Neurosymbolic AI systems where every answer is traceable to its source, every decision is explainable, and every claim is provably grounded.
No hallucinations. No black boxes. Just knowledge, structured and searchable, all the way down.
Most drug-interaction alerts arrive as a verdict with no receipt. "Major interaction." Says who? Based on what? A clinician can't tell, so — under time pressure, for
Ask a commercial interaction checker about a three-drug problem and you'll get, at best, three separate two-drug answers. That's not a limitation of the vendor'
Here is the failure mode that should scare anyone who ships clinical decision support: a drug-interaction checker that, when it doesn't know, quietly returns nothing. No alert. No
If you're building AI systems — agents, RAG pipelines, fine-tuned models, anything that learns — you already know that unit tests are necessary and completely insufficient. Tests tell you whether
Our simulation predicted 80% accuracy. Live testing delivered 54%. That's not a rounding error. That's a 26-point gap that calls into question how we validate AI
Every few months, a new paper announces a technique to "reduce hallucinations" in large language models. Retrieval-Augmented Generation. Chain-of-thought prompting. Constitutional AI. Self-consistency checking. These are patches on
Open-world video games face a problem that looks nothing like AI memory — until you squint. The Rendering Problem In a game like Zelda, Breath of the Wild, the world is
Crystallization: Teaching AI to Remember When an LLM answers a question, the answer evaporates. Next session, it's gone — no trace, no memory, no learning. The weights didn'
The Problem With Remembering Nothing Ask an LLM a brilliant question and you'll get a brilliant answer. Ask it again tomorrow and it has no memory of the