Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various domain sciences. They provide a low-dimensional emulation framework for systems that may be intrinsically high-dimensional. This is accomplished by utilizing a construction algorithm that is purely data-driven. It is no surprise, therefore, that the algorithmic advances of machine learning have led to non-intrusive ROMs with greater accuracy and computational gains. However, in bypassing the utilization of an equation-based evolution, it is often seen that the interpretability of the ROM framework suffers. This becomes more problematic when black-box deep learning methods are used which are notorious for lacking robustness outside the physical regime of the observed data. In this article, we propose the use of a novel latent-space interpolation algorithm based on Gaussian process regression. Notably, this reduced-order evolution of the system is parameterized by control parameters to allow for interpolation in space. The use of this procedure also allows for a continuous interpretation of time which allows for temporal interpolation. The latter aspect provides information, with quantified uncertainty, about full-state evolution at a finer resolution than that utilized for training the ROMs. We assess the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.

The work reported here addresses the capacity of psychophysiological sensors and measures using Electroencephalogram (EEG) and Galvanic Skin Response (GSR) to detect levels of trust for humans using AI-supported Human-Machine Interaction (HMI). Improvements to the analysis of EEG and GSR data may create models that perform as well, or better than, traditional tools. A challenge to analyzing the EEG and GSR data is the large amount of training data required due to a large number of variables in the measurements. Researchers have routinely used standard machine-learning classifiers like artificial neural networks (ANN), support vector machines (SVM), and K-nearest neighbors (KNN). Traditionally, these have provided few insights into which features of the EEG and GSR data facilitate the more and least accurate predictions - thus making it harder to improve the HMI and human-machine trust relationship. A key ingredient to applying trust-sensor research results to practical situations and monitoring trust in work environments is the understanding of which key features are contributing to trust and then reducing the amount of data needed for practical applications. We used the Local Interpretable Model-agnostic Explanations (LIME) model as a process to reduce the volume of data required to monitor and enhance trust in HMI systems - a technology that could be valuable for governmental and public sector applications. Explainable AI can make HMI systems transparent and promote trust. From customer service in government agencies and community-level non-profit public service organizations to national military and cybersecurity institutions, many public sector organizations are increasingly concerned to have effective and ethical HMI with services that are trustworthy, unbiased, and free of unintended negative consequences.

Our goal is to understand why the robustness drops after conducting adversarial training for too long. Although this phenomenon is commonly explained as overfitting, our analysis suggest that its primary cause is perturbation underfitting. We observe that after training for too long, FGSM-generated perturbations deteriorate into random noise. Intuitively, since no parameter updates are made to strengthen the perturbation generator, once this process collapses, it could be trapped in such local optima. Also, sophisticating this process could mostly avoid the robustness drop, which supports that this phenomenon is caused by underfitting instead of overfitting. In the light of our analyses, we propose APART, an adaptive adversarial training framework, which parameterizes perturbation generation and progressively strengthens them. Shielding perturbations from underfitting unleashes the potential of our framework. In our experiments, APART provides comparable or even better robustness than PGD-10, with only about 1/4 of its computational cost.

State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be accurately replicated using deep learning-based numerical models in several 2D and 3D systems with multiple agents. The deep learning-based numerical model demonstrates superior computational efficiency with plans generated 1000 times faster than the mathematical model counterpart.

Recent wildfires in the United States have resulted in loss of life and billions of dollars, destroying countless structures and forests. Fighting wildfires is extremely complex. It is difficult to observe the true state of fires due to smoke and risk associated with ground surveillance. There are limited resources to be deployed over a massive area and the spread of the fire is challenging to predict. This paper proposes a decision-theoretic approach to combat wildfires. We model the resource allocation problem as a partially-observable Markov decision process. We also present a data-driven model that lets us simulate how fires spread as a function of relevant covariates. A major problem in using data-driven models to combat wildfires is the lack of comprehensive data sources that relate fires with relevant covariates. We present an algorithmic approach based on large-scale raster and vector analysis that can be used to create such a dataset. Our data with over 2 million data points is the first open-source dataset that combines existing fire databases with covariates extracted from satellite imagery. Through experiments using real-world wildfire data, we demonstrate that our forecasting model can accurately model the spread of wildfires. Finally, we use simulations to demonstrate that our response strategy can significantly reduce response times compared to baseline methods.

In a study by IDC, it was found that the global cybersecurity market was worth $107 million in 2019 and is poised to grow up to $151 million by 2023. While most of this expenditure is towards designing software and hardware for protecting systems from hacking or compromising networks, an area which is often overlooked is the integrity of the data being used to train the datasets used by the machine learning algorithms. This is called the poisoning attack, where the intruder injects false training data to corrupt the learning model itself. It could become a significant attack that can undermine the AI systems, businesses and processes built around them. Here we will discuss the poisoning attack in particular.

In Milan, Italy, STIIMA, the National Research Council's Institute for Smart Industrial Technology Systems for Advanced Manufacturing, and the Polytechnic University of Milan have set up a joint experimental "re-manufacturing" and "de-manufacturing" facility. While still at a pilot experimental level, this is an excellent example of the enormous potential of artificial intelligence in the circular economy. This is because there are no similar plants in the world capable of managing electronic waste, understanding what the items are, dismantling them and recovering their useful or valuable components. For this reason, millions of tonnes of old TVs, monitors, broken PCs, telephones, and electrical appliances of every type, are piling up at waste sites, from where they are often taken to fuel an illegal and extremely polluting market. Its real size is difficult to estimate, but according to UNEP, the United Nations Environmental Protection agency, the global market for electronic waste is worth more than 62 billion dollars and only 20% of it is officially recycled.

With the second-generation Oculus Quest headset's release, virtual reality is promising us new ways to connect while we socially isolate during the Covid-19 pandemic. However, as we immerse ourselves in our bubbles, emerging technology companies are exploring the possibilities of VR, big data, and artificial intelligence for more critical use cases. These use cases include making more useful information available to mental health providers for cognitive assessment. For the aging community, where even "active" seniors can feel isolated during the unfolding crisis, it's helpful to take regularly cognitive assessments to understand the changes. Akili Interactive's EndeavorRx made history earlier this year by becoming the first-and-only video game approved by the U.S. Food and Drug Administration as a medical treatment, ushering in a new era in the realm of VR.

With one stroke of the pen Governor Newsom of California has insured a robust outlook for the future of electric vehicles in California. Finally, electric vehicle manufacturers are in vogue. The executive order requires that starting in 2035 all vehicles sold in the state of California can no longer have an internal combustion engine. There are many electric vehicle companies in the market. Tesla TSLA 0.6%, Nio and Rivian appear to be leading the way with large contracts and/or significant followings in the electric vehicle world.

OpenAI is an AI research and deployment company whose mission is to ensure that artificial general intelligence benefits all of humanity. In July OpenAI released the GPT-3, a new language model trained with 175 billion parameters, 10x more than any previous non-sparse language model, capable of programing, designing and even talking about politics or economy. Here there is a Twitter thread with some of the most curious cases. Even if there was a huge hype, the CEO of OpenAI and former president of Y Combinator, Sam Altman literally said "The GPT-3 hype is way too much. It is impressive but it still has serious weaknesses and sometimes makes very silly mistakes".