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How Chain-of-Thought Reasoning Helps Neural Networks Compute
Large language models do better at solving problems when they show their work. Researchers are beginning to understand why.
How Selective Forgetting Can Help AI Learn Better
Erasing key information during training results in machine learning models that can learn new languages faster and more easily.
How Quickly Do Large Language Models Learn Unexpected Skills?
A new study suggests that so-called emergent abilities actually develop gradually and predictably, depending on how you measure them.
New Theory Suggests Chatbots Can Understand Text
Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.
How to Guarantee the Safety of Autonomous Vehicles
As computer-driven cars and planes become more common, the key to preventing accidents, researchers show, is to know what you don’t know.
The Year in Computer Science
Artificial intelligence learned how to generate text and art better than ever before, while computer scientists developed algorithms that solved long-standing problems.
AI System Beats Chess Puzzles With ‘Artificial Brainstorming’
By bringing together disparate approaches, machines can reach a new level of creative problem-solving.
Tiny Language Models Come of Age
To better understand how neural networks learn to simulate writing, researchers trained simpler versions on synthetic children’s stories.
The Physical Process That Powers a New Type of Generative AI
Some modern image generators rely on the principles of diffusion to create images. Alternatives based on the process behind the distribution of charged particles may yield even better results.