Editor's note: In concert with the Defense Entrepreneurs Forum's Project Gutenberg, a futurist imagines a post-mortem on an artificial intelligence-aided Chinese invasion. The Department of Defense's chief testified before Congress, revealing details of China's efforts to deter the United States during last year's invasion of Taiwan. WASHINGTON -- The Chinese People's Liberation Army shocked the world last November when they activated nearly two million reservists, mobilized the People's Armed Forces Maritime Militia, and executed a surprisingly successful cross-strait invasion of Taiwan. In a marathon day of testimony before the House Armed Services Committee, Secretary of Defense Barry McDermott revealed how the PLA's cyber branch used artificial intelligence and the "internet of things" to help Chinese conventional forces achieve strategic military aims far from the conventional battlefield in Taiwan. McDermott told committee members the artificial-intelligence capabilities China employed will force a redefinition of "the battlefield" and must change how the US military trains for future conflict.

The context: The vast majority of Facebook's moderation is now done automatically by the company's machine-learning systems, reducing the amount of harrowing content its moderators have to review. In its latest community standards enforcement report, published earlier this month, the company claimed that 98% of terrorist videos and photos are removed before anyone has the chance to see them, let alone report them. So, what are we seeing here?: The company has been training its machine-learning systems to identify and label objects in videos--from the mundane, such as vases or people--to the dangerous, such as guns or knives. Facebook's AI uses two main approaches to look for dangerous content. One is to employ neural networks that look for features and behaviors of known objects and label them with varying percentages of confidence (as we can see in the video, above.)

There's no question that artificial intelligence and data analytics are reshaping the resources sector. These new technologies bring new challenges in the way companies consider risk, adapt to new ways of working and the skills needed for the future. These issues were the topic of conversation at a business roundtable lunch hosted by professional services firm Accenture in its new Perth Innovation Hub this week as part of the Resources Technology Showcase program of events. Invited guests, including leading policymakers and industry heavyweights, heard former SAS commander and Mettle Global managing partner Ben Pronk speak about the need to take calculated risks in the battlefield. He explained how the resources industry could adopt that philosophy to take advantage of the fourth industrial revolution.

In computational models of argumentation, the justification of statements has drawn less attention than the construction and justification of arguments. As a consequence, significant losses of sensitivity and expressiveness in the treatment of statement statuses can be incurred by otherwise appealing formalisms. In order to reappraise statement statuses and, more generally, to support a uniform modelling of different phases of the argumentation process we introduce multi-labelling systems, a generic formalism devoted to represent reasoning processes consisting of a sequence of labelling stages. In this context, two families of multi-labelling systems, called argument-focused and statement-focused approach, are identified and compared. Then they are shown to be able to encompass several prominent literature proposals as special cases, thereby enabling a systematic comparison evidencing their merits and limits. Further, we show that the proposed model supports tunability of statement justification by specifying a few alternative statement justification labellings, and we illustrate how they can be seamlessly integrated into different formalisms.

Transfer learning makes use of data or knowledge in one task to help solve a different, yet related, task. Many ex isting TL approaches are based on a joint probability distribution metric, which is a weighted sum of the marginal distribution and the c ondi-tional distribution; however, they optimize the two distri butions independently, and ignore their intrinsic dependency. This p aper proposes a novel and frustratingly easy Joint Probability Dist ribution Adaptation (JPDA) approach, to replace the frequently-use d joint maximum mean discrepancy metric in transfer learning. Duri ng the distribution adaptation, JPDA improves the transferabili ty between the source and the target domains by minimizing the joint pro b-ability discrepancy of the corresponding class, and also in creases the discriminability between different classes by maximiz ing their joint probability discrepancy. Experiments on six image cl assifica-tion datasets demonstrated that JPDA outperforms several s tate-of- the-art metric-based transfer learning approaches.

The motivation, concept, design and implementation of latent semantic search for search engines have limited semantic search, entity extraction and property attribution features, have insufficient accuracy and response time of latent search, may impose privacy concerns and the search results are unavailable in offline mode for robotic search operations. The alternative suggestion involves autonomous search engine with adaptive storage consumption, configurable search scope and latent search response time with built-in options for entity extraction and property attribution available as open source platform for mobile, desktop and server solutions. The suggested architecture attempts to implement artificial general intelligence (AGI) principles as long as autonomous behaviour constrained by limited resources is concerned, and it is applied for specific task of enabling Web search for artificial agents implementing the AGI.

Is this some sort of weird dream? It is, the scientists at the cutting edge of AI research would have you believe, a glimpse into the future of fully autonomous motor vehicles -- where you might still have to turn a key but you will not have to accelerate, indicate, steer or grumble about another car going too slowly. Once the stuff of wild fantasy, a world where the cars have no drivers behind the wheel -- where road deaths and injuries plummet -- is rapidly becoming more of a vision. Deloitte spokesman Scott Corwin -- the company runs a ''future of mobility team'', so keeps a keen eye on developments -- told the Financial Times this week that the automotive industry was spending massively to investigate self-driving technology, creating a ''race to win the Willy Wonka golden ticket''. Corwin predicted the roll-out of autonomous vehicles would gather pace but with ''limited market launches in pretty controlled environments''.

The graph represents a network of 3,989 Twitter users whose tweets in the requested range contained "futureofwork ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 27 November 2019 at 03:02 UTC. The requested start date was Monday, 25 November 2019 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 3-day, 1-hour, 59-minute period from Thursday, 21 November 2019 at 23:00 UTC to Monday, 25 November 2019 at 01:00 UTC.

Machine learning systems now play a much bigger role in many of our social institutions, from education to healthcare to criminal justice. But many scholars have shown the way these systems are built on data that result in the reproduction of structural bias and discrimination. In this talk, Professor Crawford opens the substrates of training data to uncover the historical origins, labor practices, infrastructures, and epistemological assumptions that go into the production of artificial intelligence. Rather than a focus on technically correcting biases, she argues for a recentering of justice and the enforcement of limits on centralized power. Kate Crawford, Co-Founder of the AI Now Institute, is a Distinguished Research Professor at NYU and a Principal Researcher at Microsoft Research, and she is a leading scholar of the social implications of data systems, machine learning, and artificial intelligence.

Variational inference offers scalable and flexible tools to tackle intractable Bayesian inference of modern statistical models like Bayesian neural networks and Gaussian processes. For largely over-parameterized models, however, the over-regularization property of the variational objective makes the application of variational inference challenging. Inspired by the literature on kernel methods, and in particular on structured approximations of distributions of random matrices, this paper proposes Walsh-Hadamard Variational Inference, which uses Walsh-Hadamard-based factorization strategies to reduce model parameterization, accelerate computations, and increase the expressiveness of the approximate posterior beyond fully factorized ones.