Phoenix Air Unmanned, LLC (PAU) is seeking information on the availability of Unmanned Aircraft Systems to support linear infrastructure inspections. The UAS will be operated by PAU who has been contracted by Xcel Energy as their unmanned flight service provider and they plan to purchase a minimum of 4 aircraft initially with the possibility of additional aircraft in the future. Xcel Energy, Inc. owns over 120,000 miles of transmission and distribution infrastructure across eight states (CO, MI, MN, NM, WI, ND, SD, TX) that must be inspected at regular intervals as required by state and federal regulations. Xcel Energy, Inc. is a utility holding company with a service company (Xcel Energy Services) and four wholly owned utility subsidiaries that serve electric and natural gas customers. PAU was established in 2014 for commercial UAS operations.

While the U.S.-China trade issues have dominated recent headlines, the U.S. Government and U.S. industry have been assessing the commercial environment for IPR (patents, trademarks, copyrights, industrial designs, geographical indications, trade secrets) in our trading partners for decades.

Google Assistant is honoring prominent women in March with the help of its latest feature launched for Women's History Month. Anyone with Google's smart speaker can wish it a "Happy International Women's Day" and count on their smart device to read out loud information related to a trailblazer. On devices with screens, such as a smartphone or Google Home smart display, you can also see an image of the woman with a written summary. International Women's Day is celebrated on Sunday, March 8. Google has chosen to highlight 12 women from diverse nationalities and disciplines, including labor rights activist Dolores Huerta, architect Zaha Hadidand environmental scientist Rachel Carson, among others. "Our goal is to showcase some examples of the far-ranging impact women have had on all aspects of culture, and inspire women and girls to be their own trailblazers," said Google Assistant's senior director of product management, Lilian Rincon.

Agnieszka Kurant's lower Manhattan studio stands among a scattering of cultural outposts that represent some of the most recent efforts of the avant guard to grapple with our cultural moment. When I visited in late January, a gallery two doors down was hosting a reproductive rights-themed show with works listed for upwards of $30,000. Across the street, four floors of the windowless New Museum were taken over by a retrospective of artist Hans Haacke, which included a demographic survey, a portrait of Ronald Reagan and a grass-covered mound of dirt. The seventh floor was occupied by a "mixed reality pop-up," sponsored by Ruinart champagne, in which visitors could wander about in augmented reality glasses. Minders politely asked those without reservations to "step away from the experience."

Many organisations have established an AI policy. Some companies, such as IBM (1), Google (2) and others have made these available online. Here, we propose ten – largely risk-based – considerations that synthesise the various societal, legal, ethical and engineering challenges that organisations need to consider in developing an AI. In a recent report, Accenture reported that 63% of AI adopters had an ethics committee (3). Establishing an AI ethics committee to oversee the use of AI will ensure adherence to the law, promote best practice, oversee risk and provide authority for periodic audit.

The White Paper on Artificial Intelligence (the "AI White Paper"), recently released by the European Commission, provides the clearest indication yet that the EU is seriously considering regulating the development and deployment of artificial intelligence ("AI"). If adopted, the Commission's proposals would likely increase the already significant compliance burden imposed on technology-focused and technology-dependent businesses operating in the EU, and may lead to significantly divergent practices between the EU and the rest of the world. It is evident from the text of the AI White Paper that the Commission considers that its proposals would help to establish the EU at the centre of global AI technology development and deployment, and to encourage investment in the EU in this space, although it is not entirely clear that a lack of additional regulation is currently holding the EU back. The AI White Paper charts a rough course for the potential future regulation of AI technologies in the EU. It will require much greater detail and refinement before it can realistically progress to full-blown legislation.

The identification of synthetic routes that end with a desired product has been an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited fraction of the entire reaction space. At present, emerging machine-learning technologies are overturning the process of retrosynthetic planning. The objective of this study is to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all possible pairs of purchasable reactants. We address this issue within the framework of Bayesian inference and computation. The workflow consists of two steps: a deep neural network is trained that forwardly predicts a product of the given reactants with a high level of accuracy, following which this forward model is inverted into the backward one via Bayes' law of conditional probability. Using the backward model, a diverse set of highly probable reaction sequences ending with a given synthetic target is exhaustively explored using a Monte Carlo search algorithm. The Bayesian retrosynthesis algorithm could successfully rediscover 80.3% and 50.0% of known synthetic routes of single-step and two-step reactions within top-10 accuracy, respectively, thereby outperforming state-of-the-art algorithms in terms of the overall accuracy. Remarkably, the Monte Carlo method, which was specifically designed for the presence of diverse multiple routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. We investigated the potential applicability of such diverse candidates based on expert knowledge from synthetic organic chemistry.

As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individuals fairly, without discrimination based on their age, gender, race or other sensitive traits. In recent years much progress has been made on ensuring fairness and reducing bias in standard machine learning settings. Yet, for network embedding, with applications in vulnerable domains ranging from social network analysis to recommender systems, current options remain limited both in number and performance. We thus propose DeBayes: a conceptually elegant Bayesian method that is capable of learning debiased embeddings by using a biased prior. Our experiments show that these representations can then be used to perform link prediction that is significantly more fair in terms of popular metrics such as demographic parity and equalized opportunity.

Mike Bugembe is a best-selling author, international speaker and A.I. evangelist, on a mission to address all aspects of A.I.'s diversity gap. He has worked with organizations across a range of industries as a consultant, trainer and executive adviser. Mike has a passion for all things A.I., but what excites him most is how A.I. can be used to solve the many social problems that we face today. Mike is best-known for the work he has done on using A.I. to improve generosity and charitable giving, developing algorithms that have raised millions for charitable causes. Mike's work has quickly gained him recognition as one of the UK's top digital leaders; he has won several awards and been named as one of the most influential people in data-driven business.

Humans can make mistakes, but so can machines. If we use artificial intelligence for legal work, what sort of quality is needed? A Friday ABA Techshow panel at the Hyatt Regency Chicago titled "Open the Pod Bay Doors: Problems with AI" discussed this question in detail. Is the goal to do something quickly? If the goal is to do something better than a human, which human?