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Andrew Ng says human intelligence is powerful because it learns generally and quickly While a human can learn a job just by talking, brute-forcing an AI for a single narrow task can cost a million dollars "because of this massive effort, using AI doesn't make sense for many tasks"

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Haider.

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14,611 просмотров • 5 месяцев назад •via X (Twitter)

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xAI is building “human emulators” Khan says “human emulators” will let AI bots perform any on-screen task a human can, using the same keyboard, mouse, and visual cues. The system needs zero changes to existing software because the bot simply mimics the user’s actions. Scaling from a thousand to a million digital workers is mostly an infrastructure question, not a research hurdle. With Tesla-class data centers already available, cost drops and 24/7 uptime make the model economically compelling.
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xAI is building “human emulators” Khan says “human emulators” will let AI bots perform any on-screen task a human can, using the same keyboard, mouse, and visual cues. The system needs zero changes to existing software because the bot simply mimics the user’s actions. Scaling from a thousand to a million digital workers is mostly an infrastructure question, not a research hurdle. With Tesla-class data centers already available, cost drops and 24/7 uptime make the model economically compelling.

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Yann LeCun says intelligence isn’t correlated with a desire for dominance AI will follow human goals under strict guardrails, and if one goes rogue, smarter systems can correct it. Amplifying human intelligence could start a new era: "my good AI against your bad AI"
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Yann LeCun says intelligence isn’t correlated with a desire for dominance AI will follow human goals under strict guardrails, and if one goes rogue, smarter systems can correct it. Amplifying human intelligence could start a new era: "my good AI against your bad AI"

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🇩🇪 - Germany is BACK! Agile Robots launched Agile One, a Humanoid Robot that is designed for industry. It can handle many tasks autonomously. It learns quickly by doing the tasks, weighs 69kg, and is powered by their own AI foundation models! Europe will win 🇪🇺

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AI that had human-level intelligence would actually be way above human level in capability. Obviously, if we trained a human-level AI, we could just run way more instances of it in parallel. This is a huge advantage. But it's not the only one. Right now, LLMs are much less sample-efficient than humans, but they can process each individual piece of data much more quickly. A model that was a human-level learner would be able to gain the equivalent of thousands of years of education. AIs can also be way more task-oriented than humans. Even a really well-motivated human worker can only operate with ‘human-level’ effort for relatively short spans of time. AGI could do it continuously.
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AI that had human-level intelligence would actually be way above human level in capability. Obviously, if we trained a human-level AI, we could just run way more instances of it in parallel. This is a huge advantage. But it's not the only one. Right now, LLMs are much less sample-efficient than humans, but they can process each individual piece of data much more quickly. A model that was a human-level learner would be able to gain the equivalent of thousands of years of education. AIs can also be way more task-oriented than humans. Even a really well-motivated human worker can only operate with ‘human-level’ effort for relatively short spans of time. AGI could do it continuously.

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ELON: AI IS ALREADY SUPERHUMAN, AND WE’RE JUST GETTING STARTED “It really is remarkable to see the advancement of artificial intelligence, how quickly it is evolving. I sometimes compare it to the growth of a human and how fast a human learns and gains conscious awareness and understanding. And AI is advancing just vastly faster than any human. There are some people out there who think AI can't reason. Look, it can reason at superhuman levels. And frankly, it only gets better from here.” Source: xAI Grok 4 presentation, July 2025

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37,223 просмотров • 9 месяцев назад

The AI industry is optimizing for a definition of intelligence that does not exist. Andrew Ng just said it out loud. Ng: “AGI, to me, should be less about AI that already knows everything under the sun. That seems very challenging, doesn’t seem practical.” The human brain is not the most powerful economic asset in history because of what it holds. It is powerful because of what it can pick up. Ng: “The amazing thing about the human brain is its plasticity, or its ability to learn.” That same biological hardware that earns a PhD in quantum physics could have been trained on chess, surgery, or rewriting global supply chains from scratch. Ng: “That same human brain, just given different training, could have been a chess master, or could have been amazing at playing tennis.” General intelligence is not omniscience. It is the structural capacity to master whatever you point it at. Ng: “It is through learning that we then gain these incredibly specialized intelligences.” The winner is not whoever builds the biggest model. It is whoever builds the most adaptable one. The AI that walks into a domain it has never touched and executes before a human analyst finishes reading the brief. Ng: “What makes the human brain so valuable for economic tasks, is its ability to just learn to do whatever is needed.” Every corporation on earth pays for human labor because humans adapt. Not because they already know everything. AGI is the digitization of that exact capability. At machine speed. At infinite scale. Ng: “A lot of what makes the human brain so general is not that my brain or your brain already knows everything under the sun. It’s our ability to adapt, to learn a huge range of things.” The most powerful economic asset in history was never specialized knowledge. It was the raw capacity to acquire any knowledge, in any domain, on demand. The winning AI is not an encyclopedia. It is the force that makes encyclopedias irrelevant. And once that exists, the question stops being what the AI knows. It becomes what you can teach it before your competitor wakes up. Most people dominating this conversation have not understood that yet.
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The AI industry is optimizing for a definition of intelligence that does not exist. Andrew Ng just said it out loud. Ng: “AGI, to me, should be less about AI that already knows everything under the sun. That seems very challenging, doesn’t seem practical.” The human brain is not the most powerful economic asset in history because of what it holds. It is powerful because of what it can pick up. Ng: “The amazing thing about the human brain is its plasticity, or its ability to learn.” That same biological hardware that earns a PhD in quantum physics could have been trained on chess, surgery, or rewriting global supply chains from scratch. Ng: “That same human brain, just given different training, could have been a chess master, or could have been amazing at playing tennis.” General intelligence is not omniscience. It is the structural capacity to master whatever you point it at. Ng: “It is through learning that we then gain these incredibly specialized intelligences.” The winner is not whoever builds the biggest model. It is whoever builds the most adaptable one. The AI that walks into a domain it has never touched and executes before a human analyst finishes reading the brief. Ng: “What makes the human brain so valuable for economic tasks, is its ability to just learn to do whatever is needed.” Every corporation on earth pays for human labor because humans adapt. Not because they already know everything. AGI is the digitization of that exact capability. At machine speed. At infinite scale. Ng: “A lot of what makes the human brain so general is not that my brain or your brain already knows everything under the sun. It’s our ability to adapt, to learn a huge range of things.” The most powerful economic asset in history was never specialized knowledge. It was the raw capacity to acquire any knowledge, in any domain, on demand. The winning AI is not an encyclopedia. It is the force that makes encyclopedias irrelevant. And once that exists, the question stops being what the AI knows. It becomes what you can teach it before your competitor wakes up. Most people dominating this conversation have not understood that yet.

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Andrew Ng says the concept of AGI has become meaningless because everyone defines it differently The original definition was AI that could do any intellectual task a person can — essentially, AI as intelligent as humans "by that measure, we're decades away"
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Andrew Ng says the concept of AGI has become meaningless because everyone defines it differently The original definition was AI that could do any intellectual task a person can — essentially, AI as intelligent as humans "by that measure, we're decades away"

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Anthropic CEO Dario Amodei says big, powerful AI models will spawn and orchestrate smaller models to assist with tasks, creating a swarm intelligence that will decrease the need for human input

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📁 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.

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One of the many reasons I use the largest collection of 30 second commercials curated for AI training in the world is because in 30 seconds a massive amount of concepts can be masterfully presented. No other medium can do this. It is the work product of trillions of dollars.

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Elon Musk is attempting something extraordinary. Not just building another company. He is building the largest AI training cluster in the world. A million GPUs wired together. A brute-force attempt to scale intelligence itself. The capital required to do this is staggering. Forty to sixty billion dollars for one cluster. And yet this is just one experiment. This is the scale of ambition in AI right now. It is not about incremental software. It is about rewiring the fundamental limits of what machines can learn and know.

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