Machine Learning Research

An open source model is designed to perform sophisticated object detection on edge devices like phones, cars, medical equipment, and smart doorbells.

An up-and-coming Hangzhou AI lab unveiled a model that implements run-time reasoning similar to OpenAI o1 and delivers competitive performance. Unlike o1, it displays its reasoning steps.

Researchers cut the processing required to train transformers by around 20 percent with only a slight degradation in performance.

A real-time video generator lets you explore an open-ended, interactive virtual world — a video game without a game engine.

Builders of large AI models have relied on the idea that bigger neural networks trained on more data and given more processing power would show steady improvements. Recent developments are challenging that idea.

An open source package inspired by the commercial agentic code generator Devin aims to automate computer programming and more.

A new open source large language model outperforms competitors, including the open-weights Llama 3.1 405B, on a variety of benchmarks.

Coding agents are improving, but can they tackle machine learning tasks? 

A new study suggests that leading AI models may meet the requirements of the European Union’s AI Act in some areas, but probably not in others.

API commands for Claude Sonnet 3.5 enable Anthropic’s large language model to operate desktop apps much like humans do. Be cautious, though: It’s a work in progress.

Training successive neural networks on the outputs of previous networks gradually degrades performance. Will future models succumb to the curse of recursive training?

The universe of web pages includes correct answers to common questions that are used to test large language models. How can we evaluate new models if they’ve studied the answers before we give them the test?

Researchers devised a way to cut the cost of training video generators. They used it to build a competitive open source text-to-video model and promised to release the training code.

Mistral AI launched two models that raise the bar for language models with 8 billion or fewer parameters, small enough to run on many edge devices.

Text embedding models are often used to retrieve text, cluster text, determine similarity between texts, and generate initial embeddings for text classifiers. A new embedding model comes with adapters that specialize it to each of these use cases.