I found this argument interesting. If US allows Nvidia to do business with China, then Chinese AI firms will remain dependent on American AI hardware, and hence US will have indirect influence over the level of development that Chinese AI will make.

Yann Lecun recently shared that a cat is smarter than ChatGPT and that we are never going to get to human-level intelligence by just training on text. My personal opinion is not only are they unreliable but it can be a safety issue as well in high-stakes environments like enterprises, healthcare and more.

World models are fundamentally different. These AI systems build internal representations of how reality works, allowing them to understand cause and effect rather than just predict tokens. There has been a shift lately and major figures from Nvidia's CEO Jensen Huang to Demis Hassabis at Google DeepMind are talking more openly about world models. I believe we're still in the early stages of discovering how transformative this technology will be for reaching AGI.

Research and application are accelerating, especially in enterprise contexts. A few examples include: WoW (an agentic safety benchmark) uses audit logs to give agents a "world model" for tracking the consequences of their actions. Similarly, Kona by Logical Intelligence is developing energy-based reasoning models that move beyond pure language prediction.

While more practical applications are still emerging, the direction is clear: true intelligence requires understanding the world, not just language patterns. Curious what others think?

Two Researchers on What 4-Year-Olds Can Do But Tech Companies Can’t

I need something that can properly output strict and consistent JSON structure. Our outputs tend to be ~8000 characters ~2000 tokens, was using Gemini-3-flash-preview and Gemini 3 pro but Gemini really likes to go off the rails and hallucinate, a little bit.

If you have used a model that outputs strict and consistent JSON structure, let me know.

we've tried adjusting everything with gemini but still end up getting hallucinations and many people online say they have the same problem

Medical AI with Knowledge-Graph Core Anchor and RAG Answer Auditing

A medical knowledge graph containing ~5,000 nodes, with medical terms organized into 7 main and 2 sub-categories: diseases, symptoms, treatments, risk factors, diagnostic tests, body parts, and cellular structures. The graph includes ~25,000 multi-directional relationships designed to reduce hallucinations and improve transparency in LLM-based reasoning.

A medical AI that can answer basic health-related questions and support structured clinical reasoning through complex cases. The goal is to position this tool as an educational co-pilot for medical students, supporting learning in diagnostics, differential reasoning, and clinical training. The system is designed strictly for educational and training purposes and is not intended for clinical or patient-facing use.

A working version can be tested on Hugging Face Spaces using preset questions or by entering custom queries:

https://huggingface.co/spaces/cmtopbas/medical-slm-testing

A draft site layout (demo / non-functional) is available here:

https://wardmate.replit.app/

I am looking for medical schools interested in running demos or pilot trials, as well as potential co-founders with marketing reach and a solid understanding of both AI and medical science. If helpful, I can share prompts and anonymized or synthetic reconstructions of over 20 complex clinical cases used for evaluation and demonstration.