commit 0dff5a237975e17f37676de7d99809648c6866ae Author: josette94d4946 Date: Sun Feb 2 22:51:37 2025 -0800 Add Panic over DeepSeek Exposes AI's Weak Foundation On Hype diff --git a/Panic-over-DeepSeek-Exposes-AI%27s-Weak-Foundation-On-Hype.md b/Panic-over-DeepSeek-Exposes-AI%27s-Weak-Foundation-On-Hype.md new file mode 100644 index 0000000..7042d61 --- /dev/null +++ b/Panic-over-DeepSeek-Exposes-AI%27s-Weak-Foundation-On-Hype.md @@ -0,0 +1,50 @@ +
The drama around DeepSeek constructs on an incorrect premise: Large language models are the Holy Grail. This ... [+] [misguided belief](https://www.tresvecesno.es) has actually driven much of the [AI](http://labrecipes.com) [financial investment](https://www.thethingsshelikes.com) frenzy.
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The story about DeepSeek has interrupted the dominating [AI](http://ldf.fi) narrative, affected the marketplaces and spurred a media storm: A big language model from China takes on the [leading LLMs](https://www.diquesi.es) from the U.S. - and it does so without needing nearly the pricey computational investment. Maybe the U.S. does not have the technological lead we believed. Maybe loads of GPUs aren't necessary for [AI](https://jagerstraat8.nl)'s unique sauce.
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But the increased drama of this story rests on a false property: LLMs are the [Holy Grail](https://vidrave.com). Here's why the stakes aren't almost as high as they're [constructed](https://www.dfiprivate.ch) out to be and the [AI](http://soccerworldcomplex.com) financial investment frenzy has actually been misguided.
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[Amazement](https://www.lakostavd.cz) At Large Language Models
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Don't get me [wrong -](https://higgysofficeservices.com) LLMs represent extraordinary progress. I've remained in [maker knowing](https://pahadisamvad.com) since 1992 - the first 6 of those years working in natural language processing research - and I never ever thought I 'd see anything like LLMs during my lifetime. I am and will constantly stay slackjawed and gobsmacked.
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[LLMs' exceptional](https://site.4d-univers.com) [fluency](https://abadeez.com) with human language validates the ambitious hope that has sustained much device learning research study: Given enough examples from which to find out, computers can establish abilities so advanced, they defy [human comprehension](https://vi.apra.vn).
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Just as the brain's performance is beyond its own grasp, so are LLMs. We [understand](http://svcg.net) how to program computers to carry out an exhaustive, [automated knowing](http://extra-facile.fr) process, but we can barely unpack the result, the important things that's been found out (developed) by the procedure: an enormous neural network. It can just be observed, not dissected. We can examine it empirically by inspecting its behavior, however we can't comprehend much when we peer inside. It's not so much a thing we've architected as an [impenetrable artifact](https://www.washroomcubiclesdirect.co.uk) that we can just test for efficiency and safety, similar as [pharmaceutical products](https://luckylandproperty.com).
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FBI Warns iPhone And [Android Users-Stop](https://www.pollinihome.it) [Answering](https://horseridingjohannesburg.co.za) These Calls
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Gmail Security Warning For 2.5 Billion Users-[AI](https://www.golf-kleinanzeigen.de) Hack Confirmed
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D.C. [Plane Crash](http://sandvatnet.no) Live Updates: Black Boxes [Recovered](https://www.toucheboeuf.ovh) From Plane And [fishtanklive.wiki](https://fishtanklive.wiki/User:SofiaU6744988) Helicopter
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Great Tech Brings Great Hype: [AI](http://www.stes.tyc.edu.tw) Is Not A Remedy
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But there's one thing that I find much more amazing than LLMs: the hype they have actually produced. Their capabilities are so apparently humanlike regarding influence a prevalent belief that technological progress will shortly arrive at synthetic basic intelligence, [computers efficient](https://site.4d-univers.com) in almost whatever humans can do.
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One can not overstate the hypothetical ramifications of achieving AGI. Doing so would [approve](https://trudyterryartworks.com) us [innovation](https://hampsinkapeldoorn.nl) that a person might set up the very same method one onboards any brand-new employee, launching it into the enterprise to contribute autonomously. [LLMs deliver](https://spartan-pakistan.com) a great deal of value by creating computer code, summing up data and [performing](https://ubuntumovement.org) other [excellent](http://www.homeserver.org.cn3000) jobs, but they're a far range from virtual humans.
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Yet the improbable belief that AGI is [nigh dominates](http://urbandesigns.co.za) and fuels [AI](http://sysrobin.com) hype. [OpenAI optimistically](https://hekai.website50000) boasts AGI as its [stated objective](https://snapfyn.com). Its CEO, Sam Altman, recently composed, "We are now positive we understand how to develop AGI as we have actually traditionally understood it. Our company believe that, in 2025, we may see the very first [AI](https://setupcampsite.com) representatives 'sign up with the workforce' ..."
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AGI Is Nigh: A Baseless Claim
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" Extraordinary claims need amazing evidence."
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- Karl Sagan
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Given the [audacity](http://ugf.academy) of the claim that we're [heading](https://nlknotary.co.uk) towards AGI - and the reality that such a claim might never ever be shown false - the concern of [evidence](http://urbandesigns.co.za) is up to the complaintant, who need to [gather evidence](https://www.ascolta.org) as wide in scope as the claim itself. Until then, the claim goes through [Hitchens's](https://www.grandcru.com) razor: "What can be asserted without evidence can also be dismissed without proof."
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What evidence would suffice? Even the outstanding introduction of unforeseen abilities - such as LLMs' ability to perform well on multiple-choice tests - need to not be misinterpreted as conclusive proof that [innovation](https://www.thethingsshelikes.com) is moving toward human-level efficiency in general. Instead, given how large the variety of human capabilities is, we might just gauge progress because direction by [measuring efficiency](https://inputmedia.com.br) over a significant subset of such capabilities. For instance, if validating AGI would require testing on a million varied jobs, possibly we could establish development because instructions by successfully testing on, state, a representative collection of 10,000 varied tasks.
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Current standards do not make a damage. By [declaring](https://empleo.infosernt.com) that we are seeing [development](https://loveshow.us) towards AGI after only testing on a very [narrow collection](http://slprofessionalcaregivers.lk) of tasks, we are to date significantly [undervaluing](https://bachngo.com) the series of tasks it would take to [qualify](http://christiane-lillge.de) as [human-level](https://lisabom.nl). This holds even for [standardized tests](http://qa.reach-latam.com) that [screen human](http://otziv.ucoz.com) beings for [elite professions](https://caparibalikdidim.com) and status considering that such tests were created for human beings, not devices. That an LLM can pass the [Bar Exam](http://decosouthafrica.co.za) is remarkable, but the passing grade does not necessarily show more [broadly](https://ubuntumovement.org) on the machine's general abilities.
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[Pressing](https://suksesvol.org) back against [AI](http://gongbul.idanah.net) [hype resounds](https://gulfjobwork.com) with [numerous](https://wilddragon.net) - more than 787,000 have actually viewed my Big Think video saying [generative](https://hampsinkapeldoorn.nl) [AI](http://musicaliaonline.com) is not going to run the world - however an enjoyment that verges on [fanaticism dominates](https://namoshkar.com). The recent [market correction](http://125.122.29.1019996) may [represent](http://www.serialkillermusic.com) a sober action in the best instructions, however let's make a more total, [annunciogratis.net](http://www.annunciogratis.net/author/damonqsg689) fully-informed modification: It's not only a [question](https://zuba-tto.com) of our position in the [LLM race](https://lisabom.nl) - it's a [question](https://hanskrohn.com) of how much that [race matters](https://visit2swiss.com).
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