Terry Tao is one of the world’s leading mathematicians and winner of many awards including the Fields Medal. He is Professor of Mathematics at the University of California, Los Angeles (UCLA). Following his talk, Terry is in conversation with fellow mathematician Po-Shen Loh.
The Oxford Mathematics Public Lectures are generously supported by XTX Markets.
Defense technology startup Anduril Industries Inc. has raised $1.5 billion in a new funding round and plans to spend hundreds of millions on a new facility to manufacture its rockets, underwater vehicles and other autonomous weapons systems at greater scale and speed.
The deal, which values Anduril at $14 billion, is one of the largest venture capital financings of the year so far, and reflects the company’s success getting government contracts, as well as rising investor enthusiasm for defense technology companies.
Peter Thiel’s Founders Fund and Sands Capital co-led the Series F funding round, which has been in the works for more than a month. The deal nearly doubles the startup’s valuation from its previous funding round in 2022, which raised $1.48 billion.
Unmanned aerial vehicles (UAVs), commonly known as drones, are now used to capture images and carry out a wide range of missions in outdoor environments. While there are now several UAV designs with different advantages and characteristics, most conventional aerial robots are underactuated, meaning that they have fewer independent actuators than their degrees of freedom (DoF).
Underactuated systems are often more cost-effective and can be controlled using simpler control strategies than overactuated systems (i.e., robots that have more independent actuators than their DoF). Nonetheless, they are often less reliable and not as capable of precisely controlling their position and orientation.
Researchers at Tecnalia’s Basque Research and Technology Alliance (BRTA) in Spain recently developed a new overactuated aerial robot that can independently control the position and orientation of its main body. This robot, introduced in a paper published in Robotics and Autonomous Systems, has four quadrotors that cooperatively carry its central body.
A team of engineers at Google’s DeepMind Project has demonstrated a robot capable of playing amateur-level table tennis (ping-pong). The team has published a paper on the arXiv preprint server describing how they developed the robot, how well it performed at different ability levels and how human players responded to playing with the robot.
Over the past several years, robot scientists have been combining advancements in robot design with artificial intelligence, resulting in the development of robots with ever increasing abilities. In this new effort, the research team has developed an AI-based ping-pong player with the highest performance level ever for a robot.
To build their robot system, the researchers started with a robot arm called the ABB IRB 1100—the robot is currently used in real-world industrial applications. In addition to its ability to manipulate its arm and hand very quickly, it can also quickly slide side-to-side on a rail. These features made it an ideal ping-pong-playing candidate.
Picture this: hundreds of ant-sized robots climb over rubble, under rocks and between debris to inspect the damage of a fallen building before human rescuers explore on-site.
Downscaling legged robots to the size of an insect enables access to small spaces that humans and large robots cannot reach. A swarm of small robots can even collaborate like their insect counterparts to haul objects and protect one another. Picotaur, a new robot from the labs of Sarah Bergbreiter and Aaron Johnson is the first of its size, able to run, turn, push loads and climb miniature stairs.
“This robot has legs that are driven by multiple actuators so it can achieve various locomotion capabilities,” said Sukjun Kim, a recent Ph.D. graduate advised by Bergbreiter. “With multiple gait patterns, it can walk like other hexapod robots, similar to how a cockroach moves, but it can also hop from the ground to overcome obstacles.”
In the search for less energy-hungry artificial intelligence, some scientists are exploring living computers.
Artificial intelligence systems, even those as sophisticated as ChatGPT, depend on the same silicon-based hardware that has been the bedrock of computing since the 1950s. But what if computers could be molded from living biological matter? Some researchers in academia and the commercial sector, wary of AI’s ballooning demands for data storage and energy, are focusing on a growing field known as biocomputing. This approach uses synthetic biology, such as miniature clusters of lab-grown cells called organoids, to create computer architecture. Biocomputing pioneers include Swiss company FinalSpark, which earlier this year debuted its “Neuroplatform”—a computer platform powered by human-brain organoids—that scientists can rent over the Internet for $500 a month.