Tech
Teaching robots to map large environments
A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.
Researchers have recently started building powerful machine-learning models to perform this complex task using only images from the robot’s onboard cameras, but even the best models can only process a few images at a time. In a real-world disaster where every second counts, a search-and-rescue robot would need to quickly traverse large areas and process thousands of images to complete its mission.
To overcome this problem, MIT researchers drew on ideas from both recent artificial intelligence vision models and classical computer vision to develop a new system that can process an arbitrary number of images. Their system accurately generates 3D maps of complicated scenes like a crowded office corridor in a matter of seconds.
The AI-driven system incrementally creates and aligns smaller submaps of the scene, which it stitches together to reconstruct a full 3D map while estimating the robot’s position in real-time.
Unlike many other approaches, their technique does not require calibrated cameras or an expert to tune a complex system implementation. The simpler nature of their approach, coupled with the speed and quality of the 3D reconstructions, would make it easier to scale up for real-world applications.
Beyond helping search-and-rescue robots navigate, this method could be used to make extended reality applications for wearable devices like VR headsets or enable industrial robots to quickly find and move goods inside a warehouse.
“For robots to accomplish increasingly complex tasks, they need much more complex map representations of the world around them. But at the same time, we don’t want to make it harder to implement these maps in practice. We’ve shown that it is possible to generate an accurate 3D reconstruction in a matter of seconds with a tool that works out of the box,” says Dominic Maggio, an MIT graduate student and lead author of a paper on this method.
Maggio is joined on the paper by postdoc Hyungtae Lim and senior author Luca Carlone, associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), principal investigator in the Laboratory for Information and Decision Systems (LIDS), and director of the MIT SPARK Laboratory. The research will be presented at the Conference on Neural Information Processing Systems.
Mapping out a solution
For years, researchers have been grappling with an essential element of robotic navigation called simultaneous localization and mapping (SLAM). In SLAM, a robot recreates a map of its environment while orienting itself within the space.
Traditional optimization methods for this task tend to fail in challenging scenes, or they require the robot’s onboard cameras to be calibrated beforehand. To avoid these pitfalls, researchers train machine-learning models to learn this task from data.
While they are simpler to implement, even the best models can only process about 60 camera images at a time, making them infeasible for applications where a robot needs to move quickly through a varied environment while processing thousands of images.
To solve this problem, the MIT researchers designed a system that generates smaller submaps of the scene instead of the entire map. Their method “glues” these submaps together into one overall 3D reconstruction. The model is still only processing a few images at a time, but the system can recreate larger scenes much faster by stitching smaller submaps together.
“This seemed like a very simple solution, but when I first tried it, I was surprised that it didn’t work that well,” Maggio says.
Searching for an explanation, he dug into computer vision research papers from the 1980s and 1990s. Through this analysis, Maggio realized that errors in the way the machine-learning models process images made aligning submaps a more complex problem.
Traditional methods align submaps by applying rotations and translations until they line up. But these new models can introduce some ambiguity into the submaps, which makes them harder to align. For instance, a 3D submap of a one side of a room might have walls that are slightly bent or stretched. Simply rotating and translating these deformed submaps to align them doesn’t work.
“We need to make sure all the submaps are deformed in a consistent way so we can align them well with each other,” Carlone explains.
A more flexible approach
Borrowing ideas from classical computer vision, the researchers developed a more flexible, mathematical technique that can represent all the deformations in these submaps. By applying mathematical transformations to each submap, this more flexible method can align them in a way that addresses the ambiguity.
Based on input images, the system outputs a 3D reconstruction of the scene and estimates of the camera locations, which the robot would use to localize itself in the space.
“Once Dominic had the intuition to bridge these two worlds — learning-based approaches and traditional optimization methods — the implementation was fairly straightforward,” Carlone says. “Coming up with something this effective and simple has potential for a lot of applications.
Their system performed faster with less reconstruction error than other methods, without requiring special cameras or additional tools to process data. The researchers generated close-to-real-time 3D reconstructions of complex scenes like the inside of the MIT Chapel using only short videos captured on a cell phone.
The average error in these 3D reconstructions was less than 5 centimeters.
In the future, the researchers want to make their method more reliable for especially complicated scenes and work toward implementing it on real robots in challenging settings.
“Knowing about traditional geometry pays off. If you understand deeply what is going on in the model, you can get much better results and make things much more scalable,” Carlone says.
This work is supported, in part, by the U.S. National Science Foundation, U.S. Office of Naval Research, and the National Research Foundation of Korea. Carlone, currently on sabbatical as an Amazon Scholar, completed this work before he joined Amazon.
Tech
Save Up to 40% With These Acer Promo Codes and Discounts
Acer is one of the top largest PC manufacturers in the world, perhaps best known for its gaming line and budget-friendly options. If you’ve already got your eye on an Acer product like a laptop or monitor, and are shopping at the company’s online storefront, you should be using one of these Acer promo codes and coupons to save some cash on your purchase.
Save 40% on Accessories When You Build an Acer Bundle
If you’re buying from Acer, you’re most likely shopping for either a desktop PC or laptop. With this discount, you can get a really solid deal on accessories if you bundle it with a mouse, laptop bag, or headset. When you go to purchase a PC, just click “Build Bundle” and you’ll see some of the eligible options, all of which are reduced by 40%. The Nitro Mechanical Keyboard, for example, goes from $50 to just $30. That 40% is a real discount, too, as that same keyboard costs $50 on Amazon when I checked.
Beyond peripheral add-ons, you can also save 10% off Acer Care Plus extended service plans or McAfee LiveSafe antivirus subscriptions. You can bundle up to five products together to save the most money. If you’re headed off to college (or have a kid in the family), a bundle like this can get you everything you need for a gaming or studying setup on the go.
Shop Rotating Weekly Deals on Monitors and Gaming Gear
Acer’s PC gaming offerings come in either the flagship Predator brand or the budget-tier Nitro. Acer offers rotating weekly deals on everything from monitors to gaming laptops, some of which are my favorites that I’ve tested in their given category. The Acer Nitro V 16, for example, was a budget gaming laptop that I recommended quite a lot last year because of its incredible price. The one I tested was the entry-level version with an Nvidia RTX 5050 inside, but Acer has the RTX 5060 model in its own storefront. It’s $100 off right now at $1,200, which comes with 16 GB of RAM and a terabyte of storage. In fact, it’s only $30 more than the RTX 5050 model, despite offering a significant jump in gaming performance. These discounts are reflected right on the product pages, so there’s no promo code, discount code, or coupon code required.
Acer has a wide selection of monitors available, too, whether that’s a massive 49-incher or a more modest 27-inch gaming workhorse. One of my favorite discounts I saw right now was the Acer Nitro XV2, a 27-inch 1440p display with a 300 Hz refresh rate. It’s 44% off at the time of writing, bringing the price down to just $250. Because these discounts are swapped out on a weekly basis, it’s worth checking back to see if the product you’re eyeing has a new discount.
Select Customers Can Get 15% Off Their Purchase
Acer also offers a number of added discounts at checkout, including 15% off for students. Students will need to verify through Student Beans or SheerID. Because a lot of the devices Acer offers are budget-friendly, they can be attractive for students, and the extra 15% off is the icing on the cake.
We tested the Acer Swift 16 AI last year and really enjoyed the high-resolution, OLED screen and impressively quiet performance. Acer has the smaller version of this same laptop available, the Swift 14 AI, which is currently $150 off. You also might check out the Acer Chromebook Plus 514, a laptop we liked quite a bit when we reviewed it in 2024.
Acer offers this same 15% discount for active duty military, veterans, and their families. It also applies to healthcare professionals, which can be verified through its healthcare discount portal.
Tech
AI Research Is Getting Harder to Separate From Geopolitics
The world’s top AI research conference, the Conference on Neural Information Processing Systems—better known as NeurIPS—became the latest organization this week to become embroiled in a growing clash between geopolitics and global scientific collaboration. The conference’s organizers announced and then quickly reversed controversial new restrictions for international participants after Chinese AI researchers threatened to boycott the event.
“This is a potential watershed moment,” says Paul Triolo, a partner at the advisory firm DGA-Albright Stonebridge who studies US-China relations. Triolo argues that attracting Chinese researchers to NeurIPS is beneficial to US interests, but some American officials have pushed for American and Chinese scientists to decouple their work—especially in AI, which has become a particularly sensitive topic in Washington.
The incident could deepen political tensions around AI research, as well as dissuade Chinese scientists from working at US universities and tech companies in the future. “At some level now it is going to be hard to keep basic AI research out of the [political] picture,” Triolo says.
In its annual handbook for paper submissions, issued in mid-March, NeurIPS organizers announced updated restrictions for participation. The rules stated that the event could not provide services including “peer review, editing, and publishing” to any organizations subject to US sanctions, and linked to a database of sanctioned entities. It included companies and organizations on the Bureau of Industry and Security’s entity list and those on another list with alleged ties to the Chinese military.
The new rules would have affected researchers at Chinese companies like Tencent and Huawei who regularly present work at NeurIPS. The database also includes entities from other countries such as Russia and Iran. The US places limits on doing business with these organizations, but there are no rules around academic publishing or conference participation.
The NeurIPS handbook has since been updated to specify that the restrictions apply only to Specially Designated Nationals and Blocked Persons, a list used primarily for terrorist groups and criminal organizations.
“In preparing the NeurIPS 2026 handbook, we included a link to a US government sanctions tool that covers a significantly broader set of restrictions than those NeurIPS is actually required to follow,” the event’s organizers said in a statement issued Friday. “This error was due to miscommunication between the NeurIPS Foundation and our legal team.”
Before they reversed course, the conference organizers initially said that the new rule was “about legal requirements that apply to the NeurIPS Foundation, which is responsible for complying with sanctions,” adding that it was seeking legal consultation on the issue.
Immediate Backlash
The new rule drew swift backlash from AI researchers around the world, particularly in China, which produces a large quantity of cutting-edge machine learning papers and is home to a growing share of the world’s top AI talent. Several academic groups there issued statements condemning the measure and, more importantly, discouraging Chinese academics from attending NeurIPS in the future. Some urged Chinese academics to contribute instead to domestic research conferences, potentially helping increase the country’s influence in relevant science and tech fields.
The China Association of Science and Technology (CAST), an influential government-affiliated organization for scientists and engineers, said Thursday that it would stop providing funding for Chinese scholars traveling to attend NeurIPS and would use the money instead to support domestic and international conferences that “respect the rights of Chinese scholars.”
CAST also said it will no longer count publications at the 2026 NeurIPS conference as academic achievements when evaluating future research funding. It’s unclear if the organization will reverse course now that NeurIPS has walked back the new rule.
Tech
Iranian Hackers Breached Kash Patel’s Email—but Not the FBI’s
Handala’s second claim, however—that it hacked the FBI—seems, for now, to be fiction. All evidence points to Handala having breached Patel’s older, personal Gmail account. Widely believed to be a “hacktivist” front for Iran’s intelligence agency the MOIS, Handala suggested on its website that the emails contained classified information, but the messages initially reviewed by WIRED didn’t appear to be related to any government work. TechCrunch did find, however, that Patel appears to have forwarded some emails from his Justice Department email account to his Gmail account in 2014.
Handala, which cybersecurity experts have described to WIRED as an “opportunistic” hacker group whose cyberattacks and breaches are often calculated more for their propaganda value than their tactical impacts, has nonetheless made the most of Patel’s embarrassing breach. “To the whole world, we declare: the FBI is just a name, and behind this name, there is no real security,” the group wrote in its statement. “If your director can be compromised this easily, what do you expect from your lower-level employees?”
Handala Hackers Put $50 Million Bounty on Trump and Netanyahu’s Heads
For further evidence of Handala’s bombastic rhetoric, look no further than another post on its website earlier this week (we’re intentionally not linking to it) that offered a $50 million bounty to anyone who could “eliminate” US president Donald Trump and Israeli prime minister Benjamin Netanyahu. “This substantial prize will be awarded, directly and securely, to any individual or group bold enough to show true action against tyranny,” the hackers’ statement read, along with an invitation to any would-be assassins to reach out via the encrypted messaging app Session. “All our communication and payment channels utilize the latest encryption and anonymization technologies, your safety and confidentiality are fully guaranteed.”
That bounty, Handala explained, was posted in answer to a statement about Handala published on the US Department of Justice website last week that offered $10 million for information leading to the identity or location of anyone who carries out “malicious cyber activities against US critical infrastructure” on behalf of a foreign government.
“Our message is clear: If you truly have the will and the power, come and find us!” Handala wrote in its response. “We fear no challenge and are prepared to respond to every attack with even greater force.”
In yet another post on its website this week, Handala also claimed to have doxed 28 engineers at military contractor Lockheed Martin working in Israel and threatened them with personal harm if they didn’t leave the country within 48 hours. When WIRED tried calling the phone numbers included in Handala’s leaked data, however, most of them didn’t work.
Apple says no device with its Lockdown Mode security feature enabled has ever been successfully compromised by mercenary spyware in the nearly four years since its launch. Amnesty International’s security lab head, Donncha Ó Cearbhaill, also says his team has seen no evidence of a successful attack against a Lockdown Mode–enabled iPhone. And Citizen Lab, which has documented several successful spyware attacks against iPhones, says none involve a Lockdown Mode bypass, while in two cases its researchers found the feature actively blocked attacks against NSO Group’s Pegasus and Intellexa’s Predator. Google researchers, meanwhile, found one spyware strain that simply abandons infection attempts when it detects the feature is enabled.
Lockdown Mode works by disabling commonly exploited iPhone features, such as most message attachment types and features like links and link previews. Incoming FaceTime calls are blocked unless the user has previously called that person within the past 30 days. When the iPhone is locked, it blocks connections with computers and accessories. The device will not automatically join nonsecure Wi-Fi networks, and 2G and 3G support is disabled. Apple has also doubled bounties for researchers who detect any Lockdown Mode bypass, with payouts up to $2 million.
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