It's Marvel, a four-legged robot that can quickly climb walls and walk across ceilings. The name stands for "magnetically adhesive robot for versatile and expeditious locomotion." One caveat: It only works on metal surfaces. A team of researchers from the Korea Advanced Institute of Science and Technology (KAIST) developed the robot and published a paper on its abilities in the journal Science Robotics last month. KAIST said it "climbs steel walls and crawls across metal ceilings at the fastest speed that the world has ever seen."

Simulators have been a godsend when it comes to testing robots. Real-world testing is lengthy, expensive and potentially dangerous, so anything you can do to work out as many kinks as possible ahead of time is a big win. Isaac Sim has thus far proven a success for Nvidia, as the chipmaker has looked to aggressively enter the world of robotics and automation, while roboticists search for a way to run simuations of real life working conditions. Today at CES, the company announced some key improvements to the system. Accessible via the cloud for robotics developers every where, the system is adding a very important piece of the puzzle: humans.

Nvidia has inked a new partnership with Foxconn, the world's largest tech manufacturer, to produce its Nvidia Drive Orin processors for use in highly automated (and eventually autonomous) vehicles. Foxconn will also use Nvidia Drive Orin hardware and Drive Hyperion sensor suite in its own future lineup of electric vehicles. Foxconn, perhaps best known for building iPhones, will serve as a tier-one supplier of electronic control units based on the Nvidia Drive Orin system-on-a-chip processors globally, bringing large-scale volume manufacturing to meet the demands of various automakers (such as Mercedes-Benz and Volvo) with plans to use Drive's artificial intelligence tech in their next-generation driver aid systems and automated cars. Foxconn's recent entry into the electric vehicle race is beginning to pick up speed. In 2021, it announced that it would be building an EV factory in the US by 2023 before debuting an EV platform and three electric vehicles -- the Model E luxury sedan, Model C SUV and the Model T electric bus -- under its new "Foxtron" brand.

German authorities had to pursue a Tesla that was on autopilot along a highway last week because the vehicle's driver fell asleep, according to a police statement. A patrol from the town of Bamberg tried to subject a Tesla going along the Autobahn 70 to a traffic check at around 12 p.m. local time Wednesday, but the driver did not respond to stop signals or repeated horns from officers, the Bavarian State Police said in a press release. The car kept the same distance from police as it traveled at a speed of 110 kilometers per hour (68.35 miles per hour) from the Viereth-Trunstadt and Bamberg-Hafen junctions, authorities noted. "Officers found that the Tesla driver was reclining in the seat with his eyes closed and his hands off the steering wheel," the statement read. "This strengthened the suspicion that he had left the controls to the autopilot and had fallen asleep," it added.

A robot called Golfi is the first to be able to autonomously spot and travel to a golf ball anywhere on a green and sink a putt. Golf-playing robots have been developed before, but they have needed humans to set them up in front of a ball and program them to make the correct swing. The most famous is LDRIC, a robot that hit a lengthy hole-in-one at Arizona's TPC Scottsdale golf course in 2016. In contrast, Golfi, engineered by Annika Junker at Paderborn University in Germany and her colleagues, can find golf balls and wheel itself into place thanks to input from a 3D camera that looks down on a green from above. The camera scans the green and an algorithm then approximates the surface before simulating 3000 golf swings towards the hole from random points, taking into account factors such as the speed and weight of the ball and the friction of the green, which are described by physics-based equations.

A new transforming turtle robot can explore treacherous regions where the land meets the sea--and may lead to future machines that navigate complex real-world conditions. Combining the best mobility features of an ocean-swimming turtle and a land-walking tortoise, the Amphibious Robotic Turtle (ART), described recently in Nature, can morph its limbs from turtlelike flippers to tortoiselike legs. "Most amphibious robots … use dedicated propulsion systems in each environment," says Yale University roboticist Rebecca Kramer-Bottiglio, who is the senior author on the paper. "Our system adapts a single unified propulsion mechanism for both environments: it has four limbs, and those limbs can transition between a flipper state for aquatic locomotion and a leg state for terrestrial locomotion." Each morphing limb is surrounded by a composite polymer material that is malleable when hot and stiff when cool.

The gigantic technological leap that machine learning models have shown in the last few months is getting everyone excited about the future of AI -- but also nervous about its uncomfortable consequences. After text-to-image tools from Stability AI and OpenAI became the talk of the town, ChatGPT's ability to hold intelligent conversations is the new obsession in sectors across the board. In China, where the tech community has always watched progress in the West closely, entrepreneurs, researchers, and investors are looking for ways to make their dent in the generative AI space. Tech firms are devising tools built on open source models to attract consumer and enterprise customers. Individuals are cashing in on AI-generated content.

Romain Trachel and Alexandre Peyrot, machine-learning specialists at Eidos-Sherbrooke, demonstrated a game at Unreal Fest 2022 that combines machine learning with a feature which lets developers use spatial data to inform AI decisions. This is normally handled through behavior trees, but in the demo the AI behavior was driven by a machine-learning model. The Unreal Engine Environment Query System (EQS) acts as the AI's eyes and ears, providing information about its environment, while the machine-learning model becomes its brain and decides how it should respond. "If a developer decided to activate a stronger chase mode, the only thing to do is to increase a reference value in the EQS tests," Trachel and Peyrot said in an email. "It really has the potential to simplify the development workflow, because in actual game productions, it would be up to a game designer to decide which game variables must be tuned in order to change the difficulty."

Causality will be hard to prove--was it really the words of the chatbot that put the murderer over the edge? Nobody will know for sure. But the perpetrator will have spoken to the chatbot, and the chatbot will have encouraged the act. Or perhaps a chatbot has broken someone's heart so badly they felt compelled to take their own life? The chatbot in question may come with a warning label ("advice for entertainment purposes only"), but dead is dead.

As the use of artificial intelligence and machine learning (AI/ML) continues to expand in healthcare, much attention has been given to mitigating bias in algorithms to ensure they are employed fairly and transparently. Less attention has fallen to addressing potential bias among AI/ML’s human users or factors that influence user reliance. We argue for a systematic approach to identifying the existence and impacts of user biases while using AI/ML tools and call for the development of embedded interface design features, drawing on insights from decision science and behavioral economics, to nudge users towards more critical and reflective decision making using AI/ML.