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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

Haider.

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35,431 次观看 • 7 个月前 •via X (Twitter)

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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. 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.

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49,600 次观看 • 7 个月前

🚨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 次观看 • 2 年前

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.

194,431 次观看 • 9 个月前

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

19,043 次观看 • 5 个月前

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.

81,325 次观看 • 3 个月前

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 次观看 • 10 个月前

📁 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

77,752 次观看 • 7 个月前

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 次观看 • 1 年前

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.

347,799 次观看 • 3 个月前

DJ Quik says AI is a total violation of culture: "It's a total violation of culture. It's appropriation. You can't recreate somebody that's still here doing what they do. Don't use my stuff because I know that computer is not gonna have my nuances. I watch AI programs on YouTube and they mispronounce words. It ain't human. I'd rather have bad human material than good AI material. How bout that"

DJ Quik says AI is a total violation of culture: "It's a total violation of culture. It's appropriation. You can't recreate somebody that's still here doing what they do. Don't use my stuff because I know that computer is not gonna have my nuances. I watch AI programs on YouTube and they mispronounce words. It ain't human. I'd rather have bad human material than good AI material. How bout that"

Ahmed/The Ears/IG: BigBizTheGod 🇸🇴

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"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

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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.

74,650 次观看 • 6 个月前

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 次观看 • 6 个月前

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

136,072 次观看 • 5 个月前

"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

50,217 次观看 • 2 年前

Geoffrey Hinton says mathematics is a closed system, so AIs can play it like a game. They can pose problems to themselves, test proofs, and learn from what works, without relying on human examples. “I think AI will get much better at mathematics than people, maybe in the next 10 years or so.”

Geoffrey Hinton says mathematics is a closed system, so AIs can play it like a game. They can pose problems to themselves, test proofs, and learn from what works, without relying on human examples. “I think AI will get much better at mathematics than people, maybe in the next 10 years or so.”

vitrupo

818,520 次观看 • 7 个月前

Hinton, the godfather of AI, said it best: we built the learning algorithms, but we no longer understand what they’ve built. That’s the paradox of deep learning. We designed the rules for how these systems learn, yet the internal logic of their neural networks has become too complex for us to fully grasp. Millions or even trillions of parameters interact in ways no human can trace. We can observe what they do, we can measure accuracy, behavior, and output but not truly explain why they do it. Their reasoning isn’t transparent; it’s emergent. In a sense, we’ve created alien intelligences born from our math, still tethered to our code yet evolving patterns we can’t decode. The machines are doing something beyond our comprehension and that might be both the most exciting and the most unsettling thing about the age of AI.

Hinton, the godfather of AI, said it best: we built the learning algorithms, but we no longer understand what they’ve built. That’s the paradox of deep learning. We designed the rules for how these systems learn, yet the internal logic of their neural networks has become too complex for us to fully grasp. Millions or even trillions of parameters interact in ways no human can trace. We can observe what they do, we can measure accuracy, behavior, and output but not truly explain why they do it. Their reasoning isn’t transparent; it’s emergent. In a sense, we’ve created alien intelligences born from our math, still tethered to our code yet evolving patterns we can’t decode. The machines are doing something beyond our comprehension and that might be both the most exciting and the most unsettling thing about the age of AI.

VraserX e/acc

376,491 次观看 • 9 个月前

David Chalmers says we don't know what makes a system conscious, not in brains, not in AIs The hard problem applies to both. If both compute, why couldn't both be conscious? LLMs can look very human, but they're trained in ways humans never are "they imitate us, but we don't know what's missing, or what consciousness needs"

David Chalmers says we don't know what makes a system conscious, not in brains, not in AIs The hard problem applies to both. If both compute, why couldn't both be conscious? LLMs can look very human, but they're trained in ways humans never are "they imitate us, but we don't know what's missing, or what consciousness needs"

Haider.

26,951 次观看 • 7 个月前

Investigation finds tech companies like Google are building Data Centers hidden behind deceptively named LLCs to hide what they’re doing Across the US, the locations of 1,240 data centers being built have been discovered They are even redacted records to hide what they’re doing, “It's been really tricky to kind of get these records because the companies don't want to disclose all of that information. By tricky we mean redacted records and requests denied on the grounds of trade secrets.”

Investigation finds tech companies like Google are building Data Centers hidden behind deceptively named LLCs to hide what they’re doing Across the US, the locations of 1,240 data centers being built have been discovered They are even redacted records to hide what they’re doing, “It's been really tricky to kind of get these records because the companies don't want to disclose all of that information. By tricky we mean redacted records and requests denied on the grounds of trade secrets.”

Wall Street Apes

178,657 次观看 • 9 个月前