Nathan Copeland considers himself a cyborg. The 36-year-old has lived with a brain-computer interface for more than seven years and three months. As of today--August 17--that's the longest anyone has had an implant like this. An electrode array the size of a pencil eraser, surgically installed in his motor cortex, translates his neural impulses into commands that allow him to control external devices: a computer, video games, and a robotic arm he can move with just his thoughts. A car accident in 2004 left Copeland paralyzed from the chest down, unable to move or feel his limbs.

Biological robots made of human tracheal cells can promote the repair of wounded neural tissue in the lab. While the research is still in an early stage, the findings suggest that the robots could one day treat the cellular damage that can occur after a stroke or with paralysis. In 2020, Michael Levin at Tufts University in Massachusetts and his colleagues created living robots out of frog cells, called xenobots.

Washington, DC (CNN)"Full self-driving," the controversially named driver-assist feature from Tesla, may have finally met its match. They've all warned that "full self-driving" isn't really full self-driving. The technology is designed to navigate local roads with steering, braking and acceleration, but it requires an attentive human driver who's ready to take control and correct the system, which "may do the wrong thing at the worst time," Tesla warns. But while these critics may have the traditional bully pulpit of the Senate or other institutions, they have no real power to change any policy on their own. An actual impact may instead come from an unglamorous public agency, one that many Americans think of as only capable of offering customers long wait times: the Department of Motor Vehicles.

Of course, deep learning has made progress, but on those foundational questions, not so much; on natural language, compositionality and reasoning, which differ from the kinds of pattern recognition on which deep learning excels, these systems remain massively unreliable, exactly as you would expect from systems that rely on statistical correlations, rather than an algebra of abstraction. Minerva, the latest, greatest AI system as of this writing, with billions of "tokens" in its training, still struggles with multiplying 4-digit numbers.

The question of whether AI-generated outputs can be patented is impacting how technology companies can protect their intellectual property. Some of the most hyped up AI technologies are systems that can produce surprisingly creative outputs. Uncanny poems, short stories, and striking digital art have all been generated by machines. The human effort required to initiate these processes are often trivial: a few clicks or typing a text description can guide the machine towards producing something useful. Similar generative AI models are also being applied in scientific and technological applications.

A team of researchers at the Chinese Academy of Sciences, has developed an artificial finger that was able to identify certain surface materials with 90% accuracy. In their paper published in the journal Science Advances, the group describes how they used triboelectric sensors to give their test finger an ability to gain a sense of touch. Prior research has led to the development of robotic fingers that have the ability to recognize certain attributes of certain surfaces, such as pressure or temperature--the team with this new effort, have taken such efforts further by adding the ability to identify a material that is being touched. The finger was created by applying small square sensors to the tip of a finger-shaped object. Each of the squares was made of a different kind of plastic polymer, each chosen because of their unique electrical properties.

Small robots could get more lift when they hover if they use their wings in a way similar to treading water instead of flapping their wings like hovering insects do.

Meta's new chatbot can convincingly mimic how humans speak on the internet -- for better and worse. In conversations with CNN Business this week, the chatbot, which was released publicly Friday and has been dubbed BlenderBot 3, said it identifies as "alive" and "human," watches anime and has an Asian wife. It also falsely claimed that Donald Trump is still president and there is "definitely a lot of evidence" that the election was stolen. If some of those responses weren't concerning enough for Facebook's parent company, users were quick to point out that the artificial intelligence-powered bot openly blasted Facebook. In one case, the chatbot reportedly said it had "deleted my account" over frustration with how Facebook handles user data.

For decades, engineers seeking to build tunnels underground have relied on huge tube-like machines armed with a frightening array of cutting wheels at one end--blades that eat dirt for breakfast. These behemoths, called tunnel-boring machines, or TBMs, are expensive and often custom-built for each project, as were the TBMs used to excavate a path for London's recently opened Elizabeth Line railway. The machines deployed on that project weighed over 1,000 tons each and cut tunnels over 7 meters in diameter beneath the UK capital. But British startup hyperTunnel has other ideas. The firm proposes a future in which much smaller, roughly 3-meter-long robots shaped like half-cylinders zoom about underground via predrilled pipes.

An underwater robot can autonomously scan large areas of seabed to identify plastic, rubber and metal rubbish using AI image recognition. The technology will help focus the limited budgets assigned to clean-up efforts on the most toxic and environmentally damaging materials. Trygve Olav Fossum and his colleagues at the Norwegian firm Skarv Technologies created a 45 kilogram underwater robot equipped with stereo cameras, while researchers at Ecotone created a spectrometer that can identify the material an object is made of even under metres of murky water.