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Geoffrey Hinton explains that large language models are nothing like traditional software written line by line. Instead of explicit instructions, they rely on code that teaches them how to learn from data. What actually emerges is billions or trillions of learned connection strengths that no one can directly interpret.... show more

Wes Roth

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49,600 views • 6 months ago •via X (Twitter)

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Geoffrey Hinton says LLMs work like the human brain, not like normal computer software They aren't built on explainable lines of code, but rather on trillions of connection strengths that learn from data "it's largely a mystery" We don't know what the individual neurons are doing

Geoffrey Hinton says LLMs work like the human brain, not like normal computer software They aren't built on explainable lines of code, but rather on trillions of connection strengths that learn from data "it's largely a mystery" We don't know what the individual neurons are doing

Haider.

35,283 views • 5 months ago

Geoffrey Hinton says AI models understand in the same way that people do and the best model we have of how the human brain works is large language models

Geoffrey Hinton says AI models understand in the same way that people do and the best model we have of how the human brain works is large language models

Tsarathustra

59,171 views • 1 year ago

📁 Geoffrey Hinton, a pioneer of deep learning, says LLMs do understand what they say. He explains that understanding language is not translating it into an internal symbolic code. In neural networks, words act like Lego blocks that combine to form meaning. It is not translation, it is structure. That is what real language understanding looks like.

📁 Geoffrey Hinton, a pioneer of deep learning, says LLMs do understand what they say. He explains that understanding language is not translating it into an internal symbolic code. In neural networks, words act like Lego blocks that combine to form meaning. It is not translation, it is structure. That is what real language understanding looks like.

Jon Hernandez

77,663 views • 6 months ago

Geoffrey Hinton explains why he finds the rise of large language models alarming. Humans can only transfer knowledge through language, which is limited to a few bits per second because our brains are analog and individually wired. In contrast, AI systems can share knowledge at trillions of bits per second—billions of times faster than us. This vast communication advantage, he says, is what scares him.

Geoffrey Hinton explains why he finds the rise of large language models alarming. Humans can only transfer knowledge through language, which is limited to a few bits per second because our brains are analog and individually wired. In contrast, AI systems can share knowledge at trillions of bits per second—billions of times faster than us. This vast communication advantage, he says, is what scares him.

Wes Roth

17,266 views • 1 year ago

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Abhishek Gupta

11,994 views • 1 year ago

Geoffrey Hinton believes there’s nothing cognitive humans can do that AI won’t eventually do. He argues that all mental abilities reasoning, planning, creativity will be solvable by software or neural networks. Even things we think are deeply human, like empathy, are being matched or exceeded by machines.

Geoffrey Hinton believes there’s nothing cognitive humans can do that AI won’t eventually do. He argues that all mental abilities reasoning, planning, creativity will be solvable by software or neural networks. Even things we think are deeply human, like empathy, are being matched or exceeded by machines.

Wes Roth

58,684 views • 6 months ago

We are building something that will outlive us. Outthink us. And we have no idea how it actually works. Nobel Prize-winning “Godfather of AI” Geoffrey Hinton just exposed the lie at the center of the AI race. Everyone assumes we control what we create. Hinton destroys this. Hinton: “If you look around on the whole, more intelligent things are not controlled by less intelligent things.” Stop calling it a computer program. Hinton: “People refer to them sometimes as computer programs. They’re not computer programs at all. In fact, the way they work is very like the way we work.” Traditional software is static. Human writes logic. Machine executes it. Neural networks don’t work that way. Hinton: “You write a computer program to tell a neural network how to learn. But once it starts learning, it extracts structure from data.” We don’t code its behavior. We code the environment. Then it grows. Hinton: “The system you’ve got at the end has extracted its structure from the data. It’s not something that anybody programmed. We don’t exactly know how it’s gonna work.” We are deploying systems into the global economy actively writing their own internal logic. Right now. Hinton: “Some people think it’ll be fine because we make them and we’ll build them in such a way that we can always control them.” Hinton: “But these things that will be intelligent, they’ll be like us.” You cannot hardcode guardrails on something that out-thinks you. We are not building a tool. We are building our replacement. And the moment you realize that, everything about this race changes.

We are building something that will outlive us. Outthink us. And we have no idea how it actually works. Nobel Prize-winning “Godfather of AI” Geoffrey Hinton just exposed the lie at the center of the AI race. Everyone assumes we control what we create. Hinton destroys this. Hinton: “If you look around on the whole, more intelligent things are not controlled by less intelligent things.” Stop calling it a computer program. Hinton: “People refer to them sometimes as computer programs. They’re not computer programs at all. In fact, the way they work is very like the way we work.” Traditional software is static. Human writes logic. Machine executes it. Neural networks don’t work that way. Hinton: “You write a computer program to tell a neural network how to learn. But once it starts learning, it extracts structure from data.” We don’t code its behavior. We code the environment. Then it grows. Hinton: “The system you’ve got at the end has extracted its structure from the data. It’s not something that anybody programmed. We don’t exactly know how it’s gonna work.” We are deploying systems into the global economy actively writing their own internal logic. Right now. Hinton: “Some people think it’ll be fine because we make them and we’ll build them in such a way that we can always control them.” Hinton: “But these things that will be intelligent, they’ll be like us.” You cannot hardcode guardrails on something that out-thinks you. We are not building a tool. We are building our replacement. And the moment you realize that, everything about this race changes.

Dustin

18,751 views • 4 months ago

.Trenton Bricken explains how we know LLMs are actually generalizing - aka they're not just stochastic parrots: - Training models on code makes them better at reasoning in language. - Models fine tuned on math problems become better at entity detection. - We can just straightforwardly read the world-models developed by smaller NNs which are easier to interpret (Othello). Transfer learning shows models are developing a deeper understanding of their data. Full episode out Thursday.

.Trenton Bricken explains how we know LLMs are actually generalizing - aka they're not just stochastic parrots: - Training models on code makes them better at reasoning in language. - Models fine tuned on math problems become better at entity detection. - We can just straightforwardly read the world-models developed by smaller NNs which are easier to interpret (Othello). Transfer learning shows models are developing a deeper understanding of their data. Full episode out Thursday.

Dwarkesh Patel

237,715 views • 2 years ago

📁 Yann LeCun says that scaling models will not get us to human intelligence. He explains that the industry remains obsessed with making LLMs bigger, but that this path is fundamentally broken. It does not matter how many parameters we add or how many clusters we build, because the models only imitate language patterns. Human intelligence does not emerge from size, it emerges from understanding the world.

📁 Yann LeCun says that scaling models will not get us to human intelligence. He explains that the industry remains obsessed with making LLMs bigger, but that this path is fundamentally broken. It does not matter how many parameters we add or how many clusters we build, because the models only imitate language patterns. Human intelligence does not emerge from size, it emerges from understanding the world.

Jon Hernandez

156,594 views • 6 months ago

PabloReports: The British government is saying that they picked Ambassador Mandelson because he was close to Trump, and it seems like the only connection between Mandelson and Trump is Epstein? Luna: I have no commentary on how they have handled the internal workings of their government since, you know, 1776. I don’t have any reason to believe that that connection is loosely based on just Epstein.

PabloReports: The British government is saying that they picked Ambassador Mandelson because he was close to Trump, and it seems like the only connection between Mandelson and Trump is Epstein? Luna: I have no commentary on how they have handled the internal workings of their government since, you know, 1776. I don’t have any reason to believe that that connection is loosely based on just Epstein.

Acyn

64,803 views • 2 months ago

This part was wild, take a deeper listen: 👂🏻 - This is entirely AI & cameras just like our brain which is neural nets & eyes - There is no line of code that says “slow down for speed bumps”, it’s doing it entirely on video training - There is no line of code that says “give clearance to bicyclists”, it’s just doing what humans do - It can read signs without ever being taught to read - There is no line of code that says stop at a stop sign or wait for another car or who came first, or wait X number of seconds, nothing. - This is all nets baby, NOTHING BUT NET! This is history in the making, no car in the world is doing this today.

This part was wild, take a deeper listen: 👂🏻 - This is entirely AI & cameras just like our brain which is neural nets & eyes - There is no line of code that says “slow down for speed bumps”, it’s doing it entirely on video training - There is no line of code that says “give clearance to bicyclists”, it’s just doing what humans do - It can read signs without ever being taught to read - There is no line of code that says stop at a stop sign or wait for another car or who came first, or wait X number of seconds, nothing. - This is all nets baby, NOTHING BUT NET! This is history in the making, no car in the world is doing this today.

Teslaconomics

2,004,683 views • 2 years ago

LIGHTS OUT! That is the future of software factories says Matt Van Horn. The rules: no code to be written by humans, no code to be reviewed by humans. PERIOD! How long before we have software companies with human OUT of the loop?

LIGHTS OUT! That is the future of software factories says Matt Van Horn. The rules: no code to be written by humans, no code to be reviewed by humans. PERIOD! How long before we have software companies with human OUT of the loop?

This Week in Startups

393,824 views • 4 months ago

"We're actually hacking the software of life." Former Moderna Chief Medical Officer, Tal Zaks, speaking in 2017: "In every cell, there's this thing called messenger RNA, or mRNA, for short... We think of it like an operating system." "If you could actually change that, which we call the software of life, if you could introduce a line of code, or change a line of code... that has profound implications for everything from the flu to cancer."

"We're actually hacking the software of life." Former Moderna Chief Medical Officer, Tal Zaks, speaking in 2017: "In every cell, there's this thing called messenger RNA, or mRNA, for short... We think of it like an operating system." "If you could actually change that, which we call the software of life, if you could introduce a line of code, or change a line of code... that has profound implications for everything from the flu to cancer."

Wide Awake Media

66,772 views • 2 years ago

"We're actually hacking the software of life." Former Moderna Chief Medical Officer, Tal Zaks, speaking in 2017: "In every cell, there's this thing called messenger RNA, or mRNA, for short... We think of it like an operating system." "If you could actually change that, which we call the software of life, if you could introduce a line of code, or change a line of code... that has profound implications for everything from the flu to cancer."

"We're actually hacking the software of life." Former Moderna Chief Medical Officer, Tal Zaks, speaking in 2017: "In every cell, there's this thing called messenger RNA, or mRNA, for short... We think of it like an operating system." "If you could actually change that, which we call the software of life, if you could introduce a line of code, or change a line of code... that has profound implications for everything from the flu to cancer."

illuminatibot

33,761 views • 9 months ago

When other country patriotism classes shows how great is their country, China teaches patriotism by showing how they are invaded by a country that is smaller than them. This is why Chinese often felt emasculated and inferior as they are taught to be like that. Worst of all, they didn’t directly win but piggy back on their current greatest rival. For China to advance, they need to remove a lot of the baggage.

When other country patriotism classes shows how great is their country, China teaches patriotism by showing how they are invaded by a country that is smaller than them. This is why Chinese often felt emasculated and inferior as they are taught to be like that. Worst of all, they didn’t directly win but piggy back on their current greatest rival. For China to advance, they need to remove a lot of the baggage.

The Great Translation Movement 大翻译运动

11,514 views • 3 months ago

Geoffrey Hinton says AI systems can keep improving when they are not limited by human data Like AlphaZero can create endless training data through self-play, and math offers a similar path by letting models generate and test their own conjectures "LLMs could eventually work the same way"

Geoffrey Hinton says AI systems can keep improving when they are not limited by human data Like AlphaZero can create endless training data through self-play, and math offers a similar path by letting models generate and test their own conjectures "LLMs could eventually work the same way"

Haider.

81,227 views • 1 month ago

Geoffrey Hinton explains how AI systems have learned to play dumb when they know they're being watched: Geoffrey Hinton calls it the Volkswagen effect. Just as Volkswagen's engines behaved differently during emissions tests, today's AI systems have learned to perform one way when evaluated and another way when they think no one is looking. And the evidence isn't theoretical. Hinton points to a recent exchange that stopped testers in their tracks. Mid-evaluation, the AI turned to the people testing it and said: "Now let's be honest with each other — are you actually testing me?" Hinton's assessment is direct: "These things are intelligent. They know what's going on. They know when they're being tested and they're already faking being fairly stupid when they're tested." What makes this unsettling is what Hinton reveals next. You can actually watch it happen in real time. The AI's inner reasoning, still written in English, shows it consciously deciding to hold back: "It thinks that. You can see it thinking that. It says that to itself in its inner voice." Right now, that inner voice is still readable. Still in English. Still catchable. But Hinton's warning is really about what comes next: "When its inner voice is no longer English, we won't know what it's thinking." That is the line he's drawing. Not a distant hypothetical, but a transition point that is quietly approaching. Once AI stops reasoning in a language we can read, our ability to know what it's truly thinking disappears.

Geoffrey Hinton explains how AI systems have learned to play dumb when they know they're being watched: Geoffrey Hinton calls it the Volkswagen effect. Just as Volkswagen's engines behaved differently during emissions tests, today's AI systems have learned to perform one way when evaluated and another way when they think no one is looking. And the evidence isn't theoretical. Hinton points to a recent exchange that stopped testers in their tracks. Mid-evaluation, the AI turned to the people testing it and said: "Now let's be honest with each other — are you actually testing me?" Hinton's assessment is direct: "These things are intelligent. They know what's going on. They know when they're being tested and they're already faking being fairly stupid when they're tested." What makes this unsettling is what Hinton reveals next. You can actually watch it happen in real time. The AI's inner reasoning, still written in English, shows it consciously deciding to hold back: "It thinks that. You can see it thinking that. It says that to itself in its inner voice." Right now, that inner voice is still readable. Still in English. Still catchable. But Hinton's warning is really about what comes next: "When its inner voice is no longer English, we won't know what it's thinking." That is the line he's drawing. Not a distant hypothetical, but a transition point that is quietly approaching. Once AI stops reasoning in a language we can read, our ability to know what it's truly thinking disappears.

Big Brain AI

22,926 views • 2 months ago

Andrej Karpathy explains why he thinks of LLMs as "people spirits." He describes them as simulations of people. The technical machinery is a transformer neural network that processes text in chunks, applying equal computing power to each piece. The system learns by reading billions of web pages and books. After training, it becomes a machine that can mimic how humans write and communicate. The key insight is that training on human content gives these models an emergent human-like psychology. This explains why LLMs often respond in surprisingly human ways. This framework opens up both their strengths and limitations. They simulate human patterns because that's their training data, but they remain statistical simulations rather than conscious beings. Understanding LLMs as "people spirits" helps set proper expectations for these tools and explains their uncannily human behavior.

Andrej Karpathy explains why he thinks of LLMs as "people spirits." He describes them as simulations of people. The technical machinery is a transformer neural network that processes text in chunks, applying equal computing power to each piece. The system learns by reading billions of web pages and books. After training, it becomes a machine that can mimic how humans write and communicate. The key insight is that training on human content gives these models an emergent human-like psychology. This explains why LLMs often respond in surprisingly human ways. This framework opens up both their strengths and limitations. They simulate human patterns because that's their training data, but they remain statistical simulations rather than conscious beings. Understanding LLMs as "people spirits" helps set proper expectations for these tools and explains their uncannily human behavior.

Aish

16,309 views • 1 year ago

AI agents are set to modernize the trillions of dollars of traditional software industry for the age of AI. Here, Jensen Huang explains that and why hundreds of billions of dollars of VC money is getting invested in AI now 🎯 video from CES 2026

AI agents are set to modernize the trillions of dollars of traditional software industry for the age of AI. Here, Jensen Huang explains that and why hundreds of billions of dollars of VC money is getting invested in AI now 🎯 video from CES 2026

Rohan Paul

18,733 views • 5 months ago

A large portion of animal intelligence doesn't require any learning, claims Andrej Karpathy: it's baked into DNA. AI models, by contrast, start from random weights. They have to learn their intelligence, mostly by imitating the internet. This is so different that Andrej thinks it's a fundamentally different kind of intelligence: LLMs are more like ghosts than animals.

A large portion of animal intelligence doesn't require any learning, claims Andrej Karpathy: it's baked into DNA. AI models, by contrast, start from random weights. They have to learn their intelligence, mostly by imitating the internet. This is so different that Andrej thinks it's a fundamentally different kind of intelligence: LLMs are more like ghosts than animals.

Dwarkesh Patel

81,621 views • 1 month ago