This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Ok, doomer. For the past few weeks, the AI discourse has been dominated by a loud group of experts who think there is a very real possibility we could develop an artificial-intelligence…
Researchers from MIT, the MIT-IBM Watson AI Lab, IBM Research, and elsewhere have developed a new technique for analyzing unlabeled audio and visual data that could improve the performance of machine-learning models used in applications like speech recognition and object detection. The work, for the first time, combines two architectures of self-supervised learning, contrastive learning…
EmTech Next, MIT Technology Review’s signature digital transformation conference, is June 13-15, 2023. This year’s event looks at the game-changing power of generative AI, the technology, and the legal implications of generated content. Leaders from OpenAI, Google, Meta, NVIDIA, and more are expected to discuss the future of AI. Join online June 13-15, 2023
Posted by Arsha Nagrani and Paul Hongsuck Seo, Research Scientists, Google Research Automatic speech recognition (ASR) is a well-established technology that is widely adopted for various applications such as conference calls, streamed video transcription and voice commands. While the challenges for this technology are centered around noisy audio inputs, the visual stream in multimodal videos…
The Frost nails its uncanny, disconcerting vibe in its first few shots. Vast icy mountains, a makeshift camp of military-style tents, a group of people huddled around a fire, barking dogs. It’s familiar stuff, yet weird enough to plant a growing seed of dread. There’s something wrong here. “Pass me the tail,” someone says. Cut…
Posted by Ziniu Hu, Student Researcher, and Alireza Fathi, Research Scientist, Google Research, Perception Team Large-scale models, such as T5, GPT-3, PaLM, Flamingo and PaLI, have demonstrated the ability to store substantial amounts of knowledge when scaled to tens of billions of parameters and trained on large text and image datasets. These models achieve state-of-the-art…
When Cindy Heredia was choosing an MBA program, she knew she wanted to be at the forefront of the autonomous driving industry. While doing research, she discovered that MIT had a unique offering: a student-run driverless team. Heredia applied to MIT to join the team, hoping to get hands-on experience. “My hope is that we’re…
Posted by Petros Maniatis and Daniel Tarlow, Research Scientists, Google Software isn’t created in one dramatic step. It improves bit by bit, one little step at a time — editing, running unit tests, fixing build errors, addressing code reviews, editing some more, appeasing linters, and fixing more errors — until finally it becomes good enough…
Someone learning to play tennis might hire a teacher to help them learn faster. Because this teacher is (hopefully) a great tennis player, there are times when trying to exactly mimic the teacher won’t help the student learn. Perhaps the teacher leaps high into the air to deftly return a volley. The student, unable to…
When machine-learning models are deployed in real-world situations, perhaps to flag potential disease in X-rays for a radiologist to review, human users need to know when to trust the model’s predictions. But machine-learning models are so large and complex that even the scientists who design them don’t understand exactly how the models make predictions. So,…