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.Richard Sutton, father of reinforcement learning, doesn’t think LLMs are bitter-lesson-pilled. My steel man of Richard’s position: we need some new architecture to enable continual (on-the-job) learning. And if we have continual learning, we don't need a special training phase - the agent just learns on-the-fly - like all...

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Dwarkesh Patel

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3,080,205 次观看 • 9 个月前 •via X (Twitter)

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Lots of people think it's obvious that humans learn by imitation, starting with babies imitating their parents. Richard Sutton thinks this is completely wrong. Sutton basically thinks direct imitation plays almost no role in animal learning, and at most a small role in human intelligence. When one animal ends up doing the same thing as another, he thinks this is the result of RL-style trial-and-error, not direct imitation. And if imitation isn't really a kind of learning, then training LLMs to imitate humans isn't really Bitter Lesson–pilled.
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Lots of people think it's obvious that humans learn by imitation, starting with babies imitating their parents. Richard Sutton thinks this is completely wrong. Sutton basically thinks direct imitation plays almost no role in animal learning, and at most a small role in human intelligence. When one animal ends up doing the same thing as another, he thinks this is the result of RL-style trial-and-error, not direct imitation. And if imitation isn't really a kind of learning, then training LLMs to imitate humans isn't really Bitter Lesson–pilled.

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ℏεsam

895,122 次观看 • 9 个月前

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85,856 次观看 • 1 年前

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26,952 次观看 • 9 个月前

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3:18:00

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Yacine Mahdid

57,320 次观看 • 3 个月前

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Dwarkesh Patel

2,263,770 次观看 • 1 年前

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40:27

This morning at NeurIPS, Rich Sutton reminded us that we need continual learning to reach AGI. This afternoon, Ali Behrouz presented a Google poster paper, Nested Learning, which provides new ideas on the path to continual learning. I recorded the 40 minute talk as it might be useful for some researchers in the audience. For the rest of us, I subscribe to Andrej Karpathy's suspicion that it will take a 5-10 papers like this to move us to AGI from where we are now, just like it took about 10 papers to move from 2012's AlexNet to ChatGPT. At the very end, I ask Ali how far along to continual learning this represents. Full paper link below, as well as a YouTube link. ps. sorry about the first 2 minutes of bad audio since there were 2 idiots standing beside me have a conversation right in front of this presenter in a rather packed poster presentation. Honestly, tamp down your egos guys and show come common courtesy!

Phil Trubey

235,562 次观看 • 7 个月前

The Delta of Intelligence is Human Data! Curtis G. Northcutt is Director of AI Research at Handshake AI. In this episode, Curtis shares his unique perspectives on human data, RL envs, four quadrants of the AI market, journey of Handshake and much more. 0:00:00 - INTRO 0:02:08 - The Snowman Effect: From Rural Kentucky to 7 Years at MIT 0:09:50 - Confident Learning, Two Systems of Intelligence, Classical ML Has to Prevail 0:20:37 - Origin of CleanLab and Handshake acquisition 0:28:00 - How does GPT-5 become GPT-6? 0:31:20 - The Fastest growing Data Lab: What makes Handshake different? 0:35:32 - Difference between Good Data and Bad Data 0:38:17 - New kind of Data, IA and AI 0:42:04 - Scaling Coding Benchmarks and Efficiency in Long Horizon Tasks 0:49:12 - On Pre-Training, Misconception around Human Data 0:57:13 - Open Question: Taste and Personality, Quick Fire 01:05:15 - Advice to 20yo : Skill and Obsession
1:08:18

The Delta of Intelligence is Human Data! Curtis G. Northcutt is Director of AI Research at Handshake AI. In this episode, Curtis shares his unique perspectives on human data, RL envs, four quadrants of the AI market, journey of Handshake and much more. 0:00:00 - INTRO 0:02:08 - The Snowman Effect: From Rural Kentucky to 7 Years at MIT 0:09:50 - Confident Learning, Two Systems of Intelligence, Classical ML Has to Prevail 0:20:37 - Origin of CleanLab and Handshake acquisition 0:28:00 - How does GPT-5 become GPT-6? 0:31:20 - The Fastest growing Data Lab: What makes Handshake different? 0:35:32 - Difference between Good Data and Bad Data 0:38:17 - New kind of Data, IA and AI 0:42:04 - Scaling Coding Benchmarks and Efficiency in Long Horizon Tasks 0:49:12 - On Pre-Training, Misconception around Human Data 0:57:13 - Open Question: Taste and Personality, Quick Fire 01:05:15 - Advice to 20yo : Skill and Obsession

himanshu

21,863 次观看 • 2 个月前

We are excited to share that Logan Kilpatrick joined us on The Bench to discuss Google's new Gemini 3.5 Flash: why it's deliberately more persistent and capable than previous Flash models, how it hit #1 on our FinanceAgent Benchmark taking 82 steps where competitors stopped at 13, and what justifies the price increase. We also get into why AI benchmarks need a paradigm shift, the trade-off of building everything vs staying focused, the Pope, and why Omni might kill the Subway Surfers content era. 0:11:00 – Flash is being rebased for the agent era, not just a cheaper model anymore 0:14:03 – Persistence by design: 82 tool calls vs competitors' 13 0:17:52 – Why pricing went up and how Google thinks about value per token 0:22:55 – Coding performance: from 20th to 10th place in one generation 0:28:28 – Why benchmarks have historically been misleading and what the new era of evaluation looks like 0:29:28 Logan on why Google has the best researchers in the world 0:36:16 – The cost of being Google 0:39:07 – The Pope’s encyclical on AI and whether most people see frontier intelligence as a good thing 0:51:12 – Why Omni is the thing that recently clicked
54:53

We are excited to share that Logan Kilpatrick joined us on The Bench to discuss Google's new Gemini 3.5 Flash: why it's deliberately more persistent and capable than previous Flash models, how it hit #1 on our FinanceAgent Benchmark taking 82 steps where competitors stopped at 13, and what justifies the price increase. We also get into why AI benchmarks need a paradigm shift, the trade-off of building everything vs staying focused, the Pope, and why Omni might kill the Subway Surfers content era. 0:11:00 – Flash is being rebased for the agent era, not just a cheaper model anymore 0:14:03 – Persistence by design: 82 tool calls vs competitors' 13 0:17:52 – Why pricing went up and how Google thinks about value per token 0:22:55 – Coding performance: from 20th to 10th place in one generation 0:28:28 – Why benchmarks have historically been misleading and what the new era of evaluation looks like 0:29:28 Logan on why Google has the best researchers in the world 0:36:16 – The cost of being Google 0:39:07 – The Pope’s encyclical on AI and whether most people see frontier intelligence as a good thing 0:51:12 – Why Omni is the thing that recently clicked

Vals AI

16,486 次观看 • 1 个月前

Episode w Dylan Patel and Asianometry out! A bonanza on how the semiconductor industry actually works. & what Xi could do if he becomes scaling pilled, how we can train models with 10,000x GPT-4's compute by end of decade, & much more. This one is very fun. Enjoy! Links below. 0:00:00 – Xi’s path to AGI 0:05:05 – Liang Mong Song 0:09:10 – How semiconductors get better 0:12:01 – China can centralize compute 0:19:35 – Export controls & sanctions 0:33:45 – Huawei’s intense culture 0:39:45 – Why the semiconductor industry is so stratified 0:41:50 – N2 should not exist 0:46:47 – Taiwan invasion hypothetical 0:50:15 – Mind-boggling complexity of semiconductors 1:00:06 – Chip architecture design 1:05:29 – Architectures lead to different AI models? China vs. US 1:11:06 – Being head of compute at an AI lab 1:17:17 – Scaling costs and power demand 1:37:59 – Are we financing an AI bubble?
2:11:02

Episode w Dylan Patel and Asianometry out! A bonanza on how the semiconductor industry actually works. & what Xi could do if he becomes scaling pilled, how we can train models with 10,000x GPT-4's compute by end of decade, & much more. This one is very fun. Enjoy! Links below. 0:00:00 – Xi’s path to AGI 0:05:05 – Liang Mong Song 0:09:10 – How semiconductors get better 0:12:01 – China can centralize compute 0:19:35 – Export controls & sanctions 0:33:45 – Huawei’s intense culture 0:39:45 – Why the semiconductor industry is so stratified 0:41:50 – N2 should not exist 0:46:47 – Taiwan invasion hypothetical 0:50:15 – Mind-boggling complexity of semiconductors 1:00:06 – Chip architecture design 1:05:29 – Architectures lead to different AI models? China vs. US 1:11:06 – Being head of compute at an AI lab 1:17:17 – Scaling costs and power demand 1:37:59 – Are we financing an AI bubble?

Dwarkesh Patel

260,187 次观看 • 1 年前

By now, it seems that Yann Lecun is the only one who does not believe in the future of LLMs to the extent that they will reach AGI. "At what point do we need a new architecture How do you think about what breakthroughs are needed?" Sam: "I think far enough that we can make something that will figure out the next breakthrough with the current technology."
0:32

By now, it seems that Yann Lecun is the only one who does not believe in the future of LLMs to the extent that they will reach AGI. "At what point do we need a new architecture How do you think about what breakthroughs are needed?" Sam: "I think far enough that we can make something that will figure out the next breakthrough with the current technology."

Chubby♨️

103,967 次观看 • 9 个月前

New episode Much fun chatting with Laura 🌲 ⛰️! We discuss: - personal identity, and what, exactly, we want to preserve - what a “more humane transhumanism” might look like - scientific awe - seeing inside the cell - the antimemetic qualities of reversible cryopreservation - why cryopreservation might be the most leveraged problem in longevity - and much, much more Links below. Enjoy! Timestamps: (0:00:00) - Introduction. (0:01:17) - Personal identity: what, exactly, do we want to preserve? (0:05:20) - How has Laura's emotional relation to longevity evolved? (0:10:32) - Transhumanism: what do current visions lack? (0:17:34) - The sociopolitical implications of longevity. (0:29:29) - Awe in scientific discovery. (0:37:46) - Embodied thought-experiments, & seeing inside the cell. (0:42:05) - Visualising cells to think about cryopreservation. (0:46:08) - Are some fields / types of science more amenable to embodied thinking? (0:47:13) - Scientific mentorship. (0:55:26) - Longevity drug development. (0:57:46) - Laura's current takes on theories of ageing. (1:01:17) - Key regulatory bottlenecks to longevity drugs. (1:02:40) - Patterns among The Longevity Fund's most successful investments. (1:05:18) - Laura's questions about evolution and natural selection. (1:08:57) - Why work on cryopreservation?
1:16:46

New episode Much fun chatting with Laura 🌲 ⛰️! We discuss: - personal identity, and what, exactly, we want to preserve - what a “more humane transhumanism” might look like - scientific awe - seeing inside the cell - the antimemetic qualities of reversible cryopreservation - why cryopreservation might be the most leveraged problem in longevity - and much, much more Links below. Enjoy! Timestamps: (0:00:00) - Introduction. (0:01:17) - Personal identity: what, exactly, do we want to preserve? (0:05:20) - How has Laura's emotional relation to longevity evolved? (0:10:32) - Transhumanism: what do current visions lack? (0:17:34) - The sociopolitical implications of longevity. (0:29:29) - Awe in scientific discovery. (0:37:46) - Embodied thought-experiments, & seeing inside the cell. (0:42:05) - Visualising cells to think about cryopreservation. (0:46:08) - Are some fields / types of science more amenable to embodied thinking? (0:47:13) - Scientific mentorship. (0:55:26) - Longevity drug development. (0:57:46) - Laura's current takes on theories of ageing. (1:01:17) - Key regulatory bottlenecks to longevity drugs. (1:02:40) - Patterns among The Longevity Fund's most successful investments. (1:05:18) - Laura's questions about evolution and natural selection. (1:08:57) - Why work on cryopreservation?

Joseph Noel Walker

17,580 次观看 • 1 年前

The most interesting part for me is where Andrej Karpathy describes why LLMs aren't able to learn like humans. As you would expect, he comes up with a wonderfully evocative phrase to describe RL: “sucking supervision bits through a straw.” A single end reward gets broadcast across every token in a successful trajectory, upweighting even wrong or irrelevant turns that lead to the right answer. > “Humans don't use reinforcement learning, as I've said before. I think they do something different. Reinforcement learning is a lot worse than the average person thinks. Reinforcement learning is terrible. It just so happens that everything that we had before is much worse.” So what do humans do instead? > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” > “I'd love to see during pretraining some kind of a stage where the model thinks through the material and tries to reconcile it with what it already knows. There's no equivalent of any of this. This is all research.” Why can’t we just add this training to LLMs today? > “There are very subtle, hard to understand reasons why it's not trivial. If I just give synthetic generation of the model thinking about a book, you look at it and you're like, 'This looks great. Why can't I train on it?' You could try, but the model will actually get much worse if you continue trying.” > “Say we have a chapter of a book and I ask an LLM to think about it. It will give you something that looks very reasonable. But if I ask it 10 times, you'll notice that all of them are the same.” > “You're not getting the richness and the diversity and the entropy from these models as you would get from humans. How do you get synthetic data generation to work despite the collapse and while maintaining the entropy? It is a research problem.” How do humans get around model collapse? > “These analogies are surprisingly good. Humans collapse during the course of their lives. Children haven't overfit yet. They will say stuff that will shock you. Because they're not yet collapsed. But we [adults] are collapsed. We end up revisiting the same thoughts, we end up saying more and more of the same stuff, the learning rates go down, the collapse continues to get worse, and then everything deteriorates.” In fact, there’s an interesting paper arguing that dreaming evolved to assist generalization, and resist overfitting to daily learning - look up The Overfitted Brain by Erik Hoel. I asked Karpathy: Isn’t it interesting that humans learn best at a part of their lives (childhood) whose actual details they completely forget, adults still learn really well but have terrible memory about the particulars of the things they read or watch, and LLMs can memorize arbitrary details about text that no human could but are currently pretty bad at generalization? > “[Fallible human memory] is a feature, not a bug, because it forces you to only learn the generalizable components. LLMs are distracted by all the memory that they have of the pre-trained documents. That's why when I talk about the cognitive core, I actually want to remove the memory. I'd love to have them have less memory so that they have to look things up and they only maintain the algorithms for thought, and the idea of an experiment, and all this cognitive glue for acting.”
7:01

The most interesting part for me is where Andrej Karpathy describes why LLMs aren't able to learn like humans. As you would expect, he comes up with a wonderfully evocative phrase to describe RL: “sucking supervision bits through a straw.” A single end reward gets broadcast across every token in a successful trajectory, upweighting even wrong or irrelevant turns that lead to the right answer. > “Humans don't use reinforcement learning, as I've said before. I think they do something different. Reinforcement learning is a lot worse than the average person thinks. Reinforcement learning is terrible. It just so happens that everything that we had before is much worse.” So what do humans do instead? > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” > “I'd love to see during pretraining some kind of a stage where the model thinks through the material and tries to reconcile it with what it already knows. There's no equivalent of any of this. This is all research.” Why can’t we just add this training to LLMs today? > “There are very subtle, hard to understand reasons why it's not trivial. If I just give synthetic generation of the model thinking about a book, you look at it and you're like, 'This looks great. Why can't I train on it?' You could try, but the model will actually get much worse if you continue trying.” > “Say we have a chapter of a book and I ask an LLM to think about it. It will give you something that looks very reasonable. But if I ask it 10 times, you'll notice that all of them are the same.” > “You're not getting the richness and the diversity and the entropy from these models as you would get from humans. How do you get synthetic data generation to work despite the collapse and while maintaining the entropy? It is a research problem.” How do humans get around model collapse? > “These analogies are surprisingly good. Humans collapse during the course of their lives. Children haven't overfit yet. They will say stuff that will shock you. Because they're not yet collapsed. But we [adults] are collapsed. We end up revisiting the same thoughts, we end up saying more and more of the same stuff, the learning rates go down, the collapse continues to get worse, and then everything deteriorates.” In fact, there’s an interesting paper arguing that dreaming evolved to assist generalization, and resist overfitting to daily learning - look up The Overfitted Brain by Erik Hoel. I asked Karpathy: Isn’t it interesting that humans learn best at a part of their lives (childhood) whose actual details they completely forget, adults still learn really well but have terrible memory about the particulars of the things they read or watch, and LLMs can memorize arbitrary details about text that no human could but are currently pretty bad at generalization? > “[Fallible human memory] is a feature, not a bug, because it forces you to only learn the generalizable components. LLMs are distracted by all the memory that they have of the pre-trained documents. That's why when I talk about the cognitive core, I actually want to remove the memory. I'd love to have them have less memory so that they have to look things up and they only maintain the algorithms for thought, and the idea of an experiment, and all this cognitive glue for acting.”

Dwarkesh Patel

1,050,937 次观看 • 9 个月前