MIT

Biologists used neural networks to find a new class of antibiotics. Researchers at MIT and Harvard trained models to screen chemical compounds for those that kill methicillin-resistant Staphylococcus aureus (MRSA), the deadliest among bacteria that have...

Generated images can be more effective than real ones in training a vision model to classify images. Yonglong Tian, Lijie Fan, and colleagues at Google and MIT introduced StableRep, a self-supervised method that trains vision transformers on images generated by...

Training a model to separate the objects in a picture typically requires labeled images for best results. Recent work upped the ante for training without labels.

Many workers benefit from AI in the office without knowing it, a new study found. MIT Sloan Management Review and Boston Consulting Group surveyed employees on their use of AI in their day-to-day work. Their findings...

A new algorithm defeated a championship-winning Go model using moves that even a middling human player could counter. Researchers trained a model to defeat KataGo, an open source Go-playing system that has beaten top human players.

Researchers from Emory University, MIT, Purdue University, and other institutions found that deep learning systems trained to interpret x-rays and CT scans also were able to identify their subjects as Asian, Black, or White.

Researchers have used neural networks to generate novel views of a 3D scene based on existing pictures plus the positions and angles of the cameras that took them. In practice, though, you may not know the precise camera

Key machine learning datasets are riddled with mistakes. Several benchmark datasets are shot through with incorrect labels. On average, 3.4 percent of examples in 10 commonly used datasets are mislabeled and the detrimental impact of such errors rises with model size.

A recent generative adversarial network (GAN) produced more coherent images using modified transformers that replaced fully connected layers with convolutional layers. A new GAN achieved a similar end using transformers in their original form.

Some doctors don’t trust a second opinion when it comes from an AI system. A team at MIT and Regensburg University investigated how physicians responded to diagnostic advice they received from a machine learning model versus a human expert.

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.

A neural network learned to read the genes of viruses as though they were text. That could enable researchers to page ahead for potentially dangerous mutations. Researchers at MIT trained a language model to predict mutations that would enable infectious viruses to become even more virulent.

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.

How does a convolutional neural network recognize a photo of a ski resort? New research shows that it bases its classification on specific neurons that recognize snow, mountains, trees, and houses.

Deepfakes have gone mainstream, allowing celebrities to star in commercials without setting foot in a film studio. A new method helps determine whether such endorsements — and other images produced by generative adversarial networks — are authentic.