THE AI TIMES

LinkedIn is launching its new AI-powered people search this week, after what seems like a very long wait for what should have been a natural offering for generative AI. It comes a full three years after the launch of ChatGPT and six months after LinkedIn launched its AI job search offering. For technical leaders, this timeline illustrates a key enterprise lesson: Deploying generative AI in real enterprise settings is challenging, especially at a scale of 1.3 billion users. It’s a slow, brutal process of pragmatic optimization. The following account is based on several exclusive interviews with the LinkedIn product and engineering team behind the launch. First, here’s how the product works: A user can now type a natural language query like, "Who is knowledgeable about curing cancer?" into LinkedIn’s search bar. LinkedIn's old search, based on keywords, would have been stumped. It would have looked only for references to "cancer". If a user wanted to get sophisticated, they would have ha

Google DeepMind has built a new video-game-playing agent called SIMA 2 that can navigate and solve problems in a wide range of 3D virtual worlds. The company claims it’s a big step toward more general-purpose agents and better real-world robots.    Google DeepMind first demoed SIMA (which stands for “scalable instructable multiworld agent”) last year. But…

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI is changing how we quantify pain Researchers around the world are racing to turn pain—medicine’s most subjective vital sign—into something a camera or sensor can score as reliably as blood pressure. The…

The following originally appeared on Asimov’s Addendum and is being republished here with the author’s permission. The other day, I was looking for parking information at Dulles International Airport, and was delighted with the conciseness and accuracy of Google’s AI overview. It was much more convenient than being told that the information could be found […]

Secret chatbot flings are creating new legal challenges for married couples when it comes to infidelity.

Last week, we hosted EmTech MIT, MIT Technology Review’s annual flagship conference in Cambridge, Massachusetts. Over the course of three days of main-stage sessions, I learned about innovations in AI, biotech, and robotics.  But as you might imagine, some of this climate reporter’s favorite moments came in the climate sessions. I was listening especially closely…

Another day in late 2025, another impressive result from a Chinese company in open source artificial intelligence. Chinese social networking company Weibo's AI division recently released its open source VibeThinker-1.5B—a 1.5 billion parameter large language model (LLM) that is a fine-tuned variant of rival Chinese tech firm Alibaba's Qwen2.5-Math-1.5B. It's available now for free download and usage by researchers and enterprise developers—even for commercial purposes—under a permissive MIT License on Hugging Face, GitHub and ModelScope, with a technical report on open access science publishing site arxiv.org. And yet, despite its compact size, VibeThinker-1.5B achieves benchmark-topping reasoning performance on math and code tasks, rivaling or surpassing models hundreds of times its size, even outperforming Chinese rival DeepSeek's famed R1 that went viral at the start of this year—a 671-billion parameter model—on formal reasoning benchmark. It further eclipses Mistral AI's Magistral

Anthropic believes AI models will increasingly reach into the physical world. To understand where things are headed, it asked Claude to program a quadruped.

Next-gen networking tech, sometimes powered by light instead of electricity, is emerging as a critical piece of AI infrastructure.

As software systems grow more complex and AI tools generate code faster than ever, a fundamental problem is getting worse: Engineers are drowning in debugging work, spending up to half their time hunting down the causes of software failures instead of building new products. The challenge has become so acute that it's creating a new category of tooling — AI agents that can diagnose production failures in minutes instead of hours. Deductive AI, a startup emerging from stealth mode Wednesday, believes it has found a solution by applying reinforcement learning — the same technology that powers game-playing AI systems — to the messy, high-stakes world of production software incidents. The company announced it has raised $7.5 million in seed funding led by CRV, with participation from Databricks Ventures, Thomvest Ventures, and PrimeSet, to commercialize what it calls "AI SRE agents" that can diagnose and help fix software failures at machine speed. The pitch resonates with a growing frustra