The long search is over for Uber's chief operating officer. Former Orbitz CEO Barney Harford has formally accepted the position today. Looking fwd to working w @dkhos again to help @Uber achieve its full potential https://t.co/gdnXJfFrjS In early March, former CEO Travis Kalanick disclosed he would be searching for someone to fill the position amidst allegations he had fostered a culture of sexism and achievement at any cost. The company continued to suffer a number of setbacks in the months following, including Kalanick's resignation, a lawsuit from Alphabet's self-driving car company Waymo and a Department of Justice investigation.

Cyber attacks have become more sophisticated, happen faster and cause more business disruption than ever before. Preventative tools like anti-virus and IDS, have not kept pace. This is a problem, because if we are not going to make any progress at all in defending against cyber attacks we will need both automation and artificial intelligence to help win the fight. For far too long, the key strategy in defending networks has been prevention, or not allowing bad things to happen in the first place. This strategy involves defending every single ingress and egress point and protecting against all threats to these points at all times.

One of the most important issues that Congress will face in 2018 is how and when to regulate our growing dependence on artificial intelligence (AI). During the U.S. National Governors Association summer meetings, Elon Musk urged the group to push forward with regulation "before it's too late," stating that AI was an "existential threat to humanity." Hyperbole aside, there are legitimate concerns about the technology and its use. But a rush to regulation could exacerbate current issues, or create new issues that we're not prepared to deal with along the way. To begin with, one of the biggest issues in the world of AI is the lack of clear definition for what the technology is -- and is not.

When Britain leaves the European Union, many immigrants will be forced out of the country. But many of those people provide much-needed labor, and calls to automate the jobs they leave behind are impractical. Eighteen months after the U.K. voted to leave the EU, many details of the exit remain unnegotiated. But the process is broadly expected to have one big impact: a clampdown on immigration from EU countries. In fact, immigration has already declined since the vote, with the U.K.'s Office of National Statistics reporting that net migration into the U.K. is down from 336,000 in the 12 months preceding June 2016 to 230,000 in the 12 months preceding June 2017.

During his report to the 19th CPC National Congress, President Xi Jinping disclosed his plans for the future of the People's Liberation Army (PLA): "We will make it our mission to see that by 2035, the modernisation of our national defence and our forces are basically complete; and that by the mid-21st century our people's armed forces have been fully transformed into world-class forces." The Chinese media announced: "As chairman of the Central Military Commission (CMC), Xi is tasked with ensuring the world's largest military take a "crucial leap" in the new era from being simply large to being strong." A few weeks earlier, The South China Morning Post wrote about "China building giant facial recognition database to identify any citizen:". The project is to achieve an accuracy rate of 90 per cent, though it faces formidable technological hurdles and there are serious concerns about privacy. The powerful facial recognition system should be able to identify 1.3 billion citizens within three seconds Chen Jiansheng, of the department of electrical engineering at Tsinghua University, told the Hong Kong newspaper that the government would use this system to track wanted suspects as well as for public administration.

NASA has called a press conference to reveal a breakthrough discovery from its alien-hunting Kepler telescope. The discovery was driven by Google's machine-learning artificial intelligence software. The announcement will be live-streamed on NASA's website, according to a press release. It will take place Thursday, December 14, at 1 p.m. EST. NASA's Kepler space telescope has been searching for habitable planets since 2009.

The field for NASA's next New Frontiers mission is narrowing. Officials announced the two finalists for a new robotic explorer mission -- one that would send a spacecraft to bring samples of the comet 67P/Churyumov-Gerasimenko to Earth, and another to explore Saturn's moon Titan. The two mission concepts, CAESAR and Dragonfly, detailed in a NASA briefing Wednesday, beat out 10 other proposals to explore solar system targets including a basin on the moon; the surface of Venus; and Enceladus, the icy ocean world that also circles Saturn. "The New Frontiers program is really the premier program for our principal investigators and indeed it's one of the most difficult programs to be selected for," said Jim Green, director of the Planetary Science Division at NASA Headquarters in Washington. NASA only selects about two of these medium-class missions every decade, he added.

We study the computational complexity of candidate control in elections with few voters, that is, we consider the parameterized complexity of candidate control in elections with respect to the number of voters as a parameter. We consider both the standard scenario of adding and deleting candidates, where one asks whether a given candidate can become a winner (or, in the destructive case, can be precluded from winning) by adding or deleting few candidates, as well as a combinatorial scenario where adding/deleting a candidate automatically means adding or deleting a whole group of candidates. Considering several fundamental voting rules, our results show that the parameterized complexity of candidate control, with the number of voters as the parameter, is much more varied than in the setting with many voters.

Designing new and efficient therapies is a long and ever more costly process, with less than ten percent of new treatments entering Phase I finally being approved by the FDA and commercialized [1, 2]. One of the major challenges for the improvement of drug development is to better understand how drugs interact with patients, particularly for treatments displaying heterogeneous responses. Therefore, conducting a detailed analysis of clinical trial data is critical to find subgroups of patients with higher benefit-risk ratio or to understand why a drug does not work on some subpopulation to improve existing therapeutic strategies. Moreover, understanding the relationships of patient descriptors which compose the most responsive cross-section of the population is of great importance when planning a Phase III trial, for salvaging failed trials, or accelerating advances in personalized medicine. This process of biomarker identification is critical to detect subgroups within a given indication, but, as shown recently for immunotherapies, can also provide the basis for pan-indication drug approval [3].

Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA$\theta$) which enables a continuous interpolation between different fairness definitions. More specifically, we make three main contributions to the existing literature. First, our approach allows the decision maker to continuously vary between concepts of individual and group fairness. As a consequence, the algorithm enables the decision maker to adopt intermediate "worldviews" on the degree of discrimination encoded in algorithmic processes, adding nuance to the extreme cases of "we're all equal" (WAE) and "what you see is what you get" (WYSIWYG) proposed so far in the literature. Second, we use optimal transport theory, and specifically the concept of the barycenter, to maximize decision maker utility under the chosen fairness constraints. Third, the algorithm is able to handle cases of intersectionality, i.e., of multi-dimensional discrimination of certain groups on grounds of several criteria. We discuss three main examples (college admissions; credit application; insurance contracts) and map out the policy implications of our approach. The explicit formalization of the trade-off between individual and group fairness allows this post-processing approach to be tailored to different situational contexts in which one or the other fairness criterion may take precedence.