Can TypeSafe's Jev Make AI Agents Safer Without Another LLM?

Jev vs. LLMs: When AI Moves from Generation to Decision-Making

The article reports on a benchmark where the author evaluated TypeSafe AI’s Jev against large language models (LLMs) across 3,080 classification tasks, comparing metrics such as accuracy, latency, calibration, and confidence. It examines whether Jev can serve as a practical decision layer for AI systems, effectively moving AI from merely generating outputs to making informed decisions.

Democratizing AI Safety with RiskRubric.ai

A New Kind of Model for AI Decision-Making?

Exploring TypeSafe's Jev and comparing it with OpenAI on intent classification The post A New Kind of Model for AI Decision-Making? appeared first on Towards Data Science.

Tool Calling, Explained: How AI Agents Decide What to Do Next

Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science .

Fundamental Impossibility Results in AI Safety: Trading Off between Desiderata

The paper investigates whether safe AI systems can simultaneously satisfy multiple desiderata, such as harmlessness and helpfulness. By modeling the interaction between an AI, a defender, and an attacker as a three‑player game, the authors prove a fundamental tradeoff: advice that benefits the defender must remain public, limiting its safety guarantees. They also explore whether the AI can manipulate information to favor only the defender and discuss implications for safer AI design.

An Introduction to Jev

The article titled "An Introduction to Jev" discusses an AI system that focuses on making decisions rather than generating text. It highlights Jev’s unique approach to decision-making within the broader context of AI development. The piece was originally published on Towards Data Science.