Stanford University

A new index ranks popular AI models in terms of information their developers provide about their training, architecture, and usage. Few score well.

Large language models increasingly reply to prompts with a believably human response. Can they also mimic human behavior?

It wasn’t your imagination: OpenAI’s large language models have changed. Researchers at Stanford and UC Berkeley found that the performance of GPT-4 and GPT-3.5 has drifted in recent months. In a limited selection of tasks, some prompts yielded better results than before, some worse.

One challenge to making online education available worldwide is evaluating an immense volume of student work. Especially difficult is evaluating interactive computer programming assignments such as coding a game.

How can you tell when you’re reading machine-generated text? Three recent papers proposed solutions: Watermarking, classification, and a statistical method.

Stanford’s sixth annual AI Index takes stock of a rapidly growing field. The sprawling, 386-page report from the Institute for Human-Centered AI presents the past year’s developments in AI based on a wide variety of sources including benchmarks, papers, market research, job listings, and polls.

Large datasets often contain overly similar examples that consume training cycles without contributing to learning. A new paper identifies similar training examples, even if they’re not labeled.

Tools that automatically write computer code may make their human users overconfident that the programs are bug-free. Stanford University researchers found that programmers who used OpenAI’s Codex, a model that generates computer code, were more likely...

This year we really started to see the mainstreaming of AI. Systems like Stable Diffusion and ChatGPT captured the public imagination to an extent we haven’t seen before in our field.

If a robot can predict what it’s likely to see next, it may have a better basis for choosing an appropriate action — but it has to predict quickly. Transformers, for all their utility in computer vision, aren’t well suited to this because of their steep computational and memory requirements...

When a neural network learns image labels, it may confuse a background item for the labeled object. New research avoids such mistakes.

A recent workshop highlighted the impact of poorly designed AI models in medicine, security, software engineering, and other disciplines.

What kind of beast was Aristotle? The philosopher's follower Porphyry, who lived in Syria during the third century, came up with a logical way to answer the question...

A new study showcases AI’s growing importance worldwide. What’s new: The fifth annual AI Index from Stanford University’s Institute for Human-Centered AI documents rises in funding, regulation, and performance.

While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive