UK regulators are staging the first meeting of the Artificial Intelligence Public Private Forum, a quarterly talking shop to gauge the impact of AI in financial services.

To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society. We preface our enumeration with meta-questions about whether data science is a discipline. We then describe each of the 10 challenge areas. The goal of this article is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving. Although data science builds on knowledge from computer science, engineering, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: fundamental scientific questions and pressing problems of societal importance.

An online app called Amica is now using artificial intelligence to help separating couples make parenting arrangements and divide their assets. For many people, the coronavirus pandemic has put even the strongest of relationships to the test. A May survey conducted by Relationships Australia found 42% of 739 respondents experienced a negative change in their relationship with their partner under lockdown restrictions. There has also been a surge in the number of couples seeking separation advice. The Australian government has backed the use of Amica for those in such circumstances.

Incorporating fairness constructs into machine learning algorithms is a topic of much societal importance and recent interest. Clustering, a fundamental task in unsupervised learning that manifests across a number of web data scenarios, has also been subject of attention within fair ML research. In this paper, we develop a novel notion of fairness in clustering, called representativity fairness. Representativity fairness is motivated by the need to alleviate disparity across objects' proximity to their assigned cluster representatives, to aid fairer decision making. We illustrate the importance of representativity fairness in real-world decision making scenarios involving clustering and provide ways of quantifying objects' representativity and fairness over it. We develop a new clustering formulation, RFKM, that targets to optimize for representativity fairness along with clustering quality. Inspired by the $K$-Means framework, RFKM incorporates novel loss terms to formulate an objective function. The RFKM objective and optimization approach guides it towards clustering configurations that yield higher representativity fairness. Through an empirical evaluation over a variety of public datasets, we establish the effectiveness of our method. We illustrate that we are able to significantly improve representativity fairness at only marginal impact to clustering quality.

Cory Doctorow, 49, is a British-Canadian blogger, science fiction author and tech activist. He has held various academic posts and is a visiting professor of the Open University. His latest novel, Attack Surface, was published earlier this month. The protagonist in your new novel tries to offset her job at a tech company where she is working for a repressive regime by helping some of its targets evade detection. Do you think many Silicon Valley employees feel uneasy about their work?

While I was making dinner, I yelled at Alexa. But the recipe was a little complicated, and I kept having to repeat myself to get the damn Amazon Echo to turn off the timer. And when I used my computer communication voice to ask it to play NPR One so I could catch up on the news--it had been a whole eight or nine minutes since I had checked in with the world--it tried three times to instead play "The Austin 100: A SXSW Mix From NPR Music." I feel a little bad about it, remembering Rachel Withers' (very persuasive!) 2018 piece for Future Tense about why she won't date men who are rude to Alexa: It matters how you interact with your virtual assistant, not because it has feelings or will one day murder you in your sleep for disrespecting it, but because of how it reflects on you. Alexa is not human, but we engage with her like one.

Both Lexis and Lexis demonstrate that artificial intelligence-powered legal research is a game-changer for lawyers and their firms, helping them find relevant information faster, more efficiently and cost-effectively. Artificial intelligence (AI), of course, refers to computer software and systems that rather than only relying on pre-programmed tasks, learn, plan, reason and process natural language as they go. For years now, we've incorporated AI-powered features into our legal research platforms to drive better insights in a user-friendly way, reveal previously unknowable connections in the data and incorporate real-time developments in the law. Simply stated, AI-powered legal research platforms can help lawyers do more billable work more quickly, allowing them to spend more time putting that research to good use by counseling clients, negotiating with opposing counsel or performing other higher-level work. This is particularly important for attorneys who provide their services on a flat-fee or contingency-fee basis, where more time spent on legal research could lead to lower profit margins.

Dozens of software firms have sprung up to sell companies AI recruitment tools, which they promise will cut bias out of their clients' hiring processes. In promotional materials and press releases, they argue that human recruiters are irredeemably biased while machines are objective, so companies and job candidates alike will benefit from AI-driven hiring. AI algorithms are not inherently objective, and hiring software can introduce new layers of bias and discrimination, excluding qualified job-seekers and leaving companies open to negative headlines. But if companies apply AI in thoughtful ways, and maintain a healthy dose of skepticism toward AI vendors' commercial claims, there are ways to use algorithms to cut down on bias in hiring. Textio uses machine learning to help hiring managers optimize job descriptions.

Click here to learn more about Dr. Tommy Weir. Among the many tough questions that this latest -- and potentially greatest -- recession is throwing at businesses, is what their AI strategy should look like, both in order to navigate the downturn and to bounce back when it's all over. Reimagine Your Future and Use AI to Shape It: Don't be tempted to panic and buy a lot of off-the-shelf digital solutions and hope for the best. The first and most important step is to refine your purpose and strategy. Get your team together and figure out where you could be in the next few years, especially post-recession.

Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs, which underlies both perception and learning, is the minimization of prediction errors. While motivated by high-level notions of variational inference, detailed neurophysiological models of cortical microcircuits which can implements its computations have been developed. Moreover, under certain conditions, predictive coding has been shown to approximate the backpropagation of error algorithm, and thus provides a relatively biologically plausible credit-assignment mechanism for training deep networks. However, standard implementations of the algorithm still involve potentially neurally implausible features such as identical forward and backward weights, backward nonlinear derivatives, and 1-1 error unit connectivity. In this paper, we show that these features are not integral to the algorithm and can be removed either directly or through learning additional sets of parameters with Hebbian update rules without noticeable harm to learning performance. Our work thus relaxes current constraints on potential microcircuit designs and hopefully opens up new regions of the design-space for neuromorphic implementations of predictive coding.