Voiceless in his life so far, a severely disabled 16-year-old is marvelling at being able to speak for the first time after breaking his silence with the words "Hello Mum", using a digital communication aid. James Walker is a rugby fan, likes pop music, lives with his family in Hull and has a girlfriend - Emily. He has a condition which caused hundreds of daily seizures when he was a child. Known as Lennox-Gastaut Syndrome, it left him with a severe learning disability and without the ability to walk or move. He says it's "funny" after being silent for so long that he can now communicate with friends and family and, as he puts it, "learn something exciting".

John Hennessy joined Stanford in 1977 right after receiving his Ph.D. from the State University of New York at Stony Brook. He soon became a leader of Reduced Instruction Set Computers. This research led to the founding of MIPS Computer Systems, which was later acquired for 320 million. There are still nearly a billion MIPS processors shipped annually, 30 years after the company was founded. Hennessy returned to Stanford to do foundational research in large-scale shared memory multiprocessors. In his spare time, he co-authored two textbooks on computer architecture, which have been continuously revised and are still popular 25 years later. This record led to numerous honors, including ACM Fellow, election to both the National Academy of Engineering and the National Academy of Sciences. Not resting on his research and teaching laurels, he quickly moved up the academic administrative ladder, going from the CS department chair to Engineering college dean to provost and finally to president in just seven years. He is Stanford's tenth president, its first from engineering, and he has governed it for an eighth of its existence. Since 2000, he doubled Stanford's endowment, including a record 6.2 billion for a single campaign. He used those funds to launch many initiatives--which often cross departmental lines--along with new buildings to house them. Undergraduate applications also doubled, for the first time making Stanford even more selective than Harvard.

In 2012, when reading a paper from a recent premier computer security conference, we came to believe there is a clever way to defeat the analyses asserted in the paper, and, in order to show this we wrote to the authors (faculty and graduate students in a highly ranked U.S. computer science department) asking for access to their prototype system. We thus decided to reimplement the algorithms in the paper but soon encountered obstacles, including a variable used but not defined; a function defined but never used; and a mathematical formula that did not typecheck. We asked the authors for clarification and received a single response: "I unfortunately have few recollections of the work ... " We next made a formal request to the university for the source code under the broad Open Records Act (ORA) of the authors' home state. The university's legal department responded with: "We have been unable to locate a confirmed instance of [system's] source code on any [university] system."

It described as "fundamentally vague" Stephen Goose's ethical line in his Point side of the Point/Counterpoint debate "The Case for Banning Killer Robots" in the same issue. I encourage all ACM members to read or re-read them and consider if they themselves should be working on lethal autonomous weapons or even on any kind of weapon. Ronald Arkin's Counterpoint was optimistic regarding robots' ability to "... exceed human moral performance ...," writing that a ban on autonomous weapons "... ignores the moral imperative to use technology to reduce the atrocities and mistakes that human warfighters make." This analysis involved two main problems. First, Arkin tacitly assumed autonomous weapons will be used only by benevolent forces, and the "moral performance" of such weapons is incorruptible by those deploying them.

Suresh Venkatasubramanian of the University of Utah presented a method for finding disparate impact in algorithms last year at the ACM Conference on Knowledge Discovery and Data Mining. If you have ever searched for hotel rooms online, you have probably had this experience: surf over to another website to read a news story and the page fills up with ads for travel sites, offering deals on hotel rooms in the city you plan to visit. Buy something on Amazon, and ads for similar products will follow you around the Web. The practice of profiling people online means companies get more value from their advertising dollars and users are more likely to see ads that interest them. The practice has a downside, though, when the profiling is based on sensitive attributes, such as race, sex, or sexual orientation.

One of the featured speakers at the inaugural Text By The Bay conference, held in San Francisco in April 2015, drew laughter when describing a neural network question-answering model that could beat human players in a trivia game. While such performance by computers is fairly well known to the general public, thanks to IBM's Watson cognitive computer, the speaker, natural language processing (NLP) researcher Richard Socher, said, the neural network model he described "was built by one grad student using deep learning" rather than by a large team with the resources of a global corporation behind them. Socher, now CEO of machine learning developer MetaMind, did not intend his remarks to be construed as a comparison of Watson to the academic model he and his colleagues built. As an illustration of the new technical and cultural landscape around NLP, however, the laughter Socher's comment drew was an acknowledgment that basic and applied research in language processing is no longer the exclusive province of those with either deep pockets or strictly academic intentions. Indeed, new tools and new techniques--particularly open source technologies such as Google's word2vec neural text processing tool--combined with steady increases in computing power, have broadened the potential for natural language processing far beyond the research lab or supercomputer.

In a world with more mobile phones than flush toilets, digital devices are now standard equipment among even the world's poorest and most remote people. Farmers in these areas are getting tools for their devices that help deliver water, nutrients, and medicine to plants as needed; test for crop diseases and malnourishment; and survey their soil for future planning. In some cases, these emerging apps are the biggest new technologies resource-poor farms have seen in hundreds of years. That is not very surprising to Rajiv "Raj" Khosla, professor of Precision Agriculture at the College of Agricultural Sciences of Colorado State University. "What we're finding is that many small-scale farmers in resource-poor environments are still farming in the 1500s. They're looking for leapfrog technologies," he said.

Any computer system must make trade-offs between freedoms retained by the system and guarantees made to the system's users. Designers attempt to balance conflicting goals, such as throughput and ease of use. Programming languages must make these trade-offs too. For example, a language with built-in garbage collection often retains the freedom to move objects around in memory, making it difficult to share objects with other processes or with hardware devices. C and C are based on an extreme set of trade-offs: In these languages, a wide variety of hard-to-avoid program behaviors, such as signed integer overflow and out-of-bounds array references, are "undefined behaviors."

Alexa, what can you tell me about my health? Starting Thursday, Amazon's voice assistant will tell you how well you slept and how much more exercise you need -- at least if you have a Fitbit fitness tracker and an Alexa-compatible device, such as Amazon's Echo speaker and Fire TV streaming devices. Inc.'s answer to Apple's Siri, Google Now and Microsoft's Cortana -- is part of the online retailer's ambitions to control your living room, as people start embracing a "smart," automated home. You can already use voice commands to ask Alexa for weather, movie listings and sports scores. Ask about your sleep, and Alexa will tell you when you fell asleep and for how long.

Divide and Conquer (DC) is conceptually well suited to high-dimensional optimization by decomposing a problem into multiple small-scale sub-problems. However, appealing performance can be seldom observed when the sub-problems are interdependent. This paper suggests that the major difficulty of tackling interdependent sub-problems lies in the precise evaluation of a partial solution (to a sub-problem), which can be overwhelmingly costly and thus makes sub-problems non-trivial to conquer. Thus, we propose an approximation approach, named Divide and Approximate Conquer (DAC), which reduces the cost of partial solution evaluation from exponential time to polynomial time. Meanwhile, the convergence to the global optimum (of the original problem) is still guaranteed. The effectiveness of DAC is demonstrated empirically on two sets of non-separable high-dimensional problems.