It’s been a year of supersized AI models. When OpenAI released GPT-3, in June 2020, the neural network’s apparent grasp of language was uncanny. It could generate convincing sentences, converse with humans, and even autocomplete code. GPT-3 was also monstrous in scale—larger than any other neural network ever built. It kicked off a whole new trend in…
New research from MIT neuroscientists suggests that natural soundscapes have shaped our sense of hearing, optimizing it for the kinds of sounds we most often encounter. In a study reported Dec. 14 in the journal Nature Communications, researchers led by McGovern Institute for Brain Research associate investigator Josh McDermott used computational modeling to explore factors…
Cathy Wu is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering and a member of the MIT Institute for Data, Systems, and Society. As an undergraduate, Wu won MIT’s toughest robotics competition, and as a graduate student took the University of California at Berkeley’s first-ever course on deep reinforcement learning. Now back…
As it becomes easier to create hyper-realistic digital characters using artificial intelligence, much of the conversation around these tools has centered on misleading and potentially dangerous deepfake content. But the technology can also be used for positive purposes — to revive Albert Einstein to teach a physics class, talk through a career change with your…
Summary In this developer code pattern, we demonstrate how we can use biometrics to enable a seamless check-in experience for travelers. Interested parties include airlines, airport authorities, and local and federal agencies. At the same time, we enable a means to track travelers by using biometrics, all while sharing data across different agencies at different…
Posted by Karan Singhal, Senior Software Engineer, Google Research Federated learning enables users to train a model without sending raw data to a central server, thus avoiding the collection of privacy-sensitive data. Often this is done by learning a single global model for all users, even though the users may differ in their data distributions….
The shortcomings of supervised RL Reinforcement Learning (RL) is a powerful paradigm for solving many problems of interest in AI, such as controlling autonomous vehicles, digital assistants, and resource allocation to name a few. We’ve seen over the last five years that, when provided with an extrinsic reward function, RL agents can master very complex…
For all that neural networks can accomplish, we still don’t really understand how they operate. Sure, we can program them to learn, but making sense of a machine’s decision-making process remains much like a fancy puzzle with a dizzying, complex pattern where plenty of integral pieces have yet to be fitted. If a model was…
Posted by Timothy Nguyen1, Research Engineer and Jaehoon Lee, Senior Research Scientist, Google Research For a machine learning (ML) algorithm to be effective, useful features must be extracted from (often) large amounts of training data. However, this process can be made challenging due to the costs associated with training on such large datasets, both in…
In November 2018, MIT Professor Sangbae Kim brought his mini cheetah robot onto “The Tonight Show’s” Tonight Show-botics segment. Much to the delight of host Jimmy Fallon, the mini cheetah did some yoga, got back up after falling, and executed a perfect backflip. Behind the stage, Benjamin Katz ’16, SM ’18 was remotely controlling the cheetah’s…