Computer science and technology

Auto Added by WPeMatico

Solving the “Whac-a-mole dilemma”: A smarter way to debias AI vision models

In today’s hospitals and clinics, a dermatologist may use an artificial intelligence model for classifying skin lesions to assess if the lesion is at risk of developing into a cancer or if it is benign. But if the model is biased toward certain skin tones, it could fail to identify a high-risk patient.Perhaps one of […]

Solving the “Whac-a-mole dilemma”: A smarter way to debias AI vision models Read More »

The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing

The following is a joint announcement by the MIT Schwarzman College of Computing and IBM.IBM and MIT today announced the launch of the MIT-IBM Computing Research Lab, advancing their long-standing collaboration to shape the next era of computing. The new lab expands its scope to include quantum computing, alongside foundational artificial intelligence research, with the

The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing Read More »

Enabling privacy-preserving AI training on everyday devices

A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81 percent. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.The MIT researchers boosted the efficiency of a technique known as

Enabling privacy-preserving AI training on everyday devices Read More »

A faster way to estimate AI power consumption

Due to the explosive growth of artificial intelligence, it is estimated that data centers will consume up to 12 percent of total U.S. electricity by 2028, according to the Lawrence Berkeley National Laboratory. Improving data center energy efficiency is one way scientists are striving to make AI more sustainable.Toward that goal, researchers from MIT and the

A faster way to estimate AI power consumption Read More »

MIT scientists build the world’s largest collection of Olympiad-level math problems, and open it to everyone

Every year, the countries competing in the International Mathematical Olympiad (IMO) arrive with a booklet of their best, most original problems. Those booklets get shared among delegations, then quietly disappear. No one had ever collected them systematically, cleaned them, and made them available, not for AI researchers testing the limits of mathematical reasoning, and not

MIT scientists build the world’s largest collection of Olympiad-level math problems, and open it to everyone Read More »

Teaching AI models to say “I’m not sure”

Confidence is persuasive. In artificial intelligence systems, it is often misleading.Today’s most capable reasoning models share a trait with the loudest voice in the room: They deliver every answer with the same unshakable certainty, whether they’re right or guessing. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have now traced that overconfidence to

Teaching AI models to say “I’m not sure” Read More »

Human-machine teaming dives underwater

The electricity to an island goes out. To find the break in the underwater power cable, a ship pulls up the entire line or deploys remotely operated vehicles (ROVs) to traverse the line. But what if an autonomous underwater vehicle (AUV) could map the line and pinpoint the location of the fault for a diver

Human-machine teaming dives underwater Read More »

New technique makes AI models leaner and faster while they’re still learning

Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational resources. Traditionally, obtaining a smaller, faster model either requires training a massive one first and then trimming it down, or training a small one from scratch and accepting weaker performance. Researchers at MIT’s Computer Science and Artificial Intelligence

New technique makes AI models leaner and faster while they’re still learning Read More »

Sixteen new START.nano companies are developing hard-tech solutions with the support of MIT.nano

MIT.nano has announced that 16 startups became active participants in its START.nano program in 2025, more than doubling the number of new companies from the previous year. Aimed at speeding the transition of hard-tech innovation to market, START.nano supports new ventures through the discounted use of MIT.nano shared facilities and a guided access to the

Sixteen new START.nano companies are developing hard-tech solutions with the support of MIT.nano Read More »

Helping data centers deliver higher performance with less hardware

To improve data center efficiency, multiple storage devices are often pooled together over a network so many applications can share them. But even with pooling, significant device capacity remains underutilized due to performance variability across the devices.MIT researchers have now developed a system that boosts the performance of storage devices by handling three major sources

Helping data centers deliver higher performance with less hardware Read More »