Second round of seed grants awarded to MIT scholars studying the impact and applications of generative AI
MIT News - Machine learning
by Mary Beth Gallagher | School of Engineering
3h ago
Last summer, MIT President Sally Kornbluth and Provost Cynthia Barnhart issued a call for papers to “articulate effective roadmaps, policy recommendations, and calls for action across the broad domain of generative AI.” The response to the call far exceeded expectations with 75 proposals submitted. Of those, 27 proposals were selected for seed funding. In light of this enthusiastic response, Kornbluth and Barnhart announced a second call for proposals this fall. “The groundswell of interest and the caliber of the ideas overall made clear that a second round was in order,” they said in their em ..read more
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Large language models use a surprisingly simple mechanism to retrieve some stored knowledge
MIT News - Machine learning
by Adam Zewe | MIT News
2d ago
Large language models, such as those that power popular artificial intelligence chatbots like ChatGPT, are incredibly complex. Even though these models are being used as tools in many areas, such as customer support, code generation, and language translation, scientists still don’t fully grasp how they work. In an effort to better understand what is going on under the hood, researchers at MIT and elsewhere studied the mechanisms at work when these enormous machine-learning models retrieve stored knowledge. They found a surprising result: Large language models (LLMs) often use a very simple lin ..read more
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Engineering household robots to have a little common sense
MIT News - Machine learning
by Jennifer Chu | MIT News
2d ago
From wiping up spills to serving up food, robots are being taught to carry out increasingly complicated household tasks. Many such home-bot trainees are learning through imitation; they are programmed to copy the motions that a human physically guides them through. It turns out that robots are excellent mimics. But unless engineers also program them to adjust to every possible bump and nudge, robots don’t necessarily know how to handle these situations, short of starting their task from the top. Now MIT engineers are aiming to give robots a bit of common sense when faced with situations that p ..read more
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AI generates high-quality images 30 times faster in a single step
MIT News - Machine learning
by Rachel Gordon | MIT CSAIL
1w ago
In our current age of artificial intelligence, computers can generate their own “art” by way of diffusion models, iteratively adding structure to a noisy initial state until a clear image or video emerges. Diffusion models have suddenly grabbed a seat at everyone’s table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy. Behind the scenes, it involves a complex, time-intensive process requiring numerous iterations for the algorithm to perfect the image. MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) res ..read more
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New algorithm unlocks high-resolution insights for computer vision
MIT News - Machine learning
by Rachel Gordon | MIT CSAIL
1w ago
Imagine yourself glancing at a busy street for a few moments, then trying to sketch the scene you saw from memory. Most people could draw the rough positions of the major objects like cars, people, and crosswalks, but almost no one can draw every detail with pixel-perfect accuracy. The same is true for most modern computer vision algorithms: They are fantastic at capturing high-level details of a scene, but they lose fine-grained details as they process information. Now, MIT researchers have created a system called “FeatUp” that lets algorithms capture all of the high- and low-level details of ..read more
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Five MIT faculty members take on Cancer Grand Challenges
MIT News - Machine learning
by Bendta Schroeder | Koch Institute
1w ago
Cancer Grand Challenges recently announced five winning teams for 2024, which included five researchers from MIT: Michael Birnbaum, Regina Barzilay, Brandon DeKosky, Seychelle Vos, and Ömer Yilmaz. Each team is made up of interdisciplinary cancer researchers from across the globe and will be awarded $25 million over five years.  Birnbaum, an associate professor in the Department of Biological Engineering, leads Team MATCHMAKERS and is joined by co-investigators Barzilay, the School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Com ..read more
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3 Questions: What you need to know about audio deepfakes
MIT News - Machine learning
by Rachel Gordon | MIT CSAIL
1w ago
Audio deepfakes have had a recent bout of bad press after an artificial intelligence-generated robocall purporting to be the voice of Joe Biden hit up New Hampshire residents, urging them not to cast ballots. Meanwhile, spear-phishers — phishing campaigns that target a specific person or group, especially using information known to be of interest to the target — go fishing for money, and actors aim to preserve their audio likeness. What receives less press, however, are some of the uses of audio deepfakes that could actually benefit society. In this Q&A prepared for MIT News, postdoc Nauma ..read more
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Exploring the cellular neighborhood
MIT News - Machine learning
by Alison Biester | Department of Biology
2w ago
Cells rely on complex molecular machines composed of protein assemblies to perform essential functions such as energy production, gene expression, and protein synthesis. To better understand how these machines work, scientists capture snapshots of them by isolating proteins from cells and using various methods to determine their structures. However, isolating proteins from cells also removes them from the context of their native environment, including protein interaction partners and cellular location. Recently, cryogenic electron tomography (cryo-ET) has emerged as a way to observe proteins i ..read more
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Researchers enhance peripheral vision in AI models
MIT News - Machine learning
by Adam Zewe | MIT News
2w ago
Peripheral vision enables humans to see shapes that aren’t directly in our line of sight, albeit with less detail. This ability expands our field of vision and can be helpful in many situations, such as detecting a vehicle approaching our car from the side. Unlike humans, AI does not have peripheral vision. Equipping computer vision models with this ability could help them detect approaching hazards more effectively or predict whether a human driver would notice an oncoming object. Taking a step in this direction, MIT researchers developed an image dataset that allows them to simulate peripher ..read more
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Using generative AI to improve software testing
MIT News - Machine learning
by Zach Winn | MIT News
3w ago
Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a person interacts with a software application. Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms — especially in scenarios where real-world data are limited or sensitive. For the last thre ..read more
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