The Supreme Court has dealt a major blow to the Biden administration, ending a White House requirement that employees at large businesses get a vaccine or test regularly and wear a mask on the job. The court's conservative majority concluded the administration overstepped its authority by seeking to impose the Occupational Safety and Health Administration's vaccine-or-test rule on U.S. businesses with at least 100 employees. At the same time, the court is allowing the administration to proceed with a vaccine mandate for most health care workers in the U.S., resulting in a mixed decision for the administration. Reacting to the ruling, Psaki hailed the decision allowing mandates for health care workers as "good news," saying the administration will continue to enforce it. On the OSHA ruling, Psaki said the White House will "continue to call on businesses to immediately join those those who have already stepped up, including one third of Fortune 100 companies, to institute vaccination requirements to protect their workers, customers and communities."

Responsible Artificial Intelligence (AI) - the practice of developing, evaluating, and maintaining accurate AI systems that also exhibit essential properties such as robustness and explainability - represents a multifaceted challenge that often stretches standard machine learning tooling, frameworks, and testing methods beyond their limits. In this paper, we present two new software libraries - hydra-zen and the rAI-toolbox - that address critical needs for responsible AI engineering. hydra-zen dramatically simplifies the process of making complex AI applications configurable, and their behaviors reproducible. The rAI-toolbox is designed to enable methods for evaluating and enhancing the robustness of AI-models in a way that is scalable and that composes naturally with other popular ML frameworks. We describe the design principles and methodologies that make these tools effective, including the use of property-based testing to bolster the reliability of the tools themselves. Finally, we demonstrate the composability and flexibility of the tools by showing how various use cases from adversarial robustness and explainable AI can be concisely implemented with familiar APIs.

This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems related to concerns in a CPS can be precisely formalized and implemented using Answer Set Programming (ASP). These include problems related to the dependency or conflicts between concerns, how to mitigate an issue, and what the most suitable mitigation strategy for a given issue would be. It then shows how ASP can be used to develop an implementation that addresses the aforementioned problems. The paper concludes with a discussion of the potentials of the proposed methodologies.

A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline, requirements often change, with classes being added and removed, which leads to nontrivial modifications to the source code and the possible introduction of bugs. In this work, we evaluate sequence-to-sequence models as an alternative to token-level classification methods for information extraction of legal and registration documents. We finetune models that jointly extract the information and generate the output already in a structured format. Post-processing steps are learned during training, thus eliminating the need for rule-based methods and simplifying the pipeline. Furthermore, we propose a novel method to align the output with the input text, thus facilitating system inspection and auditing. Our experiments on four real-world datasets show that the proposed method is an alternative to classical pipelines.

Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural spike trains to document streams. The clustering property is achieved via a doubly stochastic formulation: first, a set of latent events is drawn from a Poisson process; then, each latent event generates a set of observed data points according to another Poisson process. This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i.e. clusters) is a random variable, but the point process formulation makes the NSP especially well suited to modeling spatiotemporal data. While many specialized algorithms have been developed for DPMMs, comparatively fewer works have focused on inference in NSPs. Here, we present novel connections between NSPs and DPMMs, with the key link being a third class of Bayesian mixture models called mixture of finite mixture models (MFMMs). Leveraging this connection, we adapt the standard collapsed Gibbs sampling algorithm for DPMMs to enable scalable Bayesian inference on NSP models. We demonstrate the potential of Neyman-Scott processes on a variety of applications including sequence detection in neural spike trains and event detection in document streams.

The advent of artificial intelligence technology paved the way of many researches to be made within air combat sector. Academicians and many other researchers did a research on a prominent research direction called autonomous maneuver decision of UAV. Elaborative researches produced some outcomes, but decisions that include Reinforcement Learning(RL) came out to be more efficient. There have been many researches and experiments done to make an agent reach its target in an optimal way, most prominent are Genetic Algorithm(GA) , A star, RRT and other various optimization techniques have been used. But Reinforcement Learning is the well known one for its success. In DARPHA Alpha Dogfight Trials, reinforcement learning prevailed against a real veteran F16 human pilot who was trained by Boeing. This successor model was developed by Heron Systems. After this accomplishment, reinforcement learning bring tremendous attention on itself. In this research we aimed our UAV which has a dubin vehicle dynamic property to move to the target in two dimensional space in an optimal path using Twin Delayed Deep Deterministic Policy Gradients (TD3) and used in experience replay Hindsight Experience Replay(HER).We did tests on two different environments and used simulations.

I would love to learn more about your work as a government policy adviser on AI, infrastructure, education, and smart governance. Could you share about your role in crafting the National AI Roadmap under the Philippine Department of Trade and Industry? In what ways is this project important strategically for the Philippines? Many enterprises and organizations already consider data science and artificial intelligence (DSAI) as strategic capabilities. They are no longer optional, "nice-to-have" capabilities, but necessary, "must-have" -- a matter of organizational survival.

California is evaluating whether Tesla's self-driving tests require regulatory oversight, following "videos showing a dangerous use of that technology" and federal investigations into Tesla vehicle crashes, a state regulator said. The California department of motor vehicles previous said that Tesla's full self-driving, or FSD, beta requires human intervention and therefore is not subject to its regulations on autonomous vehicles. But the agency is revisiting that decision "following recent software updates, videos showing a dangerous use of that technology, open investigations by the National Highway Traffic Safety Administration (NHTSA), and the opinions of other experts", the department said in a letter on Friday to Lena Gonzalez, chair of the state senate transportation committee. The Los Angeles Times first reported the letter. Tesla did not respond to a request for comment.

After just seven months, a huge team of scientists who work with the Dark Energy Spectroscopic Instrument have already mapped a larger swath of the cosmos than all other 3D surveys combined. And since they're only 10 percent of the way through their five-year mission, there's much more to come. DESI, pronounced like Desi Arnaz's name, has revealed a spectacular cosmic web of more than 7.5 million galaxies, and it will scan up to 40 million. The instrument is funded by the US Department of Energy and installed at the Nicholas U. Mayall 4-meter Telescope at Kitt Peak National Observatory near Tucson, Arizona. It measures the precise distances of galaxies from Earth and their emitted light at a range of wavelengths, achieving quantity and quality at the same time.

The Alan Turing Institute, supported by the British Standards Institution (BSI) and the National Physical Laboratory (NPL), will pilot a new UK government initiative with the goal of helping to shape global technical standards for artificial intelligence. This initiative, called the "AI Standards Hub" will be tasked with creating practical tools for businesses, bringing the UK's AI community together through a new online platform, and developing educational materials to help organisations contribute, develop and meet global standards. The Hub is part of the UK national AI strategy. Ahead of the pilot's launch, there will be a series of roundtables with a wide range of organisations led by The Alan Turing Institute to shape the Hub's activities. The move follows the December 2021 launch of the Centre for Data Ethics and Innovation's (CDEI) roadmap to an effective AI assurance ecosystem, which is also part of the National AI Strategy.