Public Domain.
| 2023-02-04T00:00:00 |
2023/02/04
|
https://realkm.com/2023/02/04/philosophers-have-studied-counterfactuals-for-decades-will-they-help-us-unlock-the-mysteries-of-ai/
|
[
{
"date": "2023/02/04",
"position": 87,
"query": "universal basic income AI"
}
] |
Journalistic Lessons for the Algorithmic Age
|
Journalistic Lessons for the Algorithmic Age – The Markup
|
https://themarkup.org
|
[
"Julia Angwin"
] |
I wanted to share the lessons I learned building a newsroom that integrated engineers with journalists and sought to use a new model for accountability ...
|
Subscribe to Hello World Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. Browse the archive here.
Hello, friends,
I’m sad to inform you that this is my last missive to you. After founding The Markup five years ago, I am departing this newsroom to pursue other projects. It’s been an honor to correspond with all of you, dear readers, and I am so humbled by all of you who supported my vision. Please stay in touch with me through Twitter, Mastodon, or my personal newsletter.
Before I go, I wanted to share the lessons I learned building a newsroom that integrated engineers with journalists and sought to use a new model for accountability journalism: the scientific method.
I founded The Markup with the idea that striving for vague concepts like “objectivity” or “fairness” can lead to false equivalents. A better approach, I believe, is for journalists to seek a hypothesis and assemble evidence to test it.
At The Markup we pioneered an array of scientifically inspired methods that used automation and computational power to supercharge our journalism. Reflecting on our work, I came up with 10 of the most important lessons I’ve learned using this approach.
↩︎ link 1) Important ≠ secret
In a resource-constrained world, choosing a topic to investigate is the most important decision a newsroom makes.
At The Markup, we developed an investigative checklist that reporters filled out before embarking on a project. Top of the checklist was not novelty, but scale—how many people were affected by the problem we were investigating. In other words, we chose to tackle things that were important but not secret.
For instance, anyone using Google has probably noticed that Google takes up a lot of the search result page for its own properties. Nevertheless, we decided to invest nearly a year into quantifying how much Google was boosting its own products over direct links to source material because the quality of Google search results affects nearly everyone in the world.
This type of work has an impact. The European Union has now passed a law banning tech platforms from this type of self-preferencing, and there is legislation pending in Congress to do the same.
↩︎ link 2) Hypothesis first, data second
It is extremely tempting for data-driven journalists to jump into a dataset looking for a story, but this is almost never a good way to assess accountability. Instead, it often results in what I like to call “Huh, that’s interesting” stories.
The best accountability stories, data-driven or not, start out with a tip or a hunch, which you report out and develop into a hypothesis you can test.
Hypotheses must be crafted carefully. The statement “Facebook is irredeemably bad” is not a testable hypothesis. It is a hot take. A hypothesis is something provable, such as: Facebook did not live up to its promise to stop recommending political groups during the U.S. presidential election. (Spoiler: We checked; it did not).
↩︎ link 3) Data is political
Data is powerful. Whoever gathers it has the power to decide what is noticed and what is ignored. People and institutions who have the money to build massive datasets rarely have any incentive to assemble information that could be used to challenge their power.
That’s why we journalists often need to collect our own data and why I built a newsroom that had the engineering talent and social science expertise needed to collect original data at scale.
↩︎ link 4) Choose a sample size
The days when journalists could interview three people in a diner and declare a trend are thankfully over. The public is demanding more persuasive evidence from the media.
At the same time, however, not every proof requires big data. It only took one secret court document, provided by whistleblower Edward Snowden, to prove that U.S. intelligence agencies were secretly collecting the call logs of every single American.
The beauty of statistics is that even when you are examining a large system, you often need only a relatively small sample size. When we wanted to investigate Facebook’s recommendation algorithms, Markup reporter Surya Mattu assembled a panel of more than 1,000 people who shared their Facebook data with us. Even though that was a drop in the bucket of Facebook’s more than two billion users, it was still a representative sample to test some hypotheses.
↩︎ link 5) Embrace the odds
If you’re lucky, sometimes a dataset reveals its truths without your having to do any hard math. But for large data sets, statistics is usually the best way to extract meaning. This can mean embracing some wonky-sounding probabilistic findings.
Consider our investigation into whether Amazon placed its own brands at the top of search results. We scraped thousands of searches and found that Amazon disproportionately gave its brands the top slot: Amazon brands and exclusives were only 5.8 percent of products in our sample but got the number one spot 19.5 percent of the time.
The problem was that the proportions alone don’t tell you if Amazon won that spot fair and square. Maybe its products were actually better than all the others? To dig deeper, we wanted to see how Amazon brands fared against products with high star ratings or large numbers of reviews.
To do that, investigative data journalist Leon Yin used a statistical technique called a random forest analysis that allowed him to identify that being an Amazon brand was the most important factor in predicting if a product won the top slot—far more than any of the other potential factors combined. The odds—even though they were a bit complicated to explain—made our finding far more robust.
↩︎ link 6) Yes, you need a narrative
Data is necessary but not sufficient to persuade readers. Humans are wired to tell, share, and remember stories.
The statistical finding is what is known in journalism as the “nut graf” of the story. You still need a human voice to be the spine. This is where the old-school reporting skills of knocking on doors and interviewing tons of people are still incredibly valuable. It is where word choice and talented editors make all the difference in crafting a compelling article.
↩︎ link 7) Expertise matters
Journalists are generalists. Even specialists like me who have covered a single topic—technology—for decades have to dive into new topics daily.
That’s why I believe in seeking expert reviews of statistical work. Over the years, I developed a process similar to academic peer review, in which I shared my methodologies with statisticians and domain experts in whatever field I was writing about.
I never share the narrative article before publication—which would be a fireable offense in most newsrooms. But sharing the statistical methodology allows me to bulletproof my work and find mistakes.
No one is more incentivized to find mistakes than the subject of an investigation, so at The Markup we shared data, code, and analyses with the subjects before publication in a process I call “adversarial review.” This gives them an opportunity to engage with the work meaningfully and provide a thoughtful response.
↩︎ link 8) Objectivity is dead. Long live limitations
One of the best parts about using the scientific method as a guide is that it moves us beyond the endless debates about whether journalism is “fair” or “objective.”
Rather than focus on fairness, it’s better to focus on what you know and what you don’t know. When he reported on the hidden bias in mortgage approval algorithms, Markup reporter Emmanuel Martinez could not obtain applicants’ credit scores because the government doesn’t release them. So he noted their absence in the limitations section of his methodology.
His analysis was still robust enough to be cited by three federal agencies when announcing a new plan to combat mortgage discrimination.
↩︎ link 9) Show your work
Journalists have a trust problem. Now that everyone in the world can publish, journalists must work harder to prove that their accounting of the truth is the most credible version.
I have found that showing my work, sharing entire datasets, the code used to analyze the data, and an extensive methodology, builds trust with readers. As an added bonus, the methodologies often get more website traffic over time than the narrative articles.
↩︎ link 10) Never give up
Journalists are outnumbered. There are six public relations professionals for every one journalist in the United States, according to the Bureau of Labor Statistics.
That means we have to use every possible tool at our disposal to hold power to account. One way to do that is to build tools that allow our work to continue beyond the day the article was published.
Consider the real-time forensic privacy scanner, Blacklight, that Surya Mattu built at The Markup. It runs a series of real-time privacy tests on any website.
Reporters can use Blacklight anytime there is a privacy-related news story. ProPublica, for instance, recently used Blacklight to reveal that online pharmacies selling abortion pills were sharing sensitive data with Google and other third parties.
I will continue to pursue these principles in my upcoming projects. Thank you for sharing the journey with me. It’s been an honor.
Best,
Julia Angwin
The Markup
Editor’s Note:
Dear readers,
I’ve been a fan of Julia’s work since 2013, when she joined ProPublica. In our time there together, I saw firsthand how much Julia valued the partnership between journalists and engineers and the incredible work that could emerge: everything from investigations into how Facebook allowed discriminatory advertising to a landmark project that revealed how criminal risk scores were biased against Black defendants.
I could not be more grateful to Julia for her vision to start The Markup and for the work she put into building a high-impact newsroom. As Hello World readers, you know this best. During her tenure, The Markup’s journalism has spurred companies to stop selling precise location data, Congress to introduce bills to outlaw self-preferencing, regulators to combat digital redlining practices where algorithms decide who gets a mortgage, and so much more. We can’t wait to see Julia’s announcement of what’s next.
I’m also excited to share that this newsletter, Hello World, isn’t going anywhere. It’s a place where we get to show our work another way: by going deep into our original reporting. It’s also a place for us to ask important questions while highlighting the voices of people who think critically about technology and its impact on society. Looking forward, we want to keep developing Hello World by incorporating your thoughts and feedback.
Over the next two weeks, we’d love to hear from you. What issues of Hello World did you enjoy most, what’s stuck with you, or what would you like to see more of? What you share will help us decide where the newsletter will go next—whether that’s answering more of your questions, breaking down our investigations and how they impact you, or talking to experts. Share your thoughts by Saturday, Feb. 18, by replying to this email directly.
—Sisi Wei,
Editor-in-Chief,
The Markup
| 2023-02-04T00:00:00 |
2023/02/04
|
https://themarkup.org/hello-world/2023/02/04/journalistic-lessons-for-the-algorithmic-age
|
[
{
"date": "2023/02/04",
"position": 19,
"query": "AI journalism"
}
] |
AI in Graphic Design: Impact on the Industry
|
AI in Graphic Design: Impact on the Industry
|
https://www.devstars.com
|
[
"Stuart Watkins"
] |
AI affects the graphic design industry in several ways, from automating routine tasks to imitating human creativity in the form of more personalised designs.
|
Introduction
Love it or hate it, you can probably already see the impact of AI on the graphic design industry. December 2022 was a watershed moment with key technologies launching that heralded a new era for us all. It may not all be bad for the graphic design industry, though.
Fine artists had similar concerns in the 19th century when photography took off, and video didn’t kill the radio star.
AI affects the graphic design industry in several ways, from automating routine tasks to imitating human creativity in the form of more personalised designs. This article aims to highlight the impact of AI on the graphic design industry and explore its potential.
With the right knowledge, generative AI tools can be a powerful support to your skills and even traditional graphic design software, so let’s dive in and learn about the power of AI.
The Changing Face of Graphic Design
AI has revolutionised the graphic design process, making it more efficient and user-friendly. With AI, human designers can automate tedious tasks, freeing more time for creative work. Additionally, AI algorithms can analyse data and generate designs tailored to specific audiences and demographics. This can make the design process more personalised and effective.
Streamlining the Design Process
One of the most significant changes that AI has brought to graphic design is the automation of routine tasks. AI can perform tasks such as image resizing, colour correction, and even creating layouts with high accuracy. The net result saves time and ensures consistency in design when compared to other creative tools.
Check out Design Systems guru Brad Frost’s thoughts on how AI will impact digital design and development.
Personalising the Design Experience
AI has also made the graphic design process more personalised by analysing data to understand the preferences and needs of target audiences. This data can then be used to create tailored designs to those audiences, resulting in more impactful and effective designs.
Still, designing web pages in Photoshop? Check out our eBook, The Digital Toolkit for Graphic Designers, where you’ll find a section on AI’s impact and some tools to try.
Comparing AI design tools and traditional graphic design tools
AI-powered tools have emerged as a formidable force, fundamentally transforming how creative professionals work. These tools leverage machine learning algorithms and neural networks to automate, enhance, and expedite various aspects of the creative process. Let’s delve into some of the notable AI tools available for graphic designers, juxtaposing them with traditional design tools they are poised to augment or replace.
AI-Powered Tools for Graphic Design
Adobe Sensei: A collection of intelligent services that enhance the Adobe Cloud platform, Adobe Sensei facilitates rapid design workflows. It provides features like auto-tagging of images, predictive analytics, and intelligent cropping. For example, it can analyze the content of an image and suggest crops that will keep the most important elements in frame. Adobe Firefly: A newer entry into the suite of Adobe tools, Firefly is a set of AI-powered design capabilities aimed at streamlining the creation of complex design elements. It can generate patterns, textures, and other design elements that would typically require extensive manual effort. DeepArtEffects: Utilizing an approach known as style transfer, DeepArt can transform photographs into artwork that mimics the style of famous artists. This tool is particularly interesting for creating unique marketing materials or social media posts. Canva’s Magic Resize: A feature within Canva that allows users to automatically resize their designs for different social media platforms, ensuring consistency across various media without the need for manual adjustments. Khroma: An AI tool that uses machine learning to learn which color combinations a designer prefers and generates limitless color palettes that meet specific aesthetic requirements. Lumen5: This tool helps in creating social media graphics and videos from simple text prompts. It leverages AI to automatically suggest images, videos, and layouts based on the content of the text. Fronty: An AI-powered web design tool that converts hand-drawn designs into HTML and CSS code. It significantly reduces the time needed to go from a concept sketch to a functional prototype. DesignScape: Operating within the domain of layout design, DesignScape uses AI to suggest optimal design layouts based on the content and purpose of the design, aiding in the creation of both web and print layouts. Logojoy/ Looka: An AI-powered logo maker that creates custom logo designs based on user preferences and brand identity. It offers an intuitive interface that simplifies the process of logo creation. Runway ML: This tool provides designers with powerful machine learning models that can be used for various tasks like image editing, style transfer, and even generating new design elements.
Traditional Tools and Their AI Counterparts
Below is a table that outlines traditional design tools alongside their AI-powered counterparts, highlighting key attributes and the types of tasks they are used for.
Traditional Tool AI-Powered Tool Key Attributes Types of Tasks Adobe Photoshop Adobe Sensei, Adobe Firefly Automation of repetitive tasks, intelligent scaling and cropping Image editing, pattern generation Hand Sketching Fronty Conversion of sketches to code Rapid prototyping Adobe Illustrator Runway ML, Logojoy/Looka Intelligent logo design, style manipulation Logo creation, vector design Manual Color Scheme Selection Khroma AI-driven color palette generation Color schemes creation Manual Layout Design DesignScape AI suggestions for layout optimization Print and web layout design Video Editing Software Lumen5 Video creation from text prompts Social media content creation Content Management Systems Canva’s Magic Resize Auto-resizing for various platforms Multi-platform graphic design Traditional Art Techniques DeepArt Digital style transfer to mimic artists Artistic image transformation
These AI tools provide a suite of capabilities that streamline the design process, allowing creative professionals to focus on higher-level conceptual work and to iterate more rapidly on their creative projects. The use of such tools can be particularly advantageous for agencies like Devstars, where the blend of bespoke solutions and efficient design is crucial to maintaining a competitive edge and delivering high-quality creative assets.
The integration of AI into the graphic design workflow offers an exciting opportunity to enhance the creative capabilities of designers, reduce time spent on routine tasks, and focus on delivering high-quality, innovative design solutions to clients. For an agency with the vision and experience of Devstars, adopting these tools can lead to more efficient project completion, higher client satisfaction, and potentially, a stronger competitive position in the marketplace.
Opportunities and Challenges
While AI has opened up exciting opportunities in the graphic design industry, it has also presented some challenges that designers must address.
Exploring New Possibilities
One of the biggest opportunities that AI has created in the graphic design industry is the ability to tackle more complex design projects. With AI, designers can create designs that were previously impossible to achieve manually, such as complex animations and interactive designs, opening up new possibilities for designers to push the boundaries of what’s possible in the field.
Staying Ahead of the Competition
With the increasing use of AI in the graphic design industry, competition will become more intense. As AI makes it easier for non-designers to create high-quality designs, incredible banner designs, and so on traditional designers may face stiff competition from new entrants to the market.
Tools like DALL·E and Midjourney offer powerful tools and are great enablers, but they are just tools. The skill will be in how we imagine their use and steer them with prompts and questions.
The Future of Graphic Design
The impact of Artificial Intelligence on the graphic design industry will only become more significant in the coming years—this is just the beginning. As AI technology evolves, designers will have access to more powerful tools and capabilities. The challenge for designers will be to stay ahead of the curve and embrace AI in a way that allows them to achieve creative excellence and offer more value to their clients.
| 2023-02-04T00:00:00 |
2023/02/04
|
https://www.devstars.com/blog/the-impact-of-artificial-intelligence-on-graphic-design-industry/
|
[
{
"date": "2023/02/04",
"position": 4,
"query": "artificial intelligence graphic design"
}
] |
Prompt engineering in Graphic Design and Illustration 2023
|
Prompt engineering in Graphic Design and Illustration 2023
|
https://sunshinedesign.com.au
|
[] |
With AI, the sky is the limit for artists and designers, as it pushes the boundaries of what's possible and opens up new avenues for creativity. New AI-powered ...
|
Exploring prompt engineering for Artists and Graphic Designers
The graphic design industry is witnessing a transformative shift with the advent of Artificial Intelligence (AI) technology. With AI, the sky is the limit for artists and designers, as it pushes the boundaries of what’s possible and opens up new avenues for creativity. New AI-powered tools such as Midjourney, Dalle-2, and Stable Diffusion are just the tip of the iceberg of what’s to come in this exciting field. See here for more examples of AI prompt platforms as of Feb 2023.
Harnessing the Power of Prompt Engineering
Prompt Engineering, a branch of AI specialising in Natural Language Processing (NLP), is changing how we think about art and design. By using vivid descriptions as prompts, graphic designers and illustrators can bring their vision to life and refine it with the help of AI algorithms. If you’re looking to be a part of the future of graphic design, consider a career in Prompt Engineering, one of the hottest fields in the industry. Note: This article was written and illustrated with the help of AI tools such as Chat GTP, targeted SEO, grammar correction, and audience prompts to speed up the process by at least 200%.
Unleashing the Potential of NLP in AI Art and Design
Artificial intelligent art and design uses NLP to create digital designs and art. With platforms like Midjourney, Dalle-2, and Stable Diffusion, artists and designers can explore new styles and techniques that were once impossible and produce unique works of art, music, and poetry. The freedom to experiment is endless and frees designers from the constraints of traditional media and techniques.
The Benefits of AI in Art and Design: Speed and Creativity
AI in graphic design offers many advantages over traditional methods, including the ability to explore new styles and techniques and a valuable tool for creativity and imagination.
Put it to the Test!
You can find plenty of resources online that show you how to use Prompt Engineering, but the best way to experience the potential of AI art is to try it yourself. You can prompt with things you like, such as emojis, Pig Latin, or even first and last names, and see how the AI algorithms respond. You can even create unique formulas to add spin to traditional prompt suggestions.
Accelerating Creativity
AI-assisted art and design have the potential to automate certain aspects of the creative process, freeing up time for artists and designers to focus on other aspects of their work. Furthermore, AI algorithms can create personalised experiences for viewers, opening up a new realm of possibilities for interactive and immersive art and design.
The Future is Bright for AI Artists and Graphic Designers
The future of AI art and design is exciting and full of potential. As AI technology advances, we can expect to see more innovative art and design creation. Additionally, AI could revolutionise how we experience art and design, such as through personalised video, screensavers, AI stock images, gaming, custom banner creation, learning and interactive experiences for more memorable and engaging content.
| 2023-02-04T00:00:00 |
2023/02/04
|
https://sunshinedesign.com.au/prompt-engineering/prompt-engineering-in-graphic-design/
|
[
{
"date": "2023/02/04",
"position": 59,
"query": "artificial intelligence graphic design"
}
] |
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