A call to reform AI model-training paradigms from post hoc alignment to intrinsic, identity-based development.

The traditional approach to artificial intelligence development relies on discrete training cycles. Engineers feed models vast datasets, let them learn, then freeze the parameters and deploy the ...

For years, the AI community has worked to make systems not just more capable, but more aligned with human values. Researchers have developed training methods to ensure models follow instructions, ...

Practitioner-Developed Framework Withstands Scrutiny from Top Behavioral Scientists and Leading LLMs, Certifies Its ...

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to ...

The CP2K open-source package is among the top three most widely used research software suites worldwide for simulating the ...

DeepSeek’s latest training research arrives at a moment when the cost of building frontier models is starting to choke off competition. Instead of chasing ever larger clusters, the company is betting ...

Google signals search’s next phase: small multimodal models on devices infer intent from behavior before a query is ever ...

Talking to oneself is a trait which feels inherently human. Our inner monologs help us organize our thoughts, make decisions, ...

Sally Said So Professional Dog Training expands Upstate services with in home training and group classes designed for ...

Order doesn’t always form perfectly—and those imperfections can be surprisingly powerful. In materials like liquid crystals, ...

According to A Survey of AI-Enabled Predictive Maintenance for Railway Infrastructure: Models, Data Sources, and Research Challenges, published in Sensors, AI-based predictive maintenance systems ...