Computer science and technology

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New AI technique could make minimally invasive surgeries safer and more precise

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical engineering and computer science (EECS), Health care, Imaging, Jameel Clinic, Machine Learning, Medical devices, MIT Schwarzman College of Computing, MIT-IBM Computing Research Lab, National Institutes of Health (NIH), Research, School of Engineering

Researchers created a new technique that accurately and rapidly matches X-rays captured during surgery with a patient’s preoperative 3D medical scan. This method could make it easier for clinicians to precisely pilot minimally invasive surgical tools, leading to faster and safer procedures.Clinicians perform many minimally invasive surgeries using real-time X-rays to help them steer devices […]

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New method enables AI for safety-critical situations

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, Electrical engineering and computer science (EECS), IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Mechanical engineering, MIT Schwarzman College of Computing, Research, Robotics, School of Engineering

MIT researchers have developed a new technique that helps generative artificial intelligence models find solutions to high-stakes problems.In these settings, a plausible answer is not enough: The output often must also satisfy nonnegotiable safety, physical, or task-specific requirements, known as hard constraints.The researchers developed a method that helps generative models meet these strict requirements without

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MIT Schwarzman College of Computing launches pilot to help educators teach AI across disciplines

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This summer, the MIT Schwarzman College of Computing welcomed faculty from colleges and universities across Greater Boston, South Carolina, West Virginia, and Texas to campus for the inaugural AI Educators Pilot, a weeklong workshop aimed at expanding how artificial intelligence is taught across disciplines and learning environments. Inspired by MIT class C01/C51 (Modeling with Machine Learning),

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From MIT to IBM, expediting AI and quantum deployment

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The experience of transitioning from research based in theory to focusing on real-world application can vary significantly for different researchers. However, for two former MIT graduate students and a former postdoc, all now at IBM, working with the MIT-IBM Computing Research Lab (formerly the MIT-IBM Watson AI Lab) during their formative years enabled them to

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System helps humans predict when self-driving cars will make mistakes

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Artificial Intelligence, Autonomous vehicles, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Human-computer interaction, Machine Learning, MIT Schwarzman College of Computing, Research, Robotics, School of Engineering

Self-driving cars are often controlled by deep learning models that sometimes fail in unexpected situations. For instance, the car might inexplicably brake and block the path of an oncoming emergency vehicle. A human driver or passenger may need to react rapidly to prevent a collision.To help humans better anticipate a vehicle’s mistakes, researchers from MIT

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Looking beyond natural sequences

ai, AI (Artificial Intelligence), Artificial Intelligence, Biological engineering, Biology, Computer science and technology, Machine Learning, Proteins, Research, School of Engineering, School of Science

A protein’s function is determined by its structure, and structure — the way a protein folds — is determined by its sequence of amino acids, the building blocks of proteins. Many methods for designing novel proteins, including examples that could bind to a disease-causing molecule in our cells, involve a two-step process: The structure comes first,

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AI helps design new materials that work in the real world

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Today, anyone with a large enough artificial intelligence model can generate millions of new material designs in minutes. Unfortunately, that hasn’t led to a huge leap in the number of new materials being used to improve the performance of products like computer chips and rockets.One reason for the translation gap is that current models don’t

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When AI art has no author: Study finds generated images often can’t be traced to training data

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When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility.New work from a team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory

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With a feel for physics, AI models simulate a wider range of real-world scenarios

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Artificial intelligence models are jacks of many trades, including writing, generating images, and creating 3D models. But they aren’t as helpful when it comes to testing robots or designs for vehicles in diverse environments, since they don’t understand physics as well as they do pixels or text.To build an AI system that can reliably simulate

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The benefits of medical AI assistance vary based on user expertise

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A one-size-fits-all approach likely isn’t the best strategy when designing artificial intelligence systems that assist users in disease diagnosis.A new study by researchers at MIT and elsewhere found that, while AI assistance generally improved the accuracy of non-experts and clinicians in diagnosing skin diseases, AI explainability methods had different impacts depending on the users’ knowledge

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