The European Commission today unveiled a sweeping set of proposals that it hopes will establish the region as a leader in artificial intelligence by focusing on trust and transparency. The proposals would lead to changes in the way data is collected and shared in an effort to level the playing field between European companies and competitors from the U.S. and China. The EC wants to prevent potential abuses while also building confidence among citizens in order to reap the benefits promised by the technology. In a series of announcements, EC leaders expressed optimism that AI could help tackle challenges such as climate change, mobility, and health care, along with a determination to keep private tech companies from influencing regulation and dominating the data needed to develop these algorithms. "We want citizens to trust the new technology," said Ursula von der Leyen, president of the European Commission.

Researchers have used machine learning (ML) in recent years to generate highly realistic fake images and videos known as "deepfakes." Artists, pranksters, and many others have subsequently used these techniques to create a growing collection of audio and video depicting high-profile leaders, such as Donald Trump, Barack Obama, and Vladimir Putin, saying things they never did. This trend has driven fears within the national security community that recent advances in ML will enhance the effectiveness of malicious media manipulation efforts like those Russia launched during the 2016 U.S. presidential election. These concerns have drawn attention to the disinformation risks ML poses, but key questions remain unanswered. How rapidly is the technology for synthetic media advancing, and what are reasonable expectations around the commoditization of these tools?

The coronavirus crisis may have finally forced the government to get serious about fixing its computer systems. Before the pandemic, the problems might have been merely annoying, but now they're impeding an economic recovery. Japan's aging administrative pipeline has delayed aid money getting to where it's needed during the pandemic. To fix that, the government last month included a "digital new deal" in its annual policy goals, calling for a year of concentrated investment to boost data-sharing between ministries and update websites, although no price tag has been given yet. Economists say the shoddy state of the government's digital services has damped the benefits from fiscal stimulus and is hurting the country's broader competitiveness by holding back technological progress in the private sector.

The U.S. Department of Defense is getting rid of Chinese-manufactured drones over cybersecurity concerns. The U.S. Pacific Command announced on Thursday that the Defense Department and the entire federal government will have access to secure, trusted, and American-made commercial drones on the General Service Administration schedule. This new DIU initiative, dubbed Blue small unmanned aircraft systems (sUAS), is the culmination of 18 months of work by the Army and DIU to tailor the best technology from U.S. and allied companies to develop small unmanned aircraft systems that can be safely adopted by men and women in uniform, Acting Undersecretary of Defense for Research and Engineering Michael Kratsios announced at a virtual event hosted by the Defense Innovation Unit (DIU). During the Aug. 18 DIU event, Kratsios said it also has important impacts for the nation's broader economic and national security. "UAS technologies have incredible promise and potential to not only provide great economic benefit for the American people, but also to enhance safety and security for our nation. We need a strong, secure domestic UAS manufacturing base to ensure American leadership in this critical field," he said.

Precision medicine is a medical model, which proposes customization of the healthcare to a subgroup of patients, based on a genetics, lifestyle and environment. This technique allows doctors and researchers to prognosis treatment and prevention strategies for a specific disease which can work on a group of people. It is opposed to a one-size-fits-all approach, in which disease treatment and prevention techniques are advanced for the average individual with much less attention for the variations among individuals. There is an overlap between the terms "precision medication" and "personalized medicine." As per the National Research Council, "personalized medicine" is a traditional word with a meaning close to "precision medication."

This paper presents a new variable selection approach integrated with Gaussian process (GP) regression. We consider a sparse projection of input variables and a general stationary covariance model that depends on the Euclidean distance between the projected features. The sparse projection matrix is considered as an unknown parameter. We propose a forward stagewise approach with embedded gradient descent steps to co-optimize the parameter with other covariance parameters based on the maximization of a non-convex marginal likelihood function with a concave sparsity penalty, and some convergence properties of the algorithm are provided. The proposed model covers a broader class of stationary covariance functions than the existing automatic relevance determination approaches, and the solution approach is more computationally feasible than the existing MCMC sampling procedures for the automatic relevance parameter estimation with a sparsity prior. The approach is evaluated for a large number of simulated scenarios. The choice of tuning parameters and the accuracy of the parameter estimation are evaluated with the simulation study. In the comparison to some chosen benchmark approaches, the proposed approach has provided a better accuracy in the variable selection. It is applied to an important problem of identifying environmental factors that affect an atmospheric corrosion of metal alloys.

Applying machine learning (ML) on multivariate time series data has growing popularity in many application domains, including in computer system management. For example, recent high performance computing (HPC) research proposes a variety of ML frameworks that use system telemetry data in the form of multivariate time series so as to detect performance variations, perform intelligent scheduling or node allocation, and improve system security. Common barriers for adoption for these ML frameworks include the lack of user trust and the difficulty of debugging. These barriers need to be overcome to enable the widespread adoption of ML frameworks in production systems. To address this challenge, this paper proposes a novel explainability technique for providing counterfactual explanations for supervised ML frameworks that use multivariate time series data. The proposed method outperforms state-of-the-art explainability methods on several different ML frameworks and data sets in metrics such as faithfulness and robustness. The paper also demonstrates how the proposed method can be used to debug ML frameworks and gain a better understanding of HPC system telemetry data.

Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.

Legal scholars have in the last several years embarked upon an ongoing discussion and debate over a potential Legal Singularity that might someday occur, involving a variant or law-domain offshoot leveraged from the Artificial Intelligence (AI) realm amid its many decades of deliberations about an overarching and generalized technological singularity (referred to classically as The Singularity). This paper examines the postulated Legal Singularity and proffers that such AI and Law cogitations can be enriched by these three facets addressed herein: (1) dovetail additionally salient considerations of The Singularity into the Legal Singularity, (2) make use of an in-depth and innovative multidimensional parametric analysis of the Legal Singularity as posited in this paper, and (3) align and unify the Legal Singularity with the Levels of Autonomy (LoA) associated with AI Legal Reasoning (AILR) as propounded in this paper.

In their work, Dean, Rich, and Recht create a model to research recourse and availability of items in a recommender system. We used the definition of predictive multiplicity by Marx, Pin Calmon, and Ustun to examine different variations of this model, using different values for two model parameters. Pairwise comparison of their models show, that most of these models produce very similar results in terms of discrepancy and ambiguity for the availability and only in some cases the availability sets differ significantly.