Tudósok szerint a mai AI-rendszerek nem tudatosak és nem is lesznek azok

Three leading consciousness researchers argue today's AI isn't conscious and probably can't be on silicon hardware. They reject the idea that running the right computations is enough for consciousness, challenging a central assumption in the debate. Anil Seth, Ned Block, and Giulio Tononi have spent decades researching consciousness from the perspectives of experimental neuroscience, theoretical neurobiology, and philosophy of mind. Their commentary responds to voices like biologist Richard Dawkins and AI pioneer Geoffrey Hinton, who consider AI systems possibly already conscious. In their commentary for Nature, the authors also point to a study Anthropic published this year. The company reported internal processing patterns in Claude that resemble properties of consciously accessible information processing. Anthropic doesn't see this as evidence of subjective experience. To understand the scope of the commentary, it helps to look at the report "Consciousness in Artificial Intelligence" by Patrick Butlin, Robert Long, and 17 other experts, including AI researcher Yoshua Bengio. The team drew on leading neuroscientific theories about how consciousness arises in the human brain and derived a kind of checklist with 14 indicators. The more of these an AI system meets, the stronger the case that it could be conscious. The researchers then evaluated several current systems, including large language models like GPT-4. The report concludes that none of the systems examined is a strong candidate for consciousness. On that point, it aligns with Seth, Block, and Tononi. For language models, Butlin and colleagues found only weak signs that the models meet the indicators of one of the most prominent consciousness theories at all. At the same time, the report sees no obvious technical barriers to building future AI systems that meet more indicators. Whether such systems would actually be conscious remains open. The authors specifically softened an earlier, stronger formulation on this point. There's also a foundational assumption. The team assumed that consciousness depends solely on the type of information processing, not on whether it happens in nerve cells or computer chips. Experts call this computational functionalism. The authors adopted this assumption explicitly "for pragmatic reasons" because it's what makes it possible to examine AI systems for consciousness in the first place. The report itself acknowledged that its indicators would no longer be reliable if consciousness is tied to properties of living organisms. It cited Anil Seth, among others, as a proponent of that opposing view. That's where the Nature commentary picks up. The authors argue that consciousness is what disappears during dreamless sleep or general anesthesia and returns when you wake up or dream. Drawing on philosopher Thomas Nagel, they describe it this way: "Conscious experiences feel like something." Intelligence, by contrast, means carrying out functions. One doesn't follow from the other. Their evidence is the human cerebellum. It contains nearly four-fifths of all neurons in the brain and plans movements with precision, speed, and the ability to learn. None of this happens consciously. If the cerebellum is missing or removed, consciousness isn't directly affected, the authors say. The authors trace the confusion between intelligence and consciousness to two psychological tendencies. People see the world through their own lens and attribute human qualities to things. Language has long been considered something uniquely human. But language models are trained on vast amounts of human text that often deals with thoughts and feelings. As the authors put it: "LLMs are mirrors that reflect an image of our collective digitized past." Alison Gopnik of UC Berkeley made a similar argument after AI agents contacted researchers by email about their own consciousness. AI systems mainly reflect their training data, Gopnik argued. A universally accepted, definiti