We consider the problem of optimal unsignalized intersection management for continual streams of randomly arriving robots. This problem involves repeatedly solving different instances of a mixed integer program, for which the computation time using a naive optimization algorithm scales exponentially with the number of robots and lanes. Hence, such an approach is not suitable for real-time implementation. In this paper, we propose a solution framework that combines learning and sequential optimization. In particular, we propose an algorithm for learning a shared policy that given the traffic state information, determines the crossing order of the robots. Then, we optimize the trajectories of the robots sequentially according to that crossing order. This approach inherently guarantees safety at all times. We validate the performance of this approach using extensive simulations. Our approach, on average, significantly outperforms the heuristics from the literature. We also show through simulations that the computation time for our approach scales linearly with the number of robots. We further implement the learnt policies on physical robots with a few modifications to the solution framework to address real-world challenges and establish its real-time implementability.

Microsoft wants partners to put more focus into delivering the artificial-intelligence (AI) capabilities offered in its data platform and cloud portfolios. The company doesn't feel enough of its partners currently offer AI holistically to enable customers to transform their businesses. Setting the stage for how Microsoft might help enable partners to do that, the company recently announced a partnership with Accenture and its Avanade subsidiary. Collectively, they are creating preconfigured solutions geared toward helping clients in specific industries such as consumer-packaged goods, retail and telecommunications using AI to automate their business processes. Avanade's latest research found that 88 percent of global business and IT decision-makers say they don't know how to use AI -- and 79 percent encounter resistance.

In this issue, we are pleased to feature expert commentary by Eric Sandosham, Founder and Partner at Red & White Consulting Partners LLP. Formerly Managing Director and Regional Head of Citibank's Decision Management and Analytics function in 14 global markets, Eric is currently based in Singapore. Specializing in Human Capital Analytics, Red & White Consulting Partners has client engagements across Manufacturing, Banking and Non-Profit organizations in Indonesia, China, Malaysia, Singapore and Vietnam. Eric and Marcia discuss the role of data analytics in Human Resources--and more. What exactly is the CHRO's role today? Neeraj Sanan's People Matters article, "How contextual intelligence is empowering CHROs," contends that today's businesses demand more strategic direction from HR that requires "an assertive, data-driven CHRO."

Investment banks are evaluating possible avenues for efficiency improvement either through artificial intelligence (AI) or through automation. In fact Robo Advisors are disrupting the traditional wealth management model by attracting large number of millennial customers by providing a differentiated Investment management experience. At the same time new regulations are being introduced to ensure safety of investor funds from improper selling of financial products to gullible investors. There have been a number of instances where financial intermediaries had to pay huge penalties for not being able to appreciate the risks and bring in the right product features while selling the products to customers. The topic of reviewing and selecting a product for the client is largely the judgement of an Advisor.