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Personally I have been seeing some developments of AI for niche areas like ones relating to medicine. I feel like if done properly, this can be helpful for people who can't afford to visit a doctor. Of course, it's still important to be careful with what AI can advise especially to very specific or complicated situations, but these can potentially be a big help to those who need it. submitted by /u/pUkayi_m4ster [link] [comments]

I like the new ChatGPT generator but it takes too long to generate images for my purpose. I need something faster but also has the same quality. Google Gemini's Imagen seems to produce only low resolution images... I'm very uneducated in this area and really need advice. Can someone recommend me an engine? For context, I have to generate a lot of images for the B-roll of Instagram reels and TIktoks I record. submitted by /u/shouldIworkremote [link] [comments]

I read that o3 can tell where a photo was taken pretty accurately so decided to test it myself. Gotta say that I'm impressed and a bit scared at the same time. https://preview.redd.it/6yrfc6sx9uve1.jpg?width=703&format=pjpg&auto=webp&s=a749532d6c6cf9930a8b8b30daa28fcc6aad7638 submitted by /u/Altruistic-Hat9810 [link] [comments]

Interview here. submitted by /u/MetaKnowing [link] [comments]

Hey folks! I’ve been working with the team at Yandex Research on a way to make LLMs easier to run locally, without calibration data, GPU farms, or cloud setups. We just published a paper on HIGGS, a data-free quantization method that skips calibration entirely. No datasets or activations required. It’s meant to help teams compress and deploy big models like DeepSeek-R1 or Llama 4 Maverick on laptops or even mobile devices. The core idea comes from a theoretical link between per-layer reconstruction error and overall perplexity. This lets us: -Quantize models without touching the original data -Get decent performance at 3–4 bits per parameter -Cut inference costs and make LLMs more practical for edge use We’ve been using HIGGS internally for fast iteration and testing, and it's proven highl...

submitted by /u/PrincipleLevel4529 [link] [comments]

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

Sam Altman recently stated that OpenAI is no longer constrained by compute but now faces a much steeper challenge: improving data efficiency by a factor of 100,000. This marks a quiet admission that simply scaling up compute is no longer the path to AGI. Despite massive investments in data centers, more hardware won’t solve the core problem — today’s models are remarkably inefficient learners. We've essentially run out of high-quality, human-generated data, and attempts to substitute it with synthetic data have hit diminishing returns. These models can’t meaningfully improve by training on reflections of themselves. The brute-force era of AI may be drawing to a close, not because we lack power, but because we lack truly novel and effective ways to teach machines to think. This shift in und...

submitted by /u/PrincipleLevel4529 [link] [comments]

submitted by /u/PrincipleLevel4529 [link] [comments]