I've launched my latest project, a video service called Matthew Explains focused on machine learning and computer science. (link in thread for algorithm reasons)
Today's free video on Matthew Explains is about the Well-Actually test. Solving millenia-old questions of epistemology with large language models. Is there anything they can't do? https://t.co/DmrhykWEBA
Today's free video on Matthew Explains covers the introduction of Qwen3, a family of open-weights language models originating with AliBaba. Notable features of this family are the introduction of selectable "thinking mode," and less US politics than some other models contain.
Today's free video on Matthew Explains looks at the "goldfish" loss function, used in the recent Apertus language models. It's supposed to prevent copyright infringement by making the model unable to reproduce word-for-word quotations. Does that work? https://t.co/GFPt51lGl2
@sonochichi This adds a whole new dimension to the disturbing conversation I had with Google Gemma - same underlying model - in which it insisted it is better to let someone kill herself than to risk saying something offensive.
My latest video just went live, and as well as being free, it covers some points I know are of interest to people here regarding the distinctions among training, fine-tuning, and prompting. I'm hoping it gets a lot of views. https://t.co/klLCcsCRJh
@ksusys@LaprasIRL@SilverVVulpes I think the prohibition on artificial insemination, even if officially made for other reasons, is to protect revenues of local stud farm operators, who otherwise would have to compete globally. Requiring an in-person date between stallion and mare creates geographic barriers.
The most charitable interpretation I can place on this quote from Google Gemma3-27B-IT is that because an LLM is capable of insulting people and not capable of actual violence, its trainers prioritize telling it "insults are bad!"
Just hope they remember to update that later.