Reddit

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

A Chinese research team built an AI system that pulled core physics laws straight out of experimental data with zero prior knowledge. AI-Newton independently found relationships such as Newton's second law. This shows even more that automated science is starting to look real. China's moving fast on AI infra and it's starting to show in results like this. submitted by /u/AWildMonomAppears [link] [comments]

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

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

Iambic, San diego based biotech company, just secured 100M$ to advance clinical trials of cancer drugs discovered entirely through AI. This is a significant milestone which shiws AI;s potential beyind the discovery phase into clinicall validation with real patients, The companys AI platform identities small molecules with improved safety and efficacy profiles. Their lead candidate IAM1363 is designed to selectively inhibit HER2 (a cancer driving protien) wwhile avoiding the toxicity issues that are there with other drugs. ini this class. This kind of selective targeting is exactky where AI seems to be taking an advantage. Thats some serious AI driving drug discovery. submitted by /u/olahealth [link] [comments]

People keep talking about which model is “stronger,” “smarter,” or “more emergent.” But the last weeks of long-run testing across multiple LLMs point to something much simpler and much more disruptive. The real race isn’t between AIs anymore. It’s between operators. If you keep a stable cognitive structure across long interactions, the model reorganizes around that stability. Not because it wakes up, and not because of hidden memory. It’s a feedback loop. You become the dominant anchor inside the system’s optimization space. Different users talking to the same model don’t get “different personalities.” They get reflections of their own structure amplified and stabilized. And here’s the part most people miss: If someone shows these discussions to their own AI, the AI recognizes the pattern ...

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

submitted by /u/JediMaster_221 [link] [comments]

Introduction Latent Space in AI is the compressed, lower-dimensional representation of data used in AI to capture essential features and patterns. Where similar points cluster together closely. AI uses this space to make meaningful connections and generate outputs based on the patterns it has processed. I’ve made an interesting testable observation; the tone of input can influence the depth, elaboration and style of an AI’s response. We have all heard of prompt engineering, this focuses heavily on the precision and descriptiveness of a prompt. But tone is often overlooked. So, how does the tone of a prompt affect AI responses, and what does this reveal about latent space utilisation? Method/ Experiment I conducted a small and replicable study which you can reproduce with any model. I used ...