The Government Accountability Office has revealed in a new report that 10 federal agencies are planning to expand their use of facial recognition. In a survey involving 24 federal agencies on their use of facial recognition technology, the Agriculture, Commerce, Defense, Homeland Security, Health and Human Services, Interior, Justice, State, Treasury and Veterans Affairs departments told GAO that they're planning to use facial recognition in more areas through fiscal year 2023. As The Washington Post notes, most agencies already use facial recognition to give their personnel access to their phones and computers. However, there's a growing number of agencies using it to investigate crime and to track people. The Department of Agriculture apparently wants to monitor the live feeds at its facilities and scan for individuals in the watch list.

A former U.S. military interpreter says the Taliban have begun executing U.S. allies away from Kabul where there is not media attention. The House Problem Solvers Caucus has voted to officially call on President Joe Biden to extend the August 31 withdrawal date from Afghanistan as the administration scrambles to evacuate Americans stranded in Taliban-controlled Kabul. "As Democrats and Republicans, we stand united in our commitment to protecting U.S. citizens, diplomats, intelligence officers, and our foreign partners who are currently attempting to flee Afghanistan," the statement endorsed by the caucus read. "In this time of tremendous danger, politics must be put aside to advance our common goals. From this week's bipartisan Member briefing, it is apparent that the Administration's set date for departure from Afghanistan on August 31st does not provide enough time to evacuate all American citizens and our partners. We respectfully call on the Administration to reconsider its timeline and provide a clear plan to Congress that will result in the completion of our shared national objectives."

We introduce Reflective Hamiltonian Monte Carlo (ReHMC), an HMC-based algorithm, to sample from a log-concave distribution restricted to a convex body. We prove that, starting from a warm start, the walk mixes to a log-concave target distribution $\pi(x) \propto e^{-f(x)}$, where $f$ is $L$-smooth and $m$-strongly-convex, within accuracy $\varepsilon$ after $\widetilde O(\kappa d^2 \ell^2 \log (1 / \varepsilon))$ steps for a well-rounded convex body where $\kappa = L / m$ is the condition number of the negative log-density, $d$ is the dimension, $\ell$ is an upper bound on the number of reflections, and $\varepsilon$ is the accuracy parameter. We also developed an efficient open source implementation of ReHMC and we performed an experimental study on various high-dimensional data-sets. The experiments suggest that ReHMC outperfroms Hit-and-Run and Coordinate-Hit-and-Run regarding the time it needs to produce an independent sample and introduces practical truncated sampling in thousands of dimensions.

Our ability to know when to trust the decisions made by machine learning systems has not kept up with the staggering improvements in their performance, limiting their applicability in high-stakes domains. We introduce Prover-Verifier Games (PVGs), a game-theoretic framework to encourage learning agents to solve decision problems in a verifiable manner. The PVG consists of two learners with competing objectives: a trusted verifier network tries to choose the correct answer, and a more powerful but untrusted prover network attempts to persuade the verifier of a particular answer, regardless of its correctness. The goal is for a reliable justification protocol to emerge from this game. We analyze variants of the framework, including simultaneous and sequential games, and narrow the space down to a subset of games which provably have the desired equilibria. We develop instantiations of the PVG for two algorithmic tasks, and show that in practice, the verifier learns a robust decision rule that is able to receive useful and reliable information from an untrusted prover. Importantly, the protocol still works even when the verifier is frozen and the prover's messages are directly optimized to convince the verifier.

Building accurate and predictive models of the underlying mechanisms of celestial motion has inspired fundamental developments in theoretical physics. Candidate theories seek to explain observations and predict future positions of planets, stars, and other astronomical bodies as faithfully as possible. We use a data-driven learning approach, extending that developed in Lu et al. ($2019$) and extended in Zhong et al. ($2020$), to a derive stable and accurate model for the motion of celestial bodies in our Solar System. Our model is based on a collective dynamics framework, and is learned from the NASA Jet Propulsion Lab's development ephemerides. By modeling the major astronomical bodies in the Solar System as pairwise interacting agents, our learned model generate extremely accurate dynamics that preserve not only intrinsic geometric properties of the orbits, but also highly sensitive features of the dynamics, such as perihelion precession rates. Our learned model can provide a unified explanation to the observation data, especially in terms of reproducing the perihelion precession of Mars, Mercury, and the Moon. Moreover, Our model outperforms Newton's Law of Universal Gravitation in all cases and performs similarly to, and exceeds on the Moon, the Einstein-Infeld-Hoffman equations derived from Einstein's theory of general relativity.

The use of artificial intelligence (AI) and AI methods in the workplace holds both great opportunities as well as risks to occupational safety and discrimination. In addition to legal regulation, technical standards will play a key role in mitigating such risk by defining technical requirements for development and testing of AI systems. This paper provides an overview and assessment of existing international, European and German standards as well as those currently under development. The paper is part of the research project "ExamAI - Testing and Auditing of AI systems" and focusses on the use of AI in an industrial production environment as well as in the realm of human resource management (HR).

A report from the US government's National Institute of Standards and Technology (NIST) reveals the accuracy of various facial recognition algorithms. Higher numbers are better as they indicate a lower prevalence of false positives. The "N" values represent the number of individuals enrolled in each simulation of aircraft boarding. The N 42,000 simulation, for example, is designed to represent an airport security line where many people are expected. The "k" values give the number of images of each en- rollee in each gallery.

Many federal agencies said they used the software by requesting that officials in state and local governments run searches on their own software and report the results. Many searches were routed through a nationwide network of "fusion centers," which local police and federal investigators use to share information on potential threats or terrorist attacks. U.S. Customs and Border Protection, for instance, told the GAO it used Clearview's software for free by requesting help from an agent stationed at a fusion center in New York.

Artificial Intelligence has entered the domestic market of India with its smart functionalities for smart cities, industries, smart homes, consumers, and many more. Consumers have started preferring artificial intelligence over traditional workloads or systems for time-efficient and cost-efficient features. This is about the urban cities of India where there is not digital divide and poor or no network connections. But, artificial intelligence in Indian small towns is thriving in recent years while these AI models are transforming the lives of people living in these small towns. Multiple AI-based start-ups are focused on developing different AI models for Indian small towns to enhance the standard of living. Let's dive deep into how artificial intelligence is transforming the Indian small towns efficiently and effectively.

Artificial intelligence leads the top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform. The steps being taken to integrate artificial intelligence (AI) into healthcare and the use of AI techniques in the detection and management of various diseases were popularly discussed in Q2. Rafael Grossmann, a surgeon and clinical innovator, shared an article on two new companies namely Anumana and Lucem Health being launched by healthcare company Mayo Clinic that will collect and analyse patient data gathered from remote monitoring devices and tools to enable early detection and diagnosis of diseases. Mayo Clinic will launch a remote monitoring platform that will enable clinicians and physicians to make quicker and better decisions with the help of the collected and analysed patient data thereby speeding up the diagnosis before symptoms appear. It will also allow patients to take more control of their health and related decisions.