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Continual learning sometimes gets discussed as if the goal is to dissolve the context/weights distinction. Let the model just keep accumulating, fine-tuning itself on the fly. Andrej Karpathy points out, though, that this isn't how humans do it. Our working memory gets wiped regularly. What we actually have is... show more

Dwarkesh Patel

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59,993 次观看 • 2 个月前 •via X (Twitter)

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Continual Learning in 2026. And AGI with it? In July, Dario said he believes Continual Learning will turn out to be not as difficult as it seems. Sholto Douglas, Anthropic and former Deepmind researcher, predicted Continual Learning gets solved in a satisfying way in 2026. Sholto said that last week. Considering how capable AI models have become in 2025, it is unwise to dismiss this as fundraising or hype. It is much wiser to assume AGI is coming. That it will change the world. And that you can benefit from it... ...than it is to assume the opposite. "Not enough people are ready for AI to not be a bubble." - roon Choose wisely my friends.

Continual Learning in 2026. And AGI with it? In July, Dario said he believes Continual Learning will turn out to be not as difficult as it seems. Sholto Douglas, Anthropic and former Deepmind researcher, predicted Continual Learning gets solved in a satisfying way in 2026. Sholto said that last week. Considering how capable AI models have become in 2025, it is unwise to dismiss this as fundraising or hype. It is much wiser to assume AGI is coming. That it will change the world. And that you can benefit from it... ...than it is to assume the opposite. "Not enough people are ready for AI to not be a bubble." - roon Choose wisely my friends.

Dan McAteer

69,496 次观看 • 7 个月前

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.”

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

1,051,531 次观看 • 9 个月前

"When social media first started rising, we didn't necessarily realize how much of that was actually just foundational infrastructure for the future of humanity.” Ben Fielding of gensyn on why AI is about to repeat the mistake social media made, and how we can still fix it: "This isn't just a product that you chat with. It's actually new fundamental infrastructure for humanity, and we should build that learning from the lessons from before. If we let a single company build AI, they hold an enormous amount of power in the world." "If you actually distribute that out and provide it in a kind of sovereign way — it's on all of our own devices and we own it — then actually you steer the world in a very different social direction."

"When social media first started rising, we didn't necessarily realize how much of that was actually just foundational infrastructure for the future of humanity.” Ben Fielding of gensyn on why AI is about to repeat the mistake social media made, and how we can still fix it: "This isn't just a product that you chat with. It's actually new fundamental infrastructure for humanity, and we should build that learning from the lessons from before. If we let a single company build AI, they hold an enormous amount of power in the world." "If you actually distribute that out and provide it in a kind of sovereign way — it's on all of our own devices and we own it — then actually you steer the world in a very different social direction."

a16z crypto

78,712 次观看 • 3 个月前

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

Santiago

384,510 次观看 • 3 年前

Fantasy Isn't A To-Do List Not every fantasy is a plan. Sometimes your brain enjoys something in imagination that you would never actually want in real life, and that does not automatically mean something is wrong with you. Fantasy, desire, and behavior can overlap, but they are not the same thing. The better question is not always “Should I do this?” Sometimes the better question is “What does this fantasy give me?” Maybe it is power, surrender, novelty, attention, freedom, or just a weird little movie your brain wanted to play.

Fantasy Isn't A To-Do List Not every fantasy is a plan. Sometimes your brain enjoys something in imagination that you would never actually want in real life, and that does not automatically mean something is wrong with you. Fantasy, desire, and behavior can overlap, but they are not the same thing. The better question is not always “Should I do this?” Sometimes the better question is “What does this fantasy give me?” Maybe it is power, surrender, novelty, attention, freedom, or just a weird little movie your brain wanted to play.

Kate Shelor

36,944 次观看 • 1 个月前

AI agents don’t feel the pain that humans do. 3 takeaways from Mario Zechner(Mario Zechner), creator of Pi - the minimalist, self-modifying agent that OpenClaw is built on: #1 - Automation bias: agents quickly wow us and win false trust: “There are moments of brilliance in agents where they spit out perfectly fine simple code. As the steering engineer you can look at that and think: ‘Wow, this is amazing. I can just sit back and not care because it's doing the thing how I would do it.’ Two minutes later you have another agent running that spits out the worst, horrible, garbage code, but you might not notice because you have fallen into automation bias and think your agent is doing the job well.” #2 - Humans have a different capacity for learning than agents: “Agents don't learn. You can put as much stuff in your AGENTS.md, build a memory system, but it's not the same type of learning that a human does. Humans are failable as well, but they have some capability of learning.” #3 - Feeling pain is inherently human and drives us to make things well: “Humans feel pain. I think that's one of the defining things about humans. If the pain gets too big, you as a human are incentivized to fix the cause of your pain. In a code base, the cause is usually terrible interfaces and terrible complexity that you want to get rid of because you can no longer maintain that system.”

AI agents don’t feel the pain that humans do. 3 takeaways from Mario Zechner(Mario Zechner), creator of Pi - the minimalist, self-modifying agent that OpenClaw is built on: #1 - Automation bias: agents quickly wow us and win false trust: “There are moments of brilliance in agents where they spit out perfectly fine simple code. As the steering engineer you can look at that and think: ‘Wow, this is amazing. I can just sit back and not care because it's doing the thing how I would do it.’ Two minutes later you have another agent running that spits out the worst, horrible, garbage code, but you might not notice because you have fallen into automation bias and think your agent is doing the job well.” #2 - Humans have a different capacity for learning than agents: “Agents don't learn. You can put as much stuff in your AGENTS.md, build a memory system, but it's not the same type of learning that a human does. Humans are failable as well, but they have some capability of learning.” #3 - Feeling pain is inherently human and drives us to make things well: “Humans feel pain. I think that's one of the defining things about humans. If the pain gets too big, you as a human are incentivized to fix the cause of your pain. In a code base, the cause is usually terrible interfaces and terrible complexity that you want to get rid of because you can no longer maintain that system.”

The Pragmatic Engineer

11,256 次观看 • 3 个月前

Ilya on research taste: “One thing that guides me personally is an aesthetic of how AI should be by thinking about how people are. There's no room for ugliness. It's just beauty, simplicity, elegance, with correct inspiration from the brain. The more they are present, the more confident you can be in a top-down belief. The top-down belief is the thing that sustains you when the experiments contradict you. Because if you just trust the data all the time, sometimes you can be doing a correct thing, but there's a bug. How do you know if you should keep debugging or you conclude it's the wrong direction? You must say that things have to be this way, therefore we've got to keep going. That's the top-down belief, and it's based on this multifaceted beauty and inspiration by the brain.”

Ilya on research taste: “One thing that guides me personally is an aesthetic of how AI should be by thinking about how people are. There's no room for ugliness. It's just beauty, simplicity, elegance, with correct inspiration from the brain. The more they are present, the more confident you can be in a top-down belief. The top-down belief is the thing that sustains you when the experiments contradict you. Because if you just trust the data all the time, sometimes you can be doing a correct thing, but there's a bug. How do you know if you should keep debugging or you conclude it's the wrong direction? You must say that things have to be this way, therefore we've got to keep going. That's the top-down belief, and it's based on this multifaceted beauty and inspiration by the brain.”

Dwarkesh Patel

164,581 次观看 • 8 个月前

Terrence McKenna in an interview in 1998: "I think it's just going to get weirder and weirder and weirder, and finally, it's going to be so weird that people will have to talk about how weird it is. [...] This is what it's like when a species prepares to depart for the stars. You don't depart for the stars under calm and orderly conditions. It's a fire in a madhouse, and that's what we have - the fire in the madhouse at the end of time. This is what it's like when a species prepares to move on to the next dimension."

Terrence McKenna in an interview in 1998: "I think it's just going to get weirder and weirder and weirder, and finally, it's going to be so weird that people will have to talk about how weird it is. [...] This is what it's like when a species prepares to depart for the stars. You don't depart for the stars under calm and orderly conditions. It's a fire in a madhouse, and that's what we have - the fire in the madhouse at the end of time. This is what it's like when a species prepares to move on to the next dimension."

𓆝𓆜𓆞𓆢

26,389 次观看 • 1 年前

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Sir Khaycee

20,419 次观看 • 4 个月前

SAM ALTMAN: “PEOPLE JUST WANT TO TALK TO THE MODEL” “We get positive stories from people about the way they're transforming their lives, learning new things, making spiritual progress, solving problems that they've had for a long time. Our conviction that, like, people just wanna talk to the model had gotten really strong. What I hope is that this is a technology… that prioritizes amplification of your individual will for yourself.” Source: Sam Altman

SAM ALTMAN: “PEOPLE JUST WANT TO TALK TO THE MODEL” “We get positive stories from people about the way they're transforming their lives, learning new things, making spiritual progress, solving problems that they've had for a long time. Our conviction that, like, people just wanna talk to the model had gotten really strong. What I hope is that this is a technology… that prioritizes amplification of your individual will for yourself.” Source: Sam Altman

Mario Nawfal

81,329 次观看 • 1 年前

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN

Applied Compute

64,309 次观看 • 5 个月前

We asked vitalik.eth how we would protect our brain from eroding in the in the AI era. He says it means deliberately doing things manually, even when you don't have to. "Force yourself to do things manually at least sometimes, even though you don't have to. At the very least to make sure your brain stays on." "Walking is so much healthier than being in a car. In a car, you experience a city as a collection of teleport points. Walking, you have to actually think about where you're going. The whole thing follows laws of geometry." "Learning actively is 10 times more effective than learning passively, even for the same amount of time spent." sophia dew binji

We asked vitalik.eth how we would protect our brain from eroding in the in the AI era. He says it means deliberately doing things manually, even when you don't have to. "Force yourself to do things manually at least sometimes, even though you don't have to. At the very least to make sure your brain stays on." "Walking is so much healthier than being in a car. In a car, you experience a city as a collection of teleport points. Walking, you have to actually think about where you're going. The whole thing follows laws of geometry." "Learning actively is 10 times more effective than learning passively, even for the same amount of time spent." sophia dew binji

MTS

10,510 次观看 • 2 个月前

Caitlin: “What I love about you Candace though and I feel like this is something that is very lost right now especially on the women’s side is you just tell the game how it is. And it’s never in a disrespectful way you’re just trying to talk about the game how you see it.”

Caitlin: “What I love about you Candace though and I feel like this is something that is very lost right now especially on the women’s side is you just tell the game how it is. And it’s never in a disrespectful way you’re just trying to talk about the game how you see it.”

correlation

286,175 次观看 • 2 个月前

American nurse speaks out about our healthcare system “Let me be the first nurse to tell you that this medical system is not built to make you better. It is not broken. It is literally designed as a business model to keep people sick. And I know this is going to make a lot of nurses and doctors upset. It's the reality. It's what's happening in front of our faces. This is a business model to keep people coming back. It's the fact that we can't be honest with our patients is the reason that this business model is working.” “Their business model is literally keep people sick, keep them on a medication, keep telling them surgery is the answer. I know so many people who are on lifetime medications who've had multiple surgeries and who are not living their life.”

American nurse speaks out about our healthcare system “Let me be the first nurse to tell you that this medical system is not built to make you better. It is not broken. It is literally designed as a business model to keep people sick. And I know this is going to make a lot of nurses and doctors upset. It's the reality. It's what's happening in front of our faces. This is a business model to keep people coming back. It's the fact that we can't be honest with our patients is the reason that this business model is working.” “Their business model is literally keep people sick, keep them on a medication, keep telling them surgery is the answer. I know so many people who are on lifetime medications who've had multiple surgeries and who are not living their life.”

Wall Street Apes

82,849 次观看 • 5 个月前

Collins: What was your takeaways from the briefing? Gallego: We don't have a plan how to get out of this. We don't actually have a concept of victory. Our justification for going into the war is entirely different from what we're hearing from the president right now. If we don't know how we can get out of this war, we're seeing potentially a long term war.

Collins: What was your takeaways from the briefing? Gallego: We don't have a plan how to get out of this. We don't actually have a concept of victory. Our justification for going into the war is entirely different from what we're hearing from the president right now. If we don't know how we can get out of this war, we're seeing potentially a long term war.

Acyn

38,124 次观看 • 5 个月前

I’ve been trying to better understand how our thinking actually works. Not in theory, but in practice. What shapes it? What changes it? How much control that we really have over it? So to help me understand this, I’m sitting down with neuroscientist Dr David Eagleman. I didn’t expect this conversation to make me question something as basic as how I experience reality but it did. David is a Stanford professor whose work focuses on how to the brain builds our perception of the world and how it rewires itself based on experience. With rising global tensions and the rapid growth of AI, I wanted to have this conversation to show how uncertainty and risk affect the brain and how people process fear and stress. We discussed things like: - How to deal with existential fear. - Why constantly challenging yourself is the best thing you can do for your mind. - How to get learning and adapting as you get older. - Why memory is not as reliable as we assume? What became clear the longer that we spoke is that your brain is not a fixed system. It’s constantly adjusting itself depending on what you focus on, what you repeat and what you expose yourself to. It was truly illuminating speaking to David and I'm sure my neural pathways have expanded in really important ways because of him! For anyone that wants to know how to change your life by changing your brain, I recommend listening/watching this conversation.

I’ve been trying to better understand how our thinking actually works. Not in theory, but in practice. What shapes it? What changes it? How much control that we really have over it? So to help me understand this, I’m sitting down with neuroscientist Dr David Eagleman. I didn’t expect this conversation to make me question something as basic as how I experience reality but it did. David is a Stanford professor whose work focuses on how to the brain builds our perception of the world and how it rewires itself based on experience. With rising global tensions and the rapid growth of AI, I wanted to have this conversation to show how uncertainty and risk affect the brain and how people process fear and stress. We discussed things like: - How to deal with existential fear. - Why constantly challenging yourself is the best thing you can do for your mind. - How to get learning and adapting as you get older. - Why memory is not as reliable as we assume? What became clear the longer that we spoke is that your brain is not a fixed system. It’s constantly adjusting itself depending on what you focus on, what you repeat and what you expose yourself to. It was truly illuminating speaking to David and I'm sure my neural pathways have expanded in really important ways because of him! For anyone that wants to know how to change your life by changing your brain, I recommend listening/watching this conversation.

Steven Bartlett

21,490 次观看 • 3 个月前

AWFL climate doomers, hoping to shake their reputation as joyless scolds, are trying to bring "joy" to their doomsaying Spoiler alert: It's a huge cringe-fest. "If we don't center joy now, here in the process of bringing about this more vibrant, livable future, I think we actually pattern ourselves into something that isn't what we want." "The message of 'we have to fix the climate' doesn't resonate. It's more about: how do we love each other, how do we come to this place of well-being, how do we care about the well-being of ourselves, of our community, and our environment. And I think that that is joyful... There is love in this process." "If we want people to come off the sidelines and into the work, it's gotta feel good in some way. It can't be just about finger-wagging and telling people what they've done wrong if they want to be good and right and better. We have to have more openness. We have to throw a better party."

AWFL climate doomers, hoping to shake their reputation as joyless scolds, are trying to bring "joy" to their doomsaying Spoiler alert: It's a huge cringe-fest. "If we don't center joy now, here in the process of bringing about this more vibrant, livable future, I think we actually pattern ourselves into something that isn't what we want." "The message of 'we have to fix the climate' doesn't resonate. It's more about: how do we love each other, how do we come to this place of well-being, how do we care about the well-being of ourselves, of our community, and our environment. And I think that that is joyful... There is love in this process." "If we want people to come off the sidelines and into the work, it's gotta feel good in some way. It can't be just about finger-wagging and telling people what they've done wrong if they want to be good and right and better. We have to have more openness. We have to throw a better party."

Breitbart News

41,035 次观看 • 3 个月前

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!

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

235,587 次观看 • 7 个月前

This is the Elon Musk quote that made Eric Jorgenson 📚 ☀️ think, "I must write this book." "We must make stuff. Manufacturing is underrated, it is hard. I've got mad respect for the makers of things. Some people have an absurd view of the economy as a magic thing that just produces stuff. Let me break it to the fools out there. If we don't make stuff, there's no stuff. Some people have become detached from reality. This notion that the government can just send out checks to everybody and everything will be fine is not true, obviously. You can't just legislate money to solve things. If we don't make stuff, there is no stuff. Technological progress is not inevitable. It's not some kind of abstract concept. Humans make technology. If we don't do it, it will not happen. Somebody has to do the real work."

This is the Elon Musk quote that made Eric Jorgenson 📚 ☀️ think, "I must write this book." "We must make stuff. Manufacturing is underrated, it is hard. I've got mad respect for the makers of things. Some people have an absurd view of the economy as a magic thing that just produces stuff. Let me break it to the fools out there. If we don't make stuff, there's no stuff. Some people have become detached from reality. This notion that the government can just send out checks to everybody and everything will be fine is not true, obviously. You can't just legislate money to solve things. If we don't make stuff, there is no stuff. Technological progress is not inevitable. It's not some kind of abstract concept. Humans make technology. If we don't do it, it will not happen. Somebody has to do the real work."

David Senra

32,652 次观看 • 4 个月前