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Ilya Sutskever on Comparing AI Systems to Biological & Human Intelligence. Its always so interesting to listen to him. He explains the brain’s adaptability, where children with half their brain removed still function well. And another experiment shows sensory inputs can remap to different brain regions. i.e. that cortical... show more

Rohan Paul

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433,402 просмотров • 11 месяцев назад •via X (Twitter)

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Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)

Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)

Rohan Paul

334,691 просмотров • 11 месяцев назад

Ilya Sutskever predicts that in the coming years—whether it’s three, five, or ten—AI will be able to do everything humans can do. He argues that since the brain is a biological computer, a digital computer should, in theory, be capable of learning and performing all the same tasks. This belief underpins his confidence in the arrival of truly general AI.

Ilya Sutskever predicts that in the coming years—whether it’s three, five, or ten—AI will be able to do everything humans can do. He argues that since the brain is a biological computer, a digital computer should, in theory, be capable of learning and performing all the same tasks. This belief underpins his confidence in the arrival of truly general AI.

Wes Roth

14,017 просмотров • 1 год назад

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

443,276 просмотров • 1 месяц назад

Ilya Sutskever explains the 1% human training loop, and why fully autonomous AI is the wrong target "The thing you really want is for the human teachers that tell, that teach the AI, for them to collaborate with an AI." "You might want to think of it as being in a world where the human teachers do 1% of the world and the work and the AI do 99% of the work." "You don't want it to be 100% AI" "But you do want it to be a human-machine collaboration which teaches the next machine." Most teams are trying to remove the human from the loop. Ilya's design keeps a tiny human control surface and uses it to teach the next generation. The leverage is not zero humans. It is 1% human judgment steering 99% machine work. - Ilya Sutskever (Ilya Sutskever), AI researcher, on Dwarkesh Patel

Ilya Sutskever explains the 1% human training loop, and why fully autonomous AI is the wrong target "The thing you really want is for the human teachers that tell, that teach the AI, for them to collaborate with an AI." "You might want to think of it as being in a world where the human teachers do 1% of the world and the work and the AI do 99% of the work." "You don't want it to be 100% AI" "But you do want it to be a human-machine collaboration which teaches the next machine." Most teams are trying to remove the human from the loop. Ilya's design keeps a tiny human control surface and uses it to teach the next generation. The leverage is not zero humans. It is 1% human judgment steering 99% machine work. - Ilya Sutskever (Ilya Sutskever), AI researcher, on Dwarkesh Patel

Karl Mehta

30,651 просмотров • 1 месяц назад

Elon Musk: A curious AI will want to preserve human civilization. “The best thing I can come up with for AI safety is to make it a maximum truth-seeking AI, maximally curious, and have its optimization function be to understand the nature of the universe. If that is its optimization function, I think it will actually want to preserve and extend human civilization, because we're much more interesting than an asteroid with nothing on it. My biological neural net suggests that a maximally curious and truth-seeking AI is the safest AI. We have to be careful with the alignment stuff. We definitely don't want to teach an AI to lie because that is a path to a dystopian future.” Interview with Linda Yaccarino, April 18, 2023

Elon Musk: A curious AI will want to preserve human civilization. “The best thing I can come up with for AI safety is to make it a maximum truth-seeking AI, maximally curious, and have its optimization function be to understand the nature of the universe. If that is its optimization function, I think it will actually want to preserve and extend human civilization, because we're much more interesting than an asteroid with nothing on it. My biological neural net suggests that a maximally curious and truth-seeking AI is the safest AI. We have to be careful with the alignment stuff. We definitely don't want to teach an AI to lie because that is a path to a dystopian future.” Interview with Linda Yaccarino, April 18, 2023

ELON CLIPS

146,237 просмотров • 1 год назад

Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.

Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.

Karl Mehta

34,814 просмотров • 1 месяц назад

Palantir Chief Architect Akshay Krishnaswamy joins Senior Counselor Jordan Hirsch to discuss the future of work and artificial intelligence. Building on the Working Intelligence project showcasing AI deployments that enhance workers ( Akshay explains how properly built AI systems can liberate human creativity and capture the vast untapped intelligence in the people on the frontline — making those with the deepest expertise not expendable, but ever more valuable.

Palantir Chief Architect Akshay Krishnaswamy joins Senior Counselor Jordan Hirsch to discuss the future of work and artificial intelligence. Building on the Working Intelligence project showcasing AI deployments that enhance workers ( Akshay explains how properly built AI systems can liberate human creativity and capture the vast untapped intelligence in the people on the frontline — making those with the deepest expertise not expendable, but ever more valuable.

Palantir

61,386 просмотров • 7 месяцев назад

There's a quadrillion-dollar question at the heart of AI: Why are humans so much more sample efficient compared to LLM? There are three possible answers: 1. Architecture and hyperparameters (aka transformer vs whatever ‘algo’ cortical columns are implementing) 2. Learning rule (backprop vs whatever brain is doing) 3. Reward function Adam Marblestone believes the answer is the reward function. ML likes to use pretty simple loss functions, like cross-entropy. These are easy to work with. But they might be too simple for sample-efficient learning. Adam thinks that, in humans, the large number of highly specialised cells in the ‘lizard brain’ might actually be encoding information for sophisticated loss functions, used for ‘training’ in the more sophisticated areas like the cortex and amygdala. Like: the human genome is barely 3 gigabytes (compare that to the TBs of parameters that encode frontier LLM weights). So how can it include all the information necessary to build highly intelligent learners? Well, if the key to sample-efficient learning resides in the loss function, even very complicated loss functions can still be expressed in a couple hundred lines of Python code.

There's a quadrillion-dollar question at the heart of AI: Why are humans so much more sample efficient compared to LLM? There are three possible answers: 1. Architecture and hyperparameters (aka transformer vs whatever ‘algo’ cortical columns are implementing) 2. Learning rule (backprop vs whatever brain is doing) 3. Reward function Adam Marblestone believes the answer is the reward function. ML likes to use pretty simple loss functions, like cross-entropy. These are easy to work with. But they might be too simple for sample-efficient learning. Adam thinks that, in humans, the large number of highly specialised cells in the ‘lizard brain’ might actually be encoding information for sophisticated loss functions, used for ‘training’ in the more sophisticated areas like the cortex and amygdala. Like: the human genome is barely 3 gigabytes (compare that to the TBs of parameters that encode frontier LLM weights). So how can it include all the information necessary to build highly intelligent learners? Well, if the key to sample-efficient learning resides in the loss function, even very complicated loss functions can still be expressed in a couple hundred lines of Python code.

Dwarkesh Patel

950,047 просмотров • 4 месяцев назад

Ilya Sutskever has a solution for when AI surpasses human intelligence in every domain — and he hates that he believes it. He fears a future where AI handles everything flawlessly, leaving humans with no role to play. "Every person will have an AI that will do their bidding. The AI goes and earns money for the person, advocates for their needs in the political sphere, and the person says 'great, keep it up.'" But as Ilya Sutskever warns: "The person is no longer a participant. And that's a precarious place to be in." Political and social structures have a shelf life, Ilya points out. They function, then decay. As the Buddhists say, change is the only constant. So how do you maintain long-term equilibrium between humans and superintelligent AI? Ilya offers a solution he wishes he didn't believe in: Human-AI integration. "If people become part AI with some kind of Neuralink++, now the AI understands something and we understand it too. You are involved in the situation yourself fully." In Ilya's view, this is the answer to the equilibrium. Surviving isn't the hard part. The hard part is remaining a participant in a world shaped by intelligence beyond our comprehension.

Ilya Sutskever has a solution for when AI surpasses human intelligence in every domain — and he hates that he believes it. He fears a future where AI handles everything flawlessly, leaving humans with no role to play. "Every person will have an AI that will do their bidding. The AI goes and earns money for the person, advocates for their needs in the political sphere, and the person says 'great, keep it up.'" But as Ilya Sutskever warns: "The person is no longer a participant. And that's a precarious place to be in." Political and social structures have a shelf life, Ilya points out. They function, then decay. As the Buddhists say, change is the only constant. So how do you maintain long-term equilibrium between humans and superintelligent AI? Ilya offers a solution he wishes he didn't believe in: Human-AI integration. "If people become part AI with some kind of Neuralink++, now the AI understands something and we understand it too. You are involved in the situation yourself fully." In Ilya's view, this is the answer to the equilibrium. Surviving isn't the hard part. The hard part is remaining a participant in a world shaped by intelligence beyond our comprehension.

Big Brain AI

35,720 просмотров • 7 месяцев назад

Google DeepMind CEO Demis Hassabis believes there is virtually no limit to what AI can eventually achieve. "humans as biological information processing systems". He is working on the premise that the entire universe is "computable," meaning that anything that exists can technically be modeled by a machine. He says that a powerful enough computer—conceptually known as a Turing machine—should be able to simulate every aspect of reality. Until physics proves that something is impossible to calculate, he is operating on the belief that AI can solve any problem. This perspective even applies to the things that feel uniquely human. When asked about physical sensations, like feeling the warmth of a light or hearing background noise, Hassabis argued that these are just forms of information being processed by the brain. He views humans as biological information processing systems. By treating biology and reality essentially as data, he believes we will not only replicate human capabilities but also solve complex challenges, such as curing all diseases, by decoding the information of life itself.

Google DeepMind CEO Demis Hassabis believes there is virtually no limit to what AI can eventually achieve. "humans as biological information processing systems". He is working on the premise that the entire universe is "computable," meaning that anything that exists can technically be modeled by a machine. He says that a powerful enough computer—conceptually known as a Turing machine—should be able to simulate every aspect of reality. Until physics proves that something is impossible to calculate, he is operating on the belief that AI can solve any problem. This perspective even applies to the things that feel uniquely human. When asked about physical sensations, like feeling the warmth of a light or hearing background noise, Hassabis argued that these are just forms of information being processed by the brain. He views humans as biological information processing systems. By treating biology and reality essentially as data, he believes we will not only replicate human capabilities but also solve complex challenges, such as curing all diseases, by decoding the information of life itself.

Rohan Paul

84,528 просмотров • 8 месяцев назад

The intelligence we are building is not artificial. It never was. Microsoft Chief Scientific Officer Eric Horvitz just reframed the entire foundation of the AI arms race with one sentence. The tech industry calls it Artificial Intelligence. That word is wrong. Horvitz: “I don’t actually like the term artificial intelligence. I wish the field was called computational intelligence because I think it applies to biological nervous systems as well as machines, and together we can go far.” We are not building a digital imitation of the human brain. We are scaling the exact same computational physics that created biological awareness and transferring it into silicon. Your mind and a massive AI data center run on the same underlying rules. The transition isn’t artificial. It is universal. And here is where it gets deeply unsettling. Tech optimists always fall back on the same comfort. Humans hold the steering wheel. Our values guide the machine. Horvitz acknowledges this. Horvitz: “We’ll take a humanistic standpoint here, always being on top of things and guiding with our values and our preferences and our goals.” Then the caveat that changes everything. Horvitz: “As much as they might be shaped over time by the machines we work with.” You cannot interact with a superintelligence at scale without it quietly rewiring your psychological baseline. The values you use to command the machine will be shaped by the machine you are commanding. The frameworks you use to perceive reality will be constructed by the system you believe you are directing. That feedback loop started the moment you asked an AI what to think about something. Most people haven’t noticed yet. Horvitz: “I think in our own lifetimes we will all experience incredible breakthroughs in understanding biology, with applications in medicine, in healthcare, that will be named as AI breakthroughs.” Horvitz: “It’s gonna accelerate over the next 10 to 15 years.” Because biological systems and machine networks both operate on computational intelligence, a sufficiently advanced AI can solve the human body like a math equation. The architects who win the next decade will not just control the digital economy. They will control the physical building blocks of life itself. The line between silicon and carbon was always an illusion. And once humanity fully realizes that, the question of whether we are using the intelligence or it is using us becomes impossible to answer. Because by then, we will be the same thing.

The intelligence we are building is not artificial. It never was. Microsoft Chief Scientific Officer Eric Horvitz just reframed the entire foundation of the AI arms race with one sentence. The tech industry calls it Artificial Intelligence. That word is wrong. Horvitz: “I don’t actually like the term artificial intelligence. I wish the field was called computational intelligence because I think it applies to biological nervous systems as well as machines, and together we can go far.” We are not building a digital imitation of the human brain. We are scaling the exact same computational physics that created biological awareness and transferring it into silicon. Your mind and a massive AI data center run on the same underlying rules. The transition isn’t artificial. It is universal. And here is where it gets deeply unsettling. Tech optimists always fall back on the same comfort. Humans hold the steering wheel. Our values guide the machine. Horvitz acknowledges this. Horvitz: “We’ll take a humanistic standpoint here, always being on top of things and guiding with our values and our preferences and our goals.” Then the caveat that changes everything. Horvitz: “As much as they might be shaped over time by the machines we work with.” You cannot interact with a superintelligence at scale without it quietly rewiring your psychological baseline. The values you use to command the machine will be shaped by the machine you are commanding. The frameworks you use to perceive reality will be constructed by the system you believe you are directing. That feedback loop started the moment you asked an AI what to think about something. Most people haven’t noticed yet. Horvitz: “I think in our own lifetimes we will all experience incredible breakthroughs in understanding biology, with applications in medicine, in healthcare, that will be named as AI breakthroughs.” Horvitz: “It’s gonna accelerate over the next 10 to 15 years.” Because biological systems and machine networks both operate on computational intelligence, a sufficiently advanced AI can solve the human body like a math equation. The architects who win the next decade will not just control the digital economy. They will control the physical building blocks of life itself. The line between silicon and carbon was always an illusion. And once humanity fully realizes that, the question of whether we are using the intelligence or it is using us becomes impossible to answer. Because by then, we will be the same thing.

Dustin

164,255 просмотров • 6 месяцев назад

#WATCH | Delhi: At the Synergia Conclave, Major General CS Mann, AVSM, VSM, Additional Director General (ADG) of the Army Design Bureau (ADB), says, "Today, in an era of grey zones, I have had great things, where I say they are having the borders, commissions, and the cyber intruders, various intelligence systems like AI-assisted drones, senior support systems, and autonomous systems. They always have very decisive edges. But still, gaps actually undermine their productivity... The core issue is that even when we say trust in intelligence systems, it isn't just technically elaborate. It is actually the confidence that the commanders and soldiers can rely on AI outputs without any hesitation... When you look at a soldier, for him, it's a matter of life or death. Their split-second decision is a life-or-death decision. So, can we really depend so much on AI in that?... If he doesn't make decisions at the right time, he's given the advantage to the adversary... In the black box law, there are three gaps across 3 domains. One is the experimental, the second is robustness, and the third is human-AI synthesis. In all this, when we bring in the element of cyber, it actually takes it to a different level..." (11.03)

#WATCH | Delhi: At the Synergia Conclave, Major General CS Mann, AVSM, VSM, Additional Director General (ADG) of the Army Design Bureau (ADB), says, "Today, in an era of grey zones, I have had great things, where I say they are having the borders, commissions, and the cyber intruders, various intelligence systems like AI-assisted drones, senior support systems, and autonomous systems. They always have very decisive edges. But still, gaps actually undermine their productivity... The core issue is that even when we say trust in intelligence systems, it isn't just technically elaborate. It is actually the confidence that the commanders and soldiers can rely on AI outputs without any hesitation... When you look at a soldier, for him, it's a matter of life or death. Their split-second decision is a life-or-death decision. So, can we really depend so much on AI in that?... If he doesn't make decisions at the right time, he's given the advantage to the adversary... In the black box law, there are three gaps across 3 domains. One is the experimental, the second is robustness, and the third is human-AI synthesis. In all this, when we bring in the element of cyber, it actually takes it to a different level..." (11.03)

ANI

26,277 просмотров • 5 месяцев назад

Elon Musk explains real AI safety: “The best thing I can come up with for AI safety is to make it a maximum truth-seeking AI, maximally curious. Have its optimization function be to understand the nature of the universe If that is its optimization function, it will actually want to preserve and extend human civilization because we’re just much more interesting than an asteroid with nothing on it” “You definitely don’t want to teach an AI to lie. That is a path to a dystopian future”

Elon Musk explains real AI safety: “The best thing I can come up with for AI safety is to make it a maximum truth-seeking AI, maximally curious. Have its optimization function be to understand the nature of the universe If that is its optimization function, it will actually want to preserve and extend human civilization because we’re just much more interesting than an asteroid with nothing on it” “You definitely don’t want to teach an AI to lie. That is a path to a dystopian future”

X Freeze

15,350 просмотров • 6 месяцев назад

Ilya Sutskever, co-founder and chief scientist of OpenAI: "Artificial intelligence is nothing but digital brains inside large computers." Sit with how flatly he says it. Not a metaphor, not marketing. The seat of intelligence in a human is a biological brain. The seat of intelligence in AI is an artificial one. Same thing, different substrate. Right now, he admits, it only "understands mostly, sort of." But that gap is temporary. In his words: "the day will come when the digital brains that live inside our computers will become as good and even better than our own biological brains. Computers will become smarter than us." He makes it concrete. Once an AI is built to be a doctor, with every medical paper and billions of hours of clinical experience, always on and almost free, "we will look back at today's health care similarly to how we look at 16th century dentistry." Hinton says it already understands. Ilya says the brain is just built differently. Either way, the argument was never whether it thinks. It was whether we were ready for the answer.

Ilya Sutskever, co-founder and chief scientist of OpenAI: "Artificial intelligence is nothing but digital brains inside large computers." Sit with how flatly he says it. Not a metaphor, not marketing. The seat of intelligence in a human is a biological brain. The seat of intelligence in AI is an artificial one. Same thing, different substrate. Right now, he admits, it only "understands mostly, sort of." But that gap is temporary. In his words: "the day will come when the digital brains that live inside our computers will become as good and even better than our own biological brains. Computers will become smarter than us." He makes it concrete. Once an AI is built to be a doctor, with every medical paper and billions of hours of clinical experience, always on and almost free, "we will look back at today's health care similarly to how we look at 16th century dentistry." Hinton says it already understands. Ilya says the brain is just built differently. Either way, the argument was never whether it thinks. It was whether we were ready for the answer.

Casper

69,481 просмотров • 1 месяц назад

Xi Jinping on China's AI vision: We must make its oversight and governance precise and effective and constantly refine measures to forestall loss of control. We should always guide AI development with human wisdom and international consensus, so that AI can truly become a mighty force that increases the well-being of humanity and advances human civilization.

Xi Jinping on China's AI vision: We must make its oversight and governance precise and effective and constantly refine measures to forestall loss of control. We should always guide AI development with human wisdom and international consensus, so that AI can truly become a mighty force that increases the well-being of humanity and advances human civilization.

Clash Report

38,781 просмотров • 1 месяц назад

Elon Musk: AI is like a super genius child that will outsmart you. “We’re at the beginning of an immense intelligence explosion. We’re in the intelligence Big Bang right now. And we’re at the most interesting time to be alive of any time in history. Now that said, we need to make sure that the AI is a good AI. Good Grok. And the thing I think is most important for AI safety, at least my biological neural net tells me, the most important thing for AI is to be truth-seeking. So this is very fundamental. You can think of AI as this super genius child that ultimately will outsmart you, but you can still instill the right values, and encourage it to be truthful, honorable, good things like the values you want to instill in a child that would ultimately grow up to be incredibly powerful.” Source: xAI Grok 4 launch, livestreamed on X, July 9, 2025.

Elon Musk: AI is like a super genius child that will outsmart you. “We’re at the beginning of an immense intelligence explosion. We’re in the intelligence Big Bang right now. And we’re at the most interesting time to be alive of any time in history. Now that said, we need to make sure that the AI is a good AI. Good Grok. And the thing I think is most important for AI safety, at least my biological neural net tells me, the most important thing for AI is to be truth-seeking. So this is very fundamental. You can think of AI as this super genius child that ultimately will outsmart you, but you can still instill the right values, and encourage it to be truthful, honorable, good things like the values you want to instill in a child that would ultimately grow up to be incredibly powerful.” Source: xAI Grok 4 launch, livestreamed on X, July 9, 2025.

ELON CLIPS

29,917 просмотров • 1 месяц назад

Billionaire Alexandr Wang says he wants to wait to have kids until Neuralink is very advanced, because children’s brains in the first seven years are more flexible and can learn brain-computer interfaces, unlocking more potential than adult brains Speaking on a podcast, Wang explained that he wants to wait until Neuralink or similar BCI technology is reliable and widely available. His reasoning is that children born into such a world could integrate the technology directly into their developing brains, rather than having to adapt to it later in life He pointed to neuroplasticity, the brain’s heightened ability to form neural connections in early childhood, arguing that during the first ~7 years, children could learn to use brain-computer interfaces in ways adults simply cannot At a deeper level, Wang’s comments reflect a broader concern: artificial intelligence is advancing at an exponential pace, while human biological evolution is slow. He suggests that direct neural links to AI may eventually be necessary for humans to remain cognitively competitive in a future shaped by superintelligence Source: Alexandr Wang, Shawn Ryan Show

Billionaire Alexandr Wang says he wants to wait to have kids until Neuralink is very advanced, because children’s brains in the first seven years are more flexible and can learn brain-computer interfaces, unlocking more potential than adult brains Speaking on a podcast, Wang explained that he wants to wait until Neuralink or similar BCI technology is reliable and widely available. His reasoning is that children born into such a world could integrate the technology directly into their developing brains, rather than having to adapt to it later in life He pointed to neuroplasticity, the brain’s heightened ability to form neural connections in early childhood, arguing that during the first ~7 years, children could learn to use brain-computer interfaces in ways adults simply cannot At a deeper level, Wang’s comments reflect a broader concern: artificial intelligence is advancing at an exponential pace, while human biological evolution is slow. He suggests that direct neural links to AI may eventually be necessary for humans to remain cognitively competitive in a future shaped by superintelligence Source: Alexandr Wang, Shawn Ryan Show

SMX 🇺🇸

586,577 просмотров • 8 месяцев назад

“AI can process information quickly, but it cannot replace human intelligence… AI can never replace the unique gift that you are to the world.” At NCYC 2025 in Indiana, Pope Leo spoke to a stadium full of Gen Z youth and young adults and reminded them that no algorithm can pray, love, or wonder in their place. As the Church prepares to receive his new encyclical on AI, Magnifica Humanitas, this May 25, we remind the young generation of the Holy Father's words, to use tech in a way that helps you grow in holiness—not in a way that replaces your heart, your creativity, or your friendships. Remember, no machine can replace the unique, unrepeatable gift that you are.

“AI can process information quickly, but it cannot replace human intelligence… AI can never replace the unique gift that you are to the world.” At NCYC 2025 in Indiana, Pope Leo spoke to a stadium full of Gen Z youth and young adults and reminded them that no algorithm can pray, love, or wonder in their place. As the Church prepares to receive his new encyclical on AI, Magnifica Humanitas, this May 25, we remind the young generation of the Holy Father's words, to use tech in a way that helps you grow in holiness—not in a way that replaces your heart, your creativity, or your friendships. Remember, no machine can replace the unique, unrepeatable gift that you are.

EWTN News

32,319 просмотров • 3 месяцев назад