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2026 年学习 AI 最值得关注的 YouTube 频道，拒绝废话版。 收藏起来，按这个顺序学： 1. 3Blue1Brown AI / 数学基础。用可视化方式讲清楚线性代数、神经网络和底层数学直觉。 2. Andrej Karpathy 深度学习 / LLM。前 OpenAI、Tesla AI 核心人物，讲课硬核但非常清晰。 3. Yannic Kilcher AI 研究。适合跟进论文、模型架构和前沿研究动态。 3. AssemblyAI 实用 AI。大量语音识别、LLM、AI 工程化和 API 实战内容。 4. AI Explained LLM / AI 趋势。适合理解大模型能力边界、行业变化和最新进展。 5. StatQuest 机器学习理论。把统计学、机器学习算法讲得非常通俗。 6. Two Minute Papers 论文简明讲解。用短视频快速了解 AI、图形学和科研新成果。 7. Matthew Berman 生成式 AI。关注 AI 工具、开源模型、LLM 应用和最新产品。 8. Nicholas Renotte AI Agents / 实战项目。适合想动手做项目、Agent、自动化和计算机视觉的人。 9. Krish Naik 应用机器学习。数据科学、机器学习、MLOps 和实战项目内容很多。 10. Aladdin Persson PyTorch / 深度学习代码。适合系统学习 PyTorch、CNN、GAN、Transformer 等实现。 11. Serrano Academy 机器学习数学。适合补机器学习背后的数学、概率和算法直觉。 12. Lex Fridman 行业洞察。通过长访谈理解 AI、科技、创业、机器人和社会影响。 13. DeepLearningAI 真实世界 AI。Andrew Ng 团队出品，适合系统学习 AI 工程和应用落地。 我的建议： 新手先看 3Blue1Brown + StatQuest + DeepLearningAI。 想做工程项目，看 Karpathy + Nicholas Renotte + Krish Naik。 想跟前沿趋势，看 Yannic Kilcher + AI Explained + Matthew Berman。 别只收藏。 真正的学习路径是： 看一集，记一页笔记，复现一个小项目。 你也可以把最后一句改成更有传播感的一版： AI 学习最怕的不是资源不够，而是收藏太多、动手太少。

公司领导 + AI = ？ 有这么一些公司领导，他们在一些领域并不专业，甚至不懂，但就是心里觉得这个事情很简单，做不好是人的问题！ 这种领导，加上 AI，都不用 Claude Fable 5 或 GPT-5.5，豆包就行，他们在你提出方案时，会直接用 AI 去查，然后跟你说: 我问了 AI，很简单啊，根本没有你说的那么复杂，然后你就陷入了自证的阶段 😂 不是说不懂的公司领导不能用 AI，关键是，您把上下文输对啊，含含糊糊的问一句，而且带着预设倾向，AI 回复的是最接近简单场景的做法，并不解决实际复杂场景。 比如，为什么有了 OpenCV，还需要 CV 领域的 Deeplearning？是的，就是这么明显的问题，在前司甚至很多公司都在反复出现。。。

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 👉

Jensen Huang: "Intelligence is not top of my list of things I value about my abilities." Someone asks: what's the real skill that separates great builders from everyone else? Huang responds: "I hope you believe in something, something unconventional, something unexplored, but let it be informed and let it be reasoned. Then dedicate yourself to making it happen." He offers a test: "Of all of the things that I value most about my abilities, intelligence is not top of that list. My ability to endure pain and suffering, my ability to work on something for a very, very long period of time, my ability to handle setbacks and see the opportunity just around the corner ... I consider to be my superpowers." On why he doesn't wait for perfect markets: "We chose a market with no customers, a $0 billion market, and it was robotics. We built the world's first robotics computer processing an algorithm nobody understood at the time called deep learning. Ten years later, I can't be happier with what we've built." He explains why timing rarely works the way people expect: "One setback after another, we shook it off and skated to the next opportunity. Each time, we gained skills and strengthened our character. Our company is really hard to distract and really hard to discourage." Huang points to a lone gardener at the Silver Temple in Kyoto, carefully picking dead moss with a bamboo tweezer from a garden the size of a courtyard: "He said, 'I have cared for my garden for 25 years. I have plenty of time.' That was one of the most profound learnings in my life." He shares a thought experiment: "I begin each morning by doing my highest priority work first. Before I even get to work, my day is already a success. I've already completed my most important work and can dedicate my day to helping others. When people apologize for interrupting me, I always say I have plenty of time... and I do." On getting lucky with breakthroughs: "We did well in 2012 because Hinton, Krizhevsky, and Sutskever used our GPUs to win ImageNet. We did well because we believed in deep learning before anyone else did. If you build it, will they come? Our logic was: if we don't build it, they can't come." Then he delivers the point: "I hope you find a craft you want to dedicate your lifetime to perfecting, to hone the skills of, and let it be your life's work. But you don't want to spend your life waiting for the perfect moment to start.

The field of artificial intelligence was officially born at the 1956 Dartmouth workshop, where John McCarthy coined the term “artificial intelligence.” Key founders include McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon, who presented the first AI program, the Logic Theorist. Alan Turing laid the theoretical groundwork earlier with his 1950 paper and the Turing Test, asking if machines could think. So it’s more a group effort than one inventor. The original founders didn’t complete it at all. They set up the field and built early programs that solved math problems or played checkers, but the tech hit big limits. There were two “AI winters” where funding dried up because results didn’t match the hype. What we use today, like ChatGPT, comes from deep learning and neural networks that really took off around 2012 with AlexNet. That work was led by Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, decades later. The Dartmouth group laid the vision, but modern AI is a completely different approach built on massive data and computing power they couldn’t dream of.

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!