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reacted to Kseniase's post with 👍 about 2 months ago
5 Lectures and keynotes defining AI right now If you want to understand the multifaceted AI landscape in 2025 and see where the field is heading – start with (or revisit) these legendary talks. They can help you capture what’s happening in AI from multiple angles: 1. Andrej Karpathy: Software Is Changing (Again) → https://www.youtube.com/watch?v=LCEmiRjPEtQ Unveils Software 3.0 – a paradigm where LLMs are the new computers, programmed with prompts instead of code. The key: developers must now master coding, training, and prompting as AI becomes the heart of software building 2. Richard Sutton, The OaK Architecture: A Vision of SuperIntelligence from Experience → https://www.youtube.com/watch?v=gEbbGyNkR2U Unveils the OaK (Options and Knowledge) architecture – a model-based RL framework for continual intelligence, where every component learns, meta-learns & builds hierarchical abstractions 3. GTC March 2025 Keynote with NVIDIA CEO Jensen Huang → https://www.youtube.com/watch?v=_waPvOwL9Z8 Dives into the accelerated computing and the importance of Physical AI. From the Blackwell GPU architecture & AI factories to breakthroughs in agentic AI & robotics, Jensen Huang explains how NVIDIA aims to power every layer of the AI ecosystem 4. Yann LeCun "Mathematical Obstacles on the Way to Human-Level AI" → https://www.youtube.com/watch?v=ETZfkkv6V7 Yann LeCun always argues we need a new path to machines that reason about the world – not LLMs or RL. So this lecture is about self-supervised systems with world models, planning, memory and energy-based learning 5. Andrew Ng: State of AI Agents → https://www.youtube.com/watch?v=4pYzYmSdSH4 Highlights one of the most pressing topics of 2025 – agents, explaining why most effective AI agents rely on simple, linear workflows built from modular “Lego-brick” tasks + what predicts AI startup success in the new agent era Subscribe to the Turing Post: https://www.turingpost.com/subscribe –your shortcut to deep, clear AI analysis
reacted to Kseniase's post with ❤️ 5 months ago
6 Must-read books about AI and Machine Learning: Sharing some free, useful resources for you. In this collection, we’ve gathered the most recent books to give you up-to-date information on key fundamental topics. Hope this helps you master AI and machine learning: 1. Machine Learning Systems by Vijay Janapa Reddi → https://www.mlsysbook.ai/ Provides a framework for building effective ML solutions, covering data engineering, optimization, hardware-aware training, inference acceleration, architecture choice, and other key principles 2. Generative Diffusion Modeling: A Practical Handbook by Zihan Ding, Chi Jin → https://arxiv.org/abs/2412.17162 Offers a unified view of diffusion models: probabilistic, score-based, consistency, rectified flow, pre/post-training. It aligns notations with code to close the “paper-to-code” gap. 3. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges → https://arxiv.org/abs/2104.13478 Explores unified geometric principles to analyze neural networks' architectures: CNNs, RNNs, GNNs, Transformers, and guide the design of the future ones 4. Mathematical Foundations of Geometric Deep Learning by Haitz Saez de Ocariz Borde and Michael Bronstein → https://arxiv.org/abs/2508.02723 Dives into the the key math concepts behind geometric Deep Learning: geometric and analytical structures, vector calculus, differential geometry, etc. 5. Interpretable Machine Learning by Christoph Molnar → https://github.com/christophM/interpretable-ml-book Practical guide to simple, transparent models (e.g., decision trees) and model-agnostic methods like LIME, Shapley values, permutation importance, and accumulated local effects. 6. Understanding Deep Learning by Simon J.D. Prince → https://udlbook.github.io/udlbook/ Explores core deep learning concenpts: models, training, evaluation, RL, architectures for images, text, and graphs, addressing open theoretical questions Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
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