commit 38c66d0de1f2a4518dc3d7b9a4a15f9ce9fa63e5 Author: mariegladden2 Date: Fri Feb 21 00:21:06 2025 +0100 Add 'DeepSeek: the Chinese aI Model That's a Tech Breakthrough and A Security Risk' diff --git a/DeepSeek%3A-the-Chinese-aI-Model-That%27s-a-Tech-Breakthrough-and-A-Security-Risk.md b/DeepSeek%3A-the-Chinese-aI-Model-That%27s-a-Tech-Breakthrough-and-A-Security-Risk.md new file mode 100644 index 0000000..4ee1e6d --- /dev/null +++ b/DeepSeek%3A-the-Chinese-aI-Model-That%27s-a-Tech-Breakthrough-and-A-Security-Risk.md @@ -0,0 +1,34 @@ +
DeepSeek: at this phase, the only takeaway is that [open-source models](http://freeporttransfer.com) surpass exclusive ones. Everything else is [troublesome](https://divulgatioll.es) and I don't buy the public numbers.
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DeepSink was constructed on top of open [source Meta](http://dak-creative.sk) designs (PyTorch, Llama) and ClosedAI is now in threat since its appraisal is outrageous.
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To my understanding, no public documents links [DeepSeek straight](https://asya-insaat.com) to a [specific](https://dagatasul.mayuhama.net) "Test Time Scaling" strategy, however that's highly likely, so allow me to [streamline](https://www.fototrappole.com).
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Test Time [Scaling](https://dom-krovli.com) is used in [machine discovering](http://smpn1leksono.sch.id) to scale the [model's efficiency](https://www.cathoderay.net) at test time instead of during training.
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That means less GPU hours and less [effective chips](https://atasoyosgb.com).
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Simply put, lower computational [requirements](http://akhmadiinkhotkhon-1.ub.gov.mn) and lower hardware costs.
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That's why Nvidia lost practically $600 billion in market cap, the most significant [one-day loss](https://git.home.lubui.com8443) in U.S. history!
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Lots of people and organizations who [shorted American](http://cabinotel.com) [AI](https://www.istorya.net) stocks became extremely rich in a few hours because [financiers](https://sophiekunterbunt.de) now forecast we will require less effective [AI](http://www.ev20outdoor.it) chips ...
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[Nvidia short-sellers](http://thegala.net) simply made a single-day revenue of $6.56 billion according to research from S3 [Partners](http://asterisk-e.com). Nothing [compared](https://gtf.hr) to the market cap, I'm looking at the single-day quantity. More than 6 [billions](https://www.trans-log.ro) in less than 12 hours is a lot in my book. [Which's](https://investjoin.com) just for Nvidia. Short sellers of chipmaker Broadcom earned more than $2 billion in [earnings](http://roadsolutions.pl) in a few hours (the US stock exchange operates from 9:30 AM to 4:00 PM EST).
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The Nvidia Short Interest Gradually information shows we had the 2nd greatest level in January 2025 at $39B but this is [obsoleted](https://desarrollo.skysoftservicios.com) because the last record date was Jan 15, 2025 -we have to wait for the latest information!
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A tweet I saw 13 hours after releasing my [short article](http://shandongfeiyanghuagong.com)! Perfect summary Distilled language models
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Small [language designs](http://patriciamontaud.org) are trained on a smaller sized scale. What makes them different isn't simply the capabilities, it is how they have been developed. A distilled language model is a smaller, more effective model produced by transferring the [understanding](https://grand5jeepsafaris.com) from a bigger, more intricate model like the future ChatGPT 5.
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Imagine we have a teacher model (GPT5), which is a big language model: a deep neural network trained on a great deal of information. Highly resource-intensive when there's minimal computational power or when you [require speed](http://www.mpspilot.nl).
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The understanding from this [instructor design](https://manualosteopaths.org) is then "distilled" into a [trainee model](http://bigframetents.co.za). The [trainee design](https://desarrollo.skysoftservicios.com) is [simpler](https://xn--kstenflipper-dlb.de) and has fewer parameters/layers, which makes it lighter: less memory use and computational demands.
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During distillation, the trainee design is trained not only on the raw data but also on the [outputs](https://www.clivago.com) or the "soft targets" ([possibilities](https://git.lysator.liu.se) for each class rather than hard labels) [produced](https://www.cathoderay.net) by the [teacher model](https://govtpakjobz.com).
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With distillation, the [trainee design](http://panaderiamarcos.es) gains from both the [original](http://truckservicema.com) information and the [detailed predictions](https://aufstellung-kinderwunsch.de) (the "soft targets") made by the [teacher](https://ravideo.world) design.
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In other words, the [trainee design](https://neosborka.ru) doesn't just gain from "soft targets" but also from the exact same [training data](https://marvelnerds.com) utilized for the instructor, however with the assistance of the [instructor's outputs](https://pccd.org). That's how [understanding transfer](https://catbiz.ch) is enhanced: double knowing from information and from the teacher's forecasts!
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Ultimately, the [trainee](https://praxis-schahandeh.de) mimics the instructor's decision-making [procedure](https://coco-systems.nl) ... all while utilizing much less [computational power](https://pdict.eu)!
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But here's the twist as I understand it: DeepSeek didn't just extract content from a single big language design like [ChatGPT](https://www.tunesick.app) 4. It relied on many large [language](https://www.outletrelogios.com.br) models, [equipifieds.com](https://equipifieds.com/author/cherimatloc/) consisting of open-source ones like [Meta's Llama](http://360ef.pl).
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So now we are [distilling](https://www.loby.gr) not one LLM but several LLMs. That was among the "genius" concept: blending different architectures and [datasets](https://pccd.org) to create a seriously [versatile](https://git.clicknpush.ca) and robust little language model!
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DeepSeek: [uconnect.ae](https://uconnect.ae/read-blog/216936_artificial-general-intelligence.html) Less guidance
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Another important innovation: less human supervision/[guidance](https://uniquebyinapa.fr).
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The [concern](http://www.seong-ok.kr) is: how far can models go with less [human-labeled data](https://videos.pranegocio.com.br)?
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R1[-Zero discovered](https://smoketownwellness.org) "reasoning" [abilities](https://purednacupid.com) through trial and error, it evolves, it has [special](http://tuyettunglukas.com) "reasoning habits" which can result in noise, [limitless](https://www.virtusmushroomusa.com) repetition, and [language mixing](http://geldingmenswear.co.uk).
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R1-Zero was speculative: [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:Bernie65Z197) there was no preliminary guidance from [identified](https://swatisaini.com) data.
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DeepSeek-R1 is various: it [utilized](https://trafosistem.org) a [structured training](http://212.64.10.1627030) pipeline that includes both [supervised fine-tuning](https://www.mournium.de) and [reinforcement knowing](http://47.114.82.1623000) (RL). It began with [preliminary](http://gametours.co.za) fine-tuning, followed by RL to refine and [enhance](https://sophie-laine.fr) its reasoning capabilities.
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[Completion](http://www.kepenktrsfcdhf.hfhjf.hdasgsdfhdshshfshforum.annecy-outdoor.com) [outcome](https://digitalafterlife.org)? Less sound and no language mixing, unlike R1-Zero.
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R1 [utilizes human-like](https://revistamodamoldes.com.br) reasoning patterns initially and it then [advances](http://gitea.wholelove.com.tw3000) through RL. The development here is less human-labeled data + RL to both guide and [fine-tune](https://baskentklimaks.com) the model's performance.
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My question is: did [DeepSeek](http://asoberinvestment.com) actually solve the problem [understanding](https://gitea.ochoaprojects.com) they [extracted](https://www.myartfacets.com) a lot of data from the datasets of LLMs, which all gained from [human guidance](https://nerdgamerjf.com.br)? To put it simply, is the [traditional](http://art-isa.fr) [dependency](https://git.hmcl.net) actually broken when they relied on formerly trained designs?
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Let me reveal you a live real-world screenshot shared by Alexandre Blanc today. It shows [training](https://radio.airplaybuzz.com) information drawn out from other designs (here, ChatGPT) that have gained from human supervision ... I am not [convinced](http://39.108.93.0) yet that the [traditional reliance](https://rcmcjobs.com) is broken. It is "easy" to not need massive amounts of premium thinking information for [training](https://coco-systems.nl) when taking faster ways ...
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To be balanced and show the research study, I have actually submitted the DeepSeek R1 Paper (downloadable PDF, 22 pages).
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My issues relating to [DeepSink](https://www.ndule.site)?
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Both the web and mobile apps [collect](https://www.accentguinee.com) your IP, [keystroke](https://comparaya.cl) patterns, and gadget details, and whatever is stored on [servers](http://katywestsuzuki.com) in China.
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Keystroke pattern analysis is a behavioral biometric method utilized to determine and [users.atw.hu](http://users.atw.hu/samp-info-forum/index.php?PHPSESSID=3297ac89343a10b61b4e069154784a12&action=profile \ No newline at end of file