Chess is a complicated game. It's a game of strategy between two opponents, but with no hidden information and all of the potential moves known by both players at the outset. With each turn, players communicate their intent and try to anticipate the possible countermoves. The ability to envision several moves in advance is a recipe for victory, and one that mathematicians and logicians have long found intriguing. Despite some early mechanical chess-playing machines--and at least one chess-playing hoax--mechanized chess play remained hypothetical until the advent of digital computing.

More than a decade ago, Ichiro Takeuchi, professor of materials science and engineering, started applying the subfield of artificial intelligence (AI) known as machine learning (ML) to help develop new magnetic materials. At the time, ML was not widely used in materials science. "Now, it's all the rage," says Takeuchi, who also holds an appointment with the Maryland Energy Innovation Institute. Its current popularity is due in part to the deep learning revolution of 2012 and related advances in computer chip speed, data storage options, and rapid refinement of the science that drives its predictive analytics of algorithms. ML-based discovery in materials science is not just a lab exercise.

Since 2017, AI researchers have been using AI neural networks to help design better and faster AI neural networks. Applying AI in pursuit of better AI has, to date, been a largely academic pursuit--mainly because this approach requires tens of thousands of GPU hours. If that's what it takes, it's likely quicker and simpler to design real-world AI applications with the fallible guidance of educated guesswork. Next month, however, a team of MIT researchers will be presenting a so-called "neural architecture search" algorithm that can speed up the AI-optimized AI design process by 240 times or more. That would put faster and more accurate AI within practical reach for a broad class of image recognition algorithms and other related applications.

Intel has done pretty well for itself by consistently figuring out ways of making CPUs faster and more efficient. But with the end of Moore's Law lurking on the horizon, Intel has been exploring ways of extending computing with innovative new architectures at Intel Labs. Quantum computing is one of these initiatives, and Intel Labs has been testing its own 49-qubit processors. Beyond that, Intel Labs is exploring neuromorphic computing (emulating the structure and, hopefully, some of the functionality of the human brain with artificial neural networks) as well as probabilistic computing, which is intended to help address the need to quantify uncertainty in artificial intelligence applications. Rich Uhlig has been the director of Intel Labs since December of 2018, which is really not all that long, but he's been at Intel since 1996 (most recently as Director of Systems and Software Research for Intel Labs) so he seems well qualified to hit the ground running.

If you're sitting near a coffee mug, pick it up, and note how easy it is to do without really looking. You feel the curvature of the handle, the width of the cup, the slipperiness of the ceramic. Your hand glides into place and you squeeze, getting a sense of the weight, and bring the cup to your mouth. Now, imagine trying to do that with a robotic hand that gives you no sensory feedback. You get no information about the tiny adjustments that your fingers must make in order to grasp it properly.

Autonomous vehicles could transform parking as well as driving, new research suggests. Parking lots could house more driverless cars than human-driven ones, but autonomous vehicles could also lead to nightmarish gridlock if they slowly cruise the streets waiting for their owners, instead of paying to park. The typical vehicle spends 95 percent of its lifetime parked. The need to store parked vehicles has turned a lot of potentially valuable real estate into parking garages--for example, in the United States, roughly 6,500 square miles of land is devoted to parking, which is larger than the entire state of Connecticut. Autonomous vehicles could, in principle, transform parking lots.

Toyota today revealed some of the inner workings of an automation package meant to help drivers rather than replace them. The company also said that if that package had been in operation, it could have prevented or mitigated a recent three-car accident in California. The announcement came at CES 2019, which takes place this week in Las Vegas. Toyota has often spoken of its two-stage research project for self-driving cars. In the long run, it plans to offer a truly driverless technology called Chauffeur.

A coming milestone in the automobile world is the widespread rollout of Level 4 autonomy, where the car drives itself without supervision. Waymo, the company spun out of Google's self-driving car research, said it would start a commercial Level 4 taxi service by late 2018, although that hadn't happened as of press time. And GM Cruise, in San Francisco, is committed to do the same in 2019, using a Chevrolet Bolt that has neither a steering wheel nor pedals. These cars wouldn't work in all conditions and regions--that's why they're on rung 4 and not rung 5 of the autonomy ladder. But within some limited operational domain, they'll have the look and feel of a fully robotized car.

In October, the largest self-driving car project backed by the British government wrapped up three years worth of testing aimed at getting autonomous vehicles onto roads by 2021. Many of the autonomous car and pod tests took place in Milton Keynes, a town built for cars that represents one of the fastest-growing city or town economies in the United Kingdom. Originally founded as a new "model town" in 1967, Milton Keynes is a city in all but name after having grown to 280,000 people in 50 years. But the same economic success means that Milton Keynes--built in a grid layout and suburban style--faces a number of growing pains that it's looking to ease with the help of autonomous vehicle technology. The recent UK Autodrive tests were designed to test the capabilities of both self-driving cars and smaller autonomous pod vehicles made by Coventry, UK-based Aurrigo, a division of RDM Group, with an eye toward easing traffic congestion and possibly even eliminating the need for cars in the city center.

The genesis of the modern self-driving car across three Darpa challenges in the early 2000s has been well documented, here and elsewhere. Teams of universities, enthusiasts and automakers struggled to get cars to drive themselves through desert and city conditions. In the process, they kick-started the sensor, software and mapping technologies that would power today's self-driving taxis and trucks. A fascinating new book, "Autonomy" by Lawrence Burns, explores both the Darpa races and what happened next--in particular, how Google's self-driving car effort,now spun out as Waymo, came to dominate the field. Burns is a long-time auto executive, having come up through the ranks at GM and spent time championing that company's own autonomous vehicle effort, the impressive but ill-fated EN-V urban mobility concept.