Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

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.

66,849 subscribers

35,283 views • 5 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

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. This makes their internal workings fundamentally opaque, much like the human brain.

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. This makes their internal workings fundamentally opaque, much like the human brain.

Wes Roth

49,600 views • 6 months ago

🚨GEOFFREY HINTON: WE DON'T KNOW EXACTLY HOW AI WORKS AI Pioneer Geoffrey Hinton: "We have a rough idea of what it's doing, but when it gets complicated, we don't know what's going on, similar to our understanding of the brain." 60 Minutes: "What do you mean we don't know exactly how it works? It was designed by people." Hinton: "We designed the learning algorithm, like the principle of evolution. But when it interacts with data, it creates neural networks we don't fully understand." Source: 60 Minutes, Newsy on IG

🚨GEOFFREY HINTON: WE DON'T KNOW EXACTLY HOW AI WORKS AI Pioneer Geoffrey Hinton: "We have a rough idea of what it's doing, but when it gets complicated, we don't know what's going on, similar to our understanding of the brain." 60 Minutes: "What do you mean we don't know exactly how it works? It was designed by people." Hinton: "We designed the learning algorithm, like the principle of evolution. But when it interacts with data, it creates neural networks we don't fully understand." Source: 60 Minutes, Newsy on IG

Mario Nawfal

502,988 views • 2 years ago

Geoffrey Hinton says AI knows thousands of times more than humans, even with just 1% of our neural connections Humans live only about two billion seconds, limiting what we can learn, while AI trains on trillions of words They aren't like us, but already far more knowledgeable

Geoffrey Hinton says AI knows thousands of times more than humans, even with just 1% of our neural connections Humans live only about two billion seconds, limiting what we can learn, while AI trains on trillions of words They aren't like us, but already far more knowledgeable

Haider.

194,419 views • 7 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

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 says the more we understand how AI and the brain actually work, the less human thinking looks like logic. We're not reasoning machines, he says. We're analogy machines. We think by resonance, not deduction. “We're much less rational than we thought.”

Geoffrey Hinton says the more we understand how AI and the brain actually work, the less human thinking looks like logic. We're not reasoning machines, he says. We're analogy machines. We think by resonance, not deduction. “We're much less rational than we thought.”

vitrupo

770,193 views • 1 year ago

Francois Chollet says LLMs aren't enough for human-like continual learning They store skills as vector programs learned via gradient descent — not efficient, not adaptive True AGI needs to learn from experience and generalize fast "LLMs can be part of AGI, but not the solution"

Francois Chollet says LLMs aren't enough for human-like continual learning They store skills as vector programs learned via gradient descent — not efficient, not adaptive True AGI needs to learn from experience and generalize fast "LLMs can be part of AGI, but not the solution"

Haider.

50,251 views • 8 months 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 • 5 months ago

Geoffrey Hinton says LLMs are immortal. Destroy the hardware, save the weights. Rebuild them later, and “the very same being comes back to life.” But humans don't work that way. We're bound to our substrate. Not even Kurzweil gets a backup.

Geoffrey Hinton says LLMs are immortal. Destroy the hardware, save the weights. Rebuild them later, and “the very same being comes back to life.” But humans don't work that way. We're bound to our substrate. Not even Kurzweil gets a backup.

vitrupo

323,189 views • 11 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

Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"

Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"

Haider.

347,295 views • 1 month ago

"Rather than have lines like 'this is what we're doing, [...] we must do this, Matt would have a lot of lines like [...]"what do you think we should do? I'm not sure. What if we do that?" Charlie, I don't think a single one of those lines made it in the final cut bro. #Daredevil

"Rather than have lines like 'this is what we're doing, [...] we must do this, Matt would have a lot of lines like [...]"what do you think we should do? I'm not sure. What if we do that?" Charlie, I don't think a single one of those lines made it in the final cut bro. #Daredevil

Scarlett

65,503 views • 2 months ago

Geoffrey Hinton says AI hallucinations should actually be called confabulations Neither human brains nor AI models store memories in a filing cabinet; both construct recall on the fly using connection strengths Because of this, both will confidently generate plausible answers without knowing the ground truth

Geoffrey Hinton says AI hallucinations should actually be called confabulations Neither human brains nor AI models store memories in a filing cabinet; both construct recall on the fly using connection strengths Because of this, both will confidently generate plausible answers without knowing the ground truth

Haider.

74,606 views • 4 months ago

Cenk Uygur: “What I want everybody to understand, both on the left and the right, is maybe we are individual human beings, and we are not born with an ideology that fits preexisting buckets.” “Why don't I say, ‘hey you know what, I like that on the left, I like that on the right, I like that in the middle, and that's because I'm a human being, I'm an individual, I'm an American.’” “Stop listening to your leaders telling you what you have to believe.”

Cenk Uygur: “What I want everybody to understand, both on the left and the right, is maybe we are individual human beings, and we are not born with an ideology that fits preexisting buckets.” “Why don't I say, ‘hey you know what, I like that on the left, I like that on the right, I like that in the middle, and that's because I'm a human being, I'm an individual, I'm an American.’” “Stop listening to your leaders telling you what you have to believe.”

End Tribalism in Politics

54,164 views • 1 year ago

Rep. Eric Burlison believes the UFO coverup exists, not because the Govt is hiding secret technology, but because of the embarrassment of not knowing what they are. "We don't want our adversaries to know that we don't know" This is more confirmation that the UFOs we see are Galactic Federation, not a secret human UFO program. The dark forces on Earth don't want us to know that the light forces are in the crafts.

Rep. Eric Burlison believes the UFO coverup exists, not because the Govt is hiding secret technology, but because of the embarrassment of not knowing what they are. "We don't want our adversaries to know that we don't know" This is more confirmation that the UFOs we see are Galactic Federation, not a secret human UFO program. The dark forces on Earth don't want us to know that the light forces are in the crafts.

Kab

25,611 views • 5 months ago

Geoffrey Hinton says people still think LLMs are different from us, but they’re not LLMs don’t just generate words; they reflect how we process meaning: "they’re our best model of how language works" old linguistic theories failed to explain language; neural nets using feature vectors finally do

Geoffrey Hinton says people still think LLMs are different from us, but they’re not LLMs don’t just generate words; they reflect how we process meaning: "they’re our best model of how language works" old linguistic theories failed to explain language; neural nets using feature vectors finally do

Haider.

193,258 views • 1 year ago

Scientists used 200,000 human brain cells to play DOOM. The cells had never seen a computer. They learned in real time. They got better. Separately: an entire fruit fly brain, 139,000 neurons, 50 million connections, was copied into a laptop. It predicts real brain responses with 90%+ accuracy. The biological computing timeline: 2021 → neurons learn Pong 2022 → fruit fly brain fully mapped 2024 → entire fly brain simulated on a laptop 2026 → human brain cells play DOOM A GPU needs ~700W to run an LLM. The brain runs the whole show on 20W. We're spending trillions on building the next giant AI training computer but what if the solution to our energy needs was just copying the technology already running in our heads?

Scientists used 200,000 human brain cells to play DOOM. The cells had never seen a computer. They learned in real time. They got better. Separately: an entire fruit fly brain, 139,000 neurons, 50 million connections, was copied into a laptop. It predicts real brain responses with 90%+ accuracy. The biological computing timeline: 2021 → neurons learn Pong 2022 → fruit fly brain fully mapped 2024 → entire fly brain simulated on a laptop 2026 → human brain cells play DOOM A GPU needs ~700W to run an LLM. The brain runs the whole show on 20W. We're spending trillions on building the next giant AI training computer but what if the solution to our energy needs was just copying the technology already running in our heads?

Josh Kale

135,856 views • 4 months ago

"We built Jarvis!" Unaligned #11: Carlos Mats, founder and CEO of IKA Platform. He says “What would the software of 2030 look like?” Of course we talk about Apple’s Vision Pro and what his new AI-fed productivity software would work like. “We built a whole operating system around AI.”

"We built Jarvis!" Unaligned #11: Carlos Mats, founder and CEO of IKA Platform. He says “What would the software of 2030 look like?” Of course we talk about Apple’s Vision Pro and what his new AI-fed productivity software would work like. “We built a whole operating system around AI.”

Robert Scoble

50,083 views • 2 years ago

Fei-Fei Li says, she disagrees with Geoffrey Hinton, warning that there's a 10 to 20% chance that AI leads to human extinction If the human race is in trouble, that's a result of humans doing wrong things, not machines doing wrong things

Fei-Fei Li says, she disagrees with Geoffrey Hinton, warning that there's a 10 to 20% chance that AI leads to human extinction If the human race is in trouble, that's a result of humans doing wrong things, not machines doing wrong things

Haider.

51,746 views • 7 months ago

Geoffrey Hinton says AIs may already have subjective experiences, but don't realize it because their sense of self is built from our mistaken beliefs about consciousness.

Geoffrey Hinton says AIs may already have subjective experiences, but don't realize it because their sense of self is built from our mistaken beliefs about consciousness.

vitrupo

256,799 views • 9 months ago