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.

Yann LeCun says LLMs are intrinsically unsafe because they cannot be made fully reliable They can still hallucinate or take agentic actions without predicting the consequences properly "coding works because you can verify the output, but real-world tasks are harder to verify"

Haider.

68,744 subscribers

33,332 views • 2 months ago •via X (Twitter)

Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

“An AI that can act before it understands is not reliable infrastructure.” Yann LeCun, Meta’s Chief AI Scientist and one of the fathers of modern deep learning, says current LLMs are intrinsically unsafe. Not because they are evil, but because they hallucinate, lack common sense, and cannot reliably predict the consequences of agentic actions.

“An AI that can act before it understands is not reliable infrastructure.” Yann LeCun, Meta’s Chief AI Scientist and one of the fathers of modern deep learning, says current LLMs are intrinsically unsafe. Not because they are evil, but because they hallucinate, lack common sense, and cannot reliably predict the consequences of agentic actions.

Jon Hernandez

10,497 views • 2 months ago

“LLMs are intrinsically unsafe.” Yann LeCun argues that models that can’t predict the consequences of their actions can’t be made reliable.

“LLMs are intrinsically unsafe.” Yann LeCun argues that models that can’t predict the consequences of their actions can’t be made reliable.

Jacob Effron

42,383 views • 2 months ago

Yann LeCun says you cannot build a reliable agentic system without a world model LLMs don't have world models. They can't predict the consequences of their actions before taking them "they just act, and whatever happens next is someone else's problem" Without that, it's not intelligence

Yann LeCun says you cannot build a reliable agentic system without a world model LLMs don't have world models. They can't predict the consequences of their actions before taking them "they just act, and whatever happens next is someone else's problem" Without that, it's not intelligence

Haider.

332,503 views • 3 months ago

Yann LeCun argues that large language models (LLMs) cannot reach human-level or superintelligence just by scaling. He says the current LLM paradigm is hitting its limits. Many researchers are now exploring “agentic systems,” but building them on top of LLMs alone is flawed. LLMs can't plan actions well because they don’t truly understand or predict consequences. To get intelligent behavior, we need something fundamentally different.

Yann LeCun argues that large language models (LLMs) cannot reach human-level or superintelligence just by scaling. He says the current LLM paradigm is hitting its limits. Many researchers are now exploring “agentic systems,” but building them on top of LLMs alone is flawed. LLMs can't plan actions well because they don’t truly understand or predict consequences. To get intelligent behavior, we need something fundamentally different.

Wes Roth

71,832 views • 6 months ago

Yann LeCun says robotics could become a huge market, but the industry still doesn't know how to make humanoid robots useful The missing piece is AI that can understand the physical world and handle tasks with generality and adaptability "LLMs are useful, but for different problems"

Yann LeCun says robotics could become a huge market, but the industry still doesn't know how to make humanoid robots useful The missing piece is AI that can understand the physical world and handle tasks with generality and adaptability "LLMs are useful, but for different problems"

Haider.

13,727 views • 2 months ago

Yann LeCun says the real world is far more complex than the world of language LLMs can accumulate knowledge, but they fail with high-dimensional, continuous, noisy sensory data "the next revolution is physical AI" Systems that can truly plan, reason, and understand the physical environment

Yann LeCun says the real world is far more complex than the world of language LLMs can accumulate knowledge, but they fail with high-dimensional, continuous, noisy sensory data "the next revolution is physical AI" Systems that can truly plan, reason, and understand the physical environment

Haider.

68,894 views • 6 months 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.

347,702 views • 2 months ago

Yann LeCun says language isn’t intelligence. Predicting text doesn’t mean understanding reality. The real world is messy, physical, and causal and today’s LLMs barely touch that. The next leap is Physical AI: world models, cause and effect, real planning. Do you think LLMs can evolve into this, or do we need a completely new architecture?

Yann LeCun says language isn’t intelligence. Predicting text doesn’t mean understanding reality. The real world is messy, physical, and causal and today’s LLMs barely touch that. The next leap is Physical AI: world models, cause and effect, real planning. Do you think LLMs can evolve into this, or do we need a completely new architecture?

VraserX e/acc

76,194 views • 6 months ago

AI agents are coming. So is the need to verify what they do. AEREDIUM brings policy and proof into silicon - verifying actions before they happen. Trust cannot be optional in an autonomous world.

AI agents are coming. So is the need to verify what they do. AEREDIUM brings policy and proof into silicon - verifying actions before they happen. Trust cannot be optional in an autonomous world.

AEREDIUM

979,954 views • 1 month ago

Yann LeCun (AMI Labs Founder): "The AI industry is completely LLM-pilled. Everybody is working on the same thing. They're all digging the same trench." LeCun explains why no lab dares break from the pack: "They are stealing each other's engineers. So they can't afford to do something different because if they start going on a tangent, they're going to fall behind the other guys. And so they're all doing the same thing." This groupthink is exactly what drove him out of Meta. "Meta also became LLM-pilled with sort of recent reshuffling. And it's fine, a strategic decision that maybe makes sense for them. It's just not what I'm interested in." For Yann LeCun, the problem runs deeper than strategy. LLMs are missing something essential about how intelligence actually works: "I cannot imagine that we can build agentic systems without those systems having an ability to predict in advance what the consequences of their actions are going to be. The way we act in the world is that we can predict the consequences of our actions and that's what allows us to plan." His broader critique is that the industry has mistaken fluency for intelligence. Language turned out to be the easy part. The hard part is the physical world. It's why we still don't have domestic robots or level-five self-driving cars, even though today's systems can pass the bar exam and write code.

Yann LeCun (AMI Labs Founder): "The AI industry is completely LLM-pilled. Everybody is working on the same thing. They're all digging the same trench." LeCun explains why no lab dares break from the pack: "They are stealing each other's engineers. So they can't afford to do something different because if they start going on a tangent, they're going to fall behind the other guys. And so they're all doing the same thing." This groupthink is exactly what drove him out of Meta. "Meta also became LLM-pilled with sort of recent reshuffling. And it's fine, a strategic decision that maybe makes sense for them. It's just not what I'm interested in." For Yann LeCun, the problem runs deeper than strategy. LLMs are missing something essential about how intelligence actually works: "I cannot imagine that we can build agentic systems without those systems having an ability to predict in advance what the consequences of their actions are going to be. The way we act in the world is that we can predict the consequences of our actions and that's what allows us to plan." His broader critique is that the industry has mistaken fluency for intelligence. Language turned out to be the easy part. The hard part is the physical world. It's why we still don't have domestic robots or level-five self-driving cars, even though today's systems can pass the bar exam and write code.

Big Brain AI

288,789 views • 3 months ago

.Speaker Mike Johnson: "The reason the ceasefire is fragile is because you have Iran as one of the parties. They have demonstrated for us over the decades that they are not to be trusted. Ronald Reagan said 'trust but verify.' We're now in a posture where we verify and then trust."

.Speaker Mike Johnson: "The reason the ceasefire is fragile is because you have Iran as one of the parties. They have demonstrated for us over the decades that they are not to be trusted. Ronald Reagan said 'trust but verify.' We're now in a posture where we verify and then trust."

CSPAN

31,867 views • 1 year ago

📁 Demis Hassabis, CEO of DeepMind, says AI is still missing critical capabilities. He explains that current systems cannot plan far ahead or reason like top human scientists, because they lack a deep model of the world. They can process text, images, and video, but they still don’t understand physics, causality, or how actions affect outcomes over time.

📁 Demis Hassabis, CEO of DeepMind, says AI is still missing critical capabilities. He explains that current systems cannot plan far ahead or reason like top human scientists, because they lack a deep model of the world. They can process text, images, and video, but they still don’t understand physics, causality, or how actions affect outcomes over time.

Jon Hernandez

63,723 views • 6 months ago

You can’t go to war without a plan. And you can’t expect to last in bed with a woman without a strategy. Men are failing not because they lack the desire. But because they lack the process. They know what they should do… But not how they should do it. You can struggle to overcome it on your own… Or you can leverage the teachings of a 25 year veteran. Your choice. Choose wisely.

Sensitive content

You can’t go to war without a plan. And you can’t expect to last in bed with a woman without a strategy. Men are failing not because they lack the desire. But because they lack the process. They know what they should do… But not how they should do it. You can struggle to overcome it on your own… Or you can leverage the teachings of a 25 year veteran. Your choice. Choose wisely.

Erik Everhard

38,678 views • 2 years ago

Kevin Hassett: "The data can't be propaganda. The data has to be something you can trust, because decision-makers throughout the economy trust that these are the data that they can build a factory because they believe, or cut interest rates because they believe. And if the data aren't that good, then it's a real problem for the US."

Kevin Hassett: "The data can't be propaganda. The data has to be something you can trust, because decision-makers throughout the economy trust that these are the data that they can build a factory because they believe, or cut interest rates because they believe. And if the data aren't that good, then it's a real problem for the US."

Aaron Rupar

599,432 views • 1 year ago

Yann LeCun says we're fooled by LLMs because they manipulate language well, and we associate that with intelligence But language fluency doesn't mean underlying intelligence Every generation since the 1950s claimed its technique was the ticket to human-level AI All were wrong. "this generation with LLMs is also wrong"

Yann LeCun says we're fooled by LLMs because they manipulate language well, and we associate that with intelligence But language fluency doesn't mean underlying intelligence Every generation since the 1950s claimed its technique was the ticket to human-level AI All were wrong. "this generation with LLMs is also wrong"

Haider.

625,692 views • 8 months ago

📁 Yann LeCun says that AGI does not exist and is a poorly defined concept built around an idealized view of human intelligence. He argues that humans are highly specialized, good at navigating the physical and social world, but weak at many other tasks. We seem general only because we can imagine the problems we are good at. For LeCun, AGI mistakes human limits for universal laws of intelligence.

📁 Yann LeCun says that AGI does not exist and is a poorly defined concept built around an idealized view of human intelligence. He argues that humans are highly specialized, good at navigating the physical and social world, but weak at many other tasks. We seem general only because we can imagine the problems we are good at. For LeCun, AGI mistakes human limits for universal laws of intelligence.

Jon Hernandez

28,104 views • 7 months ago

So not only are they still wearing masks but they are also wearing safety glasses because you can catch COVID through the eyes🤣🤣🤣

So not only are they still wearing masks but they are also wearing safety glasses because you can catch COVID through the eyes🤣🤣🤣

Brandon

13,169 views • 6 months ago

Who are you going to be supporting in 2024? Trump. Because Trump is real. People can see the fake and they are tired of it.

Who are you going to be supporting in 2024? Trump. Because Trump is real. People can see the fake and they are tired of it.

Insurrection Barbie

633,308 views • 1 year ago

AMI Labs founder Yann LeCun on why LLMs are fooling us the same way AI has for decades: He argues that every generation of AI scientists has made the same mistake: confusing task performance with real intelligence. LeCun's core challenge to the current hype: "We're fooled into thinking those machines are intelligent because they can manipulate language. And we're used to the fact that people who can manipulate language very well are implicitly smart." He's clear that LLMs are useful, but being a useful tool and being intelligent are two very different things. The real insight is the historical pattern he's lived through. Since the 1950s, wave after wave of AI researchers have claimed their breakthrough was the path to human-level intelligence. Marvin Minsky. Herbert Simon. Frank Rosenblatt — who invented the perceptron, the first learning machine, in the 1950s — all predicted machines as smart as humans within a decade. "They were all wrong." LeCun has personally witnessed three of these cycles of hype and disappointment. And his verdict on the current one is blunt: "This generation with LLMs is also wrong. It's just another example of being fooled." The pattern: A new technique emerges → machines get good at specific tasks → we assume general intelligence The question worth asking: are we impressed by these tools because they're intelligent, or because they sound like they are?

AMI Labs founder Yann LeCun on why LLMs are fooling us the same way AI has for decades: He argues that every generation of AI scientists has made the same mistake: confusing task performance with real intelligence. LeCun's core challenge to the current hype: "We're fooled into thinking those machines are intelligent because they can manipulate language. And we're used to the fact that people who can manipulate language very well are implicitly smart." He's clear that LLMs are useful, but being a useful tool and being intelligent are two very different things. The real insight is the historical pattern he's lived through. Since the 1950s, wave after wave of AI researchers have claimed their breakthrough was the path to human-level intelligence. Marvin Minsky. Herbert Simon. Frank Rosenblatt — who invented the perceptron, the first learning machine, in the 1950s — all predicted machines as smart as humans within a decade. "They were all wrong." LeCun has personally witnessed three of these cycles of hype and disappointment. And his verdict on the current one is blunt: "This generation with LLMs is also wrong. It's just another example of being fooled." The pattern: A new technique emerges → machines get good at specific tasks → we assume general intelligence The question worth asking: are we impressed by these tools because they're intelligent, or because they sound like they are?

Big Brain AI

126,706 views • 6 months ago

Because the Covid Shots are still manufactured under the Emergency Use Authorization, they can change up to 49% of the ingredients without FDA approval.....

Because the Covid Shots are still manufactured under the Emergency Use Authorization, they can change up to 49% of the ingredients without FDA approval.....

Diamond and Silk®

420,615 views • 3 years ago