The White House Office of Science and Technology Policy, the National Science Foundation (NSF), and the US Department of Energy (DOE) announced more than $1 billion in awards for the establishment of 12 new artificial intelligence (AI) and quantum information science (QIS) research institutes nationwide. The $1 billion will go towards NSF-led AI Research Institutes and DOE QIS Research Centers over five years, establishing 12 multi-disciplinary and multi-institutional national hubs for research and workforce development in these critical emerging technologies. Together, the institutes will spur cutting edge innovation, support regional economic growth, and advance American leadership in these critical industries of the future. The National Science Foundation and additional Federal partners, including the US Department of Agriculture, are awarding $140 million for seven NSF-led AI Research Institutes over five years to accelerate a number of AI R&D areas, such as machine-learning, synthetic manufacturing, precision agriculture, and forecasting prediction. The NSF-led AI Research Institutes will be hosted by universities across the country, including at the University of Oklahoma at Norman, University of Texas at Austin, University of Colorado at Boulder, University of Illinois at Urbana-Champaign, University of California at Davis, and the Massachusetts Institute of Technology.

The White House today detailed the establishment of 12 new research institutes focused on AI and quantum information science. Agencies including the National Science Foundation (NSF), U.S. Department of Homeland Security, and U.S. Department of Energy (DOE) have committed to investing tens of millions of dollars in centers intended to serve as nodes for AI and quantum computing study. Laments over the AI talent shortage in the U.S. have become a familiar refrain. While higher education enrollment in AI-relevant fields like computer science has risen rapidly in recent years, few colleges have been able to meet student demand due to a lack of staffing. In June, the Trump administration imposed a ban on U.S. entry for workers on certain visas -- including for high-skilled H-1B visa holders, an estimated 35% of whom have an AI-related degree -- through the end of the year.

To help keep first responders safe, University of Central Florida researchers have developed an artificial intelligence method that not only rapidly and remotely detects the powerful drug fentanyl, but also teaches itself to detect any previously unknown derivatives made in clandestine batches. The method, published recently in the journal Scientific Reports, uses infrared light spectroscopy and can be used in a portable, tabletop device. Fentanyl is a leading cause of drug overdose death in the U.S. It and its derivatives have a low lethal dose and may lead to death of the user, could pose hazards for first responders and even be weaponized in an aerosol." Fentanyl, which is 50 to 100 times more potent than morphine according to the U.S. Centers for Disease Control and Prevention, can be prescribed legally to treat patients who have severe pain, but it also is sometimes made and used illegally. He said that rapid identification methods of both known and emerging opioid fentanyl substances can aid in the safety of law enforcement and military personnel who must minimize their contact with the substances.

To help keep first responders safe, University of Central Florida researchers have developed an artificial intelligence method that not only rapidly and remotely detects the powerful drug fentanyl, but also teaches itself to detect any previously unknown derivatives made in clandestine batches. The method, published recently in the journal Scientific Reports, uses infrared light spectroscopy and can be used in a portable, tabletop device. "Fentanyl is a leading cause of drug overdose death in the U.S.," says Mengyu Xu, an assistant professor in UCF's Department of Statistics and Data Science and the study's lead author. "It and its derivatives have a low lethal dose and may lead to death of the user, could pose hazards for first responders and even be weaponized in an aerosol." Fentanyl, which is 50 to 100 times more potent than morphine according to the U.S. Centers for Disease Control and Prevention, can be prescribed legally to treat patients who have severe pain, but it also is sometimes made and used illegally.

Councils are quietly scrapping the use of computer algorithms in helping to make decisions on benefit claims and other welfare issues, the Guardian has found, as critics call for more transparency on how such tools are being used in public services. It comes as an expert warns the reasons for cancelling programmes among government bodies around the world range from problems in the way the systems work to concerns about bias and other negative effects. Most systems are implemented without consultation with the public, but critics say this must change. The use of artificial intelligence or automated decision-making has come into sharp focus after an algorithm used by the exam regulator Ofqual downgraded almost 40% of the A-level grades assessed by teachers. It culminated in a humiliating government U-turn and the system being scrapped.

Hierarchical planning has attracted renewed interest in the last couple of years. As a consequence, the time was right to establish a workshop devoted entirely to hierarchical planning – an insight shared by many supporters. In this paper we report on the first ICAPS workshop on Hierarchical Planning held in Delft, The Netherlands, in 2018 as well as on the second workshop held in Berkeley, CA, USA, in 2019. Hierarchical planning approaches incorporate hierarchies in the domain model. In the most common form, the hierarchy is defined among tasks, leading to the distinction between primitive and abstract tasks.

Digital technologies are redefining what is possible in healthcare and biology. Healthcare is perhaps the most important sector in the U.S. economy. It is the largest: close to $4 trillion per year is spent on healthcare in the United States. It employs more people than any other industry, accounting for 11% of all American jobs. Nearly one quarter of all U.S. government spending is on healthcare. At the same time, healthcare is the most broken sector in the U.S. economy. Healthcare costs have spiraled out of control in recent decades, from $355 per person in 1970 to $11,172 per person in 2018.

Using artificial intelligence, they've identified possibly 50 new planets from old NASA data. University of Warwick astronomers and computer scientists built a machine learning algorithm, and with it they examined old data containing thousands of potential planet candidates. The new AI can hone in on details that help verify whether any are planets. The university said these 50 exo-planets, which orbit around other stars, range in size from as large as Neptune to smaller than Earth. Now that astronomers know the planets are real, they can prioritize them for further observation.

Adversarial perturbation of images, in which a source image is deliberately modified with the intent of causing a classifier to misclassify the image, provides important insight into the robustness of image classifiers. In this work we develop two new methods for constructing adversarial perturbations, both of which are motivated by minimizing human ability to detect changes between the perturbed and source image. The first of these, the Edge-Aware method, reduces the magnitude of perturbations permitted in smooth regions of an image where changes are more easily detected. Our second method, the Color-Aware method, performs the perturbation in a color space which accurately captures human ability to distinguish differences in colors, thus reducing the perceived change. The Color-Aware and Edge-Aware methods can also be implemented simultaneously, resulting in image perturbations which account for both human color perception and sensitivity to changes in homogeneous regions. Though Edge-Aware and Color-Aware modifications exist for many image perturbations techniques, we focus on easily computed perturbations. We empirically demonstrate that the Color-Aware and Edge-Aware perturbations we consider effectively cause misclassification, are less distinguishable to human perception, and are as easy to compute as the most efficient image perturbation techniques. Code and demo available at https://github.com/rbassett3/Color-and-Edge-Aware-Perturbations

Radiation-induced photocurrent in semiconductor devices can be simulated using complex physics-based models, which are accurate, but computationally expensive. This presents a challenge for implementing device characteristics in high-level circuit simulations where it is computationally infeasible to evaluate detailed models for multiple individual circuit elements. In this work we demonstrate a procedure for learning compact delayed photocurrent models that are efficient enough to implement in large-scale circuit simulations, but remain faithful to the underlying physics. Our approach utilizes Dynamic Mode Decomposition (DMD), a system identification technique for learning reduced order discrete-time dynamical systems from time series data based on singular value decomposition. To obtain physics-aware device models, we simulate the excess carrier density induced by radiation pulses by solving numerically the Ambipolar Diffusion Equation, then use the simulated internal state as training data for the DMD algorithm. Our results show that the significantly reduced order delayed photocurrent models obtained via this method accurately approximate the dynamics of the internal excess carrier density -- which can be used to calculate the induced current at the device boundaries -- while remaining compact enough to incorporate into larger circuit simulations.