Examples of image generators using GANsformer
Stanford University

Attention for Image Generation: Combining GANs and transformers for more believable images.

Attention quantifies how each part of one input affects the various parts of another. Researchers added a step that reverses this comparison to produce more convincing images.
Graphs and data related to ImageNet performance
Stanford University

ImageNet Performance, No Panacea: ImageNet pretraining won't always improve computer vision.

It’s commonly assumed that models pretrained to achieve high performance on ImageNet will perform better on other visual tasks after fine-tuning. But is it always true? A new study reached surprising conclusions.
Selected data from AI Index, an annual report from Stanford University
Stanford University

AI for Business Is Booming: Stanford's 2021 AI Index shows commercial AI on the rise.

Commercial AI research and deployments are on the rise, a new study highlights. The latest edition of the AI Index, an annual report from Stanford University, documents key trends in the field including the growing importance of private industry and the erosion of U.S. dominance in research.
Different data related to the phenomenon called underspecification
Stanford University

Facing Failure to Generalize: Why some AI models exhibit underspecification.

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: How an AI model feels about art.

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: Medical AI system predicts knee pain from Black patients.

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: How researchers cracked InstaHide for computer vision.

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: AI model detects earthquakes and estimates epicenters.

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

Stanford professor Fei-Fei on how a national research cloud would boost AI

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: How NLP models learn attributes of pretrained embeddings.

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: A deep learning method for medical x-rays with text

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: U.S. federal agencies lag at AI uptake

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: How battery developers are using 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: A platform for fooling language models

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: Using Active Shift Layer to analyze time series data

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.

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