Machine Learning is increasingly relevant to information security. The general idea is that it can offer better threat analysis to businesses while improving their entire IT infrastructure security. ML can also help automate menial tasks that were often given to security teams with minimal skill to handle. Data security is at severe risk in such an environment. But machine learning continues to grow in impact and adoption in cybersecurity solutions.

SHENZHEN – Chinese drone giant DJI Technology Co. built up such a successful U.S. business over the past decade that it almost drove all competitors out of the market. Yet its North American operations have been hit by internal disturbances in recent weeks and months, with a raft of staff cuts and departures, according to interviews with more than two dozen current and former employees. The loss of key managers, including some who have joined rivals, has compounded problems caused by U.S. government restrictions on Chinese companies, and raised the once-remote prospect of DJI's dominance being eroded, said four of the people, including two senior executives who were at the company until late 2020. About a third of DJI's 200-strong team in the region was laid off or resigned last year, from offices in Palo Alto, Burbank and New York, according to three former and one current employee. In February this year, DJI's head of U.S. R&D left and the company laid off the remaining R&D staff, numbering roughly 10 people, at its flagship U.S. research center in California's Palo Alto, four people said.

Historically, cybersecurity has been a field dominated by resource-intensive efforts. Monitoring, threat hunting, incident response, and other duties are often manual and time-intensive, which can delay remediation activities, increase exposure, and heighten vulnerability to cyber adversaries. Over the past few years, artificial intelligence solutions have rapidly matured to the point where they can bring substantial benefits to cyber defensive operations across a broad range of organizations and missions. By automating key elements of labor-heavy core functions, AI can transform cyber workflows into streamlined, autonomous, continuous processes that speed remediation and maximize protection.

Deep Reinforcement Learning (DRL) is an avenue of research in Artificial Intelligence (AI) that has received increasing attention within the research community in recent years, and is beginning to show potential for real-world application. DRL is one of the most promising routes towards developing more autonomous AI systems that interact with and take actions in complex real-world environments, and can more flexibly solve a range of problems for which we may not be able to precisely specify a correct ‘answer’. This could have substantial implications for people’s lives: for example by speeding up automation in various sectors, changing the nature and potential harms of online influence, or introducing new safety risks in physical infrastructure. In this paper, we review recent progress in DRL, discuss how this may introduce novel and pressing issues for society, ethics, and governance, and highlight important avenues for future research to better understand DRL’s societal implications. This article appears in the special track on AI and Society.

The Panel Vector Autoregressive (PVAR) model is a popular tool for macroeconomic forecasting and structural analysis in multi-country applications since it allows for spillovers between countries in a very flexible fashion. However, this flexibility means that the number of parameters to be estimated can be enormous leading to over-parameterization concerns. Bayesian global-local shrinkage priors, such as the Horseshoe prior used in this paper, can overcome these concerns, but they require the use of Markov Chain Monte Carlo (MCMC) methods rendering them computationally infeasible in high dimensions. In this paper, we develop computationally efficient Bayesian methods for estimating PVARs using an integrated rotated Gaussian approximation (IRGA). This exploits the fact that whereas own country information is often important in PVARs, information on other countries is often unimportant. Using an IRGA, we split the the posterior into two parts: one involving own country coefficients, the other involving other country coefficients. Fast methods such as approximate message passing or variational Bayes can be used on the latter and, conditional on these, the former are estimated with precision using MCMC methods. In a forecasting exercise involving PVARs with up to $18$ variables for each of $38$ countries, we demonstrate that our methods produce good forecasts quickly.

Though learning has become a core technology of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced solutions. The need to impose requirements on learning is therefore paramount, especially as it reaches critical applications in social, industrial, and medical domains. However, the non-convexity of most modern learning problems is only exacerbated by the introduction of constraints. Whereas good unconstrained solutions can often be learned using empirical risk minimization (ERM), even obtaining a model that satisfies statistical constraints can be challenging, all the more so a good one. In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained, finite dimensional, and deterministic. We analyze the generalization properties of this approach by bounding the empirical duality gap, i.e., the difference between our approximate, tractable solution and the solution of the original (non-convex)~statistical problem, and provide a practical constrained learning algorithm. These results establish a constrained counterpart of classical learning theory and enable the explicit use of constraints in learning. We illustrate this algorithm and theory in rate-constrained learning applications.

Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.

This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem. The proposed guidance algorithm is developed based on a general prediction-correction concept: the exact time-to-go under proportional navigation guidance with realistic aerodynamic characteristics is estimated by a deep neural network and a biased command to nullify the impact time error is developed by utilizing the emerging reinforcement learning techniques. The deep neural network is augmented into the reinforcement learning block to resolve the issue of sparse reward that has been observed in typical reinforcement learning formulation. Extensive numerical simulations are conducted to support the proposed algorithm.

China's tech industry has been hit hard by US trade battles and the economic uncertainties of the pandemic, but it's eager to bounce back in the relatively near future. According to the Wall Street Journal, the country used its annual party meeting to outline a five-year plan for advancing technology that aids "national security and overall development." It will create labs, foster educational programs and otherwise boost research in fields like AI, biotech, semiconductors and quantum computing. The Chinese government added that it would increase spending on basic research (that is, studies of potential breakthroughs) by 10.6 percent in 2021, and would create a 10-year research strategy. China has a number of technological advantages, such as its 5G availability and the sheer volume of AI research it produces.

The reason for focusing on this area of bias is regarded as important by some enterprises. This is because unconscious bias is often easy to miss. Moreover, unconscious bias is often seen to be far more pervasive in the workplace than blatant discrimination. According to some researchers, unconscious bias can be blamed for lower wages, less opportunities for advancement and high turnover. Unconscious biases are types of social stereotypes held by members of one group about other groups of people.