Picture this: As a Covid-19 patient fights for her life on a ventilator, software powered by artificial intelligence analyzes her vital signs and sends her care providers drug-dosing recommendations -- even as the same software simultaneously analyzes in real time the vital signs of thousands of other ventilated patients across the country to learn more about how the dosage affects their care and automatically implements improvements to its drug-dosing algorithm. When an algorithm encounters a real-world clinical setting, adaptive AI might allow it to learn from these new data and incorporate clinician feedback to optimize its performance. Instead of being unleashed, artificial self-control lets a manufacturer put adaptive AI on a longer leash, allowing the algorithm to explore within a defined space to find the optimal operating point. When the algorithm is ready to incorporate what it has learned from real-world data about how drug-dosing information has affected other patients on ventilators, it first goes through a controlled revalidation process, automatically testing its performance on a random sample from a large representative test dataset in the cloud, a dataset that has been carefully curated by the manufacturer to ensure it is representative of the overall population and has high quality information about drug-dosing and patient outcomes. The test is logged, and each data point used in the test is carefully controlled to ensure that the algorithm is not simply getting better and better at predicting the answer in a small test set (a common problem in machine learning called overfitting) but is instead truly improving its performance.

Active learning theories and methods have been extensively studied in classical statistical learning settings. However, deep active learning, i.e., active learning with deep learning models, is usually based on empirical criteria without solid theoretical justification, thus suffering from heavy doubts when some of those fail to provide benefits in real applications. In this paper, by exploring the connection between the generalization performance and the training dynamics, we propose a theory-driven deep active learning method (dynamicAL) which selects samples to maximize training dynamics. In particular, we prove that the convergence speed of training and the generalization performance are positively correlated under the ultra-wide condition and show that maximizing the training dynamics leads to better generalization performance. Furthermore, to scale up to large deep neural networks and data sets, we introduce two relaxations for the subset selection problem and reduce the time complexity from polynomial to constant. Empirical results show that dynamicAL not only outperforms the other baselines consistently but also scales well on large deep learning models. We hope our work would inspire more attempts on bridging the theoretical findings of deep networks and practical impacts of deep active learning in real applications.

Soil and groundwater contamination is a pervasive problem at thousands of locations across the world. Contaminated sites often require decades to remediate or to monitor natural attenuation. Climate change exacerbates the long-term site management problem because extreme precipitation and/or shifts in precipitation/evapotranspiration regimes could re-mobilize contaminants and proliferate affected groundwater. To quickly assess the spatiotemporal variations of groundwater contamination under uncertain climate disturbances, we developed a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Operator (U-FNO) to solve Partial Differential Equations (PDEs) of groundwater flow and transport simulations at the site scale.We develop a combined loss function that includes both data-driven factors and physical boundary constraints at multiple spatiotemporal scales. Our U-FNOs can reliably predict the spatiotemporal variations of groundwater flow and contaminant transport properties from 1954 to 2100 with realistic climate projections. In parallel, we develop a convolutional autoencoder combined with online clustering to reduce the dimensionality of the vast historical and projected climate data by quantifying climatic region similarities across the United States. The ML-based unique climate clusters provide climate projections for the surrogate modeling and help return reliable future recharge rate projections immediately without querying large climate datasets. In all, this Multi-scale Digital Twin work can advance the field of environmental remediation under climate change.

We propose new methods to synthesize control barrier function (CBF)-based safe controllers that avoid input saturation, which can cause safety violations. In particular, our method is created for high-dimensional, general nonlinear systems, for which such tools are scarce. We leverage techniques from machine learning, like neural networks and deep learning, to simplify this challenging problem in nonlinear control design. The method consists of a learner-critic architecture, in which the critic gives counterexamples of input saturation and the learner optimizes a neural CBF to eliminate those counterexamples. We provide empirical results on a 10D state, 4D input quadcopter-pendulum system. Our learned CBF avoids input saturation and maintains safety over nearly 100% of trials.

The Israeli medical imaging vendor Nanox says it has a vision for the future of healthcare to address health disparities and lack of access to care. It envisions a new business model and plans to leverage a package of new technologies, including cold-cathode X-ray technology to help reduce costs, coupled with a new and inexpensive imaging system that combines teleradiology with artificial intelligence (AI). The business model is to enable any clinic or hospital in the developing world or rural areas to access its technology and no upfront costs using a pay-per-exam fee. The exams will be read by remote teleradiologists, including subspecialists, and AI will help augment clinical staff and radiologists to offer additional health screenings for all patients scanned. After a few years of talk, the vendor now appears on the edge of making this a reality.

In a recent episode at Mind Matters News podcasting, "Can a computer be a person?" Wesley J. Smith: Let me ask the question in a different way. Can an AI ever be irrational? A classic example, and this happened a number of years ago, was that the Soviets during the Cold War developed a high technology to decide whether the US was being attacked by… I'm sorry, whether the Soviet Union was being attacked by the United States. And so they had these missile detectors.

British Prime Minister Rishi Sunak has announced a new air defence package worth $60m as he met Ukraine's President Volodymyr Zelenskyy during his first trip to Kyiv since becoming prime minister. "I am here today to say that the UK will continue to stand with you … until Ukraine has won the peace and security it needs and deserves," Sunak said in a news conference with Zelenskyy in Kyiv. The British prime minister said the defence package includes "120 aircraft guns, radar and anti-drone equipment". It comes as Russia has disabled nearly half of the country's energy system, forcing blackouts across the country amid winter temperatures dipping to as low as zero degrees. With friends like you by our side, we are confident in our victory.

Lawrence Livermore National Laboratory has long been one of the world's largest consumers of supercomputing capacity. With computing power of more than 200 petaflops, or 200 billion floating-point operations per second, the U.S. Department of Energy-operated institution runs supercomputers from every major U.S. manufacturer. For the past two years, that lineup has included two newcomers: Cerebras Systems Inc. and SambaNova Systems Inc. The two startups, which have collectively raised more than $1.8 billion in funding, are attempting to upend a market that has been dominated so far by off-the-shelf x86 central processing units and graphics processing units with hardware that's purpose-built for use in artificial intelligence model development and inference processing to run those models. Cerebras says its WSE-2 chip, built on a wafer-scale architecture, can bring 2.6 trillion transistors and 850,000 CPU cores to bear on the task of training neural networks. That's about 500 times as many transistors and 100 times as many cores as are found on a high-end GPU.

René Morkos is the founder of ALICE Technologies and is an adjunct professor at Stanford University's construction engineering Ph.D program. Have no fear: Contrary to the opinions of pop culture and media, the robots aren't coming for your construction job. Although the findings of a 2018 study by the Midwest Economic Policy Institute (MEPI) indicate that nearly 49% of construction tasks could be automated (paving the way for the replacement or displacement of nearly 2.7 million jobs in construction by 2057), these estimates failed to anticipate significant trends affecting the construction workforce. These include, most notably, the "aging out" of skilled labor and a global post-Covid-19 labor shortage. According to data published in 2021 by the U.S. Bureau of Labor Statistics, 65% of workers are aged 35 and older, with 21% above age 55.

Defending organizations utilize AI-powered email security measures to enhance network protection, detect advanced malware and ransomware, optimize critical data center processes, improve threat response times, and reduce human error. Unfortunately, threat actors have also identified the benefits of AI technology.