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Imagine you are typing a sentence. But instead of a keyboard, joystick, or eye tracker, a device surrounding your head reads your intentions and generates that sentence on screen.
Neural networks can forget how to perform earlier tasks as they learn new ones.
Large language models typically become less accurate and slower when they process longer contexts, but researchers enabled an LLM to keep accuracy stable and inference time constant as its context grew.
Reasoning models in the 1 to 2 billion-parameter range typically require more than 1 gigabyte of RAM to run. Liquid AI released one that runs in less than 900 megabytes, and does it with exceptional speed and efficiency.
Large language models often fail at puzzles like Sudoku, for which a solution includes multiple elements and a single mistake invalidates all of them. Researchers showed that a tiny network, by repeatedly refining its solution, can solve this sort of puzzle well.
Baidu debuted two models: a lightweight, open-weights, vision-language model and a giant, proprietary, multimodal model built to take on U.S. competitors.
The approach known as LoRA streamlines fine-tuning by training a small adapter that modifies a pretrained model’s weights at inference. Researchers built a model that generates such adapters directly.
Researchers built a model that integrates satellite imagery and other sensor readings across the entire surface of the Earth to reveal patterns of climate, land use, and other features.
DINOv2 showed that a vision transformer pretrained on unlabeled images could produce embeddings that are useful for a wide variety of tasks. Now it has been updated to improve the performance of its embeddings in segmentation and other vision tasks.
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.
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.
To date, efforts to decode what people are thinking from their brain waves often relied on electrodes implanted in the cortex. New work used devices outside the head to pick up brain signals that enabled an AI system, as a subject typed, to accurately guess what they were typing.
To date, efforts to decode what people are thinking from their brain waves often relied on electrodes implanted in the cortex. New work used devices outside the head to pick up brain signals that enabled an AI system, as a subject typed, to accurately guess what they were typing.
A new generation of robots can handle some household chores with unusual skill.
A new generation of robots can handle some household chores with unusual skill.
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