The drama around DeepSeek develops on an incorrect premise: Large language models are the Holy Grail. This ... [+] misdirected belief has driven much of the AI investment frenzy.
The story about DeepSeek has disrupted the dominating AI narrative, impacted the marketplaces and stimulated a media storm: A large language model from China takes on the leading LLMs from the U.S. - and it does so without needing almost the costly computational investment. Maybe the U.S. does not have the technological lead we thought. Maybe stacks of GPUs aren't required for AI's special sauce.
But the heightened drama of this story rests on an incorrect facility: LLMs are the Holy Grail. Here's why the stakes aren't almost as high as they're made out to be and the AI financial investment frenzy has actually been misguided.
Amazement At Large Language Models
Don't get me wrong - LLMs represent unmatched progress. I've remained in artificial intelligence since 1992 - the first six of those years working in natural language processing research - and I never ever thought I 'd see anything like LLMs throughout my lifetime. I am and will constantly stay slackjawed and gobsmacked.
LLMs' uncanny fluency with human language verifies the ambitious hope that has sustained much device discovering research: Given enough examples from which to learn, computers can establish capabilities so advanced, they defy human understanding.
Just as the brain's functioning is beyond its own grasp, so are LLMs. We understand how to configure computers to carry out an exhaustive, automated learning process, but we can hardly unload the outcome, the thing that's been learned (constructed) by the procedure: a massive neural network. It can only be observed, not dissected. We can evaluate it empirically by examining its behavior, however we can't comprehend much when we peer within. It's not so much a thing we've architected as an impenetrable artifact that we can only evaluate for efficiency and safety, much the exact same as pharmaceutical products.
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Great Tech Brings Great Hype: AI Is Not A Panacea
But there's one thing that I find a lot more fantastic than LLMs: the hype they have actually generated. Their abilities are so relatively humanlike regarding inspire a prevalent belief that technological development will quickly get here at synthetic general intelligence, computer systems efficient in practically everything human beings can do.
One can not overstate the hypothetical ramifications of attaining AGI. Doing so would give us technology that one could set up the very same method one onboards any brand-new worker, releasing it into the business to contribute autonomously. LLMs provide a great deal of worth by creating computer code, summarizing data and other outstanding tasks, but they're a far range from virtual humans.
Yet the far-fetched belief that AGI is nigh dominates and fuels AI buzz. OpenAI optimistically boasts AGI as its specified mission. Its CEO, Sam Altman, just recently wrote, "We are now positive we understand how to develop AGI as we have typically understood it. We think that, in 2025, we might see the very first AI representatives 'sign up with the workforce' ..."
AGI Is Nigh: A Baseless Claim
" Extraordinary claims need amazing evidence."
- Karl Sagan
Given the audacity of the claim that we're heading towards AGI - and the fact that such a claim could never ever be shown false - the problem of proof falls to the plaintiff, who should collect proof as broad in scope as the claim itself. Until then, the claim undergoes Hitchens's razor: "What can be asserted without proof can also be dismissed without evidence."
What proof would be sufficient? Even the excellent introduction of unforeseen abilities - such as LLMs' capability to carry out well on multiple-choice tests - should not be misinterpreted as definitive proof that technology is moving towards human-level performance in basic. Instead, provided how huge the variety of human abilities is, we could only assess progress in that direction by measuring performance over a meaningful subset of such capabilities. For instance, if validating AGI would need testing on a million differed tasks, maybe we might develop development because instructions by effectively testing on, state, a representative collection of 10,000 varied tasks.
Current standards do not make a dent. By claiming that we are experiencing development toward AGI after just testing on a very narrow collection of jobs, we are to date greatly underestimating the variety of tasks it would take to qualify as human-level. This holds even for standardized tests that evaluate people for elite professions and status since such tests were created for people, not machines. That an LLM can pass the Bar Exam is amazing, but the passing grade does not always show more broadly on the maker's overall capabilities.
Pressing back against AI buzz resounds with many - more than 787,000 have viewed my Big Think video stating generative AI is not going to run the world - but an enjoyment that verges on fanaticism controls. The recent market correction may represent a sober step in the best instructions, but let's make a more total, fully-informed change: It's not only a concern of our position in the LLM race - it's a question of just how much that race matters.
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Panic over DeepSeek Exposes AI's Weak Foundation On Hype
Christi Eudy edited this page 2025-02-03 05:29:04 +08:00