Jesse Thaler and collaborators outline a bidirectional roadmap: AI is becoming a core discovery engine across physics and math, and the physical sciences are, in turn, reshaping AI to be rigorous, interpretable, and reliable.
3 Questions: On the future of AI in physics research Read Post »
Engineering and ops leaders are adopting AI to compress detection, diagnosis, and remediation cycles. Here’s how teams can make credible, measured progress toward faster incident resolution while maintaining reliability.
How to reduce MTTR with AI code assistants: Codex-style gains for incident teams Read Post »
AI now scans vast startup signals, accelerates deal screening, and tracks portfolio health in real time. The payoff is speed and scale—along with a trade-off that can dampen outlier wins.
How AI for Venture Capital Is Transforming the Job Read Post »
Explainable AI is advancing fast, but businesses still face gaps in evaluation, trade-offs, and governance. Here’s how to use current methods responsibly—and where to push for better standards.
Model Explainability Techniques: What Works Today and What Still Needs Work Read Post »
Recent work on multimodal reasoning points to a compact training recipe, process-level safety alignment, and enterprise-ready deployment options that make specialized models viable without massive proprietary datasets.
Training Multimodal Reasoning Models: Lessons from Phi-4 Vision Read Post »
A practical playbook for game teams: use NVIDIA ACE’s small, optimized models and the NVIGI SDK’s GPU-aware scheduling to keep on-device AI responsive without blowing your frame or budget.
How to Reduce In-Game AI Inference Costs with Coding Agents Read Post »
