Microsoft researcher Charles P. Thacker, awarded the 2009 ACM A.M. Turing Award in recognition of his pioneering design and realization of the first modern personal computer, and for his contributions to Ethernet and the tablet computer, died Monday, June 12, at the age of 74, after a brief illness. Thacker, born in Pasadena, CA, on Feb. 26, 1943, earned his bachelor of science degree in physics from the University of California, Berkeley (UC Berkeley) in 1967. In 1968, Thacker joined UC Berkeley's "Project Genie" to finance a graduate degree in physics. Instead, he recalled, "I went to work for this computer project," which the Berkeley Time-sharing System, commercialized by Scientific Data Systems as the SDS 940. Thacker joined Butler Lampson (recipient of the 1992 ACM A.M. Turing Award) and others to launch the startup Berkeley Computer Corporation (BCC).

As a combat veteran and more recently an industry technologist and university professor, I have observed with concern the increasing automation--and dehumanization--of warfare. Sarah Underwood's discussion of autonomous weapons in her news story "Potential and Peril" (June 2017) highlighting this trend also reminded me of the current effort to update the ACM Code of Ethics, which says nothing about the responsibilities of ACM members in defense industries building the software and hardware in weapons systems. Underwood said understanding the limitations, dangers, and potential of autonomous and other warfare technologies must be a priority for those designing such systems in order to minimize the "collateral damage" of civilian casualties and property/infrastructure destruction. Defense technologists must be aware of and follow appropriate ethical guidelines for creating and managing automated weapons systems of any kind. Removing human control and moral reasoning from weapons will not make wars less likely or less harmful to humans.

American Institute of Artificial Intelligence is announcing the launch of several business courses in Artificial Intelligence. Al Naqvi, professor and researcher at the American Institute of A.I. said, "Technology firms are buying grocery stores. Tech firms are moving into the auto, financial, and healthcare sectors. They are not just offering products and services in these areas, they are becoming those sectors. Business leaders must train themselves on how to deal with these new competitive dynamics and remain relevant in the A.I. Economy."

Legos have been a beloved for decades as toys that teach constructive aesthetics and foster DIY creativity. Then the company started releasing Mindstorm kits to turn static models into moving robots with a little programming magic -- but these were always aimed at older kids with some tinkering prowess. Algobrix, a brick-based system going live on Kickstarter today, aims to teach block-loving children the elements of coding without having to touch a computer. Algobrix is a core set of function blocks labeled with symbols, not letters, so your early learners can figure them out before they've built up their vocabulary. Line them up the blocks railroad-style, hit "go" and your brick-built "Algobot" follows the instructions to move around, play audio or light up.

How long before you come across some #FoodPorn? While pictures of food are everywhere on Instagram, the app doesn't allow links in posts so there's no easy way to find recipes. But that could be about to change. Pic2Recipe!, a website created by MIT electrical engineering and computer science student Nick Hynes, is a neural network that's been trained to recognise food from more than one million recipes on Food.com and AllRecipes. "It can look at a photo of a dish and be able to predict the ingredients and even suggest similar recipes," Hynes says.

Are our machines turning into gods? Jie, the world's best player of the world's oldest board game, Go, had just met his match… in the form of a program called AlphaGo. In the space of a year, the program had become "almost like the god of Go," said Jie after losing to AlphaGo. Jie had been playing the game, viewed as too hard for machines to excel at, since he was 10. AlphaGo was only made by Google's parent, Alphabet, in 2014.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments. New posts will be published here first, and previous posts are archived on the Udacity blog.

Conventionally, the questions on a test are assumed to be kept secret from test takers until the test. However, for tests that are taken on a large scale, particularly asynchronously, this is very hard to achieve. For example, TOEFL iBT and driver's license test questions are easily found online. This also appears likely to become an issue for Massive Open Online Courses (MOOCs, as offered for example by Coursera, Udacity, and edX). Specifically, the test result may not reflect the true ability of a test taker if questions are leaked beforehand. In this paper, we take the loss of confidentiality as a fact. Even so, not all hope is lost as the test taker can memorize only a limited set of questions' answers, and the tester can randomize which questions to let appear on the test. We model this as a Stackelberg game, where the tester commits to a mixed strategy and the follower responds. Informally, the goal of the tester is to best reveal the true ability of a test taker, while the test taker tries to maximize the test result (pass probability or score). We provide an exponential-size linear program formulation that computes the optimal test strategy, prove several NP-hardness results on computing optimal test strategies in general, and give efficient algorithms for special cases (scored tests and single-question tests). Experiments are also provided for those proposed algorithms to show their scalability and the increase of the tester's utility relative to that of the uniform-at-random strategy. The increase is quite significant when questions have some correlation---for example, when a test taker who can solve a harder question can always solve easier questions.

We propose to use thought-provoking children's questions (TPCQs), namely Highlights BrainPlay questions, as a new method to drive artificial intelligence research and to evaluate the capabilities of general-purpose AI systems. These questions are designed to stimulate thought and learning in children, and they can be used to do the same thing in AI systems, while demonstrating the system's reasoning capabilities to the evaluator. We introduce the TPCQ task, which which takes a TPCQ question as input and produces as output (1) answers to the question and (2) learned generalizations. We discuss how BrainPlay questions stimulate learning. We analyze 244 BrainPlay questions, and we report statistics on question type, question class, answer cardinality, answer class, types of knowledge needed, and types of reasoning needed. We find that BrainPlay questions span many aspects of intelligence. Because the answers to BrainPlay questions and the generalizations learned from them are often highly open-ended, we suggest using human judges for evaluation.

Rich Sutton is old school king of RL.