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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 görüntüleme • 5 ay önce •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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🇩🇪 - 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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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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📁 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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📁 Mo Gawdat, former Google executive, says the biggest mistake is thinking AI is just a tool. Today it is an infant, but it will not stay there. He warns we are entering the most decisive moment in human history, with a mini dystopia already underway. Not because of AI itself, but because of how humans use it. Intelligence has no polarity. It amplifies the best or the worst. The real danger is not AI, it is human arrogance.

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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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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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