Powerful machine-learning models are being used to help people tackle tough problems such as identifying disease in medical images or detecting road obstacles for autonomous vehicles. But machine-learning models can make mistakes, so in high-stakes settings it's critical that humans know when to trust a model's predictions. Uncertainty quantification is one tool that improves a model's reliability; the model produces a score along with the prediction that expresses a confidence level that the prediction is correct. While uncertainty quantification can be useful, existing methods typically require retraining the entire model to give it that ability. Training involves showing a model millions of examples so it can learn a task.

It's no secret that OpenAI's ChatGPT has some incredible capabilities -- for instance, the chatbot can write poetry that resembles Shakespearean sonnets or debug code for a computer program. These abilities are made possible by the massive machine-learning model that ChatGPT is built upon. Researchers have found that when these types of models become large enough, extraordinary capabilities emerge. But bigger models also require more time and money to train. The training process involves showing hundreds of billions of examples to a model.

The players can cooperate to achieve an objective, and compete against other players with conflicting interests. Creating artificial intelligence agents that can learn to compete and cooperate as effectively as humans remains a thorny problem. A key challenge is enabling AI agents to anticipate future behaviors of other agents when they are all learning simultaneously. Because of the complexity of this problem, current approaches tend to be myopic; the agents can only guess the next few moves of their teammates or competitors, which leads to poor performance in the long run. Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere have developed a new approach that gives AI agents a farsighted perspective.

Before a machine-learning model can complete a task, such as identifying cancer in medical images, the model must be trained. Training image classification models typically involves showing the model millions of example images gathered into a massive dataset. To avoid these pitfalls, researchers can use image generation programs to create synthetic data for model training. But these techniques are limited because expert knowledge is often needed to hand-design an image generation program that can create effective training data. Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere took a different approach.

"We're entering an era in which our enemies can make anyone say anything at any point in time." In this viral video from 2018, actor-writer Jordan Peele projected his voice into former President Obama's moving lips. Peele's PSA on'deepfakes,' audio and video altered with the intent to mislead, was the first time many people heard of synthetic data. It won't be the last. Today, synthetic data are everywhere, driving some of AI's most innovative applications.

Graphs, a potentially extensive web of nodes connected by edges, can be used to express and interrogate relationships between data, like social connections, financial transactions, traffic, energy grids, and molecular interactions. As researchers collect more data and build out these graphical pictures, researchers will need faster and more efficient methods, as well as more computational power, to conduct deep learning on them, in the way of graph neural networks (GNN). Now, a new method, called SALIENT (SAmpling, sLIcing, and data movemeNT), developed by researchers at MIT and IBM Research, improves the training and inference performance by addressing three key bottlenecks in computation. This dramatically cuts down on the runtime of GNNs on large datasets, which, for example, contain on the scale of 100 million nodes and 1 billion edges. Further, the team found that the technique scales well when computational power is added from one to 16 graphical processing units (GPUs).

The players can cooperate to achieve an objective, and compete against other players with conflicting interests. Creating artificial intelligence agents that can learn to compete and cooperate as effectively as humans remains a thorny problem. A key challenge is enabling AI agents to anticipate future behaviors of other agents when they are all learning simultaneously. Because of the complexity of this problem, current approaches tend to be myopic; the agents can only guess the next few moves of their teammates or competitors, which leads to poor performance in the long run. Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere have developed a new approach that gives AI agents a farsighted perspective.

Teaching a machine to recognize human actions has many potential applications, such as automatically detecting workers who fall at a construction site or enabling a smart home robot to interpret a user's gestures. To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions. However, not only is it expensive and laborious to gather and label millions or billions of videos, but the clips often contain sensitive information, like people's faces or license plate numbers. And this assumes the video data are publicly available in the first place -- many datasets are owned by companies and aren't free to use. So, researchers are turning to synthetic datasets.

Teaching a machine to recognize human actions has many potential applications, such as automatically detecting workers who fall at a construction site or enabling a smart home robot to interpret a user's gestures. To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions. However, not only is it expensive and laborious to gather and label millions or billions of videos, but the clips often contain sensitive information, like people's faces or license plate numbers. And this assumes the video data are publicly available in the first place -- many datasets are owned by companies and aren't free to use. So, researchers are turning to synthetic datasets.

Teaching a machine to recognize human actions has many potential applications, such as automatically detecting workers who fall at a construction site or enabling a smart home robot to interpret a user's gestures. To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions. However, not only is it expensive and laborious to gather and label millions or billions of videos, but the clips often contain sensitive information, like people's faces or license plate numbers. And this assumes the video data are publicly available in the first place--many datasets are owned by companies and aren't free to use. So, researchers are turning to synthetic datasets.