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15 AI related accounts you should follow on Twitter: 1. @karpathy His tweets already create LLMs narratives that you later see on linkedin in 2 months. 2. @fchollet posts thoughtful research on intelligence, benchmarks, and AI limitations. Keras creator + ARC-AGI 3. @ylecun Yann LeCun is Deep learning pioneer & Meta Chief AI Scientist; big-picture research takes and critiques (and drama). 4. @AndrewYNg Andrew Ng is AI education legend; practical ML advice, courses, and real-world implementation. creator of deeplearning ai 5. @rasbt Sebastian Raschka posts on Practical ML/LLM implementations, "build from scratch" tutorials, and books. 6. @dair_ai Weekly ML/AI paper threads and accessible research explainers (high-signal for staying current). 7. @lilianweng Lilian Weng is ex-OpenAI, and her Lil'Log-style threads are good. has In-depth LLM research breakdowns 8. @jeremyphoward posts interesting takes on AI/crypto news, and works on democratizing practical deep learning and accessible education. 9. @simonw Simon posts Practical LLM tools, takes, experiments, prompting, and engineering breakdowns. django co-founder 10. @_akhaliq Curates the latest arXiv papers, model releases, and open-source AI drops. 11. @ID_AA_Carmack AGI/low-level optimization takes that makes you think about the problem. 12. @gwern Really high-quality long-form AI research notes and essays. 13. @goodside LLM evaluation, prompting research, and real capabilities testing 14. @drfeifei Computer vision pioneer; human-centered AI and spatial intelligence research 15. @demishassabis Been following his work for 9 years. Demmis is my hope against google usurpating their power with AI. Demmis is Google DeepMind's CEO Let me know who I missed, guys, and save it for the future

2026年学习AI的最佳YouTube频道： 1. AI Explained 👉 2. Andrej Karpathy 👉 3. Cole Medin 👉 4. DeepLearningAI 👉 5. Futurepedia 👉 6. Matthew Berman 👉 7. Skill Leap AI 👉 8. Tech With Tim 👉 9. Tina Huang 👉 10. Two Minute Papers 👉

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

The signature is alluding to NVIDIA GTC 2015, where Jensen excitedly told an audience of, at the time, mostly gamers and scientific computing professionals that Deep Learning is The Next Big Thing, citing among other examples my PhD thesis (one of the first image captioning systems that coupled image recognition ConvNet to an autoregressive RNN language model, trained end to end). This was back when most people were still unaware and somewhat skeptical but of course - Jensen was 1000% correct, highly prescient and locked in very early.

I still give the book Understanding Deep Learning by Simon J.D. Prince a good recommendation, but chapter 21: Deep learning and Ethics was sloppy. It could have been a chapter to really dig in on case studies, but it was just the basic public news story level coverage of bias and such, like: “In AI, it can be pernicious when this deviation depends on illegitimate factors that impact an output. For example, gender is irrelevant to job performance, so it is illegitimate to use gender as a basis for hiring a candidate. Similarly, race is irrelevant to criminality, so it is illegitimate to use race as a feature for recidivism prediction.” If they had stuck with “illegitimate”, then it would have been a question of societal choices, but “irrelevant” is a question about data, and your priors shouldn’t be so strong that data can’t move them. I would like to see a book or course walk through a machine learning problem with the input features being presented as something like car choices: color, style, doors, horsepower, etc. Do lots of analysis over representation, training, and generalization, then swap the feature labels to socially charged ones. What makes generalization credible in one situation but not the other?

If you're wondering whether saturating ARC-AGI-1 or 2 means we have AGI now... I refer you to what I said when we launched ARC-AGI-2 last year (which is also the same thing I said when we announced ARC-AGI-2 was coming, in Spring 2022, before the rise of LLM chatbots)... The ARC-AGI series is not an AGI threshold, it's a compass that points the research community toward the right questions. ARC-AGI-1 is a minimal test of fluid intelligence -- to pass it, you needed to show nonzero fluid intelligence. This required AI to move past the classic deep learning / LLM paradigm of pretraining scaling + static models at inference, toward test-time adaptation. ARC-AGI-2 is the same, but with tasks that probe deeper levels of reasoning complexity (particularly with regard to concept composition). Still, these are tasks that are solvable in minutes by regular people with no external tool use (we hired our test takers off the street), so it does not represent the upper bound of what human fluid intelligence can achieve (say, solving a Millennium problem). ARC-AGI-3 (launching March 2026) probes interactive reasoning: we evaluate how systems explore unknown environments, model them, set their own goals, and plan/execute towards these goals, autonomously, without instructions. We have also started work on ARC-AGI-4 and ARC-AGI-5, which I am pretty excited about!

The 3rd edition of my book Deep Learning with Python is being printed right now, and will be in bookstores within 2 weeks. You can order it now from Amazon or from Manning. This time, we're also releasing the whole thing as a 100% free website. I don't care if it reduces book sales, I think it's the best deep learning intro around, and more people should be able to read it.

There is an alternate reality where Cray took their vector supercomputers, ditched FP64 calculations, and went with one FP32 pipe and a BF16 tensor core pipe. The same instruction set, memory architecture, and vector registers would have made a sweet deep learning machine, in many ways nicer than SIMT CUDA programming on GPUs. A Y-MP class machine like that could have delivered the AlexNet and DQN moments two decades earlier. Even doing everything in FP64 with no architectural changes, a Cray-1 would have been the best machine in the world for neural networks. If @geoffreyhinton had access to one for early research, the case could have been made for the architectural modifications to 10x the performance.

The Keras team at Google is looking for part-time contractors (note: the offer is from *Google*, not me personally, and not Ndea). Globally distributed. The focus is on KerasHub model development. If you're passionate about deep learning, high-performance dev tools, and great dev UX design, consider applying! Send your resume and evidence of exceptional ability to divyasreepat@google.com. Things they're looking for: - Great Kaggle notebooks featuring Keras 3 - PRs on Keras or KerasHub repos - Top-notch open-source deep learning work Expertise in Keras 3 and JAX required.