If you thought the battle between machines and jobs – the dislocation of labor and society resulting from digitization or automation – has been one-sided so far, just wait. The next wave of attack is well underway, and it's called AI. Artificial Intelligence, most simply, refers to computers that perform tasks that normally require human intelligence – things like visual perception, speech recognition, even decision-making. Earlier this year the management consulting firm McKinsey famously wrote that "25 percent of the global workforce will either need to find new professional activities by 2020 or significantly broaden their technological skills. The World Economic Forum's "Future of Jobs Report: 2018" states, "By 2022, the skills required to perform most jobs will have shifted significantly… [and] no less than 54% of all employees will require significant re- and upskilling." The greatest concerns are not just that AI destroys jobs, but that it increases inequality – that low-wage employees get displaced, while high-wage employees maintain or even extend their value that's more difficult to replace with a machine. Of course, new technologies have disrupted existing processes for centuries. Will the experience with AI be different than with previous technologies? Most importantly, what can governments, corporations, small businesses and individual workers do to not just avoid massive disruption, but rather position themselves to take outsized advantage of the opportunities? To find out, I recently hosted an excellent roundtable discussion at Clayton, Dubilier & Rice with NYU Professor Robert Seamans. Prof. Seamans' studies how technology and governance structures affect strategic interactions between firms, affect incentives to innovate, and ultimately shape market outcomes. Previously he served under President Obama as a Senior Economist at the White House Council of Economic Advisers.

Since the American Recovery and Reinvestment Act of 2009 incentivized the adoption and use of electronic health records (EHRs), EHRs have become ubiquitous in the health care industry. Recent federal reports show about 84% adoption in hospitals and about 86% adoption in office-based practices. Patient information that was once captured on paper is now being regularly recorded and stored in EHRs, creating new opportunities for analyzing and drawing insights from these increasingly rich data sets. While EHRs can support easier access to and sharing of information by individual providers and patients, larger efforts (e.g. Why is EHR data complex?

The National Security Commission on Artificial Intelligence -- which is tasked with researching ways to advance the development of AI for national security and defense purposes -- released its initial report to Congress July 31. The group was established under the fiscal year 2019 National Defense Authorization Act. The legislation required the commission to release an initial report to Congress within 180 days of the NDAA's enactment. The panel has 15 members, led by Chairman Eric Schmidt, the former head of Google's parent company Alphabet, and Vice Chairman Robert O. Work, a former deputy secretary of defense who served in the Obama administration. The initial report provided a summary of the group's activities and its plans for the future but did not offer any recommendations to Congress.

A government panel decided Tuesday to end Saturday delivery for standard mail to deal with a labor shortage at Japan Post Co. and a drop in demand due to increased use of the internet. The Internal Affairs and Communications Ministry accepted the proposal from the panel and will seek a law amendment at an extraordinary Diet session this fall. Delivery on Saturday could be terminated possibly next year and it will be available only on weekdays. The panel also proposed that delivery for standard mail the day after posting be ended. Japan Post, a unit of Japan Post Holdings Co., has been calling for a review to trim standard mail service hours to five days a week from the current six days to address the workforce shortage.

The tragic events of the past weekend – back-to-back mass shootings in El Paso, Texas, and Dayton, Ohio leaving at least 31 dead and more than 50 wounded – has reignited the debate over the alleged correlation between video games and violent behavior. "We must stop the glorification of violence in our society," President Trump said in remarks from the White House on Monday. "This includes the gruesome and grisly video games that are now commonplace." Thousands subsequently turned to social media to challenge this claim, citing easy access to assault-style weapons without background checks as the core problem. Video games are immensely popular in several countries that do not see mass shootings, many noted.

I usually cover the developments in the field of Artificial Intelligence as part of my regular feature titled Tech Diaries, but this week was so jam-packed with AI news that I had to do write a separate piece. One story that stood out from the rest was of the AI-enabled photo-editing App called FaceApp. It is owned by Russia-based Wireless Lab and has been around since 2017, but with the recent addition of a feature which lets you see your future self made it go viral last week. My Facebook newsfeed was full of people showing off their'Now & Then' pictures -- Looked enticing but I held off the temptation to generate an older version of myself. For starters, some reports suggest that the App has collected more than 150 million photos of people's faces since its launch & according to its terms of service it can use the huge database in whatever way it wants.

Health management of complex dynamic systems has evolved from simple automated alarms into a subfield of artificial intelligence with techniques for analyzing off-nominal conditions and generating responses. This evolution took place largely apart from the development of automated system control, planning, and scheduling (generally referred to in this work as decision making). While there have been efforts to establish an information exchange between system health management and decision making, successful practical implementations of integrated architectures remain limited. This article proposes that rather than being treated as connected yet distinct entities, system health management and decision making should be unified in their formulations. Enabled by advances in modeling and algorithms, we believe that a unified approach will increase systems' resilience to faults and improve their effectiveness. We overview the prevalent system health management methodology, illustrate its limitations through numerical examples, and describe a proposed unified approach. We then show how typical system health management concepts are accommodated in the proposed approach without loss of functionality or generality. A computational complexity analysis of the unified approach is also provided.

Algorithms for scientific analysis typically have tunable parameters that significantly influence computational efficiency and solution quality. If a parameter setting leads to strong algorithmic performance on average over a set of typical problem instances, that parameter setting---ideally---will perform well in the future. However, if the set of typical problem instances is small, average performance will not generalize to future performance. This raises the question: how large should this set be? We answer this question for any algorithm satisfying an easy-to-describe, ubiquitous property: its performance is a piecewise-structured function of its parameters. We are the first to provide a unified sample complexity framework for algorithm parameter configuration; prior research followed case-by-case analyses. We present applications from diverse domains, including biology, political science, and economics.

In the recent times, automatic detection of human personality traits has received a lot of attention. Specifically, multimodal personality trait prediction has emerged as a hot topic within the field of affective computing. In this paper, we give an overview of the advances in machine learning based automated personality detection with an emphasis on deep learning techniques. We compare various popular approaches in this field based on input modality, the computational datasets available and discuss potential industrial applications. We also discuss the state-of-the-art machine learning models for different modalities of input such as text, audio, visual and multimodal. Personality detection is a very broad topic and this literature survey focuses mainly on machine learning techniques rather than the psychological aspect of personality detection.

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.