Top large language models, under experimental conditions that pressed them to choose between abandoning their prompted mission and misbehaving, resorted to harmful behavior, researchers found.
Does a reasoning model’s chain of thought explain how it arrived at its output? Researchers found that often it doesn’t.
Reducing the number of bits used to represent each parameter in a neural network from, say, 16 bits to 8 bits shrinks the network’s size and boosts its speed. Researchers took this approach to an extreme: They built a competitive large language model whose weights are limited to three values.
Apple revamped two vision-language models in a bid to catch up with fast-moving competitors.
In Northern California, old property deeds may still include racial clauses: language, made illegal decades ago, that was designed to ban people of color from owning or living in certain homes.
OpenAI launched o3-pro, a more capable version of its most advanced reasoning vision-language model.
Researchers reduced the number of tokens needed to represent video frames to be fed to a transformer.
Renowned investment analyst Mary Meeker is back with a report on the AI market, six years after publishing her last survey of the internet.
DeepSeek updated its groundbreaking DeepSeek-R1 large language model to strike another blow for open-weights performance.
DeepSeek made headlines late last year, when it built a state-of-the-art, open-weights large language model at a cost far lower than usual. The upstart developer shared new details about its method.
Anthropic continued its tradition of building AI models that raise the bar in coding tasks.
Using an 8-bit number format like FP8 during training saves computation compared to 16- or 32-bit formats, but it can yield less-accurate results. Researchers trained models using 4-bit numbers without sacrificing accuracy.
OpenAI launched an agentic software-development system.
Improving a large language model’s factual accuracy typically requires making it bigger, which in turn, involves more computation. Researchers devised an architecture that enables models to recall relevant details without significantly increasing the amount of computation required.
An open-source code generator performs comparably to the reasoning models DeepSeek-R1 and OpenAI o1 with a much smaller model.
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