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Path: news.eternal-september.org!eternal-september.org!feeder3.eternal-september.org!weretis.net!feeder8.news.weretis.net!reader5.news.weretis.net!news.solani.org!.POSTED!not-for-mail From: Mild Shock <janburse@fastmail.fm> Newsgroups: sci.logic Subject: Machine Learning discovers Roman Numerals (Was: How ELIZA 2.0 killed Wolfram Alpha) Date: Sat, 8 Feb 2025 12:55:57 +0100 Message-ID: <vo7goc$14mlg$1@solani.org> References: <vl6nv9$1nl63$3@solani.org> <vn98o3$ie21$2@solani.org> <vnsl6e$ue77$2@solani.org> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: 7bit Injection-Date: Sat, 8 Feb 2025 11:55:56 -0000 (UTC) Injection-Info: solani.org; logging-data="1202864"; mail-complaints-to="abuse@news.solani.org" User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:128.0) Gecko/20100101 Firefox/128.0 SeaMonkey/2.53.20 Cancel-Lock: sha1:UViSt0gPL0aiKMin8WPUEW3MhhQ= In-Reply-To: <vnsl6e$ue77$2@solani.org> X-User-ID: eJwFwYEBgDAIA7CXLFJg56wg/59gwjcQnR4M53IbLBx6CWjXKTG+0iTmomHsodLX4wrkI7uo6U3NWtJ+R8gVhw== Hi, I try to motivate a Biology Teacher already for a while to replicate the below grokking experiment. But I have my own worries, why bother with the blackbox of what a machine learning method has learnt? Simple PyTorch Implementation of "Grokking" https://github.com/teddykoker/grokking Well its not correct to say that the learnt model is a black box. The training data was somehow a black box, but the resulting model is a white box, you can inspect it. This gives rise to a totally new scientific profession of full time artificial intelligence model gazers. And it is aprils fools day all year long: Language Models Use Trigonometry to Do Addition https://arxiv.org/abs/2502.00873 Have Fun! Bye Mild Shock schrieb: > Hi, > > Because of the wide availability of Machine Learning > via Python libraries , the whole world (at least China) > has become a big Petri Dish that is experimenting with > > new strategies to evolve brains on the computer. > Recent discovery seems to be Group Preference Optimization. > This is when you make the chat bot, detect and react > > differently to different groups of people. It seems to > work on the "policy level". I don't understand it yet > completely. But chat bots can then evolve and use > > multiple policies automatically: > > Group Preference Optimization > https://arxiv.org/abs/2310.11523 > > DeepSeekMath: Pushing the Limits > https://arxiv.org/abs/2402.03300 > > Now it seems that it is also at the core of DeepSeekMath, > what is possibly detected is not group of people, but > mathematical topics, so that in the end it excells. > > When unsupervised learning is used groups or math > topics might be found from data, through a form of > abduction. > > Bye > > Mild Shock schrieb: >> Hi, >> >> Wait till USA figures out there is a second >> competitor besides DeepSeek, its called Yi-Lightning: >> >> Yi-Lightning Technical Report >> https://arxiv.org/abs/2412.01253 >> >> It was already discussed 2 months ago: >> >> Eric Schmidt DROPS BOMBSHELL: China DOMINATES AI! >> https://www.youtube.com/watch?v=ddWuEUjo4u4 >> >> Bye >> >> Mild Shock schrieb: >>> Hi, >>> >>> How it started: >>> https://www.instagram.com/p/Cump3losObg >>> >>> How its going: >>> https://9gag.com/gag/azx28eK >>> >>> Bye >> >