All posts tagged: MIT

Raised on AI | MIT Technology Review

Raised on AI | MIT Technology Review

Published by Miika Tikkanen

What happened? I watched the promise of the early internet give way to the reality of its potential for abuse. I myself was already fully in the throes of smartphone and social media obsession. My wife, a pediatric nurse, grew increasingly alarmed at the number of children admitted to her hospital struggling with the effects of things like body dysmorphia or cyberbullying as a result of interactions on social media. We became those parents. The ones whose kids carry flip phones and aren’t on TikTok.  We are not alone in this. A surprising—maybe troubling—number of people I know who work at big tech companies also keep their kids at arm’s distance from technology. They lock down their phones, if they have phones at all, and keep them off social media. Hell, even Mark Zuckerberg doesn’t publicly post his children’s faces on Facebook or Instagram.   If the desire to limit kids’ use of technology was once a subcurrent, it has become a raging flood. Jonathan Haidt’s best-selling 2024 book The Anxious Generation helped propel the …

Seeing through murky waters | MIT Technology Review

Seeing through murky waters | MIT Technology Review

Published by Miika Tikkanen

When remotely operated vehicles settle on the seafloor or dig through a sand bed, they can kick up clouds of sediment that onboard cameras struggle to see through. Often, the only thing to do is wait until the dust settles. But a new system developed by Amy Phung, SM ’23, PhD ’26, and her advisor Richard Camilli, SM ’00, PhD ’03, of the Woods Hole Oceanographic Institution (WHOI), offers a solution.  In this technique, the vehicle first uses sonar to quickly map its surroundings. That technology doesn’t provide detailed resolution, but it works equally well in cloudy and clear water. And it allows a vehicle to safely get close enough to specific objects for cameras to visualize them more carefully. To speed up the processing so the mapping can be done in real time, the researchers combined the sonar tech with an image-­matching algorithm developed by researchers in France that quickly estimates the relative depth of each pixel in a 2D scene. “An analogy would be if you were to go into a china shop …

Launching youth entrepreneurship | MIT Technology Review

Launching youth entrepreneurship | MIT Technology Review

Published by Miika Tikkanen

That’s why in 2012 Stach founded LaunchX, a for-profit entrepreneurship program that helps high school students build practical skills through an intensive summer program in which they start real companies. Stach says that students form teams, identify a problem, and build an actual product or service that they then take to market—often generating preorders or even revenue before the four-week program ends. Raised in Texas and the Carolinas, Stach majored in mechanical engineering at MIT and earned her MBA from Harvard Business School in 2011. She ran her first summer program on MIT’s campus with 30 students in 2013 and says “it kind of snowballed.” LaunchX now serves about 500 students per year across both in-person and online programs. Read more at www.technologyreview.com/alumni-profiles. Source link

MIT tech powered the Artemis II livestreams

MIT tech powered the Artemis II livestreams

Published by Miika Tikkanen

During the original US missions to the moon in the 1960s and ’70s, astronauts relied on radio-­frequency systems to communicate, sending grainy, low-quality images and videos to Earth. But on the Artemis II Orion mission in April, spacecraft transmitted crystal-clear footage at speeds on par with those of home internet connections.  The livestreams so many people enjoyed were made possible through the Orion Artemis II Optical Communications System (O2O), developed by MIT Lincoln Laboratory in collaboration with NASA Goddard Space Flight Center. Based on infrared laser light, which can transmit 10 to 100 times more data per second than radio waves, the system downlinked nearly half a terabyte of data at up to 260 megabits per second. This data trove contained never-before-seen views of the basins and craters on the far side of the moon, a crescent Earth setting behind the moon, a nearly hour-long total solar eclipse, and flashes of light from tiny meteoroids striking the lunar surface. “Our goal was to demonstrate O2O’s operational utility for human spaceflight, extending the high-bandwidth connections that …

Puzzle Corner | MIT Technology Review

Puzzle Corner | MIT Technology Review

Published by Miika Tikkanen

Ready for a fresh set of puzzles? Click here for the September/October 2026 Puzzle Corner, brought to you by Michael S. Branicky, ScD ’95, of the Puzzle Corner Puzzle Crew (aka PC2), which also includes Edward Faulkner ’03, MEng ’04, and Abe Kunin ’03. This column includes solutions to the May/June issue. Send problems, solutions (by October 1), and comments to [email protected]. Editor emeritus Allan Gottlieb ’67 launched Puzzle Corner in 1966. Find back issues through 2022 at cs.nyu.edu/~gottlieb/tr and more recent back issues at technologyreview.com/puzzle-corner. Source link

Inside soccer’s data renaissance | MIT Technology Review

Inside soccer’s data renaissance | MIT Technology Review

Published by Miika Tikkanen

Davis and his team of researchers employ advanced data analytics to reveal a range of (beg your pardon) game-changing findings that are shifting pro clubs’ decision-making. “His lab is the most influential sports analytics lab in soccer,” says Hugo Rios-Neto, data recruitment lead for Royal Sporting Club Anderlecht in Belgium. They’ve helped teams better evaluate their rosters, conceived ways to assess how efficient (or not) strategies are, and developed algorithms that uncover hidden tactical patterns. Like, for instance, the value of kicking the ball out of bounds close to the goal and letting your opponent throw it back into play—a move that’s been popping up in some of the world’s top leagues over the last few years. To make the statistical argument for this seemingly counterproductive move, Davis’s group built a training data set composed of more than 1.4 million passes and some 60,000 throw-ins—partly from the 2022 World Cup. They used tree ensemble models (essentially a mashup of decision trees) to simulate the tactic. The conclusion, which the researchers presented in a 2024 paper …

What’s next for IVF | MIT Technology Review

What’s next for IVF | MIT Technology Review

Published by Miika Tikkanen

This type of genetic testing is possible thanks to multiple advances in technology—not just in genomics, but also in the ability to keep embryos alive in a dish for five to six days and the technique of freezing embryos while the cells undergo testing and thawing them once the results are in. And it has become hugely popular—some clinics do PGT-A tests on all their embryos. But PGT-A won’t give you a perfect readout of a future baby’s genetics, says Sonia Gayete-Lafuente, a reproductive endocrinologist at the Center for Human Reproduction in New York City. And some of the abnormalities might be able to self-correct with time. Gayete-Lafuente and her colleagues have transferred some of those “abnormal” embryos into patients’ uteruses and seen them develop into perfectly healthy children, she says. Other forms of PGT are even more controversial. PGT-P tests are designed to predict an embryo’s chances of developing complex traits that rely on multiple genes, including medical disorders but also physical characteristics like height or cognitive factors like IQ. These tests are new, …

AI at MIT | MIT Technology Review

AI at MIT | MIT Technology Review

Published by Miika Tikkanen

Hannes Stärk, the fourth-year PhD student at CSAIL who built BoltzGen, says the model works because it actually learns—drawing inferences from the data it is trained with and then producing novel ideas inspired by that data. With machine learning, you want the model to generalize from the data you use to train it, says Stärk, who created BoltzGen over seven months, often working up to 12 hours a day. “Because otherwise,” he says, “your solution is already in your training data.” Stärk has also assembled a network of over 30 scientists both within and beyond MIT to explore the design and applications of molecular binders for use in drug development, metabolomics, and structural biology as well as in treating cancer, autoimmune diseases, and genetic diseases. “It’s nice to have one model that can do all of this,” he says. Training across all these areas also makes the model better at generalizing. Beyond drug discovery As labs working in drug development continue to reap benefits from AI, other researchers across the Institute are busy applying existing …

Enabling agent-first process redesign | MIT Technology Review

Enabling agent-first process redesign | MIT Technology Review

Published by Miika Tikkanen

In an agent-first enterprise, AI systems operate processes while humans set goals, define policy constraints, and handle exceptions. “You need to shift the operating model to humans as governors and agents as operators,” says Scott Rodgers, global chief architect and U.S. CTO of the Deloitte Microsoft Technology Practice. The agent-first imperative With technology budgets for AI expected to increase more than 70% over the next two years, AI agents, powered by generative AI, are poised to fundamentally transform organizations and achieve results beyond traditional automation. These initiatives have the potential to produce significant performance gains, while shifting humans toward higher value work. AI is advancing so quickly that static approaches to task automation will likely only produce incremental gains. Because legacy processes aren’t built for autonomous systems, AI agents require machine-readable process definitions, explicit policy constraints, and structured data flows, according to Rodgers. Further complicating matters, many organizations don’t understand the full economic drivers of their business, such as cost to serve and per-transaction costs. As a result, they have trouble prioritizing agents that can …

MIT Technology Review is a 2026 ASME finalist in reporting

MIT Technology Review is a 2026 ASME finalist in reporting

Published by Miika Tikkanen

AI is often described as a black box, but it’s not just its inner workings that are mysterious. Leading AI companies have kept figures on energy use closely guarded, making it hard to determine its climate impact. In a rigorous investigation, senior AI reporter James O’Donnell and senior climate reporter Casey Crownhart spent six months digging through hundreds of pages of reports, interviewing experts, and crunching the numbers.  The team drilled down into the energy cost of a single prompt, and then zoomed out to build a broader picture illustrating the potential impacts of AI’s current and future energy demand. Their work revealed just how big AI’s energy footprint is, where that energy comes from, and who will pay for it. In the months following the project’s publication, major AI companies including Open AI, Mistral, and Google published details about their models’ energy and water usage.  The 2026 awards will be presented in New York City on May 19.  Source link