Large Reasoning Models Hitting Limits, Say Apple Researchers
upstart writes:
Large Reasoning Models hitting limits, say Apple boffins:
Among the forever wars in geekdom, defining the difference between science fiction and fantasy is a hot potato destined to outlive the heat death of the universe.
There is no right answer and it doesn't matter, hence the abiding popularity of the question, but attempting to make that delineation can still be useful when analyzing IT industry hype. Is a promise technically feasible, or are dragon-riding pixies happening first? Yes, AI, we're talking about you again.
Look at the suggestion that IT staff should make agentic digital twins of themselves to, ahem, reduce the amount of burdensome work they have to personally do. That's a room with enough elephants to restock Africa, if it worked. If your twin mucks up, who carries the can? What's the difference between "burdensome work" and "job?" Who owns the twin when you leave? Have none of these people seen the Sorcerer's Apprentice segment of Fantasia? Fortunately, a better question leading on from that: whether the idea is science fiction or fantasy, and like all good speculative fiction there's both history and logic to help us decide.
History first. The proposal isn't new, it's a reprise of a spectacular AI failure from the mid-'80s: expert systems. The idea was to combine the then-hotness of Lisp, a language designed to work with huge lists of conceptual data to reach correct conclusions, with training acquired by analyzing how domain experts did their work. Exciting stuff, and the dollars flowed in. At last, real AI was here! Real AI was not here, sadly, and the whole field quietly died for the highly technical reason that it just didn't work.
It wasn't so much that '80s technology wasn't up to the job - there were promising early results; Moore's Law was in its exponential pomp; and there was an avalanche of money. Besides, we're now in the impossibly puissant digital world of 2025 and could run Lisp at superluminal speed if we wanted to. Nobody wants to.
The problem was that it isn't clear how humans make expert decisions. We aren't built from arrays and flow charts, and decades of experience cannot be siphoned out of the brains which own and use it. That's why new graduates come out of 15-years plus of full-time education by expert humans and aren't very good at their first job. AI can't fix that.
Even if it could break the brain bottleneck, AI is a long way from being good enough to become a digital twin of anyone, no matter how inexpert. In a science fiction scenario, it could plausibly become so over time as machines and techniques improve; in fantasy, you can't get there from here without Gandalf as team lead. There are many signs that we'll need to shop for pointy hats soon. AI isn't living up to its hype even now, and attempts to push it further aren't going well.
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