The experimental model won't compete with the biggest and best, but it could tell us why they behave in weird ways--and how trustworthy they really are. ChatGPT maker OpenAI has built an experimental large language model that is far easier to understand than typical models. That's a big deal, because today's LLMs are black boxes: Nobody fully understands how they do what they do. Building a model that is more transparent sheds light on how LLMs work in general, helping researchers figure out why models hallucinate, why they go off the rails, and just how far we should trust them with critical tasks. "As these AI systems get more powerful, they're going to get integrated more and more into very important domains," Leo Gao, a research scientist at OpenAI, told in an exclusive preview of the new work. "It's very important to make sure they're safe."

When you purchase through links in our articles, we may earn a small commission. Become a more efficient and better writer with the help of AI. ChatGPT can help with many things--creating images, looking up information, role-playing, solving math problems, programming and much more. But at the heart of everything it does are so-called "large language models"--AI algorithms trained on unimaginable amounts of text. So it's not surprising that what it does best is working with text.

Most language models (LMs) are trained and applied in an autoregressive left-to-right fashion, predicting the next token from the preceding ones. However, this ignores that the full sequence is available during training.

A smartphone shows the official website of ChatGPT Atlas. A smartphone shows the official website of ChatGPT Atlas. "It'd be really nice to have a service that was sort of just observing your life and proactively helping you when you needed it," said OpenAI CEO Sam Altman in a recent Q&A about OpenAI's plans. This vision is at the heart of a new crop of AI browsers, notably OpenAI's ChatGPT Atlas and Perplexity's Comet. AI browsers differ from traditional browsers in at least two important ways.

SIMA 2, which can figure out how to solve problems inside virtual worlds, could lead to more general-purpose agents and better robots. 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 SIMA 2 has been built on top of Gemini, the firm's flagship large language model, which gives the agent a huge boost in capability. The researchers claim that SIMA 2 can carry out a range of more complex tasks inside virtual worlds, figure out how to solve certain challenges by itself, and chat with its users.

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 push has already produced PainChek--a smartphone app that scans people's faces for tiny muscle movements and uses artificial intelligence to output a pain score--which has been cleared by regulators on three continents and has logged more than 10 million pain assessments. Other startups are beginning to make similar inroads. The way we assess pain may finally be shifting, but when algorithms measure our suffering, does that change the way we treat it? This story is from the latest print issue of MIT Technology Review magazine, which is full of fascinating stories about our bodies. Someone I know became a conspiracy theorist seemingly overnight.

OpenAI's Open-Weight Models Are Coming to the US Military The gpt-oss models are being tested for use on sensitive military computers. But some defense insiders say that OpenAI is still behind the competition. When OpenAI unveiled its first open-weight models in years this August, it wasn't just tech companies that were paying attention. The release also excited US military and defense contractors, which saw a chance to use them for highly secure operations. Initial results show that OpenAI's tools lag behind competitors in desired capabilities, some military vendors tell WIRED.

China's AI is quietly making big inroads in Silicon Valley China's AI models are quickly gaining traction in Silicon Valley, becoming integral to the operations of American companies and earning the praise of a growing list of tech leaders. Their rapid ascent has highlighted the competitive edge that Chinese developers such as Alibaba, Z.ai, Moonshot, and MiniMax have been able to gain by offering so-called "open" language models at much lower costs than their rivals in the United States. Airbnb CEO Brian Chesky generated headlines in October when he revealed that the short-term rental platform had opted for Alibaba's Qwen over OpenAI's ChatGPT, praising the Chinese model as "fast and cheap". Social Capital CEO Chamath Palihapitiya revealed the same month that his company had migrated much of its work to Moonshot's Kimi K2 as it was "way more performant" and "a ton cheaper" than models from OpenAI and Anthropic. Programmers on social media also recently highlighted evidence that two popular US-developed coding assistants, Composer and Windsurf, were built on Chinese models.

Large language models have shown remarkable language processing and reasoning ability but are prone to hallucinate when asked about private data. Retrieval-augmented generation (RAG) retrieves relevant data that fit into an LLM's context window and prompts the LLM for an answer. GraphRAG extends this approach to structured Knowledge Graphs (KGs) and questions regarding entities multiple hops away. The majority of recent GraphRAG methods either overlook the retrieval step or have ad hoc retrieval processes that are abstract or inefficient. This prevents them from being adopted when the KGs are stored in graph databases supporting graph query languages. In this work, we present GraphRAFT, a retrieve-and-reason framework that finetunes LLMs to generate provably correct Cypher queries to retrieve high-quality subgraph contexts and produce accurate answers. Our method is the first such solution that can be taken off-the-shelf and used on KGs stored in native graph DBs. Benchmarks suggest that our method is sample-efficient and scales with the availability of training data. Our method achieves significantly better results than all state-of-the-art models across all four standard metrics on two challenging Q&As on large text-attributed KGs.