Innovation in compliance can come in many forms. One such form was described by Vincent M. Walden, Managing Director at Alvarez and Marsal Holdings, LLC (A&M), in his article entitled "Profit & Loss-of-One"(P&L-of-One). In it, Walden detailed how he and his then colleagues at Ernest & Young (EY) worked in conjunction with the General Electric (GE) compliance function to "improve compliance by using forensic data analytics to provide behavioral insights to their compliance program." They did this through the innovative use of "digital twins" which Walden described as "digital replicas of physical assets that organizations can use for multiple purposes such as the maintenance of power generation equipment, jet engines and heavy machinery." In a more expansive definition, the consulting firm Gartner, Inc. described "digital twins" as dynamic software models of physical things or systems.

Service members at U.S. Indo-Pacific Command headquarters in Hawaii recently tested a prototype DARPA system designed to help military analysts and planners determine if observed events – such as increased force movements, cyber intrusions, and civil unrest – are unconnected occurrences, or if they're part of an adversary's coordinated campaign to achieve strategic objectives in a geographic region. Operational representatives from the command's intelligence and operations divisions spent three days in December trying out DARPA's COMPASS tool suite. COMPASS, which stands for Collection and Monitoring via Planning for Active Situational Scenarios, analyzes large streams of data to uncover competition campaigns, and displays results that represent the evidence and the analysis behind each hypothesis. COMPASS seeks to leverage advanced AI and other technologies to help commanders make more effective decisions regarding a competitor's complex, multi-layered competition activity. Competition refers to actions – both non-violent and violent – designed to achieve geopolitical goals without provoking full-blown armed conflict.

Our 2020 presidential candidates will be questioned about their stance on artificial intelligence (AI) policy, especially with regard to the job displacement AI could cause in manufacturing, transportation, and other industries. An over-regulation of AI could hand technical superiority to countries like China and Russia, leading to a ripple effect on America's GDP and even threatening national security. But under-regulation could lead to a massive consolidation of power among a handful of American technology companies, millions of jobs lost without replacement planning, and algorithms that show bias based on age, race, gender, and more. We're certain to hear statements about upskilling -- the process of helping displaced workers acquire new skills so they can find other employment -- and about taxing robots to slow down job loss. But the candidates will need to offer up more than a few soundbites.

Modern variational inference (VI) uses stochastic gradients to avoid intractable expectations, enabling large-scale probabilistic inference in complex models. VI posits a family of approximating distributions $q$ and then finds the member of that family that is closest to the exact posterior $p$. Traditionally, VI algorithms minimize the "exclusive KL" KL$(q\|p)$, often for computational convenience. Recent research, however, has also focused on the "inclusive KL" KL$(p\|q)$, which has good statistical properties that makes it more appropriate for certain inference problems. This paper develops a simple algorithm for reliably minimizing the inclusive KL. Consider a valid MCMC method, a Markov chain whose stationary distribution is $p$. The algorithm we develop iteratively samples the chain $z[k]$, and then uses those samples to follow the score function of the variational approximation, $\nabla \log q(z[k])$ with a Robbins-Monro step-size schedule. This method, which we call Markovian score climbing (MSC), converges to a local optimum of the inclusive KL. It does not suffer from the systematic errors inherent in existing methods, such as Reweighted Wake-Sleep and Neural Adaptive Sequential Monte Carlo, which lead to bias in their final estimates. In a variant that ties the variational approximation directly to the Markov chain, MSC further provides a new algorithm that melds VI and MCMC. We illustrate convergence on a toy model and demonstrate the utility of MSC on Bayesian probit regression for classification as well as a stochastic volatility model for financial data.

In order to increase productivity, capability, and data exploitation, numerous defense applications are experiencing an integration of state-of-the-art machine learning and AI into their architectures. Especially for defense applications, having a human analyst in the loop is of high interest due to quality control, accountability, and complex subject matter expertise not readily automated or replicated by AI. However, many applications are suffering from a very slow transition. This may be in large part due to lack of trust, usability, and productivity, especially when adapting to unforeseen classes and changes in mission context. Interactive machine learning is a newly emerging field in which machine learning implementations are trained, optimized, evaluated, and exploited through an intuitive human-computer interface. In this paper, we introduce interactive machine learning and explain its advantages and limitations within the context of defense applications. Furthermore, we address several of the shortcomings of interactive machine learning by discussing how cognitive feedback may inform features, data, and results in the state of the art. We define the three techniques by which cognitive feedback may be employed: self reporting, implicit cognitive feedback, and modeled cognitive feedback. The advantages and disadvantages of each technique are discussed.

Despite the fact that the loss functions of deep neural networks are highly non-convex, gradient-based optimization algorithms converge to approximately the same performance from many random initial points. One thread of work has focused on explaining this phenomenon by characterizing the local curvature near critical points of the loss function, where the gradients are near zero, and demonstrating that neural network losses enjoy a no-bad-local-minima property and an abundance of saddle points. We report here that the methods used to find these putative critical points suffer from a bad local minima problem of their own: they often converge to or pass through regions where the gradient norm has a stationary point. We call these gradient-flat regions, since they arise when the gradient is approximately in the kernel of the Hessian, such that the loss is locally approximately linear, or flat, in the direction of the gradient. We describe how the presence of these regions necessitates care in both interpreting past results that claimed to find critical points of neural network losses and in designing second-order methods for optimizing neural networks.

COVID-19 is an acute respiratory disease that has been classified as a pandemic by the World Health Organization. Information regarding this particular disease is limited, however, it is known to have high mortality rates, particularly among individuals with preexisting medical conditions. Creating models to identify individuals who are at the greatest risk for severe complications due to COVID-19 will be useful to help for outreach campaigns in mitigating the diseases worst effects. While information specific to COVID-19 is limited, a model using complications due to other upper respiratory infections can be used as a proxy to help identify those individuals who are at the greatest risk. We present the results for three models predicting such complications, with each model having varying levels of predictive effectiveness at the expense of ease of implementation.

The U.S. Army is seeing success in implementing machine learning (ML)-- evidenced by improved classification of previously unknown data by 20 percent-- and is developing a workforce culture where artificial intelligence (AI) adoption is possible. Speaking during the AI Experience 2020 Webcast, Deputy Assistant Secretary of Financial Information Management John Bergin spoke about how embracing shifting attitudes from top-level leadership, bringing the right team and tools together, and adopting a fail-fast mentality are vital to ML adoption. "We have to ground ourselves in some common starting point," Bergin said. Bergin said that ML and reskilling the workforce to use AI work together to improve the Army's classification of data, adding that it's up to leadership to guide the workforce through the changes. You have to train the algorithm," Bergin said, emphasizing the role humans play in adopting AI.

The new coronavirus is now officially a pandemic, and researchers are speeding to discover, test and deploy a vaccine. Some are hoping that breakthrough biotechnology and artificial intelligence can get us there faster. Much of the funding and development of a COVID-19 vaccine is likely to happen privately. The $8 billion coronavirus funding bill passed by the U.S. government includes just $800 million for the National Institutes of Health, where official U.S. vaccine research and development happens, but which is chronically underfunded. I asked Michael Greeley, co-founder and general partner with biotech investment fund Flare Capital in Boston, what uses AI could have in dealing with coronavirus.