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How could artificial intelligence harm us?
We often hear that artificial intelligence may harm or even kill humans, so it might prove dangerous.
How could artificial intelligence harm us?

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tl;dr
There are many valid reasons why people might fear (or better be concerned about) AI, not all involve robots and apocalyptic scenarios.
To better illustrate these concerns, I'll try to split them into three categories.
Conscious AI
This is the type of AI that your question is referring to. A super-intelligent conscious AI that will destroy/enslave humanity. This is mostly brought to us by science-fiction. Some notable Hollywood examples are "The terminator", "The Matrix", "Age of Ultron". The most influential novels were written by Isaac Asimov and are referred to as the "Robot series" (which includes "I, robot", which was also adapted as a movie).
The basic premise under most of these works are that AI will evolve to a point where it becomes conscious and will surpass humans in intelligence. While Hollywood movies mainly focus on the robots and the battle between them and humans, not enough emphasis is given to the actual AI (i.e. the "brain" controlling them). As a side note, because of the narrative, this AI is usually portrayed as supercomputer controlling everything (so that the protagonists have a specific target). Not enough exploration has been made on "ambiguous intelligence" (which I think is more realistic).
In the real world, AI is focused on solving specific tasks! An AI agent that is capable of solving problems from different domains (e.g. understanding speech and processing images and driving and ... - like humans are) is referred to as General Artificial Intelligence and is required for AI being able to "think" and become conscious.
Realistically, we are a loooooooong way from General Artificial Intelligence! That being said there is no evidence on why this can't be achieved in the future. So currently, even if we are still in the infancy of AI, we have no reason to believe that AI won't evolve to a point where it is more intelligent than humans.
Using AI with malicious intent
Even though an AI conquering the world is a long way from happening there are several reasons to be concerned with AI today, that don't involve robots!
The second category I want to focus a bit more on is several malicious uses of today's AI.
I'll focus only on AI applications that are available today. Some examples of AI that can be used for malicious intent:

DeepFake: a technique for imposing someones face on an image a video of another person. This has gained popularity recently with celebrity porn and can be used to generate fake news and hoaxes. Sources: 1, 2, 3
With the use of mass surveillance systems and facial recognition software capable of recognizing millions of faces per second, AI can be used for mass surveillance. Even though when we think of mass surveillance we think of China, many western cities like London, Atlanta and Berlin are among the most-surveilled cities in the world. China has taken things a step further by adopting the social credit system, an evaluation system for civilians which seems to be taken straight out of the pages of George Orwell's 1984.
Influencing people through social media. Aside from recognizing user's tastes with the goal of targeted marketing and add placements (a common practice by many internet companies), AI can be used malisciously to influence people's voting (among other things). Sources: 1, 2, 3.
Hacking.
Military applications, e.g. drone attacks, missile targeting systems.

Adverse effects of AI
This category is pretty subjective, but the development of AI might carry some adverse side-effects. The distinction between this category and the previous is that these effects, while harmful, aren't done intentionally; rather they occur with the development of AI. Some examples are:

Jobs becoming redundant. As AI becomes better, many jobs will be replaced by AI. Unfortunately there are not many things that can be done about this, as most technological developments have this side-effect (e.g. agricultural machinery caused many farmers to lose their jobs, automation replaced many factory workers, computers did the same).
Reinforcing the bias in our data. This is a very interesting category, as AI (and especially Neural Networks) are only as good as the data they are trained on and have a tendency of perpetuating and even enhancing different forms of social biases, already existing in the data. There are many examples of networks exhibiting racist and sexist behavior. Sources: 1, 2, 3, 4.

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Short term

Physical accidents, e.g. due to industrial machinery, aircraft autopilot, self-driving cars. Especially in the case of unusual situations such as extreme weather or sensor failure. Typically an AI will function poorly under conditions where it has not been extensively tested.
Social impacts such as reducing job availability, barriers for the underprivileged wrt. loans, insurance, parole.
Recommendation engines are manipulating us more and more to change our behaviours (as well as reinforce our own "small world" bubbles). Recommendation engines routinely serve up inappropriate content of various sorts to young children, often because content creators (e.g. on YouTube) use the right keyword stuffing to appear to be child-friendly.
Political manipulation... Enough said, I think.
Plausible deniability of privacy invasion. Now that AI can read your email and even make phone calls for you, it's easy for someone to have humans act on your personal information and claim that they got a computer to do it.
Turning war into a video game, that is, replacing soldiers with machines being operated remotely by someone who is not in any danger and is far removed from his/her casualties.
Lack of transparency. We are trusting machines to make decisions with very little means of getting the justification behind a decision.
Resource consumption and pollution. This is not just an AI problem, however every improvement in AI is creating more demand for Big Data and together these ram up the need for storage, processing, and networking. On top of the electricity and rare minerals consumption, the infrastructure needs to be disposed of after its several-year lifespan.
Surveillance — with the ubiquity of smartphones and listening devices, there is a gold mine of data but too much to sift through every piece. Get an AI to sift through it, of course!
Cybersecurity — cybercriminals are increasingly leveraging AI to attack their targets.

Did I mention that all of these are in full swing already?
Long Term
Although there is no clear line between AI and AGI, this section is more about what happens when we go further towards AGI. I see two alternatives:

Either we develop AGI as a result of our improved understanding of the nature of intelligence,
or we slap together something that seems to work but we don't understand very well, much like a lot of machine learning right now.

In the first case, if an AI "goes rogue" we can build other AIs to outwit and neutralise it. In the second case, we can't, and we're doomed. AIs will be a new life form and we may go extinct.
Here are some potential problems:

Copy and paste. One problem with AGI is that it could quite conceivably run on a desktop computer, which creates a number of problems:

Script Kiddies — people could download an AI and set up the parameters in a destructive way. Relatedly,
Criminal or terrorist groups would be able to configure an AI to their liking. You don't need to find an expert on bomb making or bioweapons if you can download an AI, tell it to do some research and then give you step-by-step instructions.
Self-replicating AI — there are plenty of computer games about this. AI breaks loose and spreads like a virus. The more processing power, the better able it is to protect itself and spread further.

Invasion of computing resources. It is likely that more computing power is beneficial to an AI. An AI might buy or steal server resources, or the resources of desktops and mobile devices. Taken to an extreme, this could mean that all our devices simply became unusable which would wreak havoc on the .world immediately. It could also mean massive electricity consumption (and it would be hard to "pull the plug" because power plants are computer controlled!)
Automated factories. An AGI wishing to gain more of a physical presence in the world could take over factories to produce robots which could build new factories and essentially create bodies for itself.
These are rather philosophical considerations, but some would argue that AI would destroy what makes us human:

Inferiority. What if plenty of AI entities were smarter, faster, more reliable and more creative than the best humans?
Pointlessness. With robots replacing the need for physical labour and AIs replacing the need for intellectual labour, we will really have nothing to do. Nobody's going to get the Nobel Prize again because the AI will already be ahead. Why even get educated in the first place?
Monoculture/stagnation — in various scenarios (such as a single "benevolent dictator" AGI) society could become fixed in a perpetual pattern without new ideas or any sort of change (pleasant though it may be). Basically, Brave New World.

I think AGI is coming and we need to be mindful of these problems so that we can minimise them.

I think the kind of AI that's capable of reprogramming factories to build robots for itself is a long way away. Modern "AI" is just really sophisticated pattern recognition.

I said "long term" and "AGI". AGI is, by definition, well beyond sophisticated pattern recognition. And although "sophisticated pattern recognition" is far and away the most common thing used in real-world applications, there is already plenty of work in other directions (particularly problem decomposition/action planning, which IMO is the lynchpin of these types of scenarios.)

In addition to the other answers, I would like to add to nuking cookie factory example:
Machine learning AIs basically try to fulfill a goal described by humans. For example, humans create an AI running a cookie factory. The goal they implement is to sell as many cookies as possible for the highest profitable margin.
Now, imagine an AI which is sufficiently powerful. This AI will notice that if he nukes all other cookie factories, everybody has to buy cookies in his factory, making sales rise and profits higher.
So, the human error here is giving no penalty for using violence in the algorithm. This is easily overlooked because humans didn't expect the algorithm to come to this conclusion.

This reminds me of a real-world example I saw on reddit once where someone was training an AI to climb some stairs in Unity. It discovered that it could press itself into the ground with a lot of force and the physics would glitch, causing it to fly into the air and be the fastest to the top.

Or, worse, it'd decide that humans are made out of atoms that would be better used to make cookies out of.

I've heard this argument before. One of the fallacies of predicting an AI doomsday is that we can't predict what the AI will do. It's entirely possible the AI would recognize that nuking other cookie companies might throw off the global economy and destroy any potential demand for cookies... Law of economics, supply AND demand

My favorite scenario for harm by AI involves not high intelligence, but low intelligence.  Specifically, the grey goo hypothesis.
This is where a self-replicating, automated process runs amok and converts all resources into copies of itself.
The point here is that the AI is not "smart" in the sense of having high intelligence or general intelligence--it is merely very good at a single thing and has the ability to replicate exponentially.

FWIW, humans are already grey goo. We're selfish grey goo that doesn't want to be replaced by an even more efficient grey goo.

@immibis That is of course a philosophical POV, not fact. There are plenty of people who differentiate between humans and self-replicating / self sustaining machines. Zombie movies would not be very successful if a majority carried your definition at heart =)

@immibis Did you read the gray goo article on Wikipedia that this answer references? The term refers to unintelligent (nano)machines going amok, not to any intelligent behavior. So I'd say, no, humans are not it (and neither is AI), since we didn't eat Albert Einstein when we could.

@kubanczyk the fundamental meaning of term "intelligence" seems widely misunderstood, both in academia and the general public.  Intelligence is a spectrum, generally relative (to other decision-making mechanisms), and is based on the utility of any given decision in the context of a problem.  So grey goo would be intelligent, just that the intelligence would be limited and narrow.

I have an example which goes in kinda the opposite direction of the public's fears, but is a very real thing, which I already see happening. It is not AI-specific, but I think it will get worse through AI. It is the problem of humans trusting the AI conclusions blindly in critical applications.
We have many areas in which human experts are supposed to make a decision. Take for example medicine - should we give medication X or medication Y? The situations I have in mind are frequently complex problems (in the Cynefin sense) where it is a really good thing to have somebody pay attention very closely and use lots of expertise, and the outcome really matters.
There is a demand for medical informaticians to write decision support systems for this kind of problem in the medicine (and I suppose for the same type in other domains). They do their best, but the expectation is always that a human expert will always consider the system's suggestion just as one more opinion when making the decision. In many cases, it would be irresponsible to promise anything else, given the state of knowledge and the resources available to the developers. A typical example would be the use of computer vision in radiomics: a patient gets a CT scan and the AI has to process the image and decide whether the patient has a tumor.
Of course, the AI is not perfect. Even when measured against the gold standard, it never achieves 100% accuracy. And then there are all the cases where it performs well against its own goal metrics, but the problem was so complex that the goal metric doesn't capture it well - I can't think of an example in the CT context, but I guess we see it even here on SE, where the algorithms favor popularity in posts, which is an imperfect proxy for factual correctness.
You were probably reading that last paragraph and nodding along, "Yeah, I learned that in the first introductory ML course I took". Guess what? Physicians never took an introductory ML course. They rarely have enough statistic literacy to understand the conclusions of papers appearing in medical journals. When they are talking to their 27th patient, 7 hours into their 16 hour shift, hungry and emotionally drained, and the CT doesn't look all that clear-cut, but the computer says "it's not a malignancy", they don't take ten more minutes to concentrate on the image more, or look up a textbook, or consult with a colleague. They just go with what the computer says, grateful that their cognitive load is not skyrocketing yet again. So they turn from being experts to being people who read something off a screen. Worse, in some hospitals the administration does not only trust computers, it also has found out that they are convenient scapegoats. So, a physician has a bad hunch which goes against the computer's output, it becomes difficult for them to act on that hunch and defend themselves that they chose to overrode the AI's opinion.
AIs are powerful and useful tools, but there will always be tasks where they can't replace the toolwielder.

If you're looking for more examples, the controversy around using machine learning to predict reoffending rates of applicants for bail or parole is a good one. I agree that we shouldn't expect doctors and judges to have the levels of statistical expertise needed to understand AI, in addition to their medical and legal expertise. AI designers should be aware of the fallibility of their algorithms, and provide clear guidance to its users. Maybe tell the doctor where to look on the CT scan instead of directly giving them the result.

This only intents to be a complement to other answers so I will not discuss to possibility of AI trying to willingly enslave humanity.
But a different risk is already here. I would call it unmastered technology. I have been teached science and technology, and IMHO, AI has by itself no notion of good and evil, nor freedom. But it is built and used by human beings and because of that non rational behaviour can be involved.
I would start with a real life example more related to general IT than to AI. I will speak of viruses or other malwares. Computers are rather stupid machines that are good to quickly process data. So most people rely on them. An some (bad) people develop malwares that will disrupt the correct behaviour of computers. And we all know that they can have terrible effects on small to medium organizations that are not well prepared to an computer loss.
AI is computer based so it is vulnerable to computer type attacks. Here my example would be an AI driven car. The technology is almost ready to work. But imagine the effect of a malware making the car trying to attack other people on the road. Even without a direct access to the code of the AI, it can be attacked by side channels. For example it uses cameras to read signal signs. But because of the way machine learning is implemented, AI generaly does not analyses a scene the same way a human being does. Researchers have shown that it was possible to change a sign in a way that a normal human will still see the original sign, but an AI will see a different one. Imagine now that the sign is the road priority sign...
What I mean is that even if the AI has no evil intents, bad guys can try to make it behave badly. And to more important actions will be delegated to AI (medecine, cars, planes, not speaking of bombs) the higher the risk. Said differently, I do not really fear the AI for itself, but for the way it can be used by humans.

I would say the biggest real threat would be the unbalancing/disrupting we are already seeing.  The changes of putting 90% the country out of work are real, and the results (which will be even more uneven distribution of wealth) are terrifying if you think them through.

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I think one of the most real (ie. related to current, existing AIs) risks are in blindly relying on unsupervised AIs, for two reasons.
1. AI systems may degrade
Physical error in AI systems may start producing wildly wrong results in regions in which they were not tested for because the physical system starts providing wrong values. This is sometimes redeemed by self-testing and redundancy, but still requires occasional human supervision.
Self learning AIs also have a software weakness - their weight networks or statistic representations may approach local minima where they are stuck with one wrong result.
2. AI systems are biased
This is fortunately frequently discussed, but worth mentioning: AI systems' classification of inputs is often biased because the training/testing dataset were biased as well. This results in AIs not recognizing people of certain ethnicity, for more obvious example. However there are less obvious cases that may only be discovered after some bad accident, such as AI not recognizing certain data and accidentally starting fire in a factory, breaking equipment or hurting people.

This is a good, contemporary answer. "Black box" AI such as neural networks are impossible to test in an absolute manner, which makes them less than 100% predictable - and by extension, less than 100% reliable. We never know when an AI will develop an alternative strategy to a given problem, and how this alternative strategy will affect us, and this is a really huge issue if we want to rely on AI for important tasks like driving cars or managing resources.

Human beings currently exist in an ecological-economic niche of "the thing that thinks".
AI is also a thing that thinks, so it will be invading our ecological-economic niche.  In both ecology and economics, having something else occupy your niche is not a great plan for continued survival.
How exactly Human survival is compromised by this is going to be pretty chaotic.  There are going to be a bunch of plausible ways that AI could endanger human survival as a species, or even as a dominant life form.

Suppose there is a strong AI without "super ethics" which is cheaper to manufacture than a human (including manufacturing a "body" or way of manipulating the world), and as smart or smarter than a human.
This is a case where we start competing with that AI for resources.  It will happen on microeconomic scales (do we hire a human, or buy/build/rent/hire an AI to solve this problem?).  Depending on the rate at which AIs become cheap and/or smarter than people, this can happen slowly (maybe an industry at a time) or extremely fast.
In a capitalist competition, those that don't move over to the cheaper AIs end up out-competed.
Now, in the short term, if the AI's advantages are only marginal, the high cost of educating humans for 20-odd years before they become productive could make this process slower.  In this case, it might be worth paying a Doctor above starvation wages to diagnose disease instead of an AI, but it probably isn't worth paying off their student loans.  So new human Doctors would rapidly stop being trained, and existing Doctors would be impoverished.  Then over 20-30 years AI would completely replace Doctors for diagnostic purposes.
If the AI's advantages are large, then it would be rapid.  Doctors wouldn't even be worth paying poverty level wages to do human diagnostics.  You can see something like that happening with muscle-based farming when gasoline-based farming took over.
During past industrial revolutions, the fact that humans where able to think means that you could repurpose surplus human workers to do other actions; manufacturing lines, service economy jobs, computer programming, etc.  But in this model, AI is cheaper to train and build and as smart or smarter than humans at that kind of job.
As evidenced by the ethanol-induced Arab spring, crops and cropland can be used to fuel both machines and humans.  When machines are more efficient in terms of turning cropland into useful work, you'll start seeing the price of food climb.  This typically leads to riots, as people really don't like starving to death and are willing to risk their own lives to overthrow the government in order to prevent this.
You can mollify the people by providing subsidized food and the like.  So long as this isn't economically crippling (ie, if expensive enough, it could result in you being out-competed by other places that don't do this), this is merely politically unstable.
As an alternative, in the short term, the ownership caste who is receiving profits from the increasingly efficient AI-run economy can pay for a police or military caste to put down said riots.  This requires that the police/military castes be upper lower to middle class in standards of living, in order to ensure continued loyalty -- you don't want them joining the rioters.
So one of the profit centers you can put AI towards is AI based military and policing.  Drones that deliver lethal and non-lethal ordnance based off of processing visual and other data feeds can reduce the number of middle-class police/military needed to put down food-price triggered riots or other instability.  As we have already assumed said AIs can have bodies and training cheaper than a biological human, this can also increase the amount of force you can deploy per dollar spent.
At this point, we are talking about a mostly AI run police and military being used to keep starving humans from overthrowing the AI run economy and seizing the means of production from the more efficient use it is currently being put to.
The vestigial humans who "own" the system at the top are making locally rational decisions to optimize their wealth and power.  They may or may not persist for long; so long as they drain a relatively small amount of resources and don't mess up the AI run economy, there won't be much selection pressure to get rid of them.  On the other hand, as they are contributing nothing of value, they position "at the top" is politically unstable.
This process assumed a "strong" general AI.  Narrower AIs can pull this off in pieces.  A cheap, effective diagnostic computer could reduce most Doctors into poverty in a surprisingly short period of time, for example.  Self driving cars could swallow 5%-10% of the economy.  Information technology is already swallowing the retail sector with modest AI.
It is said that every technological advancement leads to more and better jobs for humans.  And this has been true for the last 300+ years.
But prior to 1900, it was also true that every technological advancement led to more and better jobs for horses.  Then the ICE and automobile arrived, and now there are far fewer working horses; the remaining horses are basically the equivalent of human personal servants: kept for the novelty of "wow, cool, horse" and the fun of riding and controlling a huge animal.

AI that is used to solve a real world problem could pose a risk to humanity and doesn't exactly require sentience, this also requires a degree of human stupidity too..
Unlike humans, an AI would find the most logical answer without the constraint of emotion, ethics, or even greed... Only logic. Ask this AI how to solve a problem that humans created (for example, Climate Change) and it's solution might be to eliminate the entirety of the human race to protect the planet. Obviously this would require giving the AI the ability to act upon it's outcome which brings me to my earlier point, human stupidity.

In addtion to the many answers already provided, I would bring up the issue of adversarial examples in the area of image models.
Adversarial examples are images that have been perturbed with specifically designed noise that is often imperceptible to a human observer, but strongly alters the prediction of a model.
Examples include:

Affecting the predicted diagnosis in a chest x-ray
Affecting the detection of roadsigns necessary for autonomous vehicles.

Artificial intelligence can harm us in any of the ways of natural intelligence (of humans). The distinction between natural and artificial intelligence will vanish when humans start augmenting themselves more intimately. Intelligence may no longer characterize the identity and will become a limitless possession. The harm caused will be as much the humans can endure for preserving their evolving self-identity.

Few people realize that our global economy should be considered an AI:
- The money transactions are the signals over a neural net. The nodes in the neural net would be the different corporations or private persons paying or receiving money.
- It is man-made so qualifies as artificial
This neural network is better in its task then humans:
Capitalism has always won against economy planned by humans (plan-economy).
Is this neural net dangerous ?
Might differ if you are the CEO earning big versus a fisherman in a river polluted by corporate waste.
How did this AI become dangerous?
You could answer it is because of human greed.
Our creation reflects ourselves.
In other words: we didnot train our neural net to behave well.
Instead of training the neural net to improve living quality for all humans, we trained it to make rich fokes more rich.
Would it be easy to train this AI to be no longer dangerous ?
Maybe not, maybe some AI are just larger then life.
It is just survival of the fittest.


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Is AGI likely to be developed in the next decade?
AI experts like Ben Goertzel and Ray Kurzweil say that AGI   will be developed in the coming decade. Are they credible?

No. See my answer to this question, which yours is a duplicate of.

It is hard to prove a negative but if I had to bet I would say unless there is some major breakthrough it is too optimistic by an order of magnitude.

@JohnDoucette: A question about building an AGI should not really be equated with a question about the Singularity. There should be a wide gulf between AGI and Singularity in most people's minds. Proponents of the Singularity require AGI plus a usually exponentially-self-improving route to "Artificial Super Intelligence", which is pure conjecture. Whilst we already know that the GI in AGI is feasible in terms of basic physics, because we have an example that GI projects can attempt to copy.

As a riff on my answer to this question, which is about the broader concern of the development of the singularity, rather than the narrower concern of the development of AGI:
I can say that among AI researchers I interact with, it far more common to view the development of AGI in the next decade as speculation (or even wild speculation) than as settled fact.
This is borne out by surveys of AI researchers, with 80% thinking "The earliest that machines will be able to simulate learning and every other
aspect of human intelligence" is in "more than 50 years" or "never", and just a few percent thinking that such forms of AI are "near". It's possible to quibble over what exactly is meant by AGI, but it seems likely that for us to reach AGI, we'd need to simulate human-level intelligence in at least most of its aspects. The fact that AI researchers think this is very far off suggests that they also think AGI is not right around the corner.
I suspect that the reasons AI researchers are less optimistic about AGI than Kurzweil or others in tech (but not in AI), are rooted in the fact that we still don't have a good understanding of what human intelligence is. It's difficult to simulate something that we can't pin down. Another factor is that most AI researchers have been working in AI for a long time. There are countless past proposals for AGI frameworks, and all of them have been not just wrong, but in the end, more or less hopelessly wrong. I think this creates an innate skepticism of AGI, which may perhaps be unfair. Nonetheless, expert opinion on this one is pretty well settled: no AGI this decade, and maybe not ever!

I wouldn't take anything Ray Kurzweil says especially seriously. Actual AI experts spend large quantities of time reading the existing scientific literature, and working to expand it. Because Kurzweil doesn't spend much of his time actually learning about AI, he has plenty of time in which to talk about it. Loudly. This is harmful to research, because 1) a lot of the uninformed predictions he and others make resemble doomsday scenarios, and 2) the predictions of good things have insanely optimistic time frames attached, and when they don't come true, research funding may be lost because AI hasn't lived up to what people thought it promised.
AI research has been progressing very rapidly in the last decade, but if we're being honest, a lot of the credit for that has to go to the people who develop research-grade graphics cards. The ability to perform massive amounts of linear algebra in parallel has allowed us to use techniques that we've known about for a couple decades, but that were too computationally expensive to be practical at the time. And because those techniques are now practical, a lot of current research is applying those techniques to new problems, and modifying and improving them based on what we've learned. (I don't want to understate the contributions here; there have been a lot of really clever ideas developed in the last ten years. But it's mostly consistent iterative improvement of techniques that already existed, rather than completely revolutionary ideas.)
To make human-equivalent AIs, we'll probably need to make a few of those giant conceptual leaps. And each of those leaps will then need to be followed up by a decade or two of iterative improvement, because that's how the process works. Case in point, the revolutionary idea that eventually led to all the Deep Learning models out there today was this one, dated 1986. First, there was the revolutionary idea. It was followed up by a bunch of work that built on it and expanded it in new directions. The work eventually stagnated because of hardware constraints. Then hardware scientists and engineers made some advances that let us continue work, and only then did we finally start getting the major applications that we're seeing today.
We know human-level intelligence is possible, since humans manage it. I have little doubt that we'll figure out how to do it with AI eventually (maybe in my lifetime, maybe not). But if you want Kurzweil's predictions to be even remotely plausible, you might want to add a zero to the end of most of his time frames.

My simple answer is NO.
Let me elaborate. If you closely observe nature, you see that nothing changes drastically all of a sudden. Even when it does, it doesn't stay for long.
Field of AI, has just started and it needs a lot more evolution to achieve AGI. Though AI is solving many directed problems like Face Recognition, Speech Recognition and many more (applications are innumerable), all these can be considered as Narrow AI. They solve a particular task. For AI reach to the state where it can better than humans in all aspects, not only do we need breakthroughs in algorithms, we also need many more breakthroughs in electronics and physics.
Please read this article. Summary is experts(around 350) estimate that there’s a 50% chance that AGI will occur until 2060. So, there is a very bleak chance that AGI will become a reality in next decade.
https://blog.aimultiple.com/artificial-general-intelligence-singularity-timing/

4k-5k years is very long time. This is the point i wanted to stress. Drastic changes don't happen in a decade or two. And regarding the article,it may not be good, but the survey i mentioned is a very famous one and is referred to by many in a lot of ethical debates on AI and timing at which AGI will become a reality. I also wanted to tell that if few experts predict AGI to become a reality in a decade or two, there are many more who predict exactly the opposite. I want to tell that AGI becoming a reality in a decade or two is too optimistic. Thanks for the feedback. I helps to improve.

decade or two is very less compared to 4k-5k years. Agree that 4k-5k years is short if we compare full evolution

The article's exponential growth/lake analogy is particularly misleading (even if you ignore the fact that transistor density doubling every 18 months was an observation/prediction, not a law of nature, and is not expected to continue indefinitely). Putting an AI that can't solve a given problem onto hardware that's 20 billion times more powerful will just result in it getting the wrong results 20 billion times faster. Faster hardware permits us to use techniques that would have otherwise been intractable. But we still need to invent those techniques.

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

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What is the double sample problem in reinforcement learning?
According to the SBEED: Convergent Reinforcement Learning with
Nonlinear Function Approximation for convergent reinforcement learning, the Smoothed Bellman operator is a way to dodge the double sample problem? Can someone explain to me what the double sample problem is and how SBEED solves it?

The double sampling problem is referenced in Chaper 11.5 Gradient Descent in the Bellman Error in Reinforcement Learning: An Introduction (2nd edition).
From the book, this is the full gradient descent (as opposed to semi-gradient descent) update rule for weights of an estimator that should converge to a minimal distance from the Bellman error:

$$w_{t+1} = w_t + \alpha[\mathbb{E}_b[\rho_t[R_{t+1} + \gamma\hat{v}(S_{t+1},\mathbf{w})] - \hat{v}(S_{t},\mathbf{w})][\nabla\hat{v}(S_{t},\mathbf{w})- \gamma\mathbb{E}_b[\rho_t\nabla\hat{v}(S_{t+1},\mathbf{w})]]$$
[...] But this is naive, because the equation above involves the next state, $S_{t+1}$, appearing in two
expectations that are multiplied together. To get an unbiased sample of the product,
two independent samples of the next state are required, but during normal interaction
with an external environment only one is obtained. One expectation or the other can be
sampled, but not both.

Basically, unless you have an environment that you can routinely re-wind and re-sample to get two independent estimates (for $\hat{v}(S_{t+1},\mathbf{w})$ and $\nabla\hat{v}(S_{t+1},\mathbf{w})$) then the update rule that naturally arises from gradient descent on the Bellman error does will work any better than other approaches, such as semi-gradient methods. If you can do this rewind process on every step, then it may be worth it because of the guarantees of convergence, even in off-policy with non-linear approximators.
The paper proposes a workaround for this issue, keeping the robust convergence guarantees, but dropping the need to collect two independent samples of the same estimate on each step.

We cannot approximate $ E^2(x)$ by $\frac{1}{N} \sum_i x_i \frac{1}{N} \sum_j x_j $. Because $cov(x)=E(x^2)-E^2(x)$.

Hello. This answer is quite short. Maybe you could elaborate a little bit more and tell us more in detail how this answers the question(s). For example, how does this answer the question "what is the double sample problem"?

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
What are the societal risks associated with AI?
What are the actual risks to society associated with the widespread use of AI? Outside of the use of AI in a military context.
I am not talking about accidental risks or unintentional behaviour - eg, a driver-less car accidentally crashing.
And I am not talking about any transitional effects when we see the use of AI being widespread and popular.  For instance I have heard that the widespread use of AI will make many existing jobs redundant, putting many people out of work.  However this is true of any major leap forward in technology (for example the motor car killed off the stable/farrier industries).  The leaps forward in technology almost always end up creating more jobs than were lost in the long run.
I am interested in long term risks and adverse effects stemming directly from the widespread use of AI in a non-military sense.  Has anybody speculated on the social or psychological impacts that AI will produce once it has become popular?

The biggest risk is algorithmic bias. As more and more decision-making processes are taken on by AI systems, there will be an abdication of responsibility to the computer; people in charge will simply claim the computer did it, and they cannot change it.
The real problem is that training data for machine learning often contains bias, which is usually ignored or not recognised. There was a story on BBC Radio about someone whose passport photo was rejected by an algorithm because he supposedly had his mouth open. However, he belonged to an ethnic group which has larger lips than Caucasian whites, but the machine could not cope with that.
There is a whole raft of examples where similar things happen: if you belong to a minority group, machine learning can lead to you being excluded, just because the algorithms will have been trained on training data that was too restricted.
Update: Here is a link to a BBC News story about the example I mentioned.

Yes, don't get me started on that! However, I think algorithmic bias is a bigger risk, as there is little awareness of the issue. People can learn not to trust videos (as they cannot trust texts) alone, but with bias there's nothing you can do.

One risk that’s already realized:  large online vendors think they have implemented artificial intelligence in their “help” pages and therefore they can (try to) make it impossible to get to someone who can actually think.  And since the artificial stupidity (AS) usually feeds the customer articles completely unrelated to the issue, anyone sufficiently persistent to pursue it is extremely pissed off at the company before (if ever) the issue is resolved.  And because far too many people passively accept this abuse, the companies have no incentive to be more reasonable.  In other words, “AS” is reducing our expectations for customer service.
Another is the JavaScript intended to prevent invalid names, phone numbers, and email addresses in web forms which due to bugs or obsolescence rejects legitimate inputs.

IMHO the greatest risk is that AI can make people lazy.  If you can ask an AI for an answer to any problem, what's your motivation to figure out how to figure out the answer for yourself?  I have run into a lot of young people who can't add or multiply two three-digit numbers without using a calculator.  When it's possible to dump a huge mass of data into an AI, and the AI tells you the structures it finds in the data without explaining how it finds the structures so you can do it yourself, the AI wins and you lose.

when making a fire, do you use sticks to create friction to burn some kindling - or do you use a lighter?  when travelling a long distance, do you walk, ride a horse - or do you drive a car or take a train?  when you want to write something down, do you make impressions into clay, or do you use a pen and paper?  .......and does the fact that you don't rub sticks together, or walk long distances, or make impressions into clay mean that you are lazy?

The Wall-E scenario!

@Jimmery, that's a reasonable argument.  However, human beings design and make the lighters, cars, pens, and paper.  And we do remember how to walk, make impressions in clay, etc. But how to think and how to create are cultural and easily lost.  If we rely on AI to generate our art, we cease to be artists.  If only AI "understands" cosmology, we cease to be cosmologists.  If only AI does sports, we will only be spectators.  AI is extremely useful, but we should use it to strengthen humankind, not let it make us mere consumers and spectators.

@S.McGrew I think, or at least hope, that very abstract and human fields - like the arts - will never cease to be man-made. I'm not saying that in the near future AI created art won't be in our galleries, in our cinemas or even on our playlists. I'm just pointing out that art seems to be inseparable from the human. You leave a child alone in a room and when you least expect it he has already made "art"

The long human artistic trajectory also seems to me to be a good sign that it is inseparable from us; as well as its universality among all the peoples of the earth also seems to indicate this.
There is even a cave work that I find quite interesting - and profound, in a way. Although produced thousands of years ago, here in South America

Offloading of responsibility may the single greatest danger.

Where algorithmic bias may be the core issue of Machine Learning, it can be identified and mitigated.
Transferring responsibility to a robot or algorithm requires an intentional choice with moral dimension.  As the scholar Joanna Bryson put it:

In humans consciousness and ethics are associated with our morality, but that is because of our evolutionary and cultural history.  In artefacts, moral obligation is not tied by either logical or mechanical necessity to awareness or feelings.  This is one of the reasons we shouldn't make AI responsible: we can't punish it in a meaningful way.
  Source: AI Ethics: Artificial Intelligence, Robots, and Society

In a malicious sense, transferring agency to an automaton that may do something harmful which benefits me allows me to say "I didn't make the decision and have no responsibility for the outcome."  (It seems to me that companies are doing this more and more.)
There was a very good short story on the subject Unchained: A story of love, loss, and blockchain in which automate taxis develop novel strategies that have an unintended moral dimension in regard to humans.


text:
How does text classification reduce manpower costs?
(I apologize for the title being too broad and the question being not 'technical')
Suppose that my task is to label news articles. This means that given a news article, I am supposed to classify which category that news belong to. Eg, 'Ronaldo scores a fantastic goal' should classify under 'Sports'.
After much experimentation, I came up with a model that does this labeling for me. It has, say, 50% validation accuracy. (Assume that it is the best)
And so I deployed this model for my task (on unseen data obviously). Of course, from a probabilistic perspective, I should get roughly 50% of the articles labelled correctly. But how do I know that which labels are actually correct and which labels need to be corrected? If I were to manually check (say, by hiring people to do so), how is deploying such a model better than just hiring people to do the classification directly? (Do not forget that the manpower cost of developing the model could have been saved.)

Welcome to SE:AI!

"I should get roughly 50% of the articles labelled correctly." You should? If there are n categories, each article fitting one category, and articles are evenly distributed across categories, random luck would give you a 1/n chance of getting it correct for one article. If it's just sports vs non-sports, (again evenly distributed), sure, 50% is no better than random luck, but that doesn't seem to be the case.

There are several advantages:

Some text classification systems are much more accurate than 50%. For example, most spam classification systems are 99.9% accurate, or more. There will be little value to having employees review these labels.
Many text classification systems can output a confidence as well as a label. You can selectively have employees review only the examples the model is not confident about. Often these will be small in number.
You can usually test a text classification model by having it classify some unseen data, and then asking people to check the work. If you do this for a small number of examples, you can make sure the system is working. You can then confidently use the system on a much large set of unlabeled examples, and be reasonably sure about how accurate it is.
For text, it is also important to measure how much different people agree on the ratings. You are unlikely to do better than this, because this gives you a notion of the subjectivity of the specific problem you are working on. If people disagree 50% of the time anyway, maybe you can accept a 50% failure rate from the automated system, and not bother checking its work.

I don't think that the spam classification system associated with my mail client is so accurate. For example, yesterday, it's labeled two e-mails as junk, while it was not the case.

@nbro You are correct, I had misremembered the statistics. It's been at 99.9% or higher for quite a long time (more than a decade). Even though the classifiers keep improving, spam authors keep using more sophisticated techniques, so it has the nature of an arms race.

@nbro: You also need to account for the fact that the majority of the email you receive is likely to be spam. The spam that you correctly don't see counts for most of that 99.9% accuracy rating.

@NeilSlater If the majority of the e-mail I receive is spam, then I do not expect two e-mails to be marked (on the same day) as spam when they are not, if the predictive system is really understanding something about the difference between spam and not spam. In other words, if 99% of the e-mails are spam, then you can just predict always spam and you'll get a nice accuracy. Clearly, this accuracy is highly misleading.

@nbro FWIW, most spam classification literature uses AUC as a measurement. They typically have 99.99% AUC. This means that they can obtain extremely high accuracy on both classes, despite the class imbalance

I believe that point 2 actually answers my question the most. I have also come across a method that uses the predictions of multiple models. If those models all produce the same prediction, then it is likely that one can treat that prediction as 'correct' and thus save cost.

First of all to be more real, you usually expect more than 50% validation accuracy on articles predictions.
Back on your question, you should definitely try to automate this process if you are looking for a long-term solution of labeling articles. Deploying such model should not cost more than hiring employees to do this manually, at least for a long-term perspective.


text:
Is there any research on the development of attacks against artificial intelligence systems?
Is there any research on the development of attacks against artificial intelligence systems?
For example, is there a way to generate a letter "A", which every human being in this world can recognize but, if it is shown to the state-of-the-art character recognition system, this system will fail to recognize it? Or spoken audio which can be easily recognized by everyone but will fail on the state-of-the-art speech recognition system.
If there exists such a thing, is this technology a theory-based science (mathematics proved) or an experimental science (randomly add different types of noise and feed into the AI system and see how it works)? Where can I find such material?

I'm amazed nobody's posted the famous turtle gun yet! "Synthesizing Robust Adversarial Examples" (Athalye et al., 2018) And for those who haven't seen the turtle gun before, there's an xkcd for that.

Street signs that look to self-driving cars like they say "50km/h" but to humans like they say "80km/h" here

A related question: https://ai.stackexchange.com/q/8595/2444.

I'm not sure if there is something beyond adversarial examples that could answer your question, but on that topic, see CleverHans, a library to benchmark model vulnerability to adversarial examples, and also this blog post, which I think is a rather accessible introduction to the topic.

Yes, there is some research on this topic, which can be called adversarial machine learning, which is more an experimental field.
An adversarial example is an input similar to the ones used to train the model, but that leads the model to produce an unexpected outcome. For example, consider an artificial neural network (ANN) trained to distinguish between oranges and apples. You are then given an image of an apple similar to another image used to train the ANN, but that is slightly blurred. Then you pass it to the ANN, which unexpectedly predicts the object to be an orange.
Several machine learning and optimization methods have been used to detect the boundary behaviour of machine learning models, that is, the unexpected behaviour of the model that produces different outcomes given two slightly different inputs (but that correspond to the same object). For example, evolutionary algorithms have been used to develop tests for self-driving cars. See, for example, Automatically testing self-driving cars with search-based procedural content generation (2019) by Alessio Gambi et al.

Even more extreme, I saw examples of changing just a few pixels in the original image made the network classify it as something completely different, while it still looked the same to a human. Imagine a photograph of a school bus with a resolution of 1280x720 given to a well trained network for object classification for example. Changing as little as let's say 10 pixels could cause the network to classify the bus as a zebra or water bottle or not at all. It's basically exploiting edge cases of weight combinations within the network to craft behavioral extremes.

Sort of related: https://ai.stackexchange.com/questions/92/how-is-it-possible-that-deep-neural-networks-are-so-easily-fooled

@NumLock I used the example of a blurred image only to convey the idea that the images are in practice different. However, you're right: even a very small change can trick the model.

Sometimes if the rules used by an AI to identify characters are discovered, and if the rules used by a human being to identify the same characters are different, it is possible to design characters that are recognized by a human being but not recognized by an AI.  However, if the human being and AI both use the same rules, they will recognize the same characters equally well.
A student I advised once trained a neural network to recognize a set of numerals, then used a genetic algorithm to alter the shapes and connectivity of the numerals so that a human could still recognize them but the neural network could not.  Of course, if he had then re-trained the neural network using the expanded set of numerals, it probably would have been able to recognize the new ones.

Yes there are, for instance one pixel attacks described in

Su, J.; Vargas, D.V.; Kouichi, S. One pixel attack for fooling deep
  neural networks. arXiv:1710.08864

One pixels attacks are attacks in which changing one pixel in input image can strongly affect the results.

Appreciated the paper, the examples depicted are quite amusing. Graphic designers will go wild when their teapots become joysticks.

Here's an example:

How to hack your face to dodge the rise of facial recognition tech

In his recent book The Fall, Stephenson wrote about smartglasses that that project a pattern over the facial features to foil recognition algorithms (which seems not only feasible but likely;)
Here's an article from our sponsors, Adversarial AI: As New Attack Vector Opens, Researchers Aim to Defend Against It which includes this graphic of "Five ways AI hacks can lead to real world problems".
The article references the conference on The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, where you can download the full report.
I'm assuming many such examples exist in the real world, and will amend this link-based answer as I find them. Good question!

Isn't that essentially what chess does? For example, A human can recognize that a Ruy exchange offers white great winning chances (because of pawn structure) by move 4 while an engine would take several hours of brute force calculation to understand the same idea.

Not really. Humans know this because they've read about it. Chess engines also know it, because they usually have hardcoded opening tables.  Both the humans and the chess AIs would take a fair amount of time to deduce the quality of an opening from scratch.

There are many insightful comments and answers so far. I want to illustrate my idea of "color blindness test" more. Maybe it's a hint to lead us to the truth.
Imagine there are two people here. One is colorblind (AI) and another one is non-colorblind (human). If we show them a normal number "6", both of them can easily recognize it as number 6. Now, if we show them a delicately designed colorful number "6", only human can recognize it as number 6 while AI will recognize it as number 8. The interesting of this analogy is that we can not teach/train colorblind people to recognize this delicately designed colorful number "6" because of natural difference, which I believe is also the case between AI and human. AI gets results from computation while human gets results from "mind". Therefore, like @S. McGrew's answer, if we can find the fundamental difference between AI and human of how we read things, then this question is answered.

Don't forget that the AI gets results from analysis of the data, so if the data set is corrupt, the output will be weak.  Because we're talking about statistical AI, it's not about a single 6, but about a the greatest set of 6's we can find (sixes complement?;)  So I wanted to attack a text recognition algorithm, I'd spam it with bad data.  The counter there would be a mechanism to vet the data, and exclude the spam.

In otherwords, I'd train a botswarm to answer Captcha with the least likely choices, or the choices that are the most likely mistakes.  Given sufficient junk inputs, I could wreck the algorithm's output.  (This gets scary when you consider such techniques are surely already being used in regard to financial trading algorithms.)

For instance, if I knew a trading algorithm incorporated frequency of trades for a given asset, I might make a huge number of tiny trades to bias that metric and influence the overall analysis in a misleading way.  But, end of the day, Machine Learning is rooted in statistical analysis.

So even where the hack is to modify an input (such as a weird 6) to not be recognizable to algorithms or to specific algorithm, it's a function of understanding the result of the target algorithm's analysis of a data set, to choose output an algorithm trained on that data set wouldn't recognize.

Here's a live demo: https://www.labsix.org/physical-objects-that-fool-neural-nets/
Recall that neural nets are trained by feeding in the training data, evaluating the net, and using the error between the observed and the intended output to adjust the weights and bring the observed output closer to the intended. Most attacks have been on the observation that you can, instead of updating the weights, update the input neurons. That is, permute the image. However, this attack is very finnicky. It falls apart when the permuted image is scaled, rotated, blurred, or otherwise altered. That's clearly a cat to us, but guacamole to the neural net. But a slight rotation and the net starts classifying it correctly again.
However recent breakthroughs allow actual objects presented to a real camera to be reliably misclassified. That's clearly a turtle, albeit with a wonky pattern on its shell. But that net is convinced it's a rifle from practically every angle.

There are some research at least on the "foolability" of neural networks, that gives insight on potential high risk of neural nets even when they "seem" 99.99% acurate.
A very good paper on this is in Nature: https://www.nature.com/articles/d41586-019-03013-5
In a nutshell:
It shows diverse exemples of fooling neural networks/AIs, for exemple one where a few bits of scotch tape places on a "Stop" sign changes it, for the neural net, into a "limited to 40" sign... (whereas a human would still see a "Stop" sign!).
And also 2 striking exemples of turning an animal into another by just adding invisible (for humans!) colored dots, (turning in the exemple a Panda into a Gibbon, where a human hardly see anything different so still sees a Panda).
Then they elaborate on diverse research venues, involving for exemple ways to try to prevent such attacks.
The whole page is a good read to any AI researcher and shows lots of troubling problems (especially for automated systems such as cars, and soon maybe armaments).

An exerpt relevant to the question:
Hendrycks and his colleagues have suggested quantifying a DNN’s robustness against making errors by testing how it performs against a large range of adversarial examples. However, training a network to withstand one kind of attack could weaken it against others, they say. And researchers led by Pushmeet Kohli at Google DeepMind in London are trying to inoculate DNNs against making mistakes. Many adversarial attacks work by making tiny tweaks to the component parts of an input — such as subtly altering the colour of pixels in an image — until this tips a DNN over into a misclassification. Kohli’s team has suggested that a robust DNN should not change its output as a result of small changes in its input, and that this property might be mathematically incorporated into the network, constraining how it learns.
For the moment, however, no one has a fix on the overall problem of brittle AIs. The root of the issue, says Bengio, is that DNNs don’t have a good model of how to pick out what matters. When an AI sees a doctored image of a lion as a library, a person still sees a lion because they have a mental model of the animal that rests on a set of high-level features — ears, a tail, a mane and so on — that lets them abstract away from low-level arbitrary or incidental details. “We know from prior experience which features are the salient ones,” says Bengio. “And that comes from a deep understanding of the structure of the world.”

Another excerpt, near the end:
"Researchers in the field say they are making progress in fixing deep learning’s flaws, but acknowledge that they’re still groping for new techniques to make the process less brittle. There is not much theory behind deep learning, says Song. “If something doesn’t work, it’s difficult to figure out why,” she says. “The whole field is still very empirical. You just have to try things.”"


text:
Emotional Speech Synthesis
We are a team of computer science our graduation project about EmotionalSpeech Synthesis.
We've found valuable information like research papers and WaveNet, Tacotron.
A website (https://www.voicery.com/)
we were hoping to get to know more information from you.
We need more details what should we start with to grasp the fundamentals to build this idea, what is the architecture to be used in this project, whether there are papers, a GitHub Repository containing helpful documentation, datasets, some other resources, previous knowledge.

Since I am working with audio synthesis projects, I can suggest you to go through the recently released paper "MelNet: A Generative Model for Audio in the Frequency Domain", in this architecture human like speech is synthesized, I think this might be better than WaveNet where the generated audio does contain robotic nature, I think this will definitely help you with Emotional speech synthesis as it generates human-like voices.
You can check for online demos as well.
Demo
Here Is the Paper!
Related YouTube Video
Hope this helps!

Thanks for helping the demo is really great, If you please send the paper again as the link doesn't work.

Hi @AndrewMaher Glad that it helped you,I have edited the link. If this or any answer has solved your question please consider accepting it by clicking the check-mark so that others will be benefited.

Can you suggest good datasets to start with


text:
What beginner-friendly machine learning method should I use to make teams for my pickup ultimate frisbee club fairly balanced?
A bunch of friends and I play ultimate every week. Recently I wrote a program to choose our teams for us, as well as keep track of certain data (like which players were on which team, which team won, what was the score, how long the game was, etc). I wanted to use a machine learning technique to make the teams for us in order to optimize how fairly balanced the teams are (possibly measured by how many total points are scored in a game or how long a game lasts).
I am currently taking a machine learning MOOC and being introduced to very basic machine learning techniques (linear regression with gradient decent or normal equations, basic classification stuff, stuff like that). Although I hope I will come across a technique that fits my needs by the end of this course, I wanted to ask here to see if I can get a head start.
I've tried searching around everywhere, but couldn't find anything relevant. So my question is, is there an obvious technique I should look into for such a problem? If it's something too advanced for a beginner, that's fine too, but I'd like to get started learning/practicing it asap instead of waiting for my course to hopefully hit upon it.
Thank you!
EDIT: to clarify further, I would prefer something that looks at relationships between individual players like "when Steph plays with Bill she is more likely to win" or "Steph plays worse when she is on a team with players who have a high win percentage". I'd also prefer to be able to code it in python, but am willing to learn any other language

What you have on your hands is an attribution problem (and that’s a good keyword to help in your Googling). Two common approaches are computing Shapley values or a Markov chain.
In your case, I think Shapley values would be a good approach. To over-simplify, this approach attempts to first determine the total “surplus” of value created by different combinations of players, and then estimate how much was due to each player in the combination. Also under consideration are constrained combinatorial effects: e.g. having two great goalies doesn’t add much over one great goalie since they can’t play at the same time.
Depending on how hands-on you want to be you could implement yourself or find existing libraries by Googling e.g. “Shapley values python”.


text:
Digital Rights and Agents talking to humans
How does the legal question about agents talking to humans via telephone connection work? Recently Google gave a talk about Duplex, where an agent makes a call to a human to schedule a hairdresser.
I wonder if there are any regulations related to this type of scenario, if there are some limitations, if the human needs to know that he is talking to an AI.

Googling this throws a lot of debate on this issue of whether the call made to the restaurant for booking was legal or not. I found this article to put forward a lot of ideas for and against this. So it is for us to decide.
But one thing that I fully agree with you, which should be made into a regulation, is human should know that he/she is talking to a bot. This tweet by @traviskorte reasons it out why:
We should make AI sound different from humans for the same reason we put a smelly additive in normally odorless natural gas.


text:
Defining rules for an expert system
I'm doing a project for my last university examination but I'm having some troubles! I'm making an expert system who should be able to assemble a computer after asking some questions to the user. It works but according to my teacher I need to define more rules, could you give me some suggestions please? I have facts like these:
processor(P, Proc_price, Price_range),
motherboard(M, Motherboard_price, Price_range),
ram(R, Ram_price, Price_range),
case(C, Case_price, Price_range),
ali(A, Ali_price, Price_range),
video_card(V, Vga_price, Price_range),
ssd(S, Ssd_price, Price_range),
monitor(D, Monitor_price, Price_range),
hdd(H, Hdd_price, Price_range).

I ask these questions to the user: 1) choose the price range 2) choose the display size 3) choose hard disk size Then I ask 3 questions about computer utilization to define the user: 1) do you surf on internet? 2) do you play? 3) do you use editing programs?
use(gaming) :- ask("Do you play games? (y/n)").

    use(editing) :- ask("Do you use editing programs? (y/n)").

    use(surfing) :- ask("Do you surf internet?(y/n)").

    user(base) :-
        use(surfing),  \+ use(gaming), \+ use(editing).

    user(gamer) :-
        use(gaming), use(surfing), \+ use(editing).

    user(professional) :-
        use(editing), \+ use(gaming), use(surfing).

I should make more questions about user definition to make user definition more complex too and add some rules. Please help me, I'm desperate

The three questions "1) do you surf on internet? 2) do you play? 3) do you use editing programs?" are a good start, but I think your teacher is right that you need more granularity.
1) What do you use your computer for?
(a) Watching videos [leads to: "Streaming or Downloaded HD?" b/c downloading requires more local volume, and potentially a better video card.]
(b) Do you play Games [leads to: "High-end video games or simple games?" b/c playing AAA FPS requires much more powerful video cards.  If they play words with friends or Tetris, a low-end card will be sufficient.]
(c) Do you use editing programs? [leads to: "photo editing?  video editing? what size files?" b/c editing HD vid and high resolution photos is onerous with an underpowered system.]
You might want to ask in general what they use the computer for, because if it's just email, web surfing, Facebook and Youtube, etc., they can probably get by with a Windows surface (I'm a regular critic of MS, but my understanding is you can get the fully functional Office Suite on Surface, which has utility value.)
--------------------------
I think you're on the right track, but you should step back and think like a Product Manager here, as opposed to a developer.  These questions might help you clarify your intent, and expand your initial template to a more fully featured system your teacher is looking for:

Who is the customer for my system?
What level of technical knowledge will the average user have?
Am I covering every aspect with the proper amount of detail?
Is the order of my questions correct?  (Why ask price range before monitor size?)
Is there any mechanism to help a user if they don't know what answer to choose?

Again, these are just some initial thoughts. Only you know what product you want to create, and what the capabilities should be.

We cannot do homework for students in this network, however I can suggest that several items affecting cost and several usage patterns are missing and the number of rules is shy by an order of magnitude.  I wholeheartedly agree with the educational directives you received.
Consider first developing your lists further to include peripherals like DVD burner, USB devices, and audio.  Whether the user does scientific programming, watches movies on the monitor, develops software, and other specific usage scenarios is also more specific and therefore will produce a better tailored system than the answer to the question of whether the user is a professional.
It is not the metric of the number of rules that is of most importance.  It is the number of operations contained in the rule set that is the guiding metric.  This is because rules in Prolog can be aggregated.  The rough estimate of rule operator count to complete a system is sqrt(i*o)/4, where i is the number of input permutations and o is the number of output permutations.
(This is the application of Shannon Information Theory, that number of bits n = log2 (P'/P), where P' and P are the a posteriorii and a priori probabilities respectively.  The divisor of four is because there are about 16 = 24 operators normally used.)
You may end up with thirty or forty rules.
Create some use cases that exercise the extremes as well as some of the typical cases from among the permutations in inputs and outputs.  Run your system on those cases and observe the system behavior.  Learn how to debug by outputting intermediate results or stepping through rule execution.
There are no shortcuts to researching and developing other than not wasting time worrying about how much time it will take.  You can also optimize your homework time by learning the tools and then stepping back, taking a deep breath, and saying, "I can do this!"

Although my answer may have been more in line with what the OP was looking for--pure luck, imo--this answer deserves recognition (I certainly upvoted) for the technical points you make re Prolog and Shannon.  Thanks for contributing!

@DukeZhou I've panicked when not sure of myself with regard to academic assignments at the university. In this specific case, I read between the lines, supported what the professor was doing (which seemed to be in the best interest of the student), and provided a wider context to understand what appeared to be the assignment. I am interested in what you mean when you say the author of the question was looking for an answer involving pure luck. Can you expound?

Just that I took a shot in the dark that the list of questions itself needed to be supplemented, without thinking much about the underlying mechanics.  (It seems to me "level of use" is the primary question these days re: computer purchase--with the rise of the tablet, only PC gamers and techs really need PCs anymore, outside of an office setting.  That said, my answer doesn't involve AI!)


text:
Is there any opensource 2d open-world simulation with python API?
For my pet project I’m looking for a grid-like world simulation with some kind of resources that requires from agent incrementally intelligent behaviour to survive.
Something like this steam game, but with API.  I’ve seen minecraft fork, but it’s too complex for my task. There is pycolab, i can build some world on this engine, but I’d prefer ready-to-use simulations.
Is there any option? I'll appreciate any suggestion.

You could try Mesa.
It has various examples that are commonly-used in agent-based modelling, like Epstein's model, a wolf/sheep predator/prey model, and many more.
There is also an introductory tutorial.


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Can I recognize the faces of people around the world?
I created a system where every moment takes photos of the face of who is in the vision of the camera. Initially I took 500 photos of me, to recognize its creator. This takes approximately 20 seconds.
Then every moment he recognizes faces and if it is me, he knows that his creator is present. Any different face, it creates a dataset with a different name and starts taking up to 500 photos to also recognize these faces.
When I say a certain command, it returns all faces I have found that have no ID.
I'm looking for ways to capture the images through some camera that I can carry while walking on the street and in public places.
The problem is that there would be several faces to name. I'm partially solving this problem by trying to recognize these people in social networks. I check the region where the photo was taken and try to find people who have checked in or liked the area on Facebook. But anyway, this is not the big problem, although it is looking for more effective solutions.

My big problem is: Can I do this? Do I have this right? Can I record a
  robber robbery and recognize his face in other places? Record an
  aggression, an act of prejudice and things like that?

The main purpose would be this, but could also be used for other purposes. My fear is being arrested for doing this. Both because he would be taking pictures of people without his consent.
ps: I'm thinking of having a camera in the palm of my hand. It would be a micro camera (I'm trying to find the product on the Internet), to be as discreet as possible.

The right to take pictures of person without his/her consent would be violation of right to privacy. You should check the local laws of every region before doing such a thing.

I thought it might be a helpful place to start:
https://en.wikipedia.org/wiki/Google_Street_View_privacy_concerns
The wiki is well cited, so should lead to some useful tidbits.  They break it down by continent and country.
Seems like a parallel to what you're doing, although, if you're not making your data public, I doubt you'll be facing the same privacy issues.
Also relevant would be: https://en.wikipedia.org/wiki/Expectation_of_privacy

Examples of places where a person has a reasonable expectation of privacy are a person's residence or hotel room and public places which have been specifically provided by businesses or the public sector in order to ensure privacy, such as public restrooms...
In general, one cannot have a reasonable expectation of privacy in things held out to the public.

My understanding is that there is no expectation of privacy in public places where you'd be capturing the images.

Thank you very much, this material will help me. Anyway, I'm going to consult a lawyer to be well-guarded.

@GuilhermeIA   Consulting with an attorney is definitely a good idea--most attorneys will do the initial consult for free. I'm facing different potential issues with my own venture, but early legal consults were critical in forming the long-term strategy and business plan.


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Clarification regarding "Image Crowd Counting Using Convolutional Neural Network and Markov Random Field"
I am currently reading the research paper Image Crowd Counting Using Convolutional Neural Network and Markov Random Field  by Kang Han, Wanggen Wan, Haiyan Yao, and Li Hou.
I did not understand the following context properly:

We employ the residual network, which is trained on ImageNet dataset for image classication task, to extract the deep features to represent the density of the crowd. This pre-trained CNN network created a residual item for every three convolution layer to bring the layer of the network to 152. We resize the image patches to the size of 224 × 224 as the input of the model and extract the output of the fc1000 layer to get the 1000 dimensional features. The features are then used to train 5 layers fully connected neural network. The network's input is 1000dimensional, and the number of neurons in the network is given by 100-100-50-50-1. The network's output is the local crowd count

Can anyone explain the above part in detail?

I will try to do it part by part :

We employ the residual network, which is trained on ImageNet dataset for image classication task, to extract the deep features to represent the density of the crowd.

If you look at the figure 2, you can see that they use the Neural network architecture ResNet. This is a deep network, here is the paper. It has good performance and do image classification.

This pre-trained CNN network created a residual item for every three convolution layer to bring the layer of the network to 152

If you are in the layer k, it means this layer has in input, the ouput of the k-3th layer. See the paper, figure 5 explains it well without much explications needed. Furthermore, Resnet has 3 different architectures wih different number of layers, and they take the deeper one, the 152 layers deep Resnet.

We resize the image patches to the size of 224 × 224 as the input of the model and extract the output of the fc1000 layer to get the 1000 dimensional features

The input of Resnet is images of size 224x224, so they need to resize them to fit the input requirement of Resnet. The output of Resnet is 1000 because Imagenet is a dataset of 1000 classes.

The features are then used to train 5 layers fully connected neural network. The network's input is 1000dimensional, and the number of neurons in the network is given by 100-100-50-50-1.

Then they give the output of Resnet to their own Network, which is 5 layer deep. See figure 2 of their paper. Obviously, the input layer has 1000 inputs because of the output of Resnet. The network have layers of 100, 100, 50 and finally 1 neuron. See figure 2.

The network's output is the local crowd count

I don't think I need to explain it, they want only the number of people in the crowd, so they need only one output. This is obviously not a classifcation problem, but a regression problem.
Has you don't really point out what you don't understand, I don't explain it in details. Feel free to ask more precise question if some part are still blurred to you !

Thank you very much. Actually, I also  wanted an explanation of how exactly they are using MRF for Image Counting. To be specific, I wanted to know how exactly can I implement the method described in section 3.2, Images Counting.

@Ronith Then, maybe edit your question or ask another for this part of the paper

Okay, I'll do that in a minute.

Check out this question: https://ai.stackexchange.com/questions/7952/doubt-regarding-research-paper. Hoping to see an answer soon.


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How does a robot protect its own existence
What are the many ways that artificial intelligence robots protect their existence?
Isaac Asimov's "Three Laws of Robotics"
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Right now, the scenarios where automata protect their own existence is limited.
In the case of autonomous vehicles, primary goals would certainly be collision avoidance, and other hazards (deep water, cliff faces, etc.)
It's possible that arial drones could have certain automated defense mechanisms specific to airborne threats such a missiles.
Bots might be said to protect their existence by copying themselves onto new systems.


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Viola Jones Algorithm
Can Viola Jones algorithm be used to detect the facial emotion. Actually it was used in creating harr-cascade file for object and facial detection, but what confused me is whether it can be used to train for emotion detection.
If not, what algorithms can I use? and what are the mathematical bases? (i.e. what mathematics should I be studying?)

Welcome to AI!  Very interesting question that I'd also be interested in knowing the answer to.  (My guess would be that there is some overlap, but I know next to nothing about this subject!)

An introduction to the Haar features is provided in the youTube video.  The video indicates the VJ face detector leverages a selected combination of Haar features (convolutional kernels) to detect facial features (weak classifiers), such as the nose bridge.  The binary presence of the weak classifiers are summed to determine if the window contains a face.
The ability for a VJ algorithm to detect emotion would rely on the ability to assign a set of Haar features (kernels) to recognize features associated with a particular emotion label (surprise, anger, content, fear).
It is conceivable that the initial stage of an  emotion classifier could use a the VJ algorithm to identify a face for additional stages to classify emotion.

I have once tried Viola Jones Algorithm to do that, it does not capture the subtle differences in the direction of facial segments which are important to detect emotion. Features like HOG (Available in openCV and many famous image processing libraries) can extract better information from the face to classify emotion.
Also there are many other approaches including ANNs and pure rule based approaches But almost everywhere a good alignment approach for faces become the most important aspect of the exercise. So I will suggest exploring some facial alignment approaches and then Features like HOG instead of Viola Jones/ HARR.
For the mathematics part, it is upto you to dive deep into mathematics or just exploring different approaches by codes. A good understanding of Linear Algebra and a little Geometry will help a lot.
Also if you are new to Machine Learning, understanding the basic algorithms might be relevant to you.

I'm trying to feed the values that i got from the HOG descriptor to the keras Deep learning model, but the problem arises when feeding the feature to that model, as feature can take any random size from images to images. Can i perform SVM on top. Please refer to any articles, blog post.

No you can not model with any algorithm with changing feature size. Each face will differ in many aspects. I would suggest that you first get some Canonical and normalised face before you extract any features from it. A pre decided size of the crop and location of the eye works in many cases.

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What to do with duplicate Innovations in a genome?
I´m currently implementing NEAT.
What should I do when in a mutation the same Innovation occurs which has already happened to that genome?
Should I simply ignore it?
If not what do I do with it in the mating part?

What do you mean exactly? The NEAT algorithm doesn't make it possible that the same innovation occurs twice in the same genome.

@ThomasW where exactly does he says this. I can´t remember reading this.

Because this is more of an answer than a comment:

In the add connection
  mutation, a single new connection gene with a random weight is added connecting
  two previously unconnected nodes.

So two nodes that have been mutated with a connection, can't remutate because the nodes should be unconnected.

In the add node mutation, an existing connection is
  split and the new node placed where the old connection used to be. The old connection
  is disabled and two new connections are added to the genome.

The old connection is disabled, so the same mutation can't occur again on this connection. In practice, you can just remove the disabled connection, because it will be formed again with the add connection mutation.

Okay. I must have over read the previously unconnected part. Thanks

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How to implement an Automatic Learning Rate for a Neural Network?
I'm learning Neural Networks, and everything works as planned but, like humans do, adjusting themselves to learn more efficiently, I'm trying to understand conceptually how one might implement an auto adjusting learning rate for a Neural Network.
I have tried to make it based on error, something like how bigger is error learning rate is getting bigger as well. [Could use some clarification here--not entirely sure what you're saying.  If can clarify, I'm happy to clean up the English. -DukeZhou]
*If you want give me an example give it on a C based language or math because I don't have experience with Python or Pascal.

Google "adaptive learning rate"

http://cs231n.github.io/neural-networks-3/#ada

thx, you can make this comment an answer to mark it as best answer?

@SmallChess very few people seem to be voting right now, but I'd definitely upvote a formal answer!

Adjusting for learning rate is a common scenario in machine learning. There is rich literature about it, countless papers. The most common implementation:

AdaGrad
RMSProp
Adam

There are many more variants. You'll need to do some research. Please take a look at:

https://en.wikipedia.org/wiki/Stochastic_gradient_descent

Apart from the already mentioned methods there is one which I came across recently is the Cyclic Learning Rates.
Mainstream methods usually reduce the learning rates monotonically. In most of the cases they work well. Yet they do not do well with handling saddle points and local minima. CLR method claims to solve the problem by effectively handling the case of saddle points.
Ref: https://arxiv.org/abs/1506.01186

While adjusting the learning rate during the training is certainly interesting, so is finding of a good initial learning rate, too.
Cyclical Learning Rates for Training Neural Networks, fast.ai (pytorch) implementation.
Here's a good practitioner's overview of learning rate schedules (python, but rather readable and with nice plots).


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How can I use Neural Network in motion identification
I'm quite new to image processing and AI. But I have the expertise to create a network that can be used in object detection and recognition. Most of the time I've used ANN or Naive Bayes.
Now, I want to develop a method of action recognition, something like identifying whether one is jogging, running or walking by applying ANN. However, I really don't have idea how the sequence of frames can be classified.
In static image, segmentation and feature extraction is easy. But in regard to a moving image, I'm unsure of the approach.
Thanks in advance!

Depending on the complexity of the problem you could go with the full blown approach of LSTMs (like dant suggested) or you could try to address the challenge using pre-processing and stick with your already established CNN architecture.
If you want to use pre-processing, you basically calculate delta images between frames and use them like "normal" frames in your CNN. The DQN of Deepmind used this approach successfully to understand motion when playing ATARI games. Those images were rather simple and the amount of motion limited especially compared with videos of real life situations, so this simple approach may not be sufficient for your problem, but I consider it worth trying.

LSTM's provide a simple way to process sequential data (assuming all video sequences have the same number of frames and resolution).


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How is creativity generated in a currently rule based neural network?
I  recently became interested in how creativity is generated in NN. My understanding of NNs is that the output always known, given that it they are trained with target values, but how does one train a network to be creative, I mean in such cases would the task be to create something novel, but the actual target would not be known.  How does creativity express itself in a rule based system, such as NN?
One would need to redefine the cost-function, but how does one imply creativity in a rule based system?

Great subject.  For further research, I'd recommend looking at the computational creativity wiki and the cited papers and publications.  You may also want to look into how monte-carlo tree search in Machine Learning algorithms.

Your question is a bit broad. Some examples of creativity and how it's achieved (in all of them there is an ordinary convolutional network trained via supervised learning, but applied differently):

Google Deep Dream uses backpropagation to the image to tweak the image towards maximum activation in the convolutional layer. As a result, it finds lots of "things" in the image and enhances them:

Generative Adversarial Networks use two competing networks (generator and discriminator) to generate samples from the training manifold:

Neuro style method captures the style of one image via Gram matrix of convolutional layer activations and trains (another) neural network with a special loss function that minimizes the joint distance to the incoming image (content) and to the Gram matrix (style). The result:

Thanks for the reply.. And for giving this overall reply.. It surely helps with the understanding, or what too google for too for my further investigations.


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OpenAI Gym: How is "experience" stored?
I have been playing around with the Pong-v0 game. After few days, the AI was able to beat it. I stopped the script for a moment and ran it again. For my surprise it seems it started all over again.
Where and how does OpenAI store the experience?
How can I continue the learning procedure in another computer?

I think what I'm looking is to learn about tensorflow' saver function...

Related: https://discuss.openai.com/t/how-do-i-save-and-load-a-trained-agent/570

@Bobs I think we have a winner

@Bobs Perception!! :)

This is sort of a tricky question to answer, by results you could mean 2 possible things. History, which is a culmination of actions, observations and rewards. Or the value func/policy which is the thing you are trying to improve.
With that being said, you for most instances of RL will only be dealing with the last few events in the history, or sampling uniform randomly from it according to your route of exploration. So I don't think this is what you are talking about.
As far as storing your value func/policy OpenAI offers a way to post and to a certain extent store your result. This Readme will show you how to do so.
Edit: OpenAI does not offer anything to store experience out of the box, as I noted in my comment you could pretty easily whip up some code that writes JSON to some file and saving that would be a store of experience. Although, it looks like you already found something that does this in tensorflow.
OpenAI likely hasn't included it up to this point because they don't put a ton into this framework. I would encourage you check out SerpentAI on Github, as it has a lot of features missing in OpenAI.
Hope I answered your question!

By results I mean the "experience" or "progress" during learning. I followed the link you posted but I don't see where it explain how to store that experience. I replaced "results" for "experience", I hope its clear now.

Ah ok, I do not believe OpenAI offers anything to do this out of the box. You could pretty easily whip up code to write JSON and save the file which would have as much data as you told it to save.

Thank you. Do you know why it is not included? Can you edit your answer, so it address the question (as you did with your comment), so I can accept it? (otherwise, it will not be clear why I accepted it).

It has been done:)


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Which technologies should humanity develop before developing an AI with general superhuman intelligence?
The sequence in which new technologies are developed can be crucial for it's success and it's safe use. For example, before developing a nuclear reactor you want to have technologies like reliable cooling systems and secure ultimate disposal zones available and tested. Otherwise we might have a bad time.
I see AI with general superhuman intelligence as important predecessor for dangerous technologies like nanobots or generators using nuclear fusion, because superhuman intelligence can be helpful in foreseeing dangers that might lead to catastrophe using these technologies in an insecure way.
But what about important technologies we want to have before inventing an AI with general superhuman intelligence? Solving the control-problem is obvious, but are there other technologies we should have in advance?

I think most answers that come to mind are going to be connected to the control problem in some way---e.g. sandboxing frameworks, AI behavior monitoring protocols, etc.
However, if you aren't interested in those for now, that pretty much leaves two possible needs such technology could satisfy: the technical challenges of AI itself, and the need to optimize the social impact on AI.
The technologies satisfying the first need are relatively "obvious" once you have the AI implementation figured out---depending on what that is, there will be certain hardware and software requirements (massively parallel processing, neural network frameworks, etc). Most of these technologies seem likely to arise "naturally" from the status quo---if we just keep developing our hardware and software as we are now, we will eventually get there.
Coming up with technologies satisfying the second need is a challenge, and far more interesting in my opinion. We need to start a discussion about this, so you have a great question here. I don't know that I can contribute much, but maybe I can provide some starter ideas:

More robust encryption schemes, so that the massive amount of information handled by AI is secure even in the face of AI-capable adversaries. We'd want these encryption schemes to be information-theoretic secure, so that it is mathematically impossible to recover the plaintext, rather than just computationally difficult, since an AI may be able to improve it's own computational efficiency with unprecedented ease. Quantum cryptography may help.
Improved robotics, which would require improved battery technology, to multiply by a great factor the ability of AI to manipulate the physical world and assist with physical tasks (obviously requires a fantastic solution to the control problem).
If you consider it separate from the control problem (and I think there is a useful distinction here), lots of AI-monitoring technology will be necessary. Think of technologies acting as regulators for newly automated tasks---e.g. traffic monitoring algorithms to watch for undesirable behavior among automated vehicles.

Answering this question is very tough, particularly because you are asking for technologies which we'd have to develop before we have AI. Of course, once we have general AI we can instruct it to develop any of these technologies for us, supposing the control problem is adequately solved. Nevertheless it seems wise to solve develop improved cryptography immediately since even with weak AI (or a weak control problem solution) the need is urgent.

Great answer, thx, upvoted! I will leave the question open a little longer, in case your answer inspired others as well. The best answer after the weekend will be accepted.