When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for semi-automatic fault localization in ontologies have been proposed. Often, these approaches involve a human expert who provides answers to system-generated questions about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting user. In this work, we characterize and discuss different user types and show that existing approaches do not achieve optimal efficiency for all of them. As a remedy, we suggest a new type of expert question which aims at fitting the answering behavior of all analyzed experts. Moreover, we present an algorithm to optimize this new query type which is fully compatible with the (tried and tested) heuristics used in the field. Experiments on faulty real-world ontologies show the potential of the new querying method for minimizing the expert consultation time, independent of the expert type. Besides, the gained insights can inform the design of interactive debugging tools towards better meeting their users' needs.

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The next day at the Slack office, people were quite literally sobbing in the cafeteria. I was mostly keeping my shit together until my parents called from Canada. I went into one of the little phone booths and just sobbed on the phone. It took a bit of time to grieve, but then you also have to act. The space that Maciej1 created in Tech Solidarity was incredibly important. To show up at that first meeting at the Stripe offices and see hundreds of other people who are figuring out what the hell to do next was incredibly gratifying. "Oh, Joe who works over at the security team at a text-editor company actually cares about the fate of Muslim people in America." There were lots of pleasant surprises like that. I think one of the things that Tech Solidarity got really right was: "Don't show up at these organizations offering to make an app for them that you're going to abandon. Show up and help them fix their printer. Show up and just give them money. You made a lot of money on the IPO or whatever. Just give them your money." It was after the first meeting that I thought about the pledge.

When Stanford announced a new artificial intelligence institute, the university said the "designers of AI must be broadly representative of humanity" and unveiled 120 faculty and tech leaders partnering on the initiative. Some were quick to notice that not a single member of this "representative" group appeared to be black. The backlash was swift, sparking discussion on the severe lack of diversity across the AI field. But the problems surrounding representation extend far beyond exclusion and prejudice in academia. Major tech corporations have launched AI "ethics" boards that not only lack diversity, but sometimes include powerful people with interests that don't align with the ethics mission.

BEIJING - People.cn, the online unit of China's influential People's Daily, is boosting its numbers of human internet censors backed by artificial intelligence to help firms vet content on apps and adverts, capitalizing on its unmatched Communist Party lineage. Demand for online censoring services provided by the Shanghai-listed People.cn has soared since last year after China tightened its already strict online censorship rules. As a unit of the People's Daily -- the ruling Communist Party's mouthpiece -- it is seen by clients as the go-to online censor. Investors concur, lifting shares in People.cn "The biggest advantage of People.cn is its precise grasp of policy trends," said An Fushuang, an independent analyst based in Shenzhen.

Freed from a variety of tasks by artificial intelligence, doctors will have more time with patients, Topol predicts. In 1970 in The New England Journal of Medicine, William Schwartz predicted that by the year 2000, much of the intellectual function of medicine could be either taken over or at least substantially augmented by "expert systems"--a branch of artificial intelligence (AI). Schwartz hoped that the medical school curriculum would be "redirected toward the social and psychologic aspects of health care" and that medical schools would attract applicants interested in "behavioral and social sciences and … the information sciences and their application to medicine." But Schwartz's dream of smart medical technologies, for the most part, remains just that. Eric Topol, however, is optimistic about the future of health care.

Air pollution kills--scientists have known this for many years. But how do they know? The global scientific community has developed and agreed upon a framework that draws on multiple lines of evidence across different scientific disciplines to assess the existence and strength of links between air pollution and health. In the United States, federal policies require use of this science-based framework to ensure that air pollution standards protect the public's health. But now this science-based policy process--and public health--are at risk.

AI software being tested by the US defence department could one day lead to systems for keeping tabs on employees, experts have warned. The Defense Security Service (DSS) project monitors all online activity of employees with top-secret clearance - including emails, social media use and websites visited. It's trained to detect'micro changes' in the behaviour of employees looking for evidence untrustworthy employees and the future risks they may pose. The system would analyse employee data from their online activity and the information they provided through initial screening processes. If the pilot proves successful it could provide a model for the future of corporate AI, civil liberties groups suggest.

For our eyes, reading is hardly a smooth ride. They stutter across the page, lingering over words that surprise or confuse, hopping over those that seem obvious in context (you can blame that for your typos), pupils widening when a word sparks a potent emotion. All this commotion is barely noticeable, occurring in milliseconds. But for psychologists who study how our minds process language, our unsteady eyes are a window into the black box of our brains. Nora Hollenstein, a graduate student at ETH Zurich, thinks our reader's gaze could be useful for another task: helping computers learn to read. Researchers are constantly looking for ways to make artificial neural networks more brainlike, but brain waves are noisy and poorly understood.

Steve Nash, who has met me at a court in Manhattan Beach on a cloudy Monday afternoon to shoot free throws, glances over and chuckles at his miss. "It's been a while," he says. When he retired from the NBA in 2015, Nash, a two-time MVP, left with a career average 90.43 percent from the line--the highest in league history. But he hasn't worked on his foul shot since. For an instant, I feel anxious for him.