All the sessions from Transform 2021 are available on-demand now. In early June, border officials "quietly deployed" the mobile app CBP One at the U.S.-Mexico border to "streamline the processing" of asylum seekers. While the app will reduce manual data entry and speed up the process, it also relies on controversial facial recognition technologies and stores sensitive information on asylum seekers prior to their entry to the U.S. The issue here is not the use of artificial intelligence per se, but what it means in relation to the Biden administration's pre-election promise of civil rights in technology, including AI bias and data privacy. When the Democrats took control of both House and Senate in January, onlookers were optimistic that there was an appetite for a federal privacy bill and legislation to stem bias in algorithmic decision-making systems. This is long overdue, said Ben Winters, Equal Justice Works Fellow of the Electronic Privacy Information Center (EPIC), who works on matters related to AI and the criminal justice system.

"The Russian military seeks to be a leader in weaponizing AI technology," Lt. Gen. Michael Groen, director of the Pentagon's Joint Artificial Intelligence Center, told National Defense. The JAIC -- which has been working to facilitate AI adoption across the Defense Department since 2018 -- recently commissioned a report by CNA, a research organization based in Arlington, Virginia, to examine Russia's developments. The report -- titled "Artificial Intelligence and Autonomy in Russia" -- identified more than 150 AI-enabled military systems in various stages of development, Groen said in an email in June. Key areas of interest include autonomous air, underwater, surface and ground platforms. The nation wants to use AI for electronic warfare, intelligence, surveillance, reconnaissance and strategic decision-making processes as leaders pursue information dominance on the battlefield, Groen said.

Artificial intelligence is a fifth more accurate at predicting whether individuals are likely to become long-term recipients of benefits. A new method of predicting welfare dependency, developed by Dr. Dario Sansone from the University of Exeter Business School and Dr. Anna Zhu from RMIT University, could save governments billions in welfare costs as well as help them make earlier interventions to prevent long-term economic disadvantage and social exclusion. Their study found that machine learning algorithms, which improve through several iterations and use of big data, are 22% more accurate at predicting the proportion of time individuals are on income support than the standard early warning systems. The researchers were able to apply the off-the-shelf algorithms to the entire population of people enrolled in the Australian social security system between 2014 and 2018. This included demographic and socio-economic data of anyone who received a welfare payment from Australia's social security system Centrelink, whether on the grounds of unemployment, disability, having children, or being a carer, a student or of pensionable age.

Many governments are increasingly approaching artificial intelligence with an almost religious zeal. By 2018 at least 22 countries around the world, and also the EU, had launched grand national strategies for making AI part of their business development, while many more had announced ethical frameworks for how it should be allowed to develop. The latest is Ireland, which has just announced its national artificial intelligence strategy, "AI – Here for Good". It aims to become "an international leader in using AI to benefit our economy and society, through a people-centred, ethical approach to its development, adoption and use". This is to be obtained via eight policy commandments, including increasing trust in and understanding of AI by using an "AI ambassador" – a veritable AI high priest – to spread the message around the country.

Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.

Abstract-- This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs). The uncertainty propagation over a task-allocation process is done by using the Unscented transform that uses the Sigma-Point sampling mechanism. It has great potential to be employed for generic task-allocation schemes, in the sense that there is no need to modify an existing task-allocation method that has been developed without considering the uncertainty in the situational awareness. The proposed framework was tested in a simulated environment where the decision-maker needs to determine an optimal allocation of multiple locations assigned to multiple mobile flying robots whose locations come as random variables of known mean and covariance. The simulation result shows that the proposed stochastic task allocation approach generates an assignment with 30% less overall cost than the one without considering the uncertainty.

This paper is an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of unknown terrains. Within this scope, MarsExplorer, an openai-gym compatible environment tailored to exploration/coverage of unknown areas, is presented. MarsExplorer translates the original robotics problem into a Reinforcement Learning setup that various off-the-shelf algorithms can tackle. Any learned policy can be straightforwardly applied to a robotic platform without an elaborate simulation model of the robot's dynamics to apply a different learning/adaptation phase. One of its core features is the controllable multi-dimensional procedural generation of terrains, which is the key for producing policies with strong generalization capabilities. Four different state-of-the-art RL algorithms (A3C, PPO, Rainbow, and SAC) are trained on the MarsExplorer environment, and a proper evaluation of their results compared to the average human-level performance is reported. In the follow-up experimental analysis, the effect of the multi-dimensional difficulty setting on the learning capabilities of the best-performing algorithm (PPO) is analyzed. A milestone result is the generation of an exploration policy that follows the Hilbert curve without providing this information to the environment or rewarding directly or indirectly Hilbert-curve-like trajectories. The experimental analysis is concluded by comparing PPO learned policy results with frontier-based exploration context for extended terrain sizes. The source code can be found at: https://github.com/dimikout3/GeneralExplorationPolicy.

State-of-the-art driver-assist systems have failed to effectively mitigate driver inattention and had minimal impacts on the ever-growing number of road mishaps (e.g. life loss, physical injuries due to accidents caused by various factors that lead to driver inattention). This is because traditional human-machine interaction settings are modeled in classical and behavioral game-theoretic domains which are technically appropriate to characterize strategic interaction between either two utility maximizing agents, or human decision makers. Therefore, in an attempt to improve the persuasive effectiveness of driver-assist systems, we develop a novel strategic and personalized driver-assist system which adapts to the driver's mental state and choice behavior. First, we propose a novel equilibrium notion in human-system interaction games, where the system maximizes its expected utility and human decisions can be characterized using any general decision model. Then we use this novel equilibrium notion to investigate the strategic driver-vehicle interaction game where the car presents a persuasive recommendation to steer the driver towards safer driving decisions. We assume that the driver employs an open-quantum system cognition model, which captures complex aspects of human decision making such as violations to classical law of total probability and incompatibility of certain mental representations of information. We present closed-form expressions for players' final responses to each other's strategies so that we can numerically compute both pure and mixed equilibria. Numerical results are presented to illustrate both kinds of equilibria.

In the last post, we discussed an outline of AI powered cyber attacks and their defence strategies. In this post, we will discuss a specific type of attack which is called adversarial attack. Adversarial attacks are not common now because there are not many deep learning systems in production. But soon, we expect that they will increase. Adversarial attacks are easy to describe.

'Gutfeld!' panel debates whether CNN will change their coverage This is a rush transcript from "Gutfeld!," This copy may not be in its final form and may be updated. I want to protect free speech. No, we want people to be protected from disinformation, to be protected from dying in this country, to be protected from people like Donald Trump who spread this information for -- who love to make sure that the division and the death continues. That was a rough weekend, and not just for Kat. But at least she kept her clothes on unlike our other guests, Jimmy Failla. But it was a far worse weekend for CNN. First let's go to our roly-poly guacamole gossip goalie. See how bad it got unreliable fart noises. Here's Michael Wolff delivering that smack to the hack. You know, you become part of -- one of the parts of the problem of the media. You know, you come on here and you -- and you have a, you know, a monopoly on truth. You know, you know exactly how things are supposed to be done. You know, you are why one of the reasons people can't stand the media. You should see the rest of the world, buddy. Can I hear that chuckle again? But if that was a heavyweight fight, and it is because, you know, Stelter, it would have been stopped in the first 25 seconds. It got worse, meaning better, lots better. STELTER: It's -- how -- so what should I do differently, Michael? WOLFF: You know, don't talk so much. Listen more, you know, people have genuine problems with the media. The media doesn't get the story right.