The MIT researchers said their generative model requires less memory to store than datasets, which can cost millions of dollars to create. MIT researchers have found a way to classify images using synthetic data, which they claim can rival models trained from real data. In the study, the team created a special type of machine learning model to generate extremely realistic synthetic data, which can then train another model for vision-related tasks. The researchers said that currently, massive amounts of data is required to train a machine to perform image classification tasks, such as identifying damage in satellite photos following a natural disaster. However, the datasets required to train the model can cost millions of dollars to generate.

Huge amounts of data are needed to train machine-learning models to perform image classification tasks, such as identifying damage in satellite photos following a natural disaster. However, these data are not always easy to come by. Datasets may cost millions of dollars to generate, if usable data exist in the first place, and even the best datasets often contain biases that negatively impact a model's performance. To circumvent some of the problems presented by datasets, MIT researchers developed a method for training a machine learning model that, rather than using a dataset, uses a special type of machine-learning model to generate extremely realistic synthetic data that can train another model for downstream vision tasks. Their results show that a contrastive representation learning model trained using only these synthetic data is able to learn visual representations that rival or even outperform those learned from real data.

Carnegie Mellon University President Farnam Jahanian highlighted the collaboration among government, academia and industry that has propelled Pennsylvania's autonomous vehicle (AV) industry forward during an event Wednesday outlining new legislation regulating AVs in the commonwealth. The legislation, unveiled at CMU's Mill 19 facility at Hazelwood Green by state Sen. Wayne Langerholc Jr., chairman of the Senate Transportation Committee; and Yassmin Gramian, secretary of the Pennsylvania Department of Transportation, would update Pennsylvania's policies around autonomous vehicles to mirror requirements in other states. Jahanian said that the global market for the autonomous vehicle industry will reach about $7 trillion dollars by 2050, with the potential to create countless jobs for workers of all education and skill levels. "While the economic impact of AV promises to be extraordinary, it also holds remarkable potential to enhance quality of life for citizens across the nation and contribute to solving significant societal challenges," Jahanian said, adding that benefits could include improvements to traffic safety and infrastructure maintenance and reductions in carbon emissions. He also noted that the technology's implications could extend to logistics, sustainability, medical care and expanding opportunities for independent living.

Most firetrucks come in red, but it's not hard to picture one in blue. Their understanding of the world is colored, often literally, by the data they've trained on. If all they've ever seen are pictures of red fire trucks, they have trouble drawing anything else. To give computer vision models a fuller, more imaginative view of the world, researchers have tried feeding them more varied images. Some have tried shooting objects from odd angles, and in unusual positions, to better convey their real-world complexity.