Bottom line: Text generative AIs do not capture meaning. They capture relationships which approximate meaning well enough to be useful.
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By 2020, authors Yudlowsky and Soares were already Doomers but the rapid success of ChatGPT and similar models heightened their worries.
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If intelligence essentially involves agency, artificial general intelligence (AGI) in the human sense is not attainable by computation alone.
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Mappouras doesn’t rule out creativity in machines; he wants to develop a test that could recognize it.
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Human trainers can nudge LLMs to give good answers for specific questions but this training doesn’t prepare them for subtle variations.
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As prominent neuroscientist Christof Koch’s recent difficulties with Cancel Culture show, the cracks in materialist neuroscience are getting harder to paper over.
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AI can never be more than a machine; the human mind soars far above any machine model. Be nice to your robot. But don’t think she is human.
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The Turing Test 2.0 mirrors the way good teachers test students — not asking for memorized answers but for concepts to be applied in unfamiliar situations.
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The fundamental problem remains; models like GPT-5 are hobbled by their inherent inability to relate the text they input and output to the real world.
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Mappouras’s General Intelligence Threshold offers a test question: Can the system generate insights that were not directly programmed into it?
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Here’s the problem with artificial general intelligence: asking how to make systems “general” is asking how to remove the very constraints that made them work.
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Even when we get a quantum leap forward, it quickly signals that it too is a dead end for the bolder ambitions of true AGI.
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When I first wrote this almost a decade ago, “AI” was already a cultural Rorschach test. To some, it was exciting and futuristic. To others, it was ominous, Orwellian, or just marketing spin. Automation, by contrast, was the unglamorous cousin that conjured images of soulless machines taking over the last shreds of human purpose. But from the start, my view was simple: what we call “AI” today is still just automation. And automation is not a mind. That argument has aged better than I expected. In the years since, we’ve seen an explosion of so-called AI — from self-driving cars to ChatGPT — yet the distinction between AI and automation remains almost universally misunderstood. Recently, computational linguist Emily Bender and Read More ›
At present, it doesn’t sound as though consciousness studies can take us beyond our natural folk understanding of consciousness.
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It’s no mystery why LLMs aren’t intelligent in any meaningful way. The real mystery is why so many otherwise intelligent people still take the claims seriously.
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One change is that Sam Altman, CEO of OpenAI, the maker of ChatGPT-5, seems to be acknowledging that the AI bubble might soon burst.
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We remain confronted by the same old mystery: who, or what, imagines the birdhouse in the first place?
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AI does not, at present, originate the kind of deep insight required to solve a open mathematical problem.
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If this is a tech Gold Rush, based on recent news, we might be better off selling shovels than investing in the gold.
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Avian is perfectly clear: There is no mind at all in Coordinated Avian Models (CAMs). And yet, they’re behind a staggering number of new designs.
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