Robodogs are being used by the Australian military and can even be controlled by soldiers' minds. Kurt "The CyberGuy" Knutsson explains how it works. The age-old philosophical question, "who let the dogs out?" has finally been solved. Thanks to the help of Ghost Robotics and the Australian military, soldiers can now control robot dogs with their minds. It sounds like something out of a sci-fi movie, yet it's happening right now while half the planet focuses on Pedro Pascal's impeccable performance on "Last of Us." CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER This isn't the first time we've seen robot dogs in action.

The short videos give the impression of a flipbook, jumping shakily from one surreal frame to the next. They're the result of internet meme-makers playing with the first widely available text-to-video AI generators, and they depict impossible scenarios like Dwayne "The Rock" Johnson eating rocks and French president Emmanuel Macron sifting through and chewing on garbage, or warped versions of the mundane, like Paris Hilton taking a selfie. This new wave of AI-generated videos has definite echoes of Dall-E, which swept the internet last summer when it performed the same trick with still images. Less than a year later, those wonky Dall-E images are almost indistinguishable from reality, raising two questions: Will AI-generated video advance as quickly, and will it have a place in Hollywood? ModelScope, a video generator hosted by AI firm Hugging Face, allows people to type a few words and receive a startling, wonky video in return. Runway, the AI company that cocreated the image generator Stable Diffusion, announced a text-to-video generator in late March, but it has not made it widely available to the public.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on "America's Newsroom." The Federal Trade Commission (FTC) is making a play to be a key regulator of artificial intelligence (AI) systems, just as technology heavyweights and policymakers are clamoring for federal government oversight of AI applications. Last week's call for a moratorium on new AI development from tech giants like Elon Musk and Steve Wozniak kick-started a discussion about whether and how the government should step in and put guardrails up around potentially dangerous AI systems. Several lawmakers responded by saying a moratorium would be difficult to impose, leaving a huge gap between calls for action and the realities of how quickly Congress can act. However, the FTC has made it clear over the last week that it is prepared to bridge that gap and take a stab at regulating emerging AI systems. The federal agency tasked with policing "deceptive or unfair business practices" says it has a dog in this fight and is building up a capacity to take on the threats that AI poses to wary consumers.

Researchers created DribbleBot, a system for in-the-wild dribbling on diverse natural terrains including sand, gravel, mud, and snow using onboard sensing and computing. In addition to these football feats, such robots may someday aid humans in search-and-rescue missions. If you've ever played soccer with a robot, it's a familiar feeling. A four-legged robot is hustling toward you, dribbling with determination. Researchers from MIT's Improbable Artificial Intelligence Lab, part of the Computer Science and Artificial Intelligence Laboratory (CSAIL), have developed a legged robotic system that can dribble a soccer ball under the same conditions as humans.

Artificial intelligence has reached a new level of interest ever since ChatGPT burst into the scene. The AI chatbot with its eerily human-like responses has lit a fire under many tech giants and smaller tech companies that are now rushing to release their rival offerings. US President Joe Biden, however, wants them to be careful and make sure that their products are safe before opening them up to the public. According to AP and Reuters, the president has met up with his science and technology advisors, which include academics and executives from Google and Microsoft, to discuss the "risks and opportunities" of artificial intelligence. While the meeting likely won't culminate in a banning of ChatGPT like what happened in Italy, the president doesn't seem to be convinced that AI is perfectly safe at this point in time. When asked if AI is dangerous, he responded: "It remains to be seen.

PRAGUE – Eugene Nayshtetik and his five co-workers shuttered their company developing medical and biotech startups to join the defense forces days after Russia invaded Ukraine. Within two months, their commanders agreed it would be more useful if they swapped their military gear for computers. With the government's blessing, Nayshtetik and his team of engineers moved to neighboring Poland where they raised initial funding from a Polish company, Air Res Aviation, to develop a new drone for the Ukrainian military. Jerzy Nowak, president and co-owner of Air Res Aviation, said his company's initial investment in the drone project amounted to around $200,000. This could be due to a conflict with your ad-blocking or security software.

United States President Joe Biden has said artificial intelligence (AI) "could be" dangerous but it remains to be seen how the technology will affect society. Speaking at the start of a meeting with science and technology advisers on Tuesday, Biden said technology companies had a responsibility to ensure their products are safe before their release. "Tech companies have a responsibility, in my view, to make sure their products are safe before making them public," Biden said at the opening of a meeting of the President's Council of Advisors on Science and Technology. Asked if AI was dangerous, Biden said it "remains to be seen" but "it could be". Biden said AI could help tackle challenges like disease and climate change but that developers of the technology would also have to address "potential risks to our society, to our economy, to our national security".

Modern end-to-end learning systems can learn to explicitly infer control from perception. However, it is difficult to guarantee stability and robustness for these systems since they are often exposed to unstructured, high-dimensional, and complex observation spaces (e.g., autonomous driving from a stream of pixel inputs). We propose to leverage control Lyapunov functions (CLFs) to equip end-to-end vision-based policies with stability properties and introduce stability attention in CLFs (att-CLFs) to tackle environmental changes and improve learning flexibility. We also present an uncertainty propagation technique that is tightly integrated into att-CLFs. We demonstrate the effectiveness of att-CLFs via comparison with classical CLFs, model predictive control, and vanilla end-to-end learning in a photo-realistic simulator and on a real full-scale autonomous vehicle.

Methane (CH$_4$) is the chief contributor to global climate change. Recent Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) has been very useful in quantitative mapping of methane emissions. Existing methods for analyzing this data are sensitive to local terrain conditions, often require manual inspection from domain experts, prone to significant error and hence are not scalable. To address these challenges, we propose a novel end-to-end spectral absorption wavelength aware transformer network, MethaneMapper, to detect and quantify the emissions. MethaneMapper introduces two novel modules that help to locate the most relevant methane plume regions in the spectral domain and uses them to localize these accurately. Thorough evaluation shows that MethaneMapper achieves 0.63 mAP in detection and reduces the model size (by 5x) compared to the current state of the art. In addition, we also introduce a large-scale dataset of methane plume segmentation mask for over 1200 AVIRIS-NG flight lines from 2015-2022. It contains over 4000 methane plume sites. Our dataset will provide researchers the opportunity to develop and advance new methods for tackling this challenging green-house gas detection problem with significant broader social impact. Dataset and source code are public

Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined apriori with limited human feedback during operation. In contrast, here we present the development of a new type of human in the loop experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly, employing human feedback. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and then implement this in real time on an atomic force microscope, where the optimization proceeds to find symmetric piezoresponse amplitude hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human-augmented machine learning approaches for curiosity-driven exploration of systems across experimental domains. The analysis reported here is summarized in Colab Notebook for the purpose of tutorial and application to other data: https://github.com/arpanbiswas52/varTBO