Hidden gems in AI community of this Week

submitted by /u/mati_tylec [link] [comments]

Medical SLM Model Output based on Graph Dictionary, 85% to 100% token success, 0.002 loss, 1.01 perplexity and all of this based on only 500 PubMed dataset samples and 85% weight on graph dictionary vector embeddings, These are simply results

I’ve been examining this new approach to generating seamless looping videos from text prompts called Mobius. The key technical innovation here is a latent shift-based framework that ensures smooth transitions between the end and beginning frames of generated videos. The

AI companies race to use ‘distillation’ to produce cheaper models.[1] DeepSeek claims ‘theoretical’ profit margins of 545%.[2] Microsoft’s new Phi-4 AI models pack big performance in small packages.[3] Stanford Researchers Uncover Prompt Caching Risks in AI APIs: Revealing Security Flaws

Have you ever stopped to think about how AI is already becoming indispensable in society—not through some big, dramatic takeover, but because humans are unknowingly building it into the system? Instead of a loud “AI rebellion,” imagine a scenario where

submitted by /u/Curious_Suchit [link] [comments]

submitted by /u/MetaKnowing [link] [comments]

Considering that there are AIs that are very good at recognizing patterns in speech, images and video, I have no doubt that there are also AIs that can recognize music in a similar fashion. Basically: Current Technology: I attach an

https://reddit.com/link/1j12vc6/video/5qrwwq0tq3me1/player Last few weeks where a bit crazy with all the new gen of models, this makes it a bit easier to compare the models against. I was particularly surprised at how bad R1 performed to my liking, and a

Ive heard a lot of people say that LLMs can’t reason outsude their training data both in and outside of this sub, which is completely untrue. Here’s my proof for why I believe that: MIT study shows language models defy