As an initiative to provide protection to the military's artificial intelligence systems from cyber-attacks, researchers from Delhi University and the Army have joined hands, as per a recent Army news release. As the Army increasingly utilizes AI frameworks to identify dangers, the Army Research Office is investing in more security. This move was a very calculated one in fact as it drew reference from the NYU supported CSAW HackML competition in 2019 where one of the many major goals was to develop such a software that would prevent cyber attackers from hacking into the facial and object recognition software the military uses to further train its AI. MaryAnne Fields, program manager for the ARO's intelligent systems, said in a statement, "Object recognition is a key component of future intelligent systems, and the Army must safeguard these systems from cyber-attack. This work will lay the foundations for recognizing and mitigating backdoor attacks in which the data used to train the object recognition system is subtly altered to give incorrect answers."

Many developments show that states have turned AI technology into a part of the arms race. The "Summary of the 2018 Department of Defense Artificial Intelligence Strategy" report prepared by the U.S. Department of Defense highlighted Chinese and Russian investments in AI weapons technologies and stated the steps to be taken within the framework of such competition. Moreover, the Pentagon's budget for AI arming, worth $2 billion, and the "Executive Order on Maintaining American Leadership in AI" published by U.S. President Donald Trump reveal the importance of arming in AI technology. U.S. Defense Secretary Mark Esper recently said that the growing threats posed by great power competitors such as China and Russia warrant refocusing on high-intensity conflict across all of the military services. Esper also stressed the necessity of modernizing the military in AI, robotics, directed energy and hypersonic technologies.

Tesla is facing calls from a U.S. Senator to make safety fixes to its autopilot system. In a press release, Democrat Senator Edward Markey of Massachuttes took issues with certain areas of its autopilot feature that enable a Tesla vehicle to center itself in a lane, provide speed changing cruise control and self-park among other things. Markey sits on the Commerce, Science and Transportation Committee. Markey said that by calling it Autopilot it encourages users to "over-rely" on the technology and think they can take their hands off the steering wheel. To get around that the Senator is calling on Tesla to rebrand and remarket Autopilot to make it clear that its a driver's assistance system not a fully autonomous capability.

The majority of Information Security teams' cybersecurity analysts are overwhelmed today analyzing security logs, thwarting breach attempts, investigating potential fraud incidents and more. The following graphic compares the percentage of organizations by industry who are relying on AI to improve their cybersecurity. The bottom line is all organizations have an urgent need to improve endpoint security and resilience, protect privileged access credentials, reduce fraudulent transactions, and secure every mobile device applying Zero Trust principles. Many are relying on AI and machine learning to determine if login and resource requests are legitimate or not based on past behavioral and system use patterns. Several of the top ten companies to watch take into account a diverse series of indicators to determine if a login attempt, transaction, or system resource request is legitimate or not.

The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.

Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision.  This article is part of the special track on AI and Society.

We propose a new method for clustering multivariate time-series data based on Dynamic Linear Models. Whereas usual time-series clustering methods obtain static membership parameters, our proposal allows each time-series to dynamically change their cluster memberships over time. In this context, a mixture model is assumed for the time series and a flexible Dirichlet evolution for mixture weights allows for smooth membership changes over time. Posterior estimates and predictions can be obtained through Gibbs sampling, but a more efficient method for obtaining point estimates is presented, based on Stochastic Expectation-Maximization and Gradient Descent. Finally, two applications illustrate the usefulness of our proposed model to model both univariate and multivariate time-series: World Bank indicators for the renewable energy consumption of EU nations and the famous Gapminder dataset containing life-expectancy and GDP per capita for various countries.

Xenofon Koutsoukos V anderbilt University Nashville, TN xenofon.koutsoukos@vanderbilt.edu Abstract --Cyber-physical systems (CPS) greatly benefit by using machine learning components that can handle the uncertainty and variability of the real-world. Typical components such as deep neural networks, however, introduce new types of hazards that may impact system safety. The system behavior depends on data that are available only during runtime and may be different than the data used for training. Out-of-distribution data may lead to a large error and compromise safety. The paper considers the problem of efficiently detecting out-of-distribution data in CPS control systems. Detection must be robust and limit the number of false alarms while being computational efficient for real-time monitoring. The proposed approach leverages inductive confor-mal prediction and anomaly detection for developing a method that has a well-calibrated false alarm rate. We use variational autoencoders and deep support vector data description to learn models that can be used efficiently compute the nonconformity of new inputs relative to the training set and enable real-time detection of out-of-distribution high-dimensional inputs. We demonstrate the method using an advanced emergency braking system and a self-driving end-to-end controller implemented in an open source simulator for self-driving cars. The simulation results show very small number of false positives and detection delay while the execution time is comparable to the execution time of the original machine learning components. I NTRODUCTION Learning-enabled components (LECs) such as neural networks are used in many classes of cyber-physical systems (CPS). Semi-autonomous and autonomous vehicles, in particular, are CPS examples where LECs can play a significant role for perception, planning, and control if they are complemented with methods for analyzing and ensuring safety [1], [2]. However, there are several characteristics of LECs that can complicate safety analysis. LECs encode knowledge in a form that is not transparent.

New Jersey's attorney general, Gurbir S. Grewal, has instructed prosecutors across the state to stop using Clearview AI, a private facial recognition software. Clearview AI's tools allow law enforcement officials to upload a photo of an unknown person they'd like to identify, and see a list of matches culled from a database of over 3 billion photos. The photos are taken from a variety of controversial sources, including Facebook, YouTube, Twitter, and even Venmo. New Jersey attorney general Gurbir S. Grewal told the state's prosecutor's to stop using Clearview AI, private facial recognition software that he worried might compromise the integrity of the state's investigations Grewal decided to issue the ban after seeing Clearview had used footage from a 2019 sting operation in New Jersey promoting its own services, something even he hadn't been aware of at the time. 'I was surprised they used my image and the office to promote the product online," Grewal told the New York Times. 'I was troubled they were sharing information about ongoing criminal prosecutions.' After investigating the issue, Gerwal confirmed one of the 19 people arrested in the sting had been identified using Clearview, but since all the cases were ongoing, he felt uncomfortable with them being used in promotional material. Another report in the Times, on the controversial practices used to generate the company's massive database, further worried Grewal and prompted him to issue the ban. 'Until this week, I had not heard of Clearview AI," he said.

LONDON – British Prime Minister Boris Johnson on Monday insisted the U.K. can have technological progress while preserving national security, as he prepared to approve a role for Chinese telecom giant Huawei in developing its 5G telecom network despite strong U.S. opposition. Johnson spoke after U.S. Secretary of State Mike Pompeo on Sunday tweeted: "The UK has a momentous decision ahead on 5G." The United States has banned Huawei from the rollout of its next generation 5G mobile networks because of concerns -- strongly denied -- that the firm could be under the control of Beijing. With Washington heaping pressure on Johnson to sideline Huawei totally, the Financial Times reported that Britain was Tuesday "expected to approve a restricted role" for the group. It comes after a senior U.K. official last week strongly hinted at a green light for Huawei.