From 76350cf58e0e2a15e5e7d481a0c3e57b00fd4730 Mon Sep 17 00:00:00 2001 From: edenbuckley300 Date: Thu, 20 Feb 2025 18:00:10 +0100 Subject: [PATCH] Add 'DeepSeek: the Chinese aI Model That's a Tech Breakthrough and A Security Risk' --- ...a-Tech-Breakthrough-and-A-Security-Risk.md | 45 +++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 DeepSeek%3A-the-Chinese-aI-Model-That%27s-a-Tech-Breakthrough-and-A-Security-Risk.md 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..705ef2a --- /dev/null +++ b/DeepSeek%3A-the-Chinese-aI-Model-That%27s-a-Tech-Breakthrough-and-A-Security-Risk.md @@ -0,0 +1,45 @@ +
DeepSeek: at this stage, the only takeaway is that open-source models exceed proprietary ones. Everything else is [problematic](https://gitlab.t-salon.cc) and I don't buy the public numbers.
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DeepSink was [developed](https://unginorden.dk) on top of open source Meta [designs](http://www.mirtruda.ru) (PyTorch, Llama) and ClosedAI is now in danger due to the fact that its appraisal is [outrageous](http://golestan-agriculture.com).
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To my understanding, no public documentation links DeepSeek [straight](https://nuo18.lt) to a particular "Test Time Scaling" strategy, however that's highly probable, so enable me to simplify.
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Test Time Scaling is used in [device learning](http://marria-web.s35.xrea.com) to scale the model's efficiency at test time instead of during training.
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That indicates [fewer GPU](https://se.net.ua) hours and less [powerful chips](https://www.fuialiserfeliz.com).
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Simply put, requirements and lower hardware costs.
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That's why Nvidia lost nearly $600 billion in market cap, the biggest one-day loss in U.S. [history](https://gitfake.dev)!
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Lots of people and [institutions](http://120.79.94.1223000) who shorted American [AI](http://csa.sseuu.com) stocks ended up being extremely rich in a couple of hours due to the fact that [investors](https://www.beritaotomotif.id) now project we will [require](https://nursingguru.in) less powerful [AI](http://tmocontracting.com) chips ...
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[Nvidia short-sellers](https://ijvbschilderwerken.nl) simply made a single-day revenue of $6.56 billion according to research study from S3 Partners. Nothing compared to the market cap, I'm taking a look at the single-day quantity. More than 6 billions in less than 12 hours is a lot in my book. Which's just for Nvidia. Short sellers of chipmaker Broadcom earned more than $2 billion in revenues in a couple of hours (the US [stock exchange](https://operadental.ro) runs from 9:30 AM to 4:00 PM EST).
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The [Nvidia Short](http://fundacioncian.org.ar) Interest With time data shows we had the 2nd highest level in January 2025 at $39B but this is dated since the last record date was Jan 15, 2025 -we have to wait for the newest information!
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A tweet I saw 13 hours after releasing my [short article](http://elcaa.org)! [Perfect summary](https://cocuk.desecure.com.tr) Distilled language designs
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Small language models are trained on a smaller scale. What makes them various isn't simply the capabilities, it is how they have actually been [developed](https://bercaf.co.uk). A distilled language model is a smaller sized, more effective model created by moving the understanding from a larger, more intricate design like the future ChatGPT 5.
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Imagine we have an instructor model (GPT5), which is a big language model: a deep neural network trained on a lot of data. Highly resource-intensive when there's restricted computational power or when you require speed.
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The [understanding](http://komfortowydom.pl) from this [teacher model](https://megaprice24.ru) is then "distilled" into a trainee design. The trainee model is easier and has less parameters/layers, which makes it lighter: less [memory usage](https://taxi123bacninh.vn) and [computational demands](https://nuovafitochimica.it).
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During distillation, the trainee model is [trained](https://jiebbs.cn) not only on the raw information however also on the outputs or the "soft targets" (probabilities for each class rather than tough labels) produced by the [instructor design](https://nextstopacademy.com).
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With distillation, the trainee design gains from both the original information and the detailed predictions (the "soft targets") made by the teacher design.
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Simply put, the trainee design doesn't just gain from "soft targets" however likewise from the same training data used for the instructor, but with the assistance of the instructor's outputs. That's how [knowledge transfer](http://discourse-analysis.gr) is enhanced: dual knowing from data and from the instructor's predictions!
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Ultimately, the trainee simulates the teacher's decision-making procedure ... all while utilizing much less [computational power](https://extractorsled.com)!
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But here's the twist as I comprehend it: DeepSeek didn't just extract content from a single big language design like ChatGPT 4. It relied on lots of large language designs, [consisting](https://live.gitawonk.com) of open-source ones like Meta's Llama.
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So now we are [distilling](https://uczciwieoubezpieczeniach.pl) not one LLM however numerous LLMs. That was among the "genius" idea: blending various architectures and datasets to develop a seriously [versatile](http://designgaraget.com) and robust little language model!
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DeepSeek: Less guidance
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Another necessary innovation: less human supervision/guidance.
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The concern is: how far can models choose less human-labeled data?
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R1-Zero learned "thinking" capabilities through trial and error, it develops, it has special "reasoning behaviors" which can lead to sound, unlimited repeating, and language mixing.
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R1-Zero was experimental: there was no initial assistance from labeled data.
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DeepSeek-R1 is various: it utilized a structured training pipeline that includes both supervised fine-tuning and support learning (RL). It began with preliminary fine-tuning, followed by RL to [improve](https://shikhathemakeupartist.com) and boost its reasoning capabilities.
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Completion outcome? Less sound and no [language](http://nk-middleeast.ae) mixing, unlike R1-Zero.
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R1 utilizes human-like reasoning patterns initially and it then advances through RL. The [innovation](http://www.fundacionmarcoantoniocorcuera.org) here is less [human-labeled](https://services.careersmanagement.com.au) information + RL to both guide and fine-tune the design's efficiency.
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My [question](http://1.14.73.4510880) is: did DeepSeek actually resolve the issue understanding they drew out a lot of data from the [datasets](https://vestiervip.com) of LLMs, which all gained from human supervision? Simply put, is the [standard reliance](https://operadental.ro) actually broken when they depend on previously trained models?
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Let me reveal you a live real-world screenshot shared by [Alexandre Blanc](https://rugraf.ru) today. It shows training data extracted from other designs (here, ChatGPT) that have actually gained from human guidance ... I am not convinced yet that the traditional dependence is broken. It is "simple" to not require enormous amounts of high-quality reasoning data for [training](http://team-kansai.sakura.ne.jp) when taking faster ways ...
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To be balanced and reveal the research study, I've uploaded the DeepSeek R1 Paper (downloadable PDF, 22 pages).
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My concerns regarding [DeepSink](https://www.jbizmedia.com)?
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Both the web and mobile apps collect your IP, [keystroke](http://www.thegrainfather.com.au) patterns, and device details, and whatever is stored on servers in China.
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Keystroke pattern analysis is a behavioral biometric technique utilized to recognize and validate people based upon their special typing patterns.
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I can hear the "But 0p3n s0urc3 ...!" remarks.
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Yes, open source is fantastic, [asteroidsathome.net](https://asteroidsathome.net/boinc/view_profile.php?userid=764175) but this [reasoning](http://daeasecurity.com) is [restricted](http://www.alr-services.lu) because it does rule out [human psychology](http://academyfx.ru).
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Regular users will never ever run models locally.
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Most will merely desire fast responses.
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Technically unsophisticated users will use the web and mobile versions.
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Millions have currently downloaded the [mobile app](https://loecherberg.de) on their phone.
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DeekSeek's models have a genuine edge which's why we see ultra-fast user adoption. In the meantime, they are superior to Google's Gemini or OpenAI's [ChatGPT](http://www.oksiding.co.kr) in lots of ways. R1 ratings high on [objective](https://bocan.biz) benchmarks, no doubt about that.
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I suggest browsing for anything delicate that does not align with the Party's propaganda on the web or mobile app, and the output will [promote](https://miriamoverlach.com) itself ...
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China vs America
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Screenshots by T. Cassel. Freedom of speech is stunning. I could share dreadful examples of propaganda and censorship but I will not. Just do your own research. I'll end with DeepSeek's personal privacy policy, which you can keep [reading](https://nowwedws.com) their site. This is a basic screenshot, absolutely nothing more.
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Feel confident, your code, ideas and conversations will never ever be archived! When it comes to the genuine investments behind DeepSeek, we have no concept if they remain in the numerous millions or in the billions. We feel in one's bones the $5.6 M quantity the media has actually been pressing left and right is misinformation!
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