Stanford University

Large language models sometimes generate false statements. New work makes them more likely to produce factual output.

Researchers used an AI system to identify animal cell types from gene sequences, including a cell type that conventional approaches had discovered only in the past year. 

Research aims to help users select large language models that minimize expenses while maintaining quality.

Machine learning models typically learn language by training on tasks like predicting the next word in a given text. Researchers trained a language model in a less focused, more human-like way.

Researchers proposed a way for robots to find objects in households where things get moved around. Andrey Kurenkov and colleagues at Stanford University introduced Node Edge Predictor, a model that learned to predict where objects were located in houses.

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...