Science Archaeology Ancient'dirty dishes' may have led archaeologists astray for decades A new study questions if Bronze Age dishes really do have traces of olive oil. Breakthroughs, discoveries, and DIY tips sent every weekday. As far as kitchen staples, you don't really get much better than olive oil . It can do it all--jazz up a salad, sauté vegetables, add a nice crisp to some noodles, and more. Humans have been using olive oil for about 8,000 years, so archeologists often report olive oil residue on excavated pottery.

The skill to drift a car--i.e., operate in a state of controlled oversteer like professional drivers--could give future autonomous cars maximum flexibility when they need to retain control in adverse conditions or avoid collisions. We investigate real-time drifting strategies that put the car where needed while bypassing expensive trajectory optimization. To this end, we design a reinforcement learning agent that builds on the concept of tire energy absorption to autonomously drift through changing and complex waypoint configurations while safely staying within track bounds. We achieve zero-shot deployment on the car by training the agent in a simulation environment built on top of a neural stochastic differential equation vehicle model learned from pre-collected driving data. Experiments on a Toyota GR Supra and Lexus LC 500 show that the agent is capable of drifting smoothly through varying waypoint configurations with tracking error as low as 10 cm while stably pushing the vehicles to sideslip angles of up to 63{\deg}.

Losing traction while driving at high speed is generally very bad news. Scientists from the Toyota Research Institute and Stanford University have developed a pair of self-driving cars that use artificial intelligence to do it in a controlled fashion--a trick better known as "drifting"--to push the limits of autonomous driving. The two autonomous vehicles performed the daredevil stunt of drifting tandem around the Thunderhill Raceway Park in Willows, California, in May. In a promotional video, the two cars roar around the track a few feet from one another after human drivers relinquish control. Chris Gerdes, a professor at Stanford University who led its involvement with the project, tells WIRED that the techniques developed for the feat could eventually help future driver-assistance systems.

The Gran Turismo Sophy A.I. does a lap of the course. The Gran Turismo Sophy A.I. does a lap of the course. An artificial intelligence program has beaten the world's best players in the popular PlayStation racing game Gran Turismo Sport, and in doing so may have contributed towards designing better autonomous vehicles in the real world, according to one expert. The latest development comes after an interesting couple of decades for A.I. playing games. It began with chess, when world champion Garry Kasparov lost to IBM's Deep Blue in a match in 1997.

To hurtle around a corner along the fastest "racing line" without losing control, race car drivers must brake, steer and accelerate in precisely timed sequences. The process depends on the limits of friction, and they are governed by known physical laws--which means self-driving cars can learn to complete a lap at the fastest possible speed (as some have already done). But this becomes a much knottier problem when the automated driver has to share space with other cars. Now scientists have unraveled the challenge virtually by training an artificial intelligence program to outpace human competitors at the ultrarealistic racing game Gran Turismo Sport. The findings could point self-driving car researchers toward new ways to make this technology function in the real world.

Sony has developed what it's calling a breakthrough artificial intelligence program for the Gran Turismo series of PlayStation racing games. The software, called Gran Turismo Sophy, is so sophisticated, Sony says, that it handily beat a group of the world's best virtual race car drivers in test version of the 2017 game Gran Turismo Sport in October. "Outracing human drivers so skillfully in a head-to-head competition represents a landmark achievement for AI," Chris Gerdes, a Stanford professor specializing in autonomous driving, wrote in a Nature article published alongside Sony's research. Gerdes said this research could one day affect self-driving car development, according to Wired. "GT Sophy's success on the track suggests that neural networks might one day have a larger role in the software of automated vehicles than they do today," Gerdes wrote.

Takuma Miyazono began driving virtual race cars at age 4, when his father brought home the highly realistic motorsport game Gran Turismo 4. Sixteen years later, in 2020, Miyazono became the Gran Turismo world champion, winning an unprecedented "triple crown" of esports motor racing events. But he had never faced a Gran Turismo driver quite like GT Sophy, an artificial intelligence developed by Sony and Polyphony Digital, the studio behind the Gran Turismo franchise. "Sophy is very fast, with lap times better than expected for the best drivers," he says via a translator. "But watching Sophy, there were certain moves that I only believed were possible afterward." Video games have become an important sandbox for AI research in recent years, with computers mastering a growing array of titles.

High-end Italian brake manufacturer Brembo announced in October its plan to release Sensify, an AI-enhanced braking system that promises both "driving pleasure and total safety" when it rolls out via an unnamed manufacturer in 2024. Going beyond anti-lock brakes, traction, and stability control, it replaces hydraulic controls with electronic ones for design flexibility and, potentially, more precise control. Incorporating AI into vehicles isn't new, as algorithms control playlists, maps, driver assistance, and even "self-driving" to various degrees. AI-based brake systems, however, are enough to raise eyebrows about how exactly they will work or enhance safety. "As you start to deal with artificial intelligence and neural networks, they're only as good as the training data you have," said J. Christian Gerdes, an engineering professor and co-director of the Center of Automotive Research at Stanford University.

Now, it's also the latest company to seek an edge in artificial intelligence by making its own silicon chips. At a promotional event last month, Tesla revealed details of a custom AI chip called D1 for training the machine-learning algorithm behind its Autopilot self-driving system. The event focused on Tesla's AI work and featured a dancing human posing as a humanoid robot the company intends to build. Tesla is the latest nontraditional chipmaker to design its own silicon. As AI becomes more important and costly to deploy, other companies that are heavily invested in the technology--including Google, Amazon, and Microsoft--also now design their own chips.

As the DeLorean rolled to a stop and the cloud of tire smoke cleared, Jon Goh peeked out the sliver of the passenger-side window to see dozens of gathered spectators cheering and high-fiving the successful test. The crowd, and anticipation, had built throughout the afternoon, while Goh, a recent mechanical engineering Ph.D. graduate from Stanford, had been outlining a kilometer-long obstacle course in traffic cones at Thunderhill Raceway in Northern California. The sun was setting fast, but Goh and his co-pilot, another grad student named Tushar Goel, couldn't wait until morning to take a shot at the twisty course. Besides, MARTY, the driver, didn't need to see the track--it needed only GPS coordinates and the algorithms on Goh's laptop to chart its path. MARTY is a 1981 DeLorean that Goh and his colleagues at Stanford's Dynamic Design Lab converted into an all-electric, autonomous drift car.