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Generative AI improves a wireless vision system that sees through obstructions

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Generative AI improves a wireless vision system that sees through obstructions



MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items.

Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot’s ability to reliably grasp and manipulate objects that are blocked from view.

This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model.

The researchers also introduced an expanded system that uses generative AI to accurately reconstruct an entire room, including all the furniture. The system utilizes wireless signals sent from one stationary radar, which reflect off humans moving in the space.  

This overcomes one key challenge of many existing methods, which require a wireless sensor to be mounted on a mobile robot to scan the environment. And unlike some popular camera-based techniques, their method preserves the privacy of people in the environment.

These innovations could enable warehouse robots to verify packed items before shipping, eliminating waste from product returns. They could also allow smart home robots to understand someone’s location in a room, improving the safety and efficiency of human-robot interaction.

“What we’ve done now is develop generative AI models that help us understand wireless reflections. This opens up a lot of interesting new applications, but technically it is also a qualitative leap in capabilities, from being able to fill in gaps we were not able to see before to being able to interpret reflections and reconstruct entire scenes,” says Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science, director of the Signal Kinetics group in the MIT Media Lab, and senior author of two papers on these techniques. “We are using AI to finally unlock wireless vision.”

Adib is joined on the first paper by lead author and research assistant Laura Dodds; as well as research assistants Maisy Lam, Waleed Akbar, and Yibo Cheng; and on the second paper by lead author and former postdoc Kaichen Zhou; Dodds; and research assistant Sayed Saad Afzal. Both papers will be presented at the IEEE Conference on Computer Vision and Pattern Recognition.

Surmounting specularity

The Adib Group previously demonstrated the use of millimeter wave (mmWave) signals to create accurate reconstructions of 3D objects that are hidden from view, like a lost wallet buried under a pile.

These waves, which are the same type of signals used in Wi-Fi, can pass through common obstructions like drywall, plastic, and cardboard, and reflect off hidden objects.

But mmWaves usually reflect in a specular manner, which means a wave reflects in a single direction after striking a surface. So large portions of the surface will reflect signals away from the mmWave sensor, making those areas effectively invisible.

“When we want to reconstruct an object, we are only able to see the top surface and we can’t see any of the bottom or sides,” Dodds explains.

The researchers previously used principles from physics to interpret reflected signals, but this limits the accuracy of the reconstructed 3D shape.

In the new papers, they overcame that limitation by using a generative AI model to fill in parts that are missing from a partial reconstruction.

“But the challenge then becomes: How do you train these models to fill in these gaps?” Adib says.

Usually, researchers use extremely large datasets to train a generative AI model, which is one reason models like Claude and Llama exhibit such impressive performance. But no mmWave datasets are large enough for training.

Instead, the researchers adapted the images in large computer vision datasets to mimic the properties in mmWave reflections.

“We were simulating the property of specularity and the noise we get from these reflections so we can apply existing datasets to our domain. It would have taken years for us to collect enough new data to do this,” Lam says.

The researchers embed the physics of mmWave reflections directly into these adapted data, creating a synthetic dataset they use to teach a generative AI model to perform plausible shape reconstructions.

The complete system, called Wave-Former, proposes a set of potential object surfaces based on mmWave reflections, feeds them to the generative AI model to complete the shape, and then refines the surfaces until it achieves a full reconstruction.

Wave-Former was able to generate faithful reconstructions of about 70 everyday objects, such as cans, boxes, utensils, and fruit, boosting accuracy by nearly 20 percent over state-of-the-art baselines. The objects were hidden behind or under cardboard, wood, drywall, plastic, and fabric.

Seeing “ghosts”

The team used this same approach to build an expanded system that fully reconstructs entire indoor scenes by leveraging mmWave reflections off humans moving in a room.

Human motion generates multipath reflections. Some mmWaves reflect off the human, then reflect again off a wall or object, and then arrive back at the sensor, Dodds explains.

These secondary reflections create so-called “ghost signals,” which are reflected copies of the original signal that change location as a human moves. These ghost signals are usually discarded as noise, but they also hold information about the layout of the room.

“By analyzing how these reflections change over time, we can start to get a coarse understanding of the environment around us. But trying to directly interpret these signals is going to be limited in accuracy and resolution.” Dodds says.

They used a similar training method to teach a generative AI model to interpret those coarse scene reconstructions and understand the behavior of multipath mmWave reflections. This model fills in the gaps, refining the initial reconstruction until it completes the scene.

They tested their scene reconstruction system, called RISE, using more than 100 human trajectories captured by a single mmWave radar. On average, RISE generated reconstructions that were about twice as precise than existing techniques.

In the future, the researchers want to improve the granularity and detail in their reconstructions. They also want to build large foundation models for wireless signals, like the foundation models GPT, Claude, and Gemini for language and vision, which could open new applications.

This work is supported, in part, by the National Science Foundation (NSF), the MIT Media Lab, and Amazon.



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A Kid With a Fake Mustache Tricked an Online Age-Verification Tool

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A Kid With a Fake Mustache Tricked an Online Age-Verification Tool


Meta is beefing up its age-verification mechanisms with an AI system that analyzes images and videos on Instagram and Facebook for “visual cues,” such as height and bone structure, to identify and delete accounts of users under the age of 13. The company announced the move amid a wave of cases in which hundreds of children have managed to evade social network access restrictions, even through simple tricks such as drawing on a mustache.

The new approach is part of a series of measures Meta adopted as part of an AI-based security strategy designed to correct the limitations of traditional methods, which rely heavily on self-reported age. With this change, the company seeks to reduce the ease with which minors access platforms that, in theory, are restricted to them.

In a press release, Meta explained that it is implementing several tools to identify contextual indicators that allow estimating a person’s age. This process includes the analysis of posts, comments, bios, and descriptions, with special attention to references related to school years or birthday celebrations—elements that can offer clues about the real age of the person who manages the account.

These tools are in addition to automated analysis techniques aimed at detecting physical traits from imagery shared to Meta’s social platforms. These include characteristics such as height and bone structure. Meta is careful to stipulate that this system is not face recognition, as it does not seek to identify specific individuals in images or videos. Instead, the company notes that, “by combining these visual insights with our analysis of text and interactions, we can significantly increase the number of underage accounts we identify and remove.”

If, based on these elements, Meta suspects that an account is managed by a child under 13, it will be suspended. The user will have to revalidate their age using the procedures established by the company to regain access; otherwise, the profile will be permanently deleted.

Meta also announced that it will expand the scope of its technology to detect users between the ages of 13 and 15 and automatically assign them teen accounts. This type of profile incorporates content restrictions and parental controls enabled by default, with the aim of providing a safer environment for this age group.

Meta began implementing age-verification tech in 2024 for Instagram users in the United States, Australia, Canada, and the United Kingdom. Now, the mechanism will be extended to Instagram accounts in Brazil and 27 European Union countries. In addition, these practices will be applied for the first time to Facebook users in the US, with plans to expand to the EU and UK next month.

Looking All Grown-Up

The new measures have been interpreted as a response to a preliminary ruling recently issued by the European Commission, which concluded that the company led by Mark Zuckerberg is in breach of the Digital Services Act for allegedly failing to effectively prevent children under 13 from using its platforms. The EU body found that the company lacks sufficiently effective mechanisms to block such access and that its current systems for identifying and suspending accounts below the age threshold are insufficient.

These criticisms are supported by the results of a survey conducted by the nonprofit Internet Matters. After surveying nearly 1,300 children and their parents in the UK, the study revealed that approximately one-third of children have successfully evaded government-imposed restrictions on access to social networking sites. In some cases, the methods employed are particularly striking.

The report, titled “The Online Safety Act: Are Children Safe Online?” showed that 46 percent of 9- to 16-year-olds believe that circumventing age controls is very easy. In total, however, only 32 percent admitted to breaking the rules.



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Trump’s Team Wants Him to Accept an Iran Deal He’s Already Rejected

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Trump’s Team Wants Him to Accept an Iran Deal He’s Already Rejected


President Donald Trump’s negotiators face the arduous task of trying to convince the president that a deal he previously rejected is their best option in Iran.

Last month, Trump initially gave his blessing for a so-called “cash for uranium” deal, under which the US would release around $20 billion in frozen funds in exchange for Iran handing over its stockpile of highly enriched uranium, sources familiar with the matter tell WIRED.

Trump’s negotiators, vice president JD Vance, special envoy Steve Witkoff and Jared Kushner, Trump’s son in law, received repeated approvals from the president while they were in Islamabad, giving them confidence a deal was close.

But the deal unraveled, in part because Trump was warned by his team that there was a risk he could be seen as giving Iran “pallets of cash”—an echo of his own oft-stated criticism of Barack Obama’s Iran deal—and he pulled the plug, the sources said.

Except now, that’s once again the cornerstone of the current proposal.

The current negotiations for a memorandum of understanding that could guide talks on a nuclear deal center on Iran handing over its stockpile of highly enriched uranium, and a moratorium on further uranium enrichment for somewhere around 12 to 15 years, Axios earlier reported.

In exchange, the US would offer a combination of billions in sanctions relief and the gradual release of frozen funds after gaining control of the enriched uranium, in order to destroy it or blend it down so it cannot be used for a nuclear weapon.

While a memorandum of understanding might get Iran to the table, that framework is not materially different from what was discussed previously in Islamabad and rejected by Trump, who has repeatedly told advisers in recent weeks he is against sending money to Iran, sources tell WIRED.

Some of Trump’s advisers say the decision of whether Trump ultimately blesses the framework is likely to come down to how badly he wants a deal. There are few options to incentivize Iran, they add, and financial aid has been the most compelling.

“They are going to have to do something like that, and it’s better than the Obama deal, so he should take it,” one Trump adviser said on the condition of anonymity, referring to the Joint Comprehensive Plan of Action. Trump has long criticized that deal for having provisions similar to ones currently under discussion, like a sunset clause on nuclear enrichment and the US lifting some sanctions.

For all the machinations in the West Wing, it has not gone unnoticed by Trump’s orbit that some of his top players have been conspicuous in their absence on Iran, according to two administration officials familiar with the matter.

Marco Rubio, the secretary of state and national security adviser, has been part of the group advising Trump on Iran and, physically speaking, spends most of his time in his West Wing office overlooking West Executive Avenue instead of at the State Department.

Rubio was happy to brief reporters on Tuesday, but he only did so at the request of the White House, a person familiar with the matter said, with his advisers wary of him getting involved in Iran negotiations that could as easily unravel as succeed.

In fact, given the downside risk, Rubioworld has been saying they were surprised that Vance asked to be a part of the Iran talks—a contention denied by people close to the vice president, who said he was ordered to by Trump.

Rubio instead has been more focused on Cuba, and on Venezuela, where assistant secretary of state Caleb Orr has been involved in overseeing new private equity investment to rebuild the country’s oil infrastructure.



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Mexico City Is Sinking. A Powerful NASA Satellite Just Exposed How Fast

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Mexico City Is Sinking. A Powerful NASA Satellite Just Exposed How Fast


Mexico City is one of the fastest sinking cities in the world. Now, a powerful satellite from the US National Aeronautics and Space Administration (NASA) confirms the accelerated advance of this silent threat that puts nearly 20 million people at risk.

The satellite designed by NASA and the Indian Space Research Organization (ISRO), known as NISAR (NASA-ISRO Synthetic Aperture Radar), was able to capture with unprecedented precision the magnitude and evolution of this phenomenon in different areas of the Mexican capital. The analysis is based on preliminary measurements taken from space between October 2025 and January of this year, during the dry season in Mexico City.

Their findings were captured in a map that shows how the subsurface of the metropolis is shifting. In the map, NASA identified areas with subsidence greater than 2 centimeters per month (marked in dark blue). The agency specifies that the areas marked in yellow and red could correspond to background signals (or noise) that are expected to diminish as the satellite instrument collects more data.

The image also highlights the location of Benito Juarez International Airport, located near Lake Nabor Carrillo, which operates in the middle of an area with accelerated subsidence. “Images like this confirm that the NISAR measurements are in line with expectations,” said Craig Ferguson, deputy director of the project.

Mexico City sits atop the clay and lake bed of ancient Lake Texcoco. NASA explains that this process is a consequence of intense groundwater pumping and the increasing weight associated with urban development. Both factors have caused the compaction of the ancient lake soil for more than a century.

The phenomenon was first documented in 1925 by engineer Roberto Gayol. Between the 1900s and 2000s, some areas experienced a drop of nearly 35 centimeters per year, causing damage to infrastructure such as the Metro, one of the largest mass transit systems in the Americas.

A study conducted in 2024 by Dario Solano-Rojas, a remote-sensing specialist at the National Autonomous University of Mexico, found that subsidence is not uniform. After analyzing changes in the city’s elevation between 2011 and 2020, the researcher and his team concluded that subsidence rates are highly variable: While some areas register up to 50 centimeters per year, in others the phenomenon is almost imperceptible.

This creates “differential subsidence,” where the ground sinks unevenly not only across square kilometers or city blocks, but even on a meter scale. When a street, railway, or building sinks differently at one end compared to the other, its stability is compromised.



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