Zoom, the company that normalized attending business meetings in your pajama pants, was forced to unmute itself this week to reassure users that it would not use personal data to train artificial intelligence without their consent. A keen-eyed Hacker News user last week noticed that an update to Zoom's terms and conditions in March appeared to essentially give the company free rein to slurp up voice, video, and other data, and shovel it into machine learning systems. The new terms stated that customers "consent to Zoom's access, use, collection, creation, modification, distribution, processing, sharing, maintenance, and storage of Service Generated Data" for purposes including "machine learning or artificial intelligence (including for training and tuning of algorithms and models)." The discovery prompted critical news articles and angry posts across social media. On Monday, Zoom's chief product officer, Smita Hasham, wrote a blog post stating, "We will not use audio, video, or chat customer content to train our artificial intelligence models without your consent." The company also updated its terms to say the same.

Fox News anchor Julie Banderas reacts to the vice president's gaffe and CNN calling Dylan Mulvaney a man on'Jesse Watters Primetime.' The White House launched a two-year competition the week that will award millions of dollars in prize money to teams that develop artificial intelligence tools that can be used to protect critical U.S. computer code. "This competition, which will feature almost $20 million in prizes, will drive the creation of new technologies to rapidly improve the security of computer code, one of cybersecurity's most pressing challenges," the White House said Wednesday. "It marks the latest step by the Biden-Harris Administration to ensure the responsible advancement of emerging technologies and protect Americans." The AI Cyber Challenge will be hosted by the Defense Advanced Research Projects Agency and will let AI development teams show the agency early next year how their AI-powered tools can protect U.S. code that "helps run the internet and other critical infrastructure."

Bogged down in small-scale infantry attacks and incremental advances, Ukraine sought to gain an advantage in the 76th week of the war by attacking Russian shipping at range and was accused of drone strikes targeting Moscow. Drone footage Ukraine released on August 4 showed the prow of a surface drone approaching the Olenegorsky Gornyak, a Ropucha-class Russian landing ship, before going blank at contact range. The attack happened just outside Novorossiysk harbour, supposedly a safe port on the eastern edge of the Black Sea, to which Russia had relocated much of its navy fleet based in Sevastopol after Ukraine sank its Black Sea flagship in May. Daylight footage showed the Olenegorsky Gornyak listing severely to port as it was towed to Novorossiysk harbour. "This poses a great dilemma for the Russians," wrote Phillips O'Brien, professor of strategy at St Andrews University.

The thread also highlighted the appointment of experts Matt Clifford and Jonathan Black, tasked with leading the preparations for the first major international summit on the responsible use of AI, which the UK is set to host later this year.

Russian forces took down more than a dozen Ukrainian drones flying towards the capital Moscow and the city of Sevastopol in the annexed Crimean peninsula, according to the country's defence ministry. The attack on Moscow on Thursday is the latest in a series of Ukrainian drone raids deep inside Russian territory. The defence ministry said in a statement that two drones "flying in the direction of the city of Moscow were destroyed", while 11 others were brought down near the city of Sevastopol. Two of the Ukrainian drones were "hit by on-duty anti-aircraft defence equipment, another nine were suppressed by means of electronic warfare and crashed in the Black Sea before reaching the target", the ministry said of the attack. There was no immediate comment from Ukraine.

U.S. President Joe Biden on Wednesday signed an executive order that will prohibit some new U.S. investment in China in sensitive technologies like computer chips, and require government notification in other technology sectors. The long-awaited order authorizes the U.S. Treasury secretary to prohibit or restrict U.S. investments in Chinese entities in three sectors: semiconductors and microelectronics, quantum information technologies and certain artificial intelligence systems. The administration said the restrictions would apply to "narrow subsets" of the three areas but did not give specifics. The proposal is open for public input.

The state-of-the-art predictive maintenance (PdM) techniques have shown great success in reducing maintenance costs and downtime of complicated machines while increasing overall productivity through extensive utilization of Internet-of-Things (IoT) and Deep Learning (DL). Unfortunately, IoT sensors and DL algorithms are both prone to cyber-attacks. For instance, DL algorithms are known for their susceptibility to adversarial examples. Such adversarial attacks are vastly under-explored in the PdM domain. This is because the adversarial attacks in the computer vision domain for classification tasks cannot be directly applied to the PdM domain for multivariate time series (MTS) regression tasks. In this work, we propose an end-to-end methodology to design adversarially robust PdM systems by extensively analyzing the effect of different types of adversarial attacks and proposing a novel adversarial defense technique for DL-enabled PdM models. First, we propose novel MTS Projected Gradient Descent (PGD) and MTS PGD with random restarts (PGD_r) attacks. Then, we evaluate the impact of MTS PGD and PGD_r along with MTS Fast Gradient Sign Method (FGSM) and MTS Basic Iterative Method (BIM) on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Bi-directional LSTM based PdM system. Our results using NASA's turbofan engine dataset show that adversarial attacks can cause a severe defect (up to 11X) in the RUL prediction, outperforming the effectiveness of the state-of-the-art PdM attacks by 3X. Furthermore, we present a novel approximate adversarial training method to defend against adversarial attacks. We observe that approximate adversarial training can significantly improve the robustness of PdM models (up to 54X) and outperforms the state-of-the-art PdM defense methods by offering 3X more robustness.

Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. We demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.

Neural networks (NN) have become almost ubiquitous with image classification, but in their standard form produce point estimates, with no measure of confidence. Bayesian neural networks (BNN) provide uncertainty quantification (UQ) for NN predictions and estimates through the posterior distribution. As NN are applied in more high-consequence applications, UQ is becoming a requirement. BNN provide a solution to this problem by not only giving accurate predictions and estimates, but also an interval that includes reasonable values within a desired probability. Despite their positive attributes, BNN are notoriously difficult and time consuming to train. Traditional Bayesian methods use Markov Chain Monte Carlo (MCMC), but this is often brushed aside as being too slow. The most common method is variational inference (VI) due to its fast computation, but there are multiple concerns with its efficacy. We apply and compare MCMC- and VI-trained BNN in the context of target detection in hyperspectral imagery (HSI), where materials of interest can be identified by their unique spectral signature. This is a challenging field, due to the numerous permuting effects practical collection of HSI has on measured spectra. Both models are trained using out-of-the-box tools on a high fidelity HSI target detection scene. Both MCMC- and VI-trained BNN perform well overall at target detection on a simulated HSI scene. This paper provides an example of how to utilize the benefits of UQ, but also to increase awareness that different training methods can give different results for the same model. If sufficient computational resources are available, the best approach rather than the fastest or most efficient should be used, especially for high consequence problems.

Establishing a human settlement on Mars is an incredibly complex engineering problem. The inhospitable nature of the Martian environment requires any habitat to be largely self-sustaining. Beyond mining a few basic minerals and water, the colonizers will be dependent on Earth resupply and replenishment of necessities via technological means, i.e., splitting Martian water into oxygen for breathing and hydrogen for fuel. Beyond the technical and engineering challenges, future colonists will also face psychological and human behavior challenges. Our goal is to better understand the behavioral and psychological interactions of future Martian colonists through an Agent-Based Modeling (ABM simulation) approach. We seek to identify areas of consideration for planning a colony as well as propose a minimum initial population size required to create a stable colony. Accounting for engineering and technological limitations, we draw on research regarding high performing teams in isolated and high stress environments (ex: submarines, Arctic exploration, ISS, war) to include the 4 basic personality types within the ABM. Interactions between agents with different psychological profiles are modeled at the individual level, while global events such as accidents or delays in Earth resupply affect the colony as a whole. From our multiple simulations and scenarios (up to 28 Earth years), we found that an initial population of 22 was the minimum required to maintain a viable colony size over the long run. We also found that the agreeable personality type was the one more likely to survive. We find, contrary to other literature, that the minimum number of people with all personality types that can lead to a sustainable settlement is in the tens and not hundreds.