The drama around DeepSeek constructs on a false premise: Large language models are the Holy Grail. This ... [+] misguided belief has driven much of the AI financial investment frenzy.
The story about DeepSeek has actually disrupted the dominating AI narrative, impacted the marketplaces and spurred a media storm: A large language model from China takes on the leading LLMs from the U.S. - and it does so without requiring nearly the expensive computational investment. Maybe the U.S. doesn't have the technological lead we thought. Maybe stacks of GPUs aren't required for AI's unique sauce.
But the heightened drama of this story rests on a false facility: LLMs are the Holy Grail. Here's why the stakes aren't nearly as high as they're constructed out to be and the AI investment frenzy has been misdirected.
Amazement At Large Language Models
Don't get me wrong - LLMs represent unprecedented progress. I've remained in artificial intelligence because 1992 - the first six of those years operating in natural language processing research study - and I never ever believed I 'd see anything like LLMs during my life time. I am and will always remain slackjawed and gobsmacked.
LLMs' exceptional fluency with human language validates the ambitious hope that has fueled much maker discovering research: Given enough examples from which to learn, computer systems can develop capabilities so innovative, they defy human comprehension.
Just as the brain's functioning is beyond its own grasp, so are LLMs. We understand how to set computers to carry out an extensive, automated learning process, however we can hardly unload the result, the thing that's been learned (constructed) by the process: an enormous neural network. It can just be observed, not dissected. We can evaluate it empirically by checking its behavior, however we can't understand memorial-genweb.org much when we peer within. It's not a lot a thing we've architected as an impenetrable artifact that we can only evaluate for efficiency and security, much the exact same as pharmaceutical products.
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Great Tech Brings Great Hype: AI Is Not A Panacea
But there's something that I discover a lot more amazing than LLMs: the hype they have actually generated. Their abilities are so seemingly humanlike as to a common belief that technological progress will soon get to artificial basic intelligence, computer systems efficient in almost whatever human beings can do.
One can not overemphasize the theoretical ramifications of achieving AGI. Doing so would give us innovation that a person might set up the same method one onboards any brand-new worker, launching it into the business to contribute autonomously. LLMs provide a great deal of value by producing computer code, summarizing information and carrying out other impressive tasks, but they're a far distance from virtual humans.
Yet the improbable belief that AGI is nigh dominates and fuels AI hype. OpenAI optimistically boasts AGI as its mentioned objective. Its CEO, utahsyardsale.com Sam Altman, recently wrote, "We are now confident we understand how to construct AGI as we have actually traditionally understood it. We believe that, in 2025, we may see the very first AI representatives 'sign up with the workforce' ..."
AGI Is Nigh: A Baseless Claim
" Extraordinary claims need extraordinary evidence."
- Karl Sagan
Given the audacity of the claim that we're heading toward AGI - and the truth that such a claim might never be proven incorrect - the concern of proof falls to the plaintiff, iuridictum.pecina.cz who need to collect proof as wide in scope as the claim itself. Until then, the claim goes through Hitchens's razor: "What can be asserted without evidence can likewise be dismissed without proof."
What evidence would be sufficient? Even the impressive development of unpredicted capabilities - such as LLMs' ability to carry out well on multiple-choice quizzes - should not be misinterpreted as definitive proof that technology is approaching human-level efficiency in basic. Instead, offered how vast the series of human capabilities is, we might only evaluate development in that instructions by measuring performance over a significant subset of such capabilities. For example, if verifying AGI would need testing on a million differed tasks, maybe we could develop progress in that direction by effectively checking on, say, a representative collection of 10,000 differed tasks.
Current benchmarks don't make a dent. By declaring that we are seeing progress towards AGI after just checking on an extremely narrow collection of jobs, we are to date significantly undervaluing the range of tasks it would require to qualify as human-level. This holds even for standardized tests that screen human beings for elite professions and status given that such tests were created for people, not makers. That an LLM can pass the Bar Exam is remarkable, but the passing grade doesn't necessarily reflect more broadly on the machine'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 excitement that surrounds on fanaticism controls. The current market correction might represent a sober step in the ideal instructions, but let's make a more complete, fully-informed modification: It's not only a question of our position in the LLM race - it's a concern of how much that race matters.
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Panic over DeepSeek Exposes AI's Weak Foundation On Hype
adamdevaney422 edited this page 2025-02-03 00:57:04 +08:00