Different data related to the phenomenon called underspecification
Stanford University

Facing Failure to Generalize

The same models trained on the same data may show the same performance in the lab, and yet respond very differently to data they haven’t seen before. New work finds this inconsistency to be pervasive.
Art pieces with subjective commentary regarding their emotional impact
Stanford University

How Art Makes AI Feel

An automated art critic spells out the emotional impact of images. Led by Panos Achlioptas, researchers at Ecole Polytechnique, King Abdullah University, and Stanford University trained a deep learning system to generate subjective interpretations of art.
Stanford University

Pain Points in Black and White

A model designed to assess medical patients’ pain levels matched the patients’ own reports better than doctors’ estimates did — when the patients were Black.
Examples of InstaHide scrambling images
Stanford University

A Privacy Threat Revealed

With access to a trained model, an attacker can use a reconstruction attack to approximate its training data. A method called InstaHide recently won acclaim for promising to make such examples unrecognizable to human eyes while retaining their utility for training.
Data and graphs related to a new model capable of detecting tremors
Stanford University

Quake Watch

Detecting earthquakes is an important step toward warning surrounding communities that damaging seismic waves may be headed their way. A new model detects tremors and provides clues to their epicenter.
Fei-Fei Li
Stanford University

Fei-Fei Li: Invigorating the U.S. AI Ecosystem

The United States has been a leader in science and technology for decades, and all nations have benefitted from its innovations. But U.S. leadership in AI is not guaranteed.
Data showing how new pretrained language models might learn facts like weight and cost
Stanford University

The Measure of a Muppet

The latest pretrained language models have shown a remarkable ability to learn facts. A new study drills down on issues of scale, showing that such models might learn the approximate weight of a dog or cost of an apple, at least to the right order of magnitude.
Examples of contrastive learning
Stanford University

Learning From Words and Pictures

It’s expensive to pay doctors to label medical images, and the relative scarcity of high-quality training examples can make it hard for neural networks to learn features that make for accurate diagnoses.
Graphs with data related to AI use cases
Stanford University

Washington Wrestles with AI

The U.S. government’s effort to take advantage of AI has not lived up to its promise, according to a new report. Implementations of machine learning systems by federal agencies are “uneven at best, and problematic and perhaps dangerous at worst".
Information and components of a battery
Stanford University

Getting a Charge From AI

Machine learning is helping to design energy cells that charge faster and last longer. Battery developers are using ML algorithms to devise new chemicals, components, and charging techniques faster than traditional techniques allow.
Screen captures of online platform Dynabench
Stanford University

Dynamic Benchmarks

Benchmarks provide a scientific basis for evaluating model performance, but they don’t necessarily map well to human cognitive abilities. Facebook aims to close the gap through a dynamic benchmarking method that keeps humans in the loop.
Graphs and data related to RubiksShift
Stanford University

More Efficient Action Recognition

Recognizing actions performed in a video requires understanding each frame and relationships between the frames. Previous research devised a way to analyze individual images efficiently known as Active Shift Layer (ASL). New research extends this technique to the steady march of video frames.
Screen capture showing how Diffbot works
Stanford University

The Internet in a Knowledge Graph

An ambitious company is using deep learning to extract and find associations from all the information on the internet — and it isn’t Google. Diffbot built a system that reads web code, parses text, classifies images, and assembles them into what it says is the world’s largest knowledge graph.
Information and examples of CheXbert, a network that labels chest X-rays
Stanford University

Human-Level X-Ray Diagnosis

Like nurses who can’t decipher a doctor’s handwriting, machine learning models can’t decipher medical scans — without labels. Conveniently, natural language models can read medical records to extract labels for X-ray images.
Road sign with the word "trust"
Stanford University

Toward AI We Can Count On

A consortium of top AI experts proposed concrete steps to help machine learning engineers secure the public’s trust. Dozens of researchers and technologists recommended actions to counter public skepticism toward artificial intelligence, fueled by issues like data privacy.

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