Nils J. Nilsson, a computer scientist who helped develop the first general-purpose robot and was a co-inventor of algorithms that made it possible for the machine to move about efficiently and perform simple tasks, died on Sunday at his home in Medford, Ore. His death was confirmed by his wife, Grace Abbott. Dr. Nilsson was a member of a small group of computer scientists and electrical engineers at the Stanford Research Institute (now known as SRI International) who pioneered technologies that have proliferated in modern life, whether in navigation software used in more than a billion smartphones or in such speech-control systems as Siri. The researchers had been recruited by Charles Rosen, a physicist at the institute, who had raised Pentagon funding in 1966 to design a robot that would be used as a platform for doing research in artificial intelligence. Although the project was intended to create a general-purpose mobile "automaton" and be a test bed for A.I. programs, Mr. Rosen had secured the funding by selling the idea to the Pentagon that the machine would be a mobile sentry for a military base.

The Immigration Services Agency plans to strengthen its eligibility standards for Japanese-language schools, it was learned Saturday. The move comes as Japanese-language schools have been under fire for accepting many foreign students whose purpose is to work in Japan. The number of Japanese-language schools recognized by the government grew 1.6 times over the past five years to 749 as of April 2. The government late last year outlined plans to improve the quality of Japanese-language schools as part of efforts to bring in more foreign workers to the country. Under the agency's plan, the requirement for the average student attendance rate would be revised from the current 50 percent or more in a month to 70 percent or more in a period of seven months. Schools failing to meet the requirement would not be allowed to accept foreign students.

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.

Demand for more powerful big data analytics solutions has spurred the development of novel programming models, abstractions, and platforms for next-generation systems. For these problems, a complete solution would address data wrangling and processing, and it would support analytics over data of any modality or scale. It would support a wide array of machine learning algorithms, but also provide primitives for building new ones. It would be customizable, scale to vast volumes of data, and map to modern multicore, GPU, coprocessor, and compute cluster hardware. In pursuit of these goals, novel techniques and solutions are being developed by machine learning researchers,4,6,7 in the database and distributed systems research communities,2,5,8 and by major players in industry.1,3

A robotic surgical device has learned to autonomously navigate inside a beating heart. Using only a small camera for vision, it successfully travelled to the correct location in the hearts of pigs for surgeons to then complete the operation. Pierre Dupont at Harvard Medical School in Boston and his colleagues created a robotic catheter --a thin tube widely used in surgeries to deliver devices or drugs. The device has a camera and LED light on its tip and is connected to a motor system that controls its movement from the other end. The team used 2000 images of the interior of a heart to train an algorithm to control the movement of the catheter.

Today, Zipline is officially opening the first of four distribution centers in Ghana, inaugurating a drone-delivery network that will eventually serve 2,000 hospitals and clinics covering 12 million people. Here's what Zipline says in a press release about the new operation: The revolutionary new service will use drones to make on-demand, emergency deliveries of 148 different vaccines, blood products, and life-saving medications. The service will operate 24 hours a day, seven days a week, from 4 distribution centers--each equipped with 30 drones--and deliver to 2,000 health facilities serving 12 million people across the country. Together, all four distribution centers will make up to 600 on-demand delivery flights a day on behalf of the Government of Ghana. Each Zipline distribution center has the capacity to make up to 500 flights per day.

When Seven Dreamers Laboratories Inc. unveiled its prototype laundry-folding robot in 2015, it generated a buzz, with people saying they couldn't wait to buy one if it ever went to market. But the AI-based tidying device dubbed Laundroid is apparently coming to an end before its commercial debut, as the Tokyo-based developer filed for bankruptcy Tuesday with the Tokyo District Court, citing insufficient funds to continue operations. A spokesperson for Seven Dreamers, a contest-winning startup that had received over ¥10 billion in funding, said development of robot is over for now. According to Teikoku Databank Ltd., a credit research company, Seven Dreamers Laboratories had accumulated ¥2.2 billion in debt as it struggled to ship the robot and invested heavily in research and development. After postponing its initial sales goal in fiscal 2017, it had to push back its goal for fiscal 2018, too.

Who's flying that drone over my house, and what exactly are they looking for? Is the pilot a police officer, a search-and-rescue volunteer, or Creepy Steve from four doors down? These concerns over the origin and intention of small drones have bedeviled the drone industry for as long as it has existed. Our inability to figure out who is piloting the weird quadcopter over our neighborhoods surely has a lot to do with why so many still distrust drones. People are working on it, though.

Ousmane Bah, an 18-year-old college student from New York, filed a lawsuit against Apple on Monday for allegedly relying on facial recognition systems that misidentified him as a serial shoplifter. The suit claims that Apple and its contractor, Security Industry Specialists, caused Bah to suffer emotional distress as a result. Apple has subsequently denied that it uses facial recognition in its stores. According to the lawsuit, Bah received a summons arraignment last summer from a Boston municipal court for the theft of $1,200 worth of products from an Apple Store in the city. The police report indicated that a Security Industry Specialists loss prevention associate saw the theft on a security video and recognized Bah from a similar incident at an Apple Store in Connecticut.

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.