Letting probabilistic AI models autonomously operate inside production networks creates real safety and auditability issues, and that core security validation still needs deterministic guardrails. And ...

The compiler analyzed it, optimized it, and emitted precisely the machine instructions you expected. Same input, same output.

Abstract: In this paper, we investigate a dependency-aware task scheduling problem in connected autonomous vehicle (CAV) networks. Specifically, each CAV task consists of multiple dependent subtasks, ...

The automation tech leaders building the systems that are enabling much of today’s AI-powered workflows describe how the markets are shifting as organizations and individuals learn how to implement AI ...

A research article by Horace He and the Thinking Machines Lab (X-OpenAI CTO Mira Murati founded) addresses a long-standing issue in large language models (LLMs). Even with greedy decoding bu setting ...

Richie Etwaru, Co-founder & CEO of Mobeus, is an evangelist for the probabilistic math revolution and a pioneer in emerging technologies. For most of business history, systems followed deterministic ...

Abstract: In this note, we consider infinite horizon optimal control problems with deterministic systems. Since exact solutions to these problems are often intractable, we propose a parallel model ...

The vast majority of businesses approach generative AI as a new performance tool. They want faster answers, automated summaries, and higher-quality content that is accurate and produced quickly. In ...

Everywhere you look, people are talking about AI agents like they’re just a prompt away from replacing entire departments. The dream is seductive: Autonomous systems that can handle anything you throw ...

Some readers are fed up with me! “Don’t guilt trip me” is a refrain I heard from many readers of my recent columns from West Africa and South Sudan about children dying because of cuts in American ...