The Autonomous Truck-Mounted Attenuator (ATMA) system is a lead-follower vehicle system based on autonomous driving and connected vehicle technologies. The lead truck performs maintenance tasks on the road, and the unmanned follower truck alerts passing vehicles about the moving work zone and protects workers and the equipment. While the ATMA has been under testing by transportation maintenance and operations agencies recently, a simulator-based testing capability is a supplement, especially if human subjects are involved. This paper aims to discover how passing drivers perceive, understand, and react to the ATMA system in road maintenance. With the driving simulator developed for this ATMA study, the paper performed a simulation study wherein a screen-based eye tracker collected sixteen subjects' gaze points and pupil diameters. Data analysis evidenced the change in subjects' visual attention patterns while passing the ATMA. On average, the ATMA starts to attract subjects' attention from 500 ft behind the follower truck. Most (87.50%) understood the follower truck's protection purpose, and many (60%) reasoned the association between the two trucks. Nevertheless, nearly half of the participants (43.75%) did not recognize that ATMA is a connected autonomous vehicle system. While all subjects safely changed lanes and attempted to pass the slow-moving ATMA, their inadequate understanding of the ATMA is a potential risk, like cutting into the ATAM. Results implied that transportation maintenance and operations agencies should consider this in establishing the deployment guidance.

This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2022. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a real-time online evaluation system for the participants. In the presented case, the data constitute 47,420 road images collected from India, Japan, the Czech Republic, Norway, the United States, and China to propose methods for automatically detecting road damages in these countries. More than 60 teams from 19 countries registered for this competition. The submitted solutions were evaluated using five leaderboards based on performance for unseen test images from the aforementioned six countries. This paper encapsulates the top 11 solutions proposed by these teams. The best-performing model utilizes ensemble learning based on YOLO and Faster-RCNN series models to yield an F1 score of 76% for test data combined from all 6 countries. The paper concludes with a comparison of current and past challenges and provides direction for the future.

Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more. However, due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies. Research has shown that these risks can range anywhere from regulatory, compliance, reputational, and user trust, to financial and even societal risks. Depending on the nature and size of the organization, AI technologies can pose a significant risk, if not used in a responsible way. This position paper seeks to present a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI with the goal of preventing and mitigating risks. Having such a framework will not only manage risks but also gain maximum value out of AI projects and develop consistency for organization-wide adoption of AI.

Offline reinforcement learning (RL) learns policies entirely from static datasets, thereby avoiding the challenges associated with online data collection. Practical applications of offline RL will inevitably require learning from datasets where the variability of demonstrated behaviors changes non-uniformly across the state space. For example, at a red light, nearly all human drivers behave similarly by stopping, but when merging onto a highway, some drivers merge quickly, efficiently, and safely, while many hesitate or merge dangerously. Both theoretically and empirically, we show that typical offline RL methods, which are based on distribution constraints fail to learn from data with such non-uniform variability, due to the requirement to stay close to the behavior policy to the same extent across the state space. Ideally, the learned policy should be free to choose per state how closely to follow the behavior policy to maximize long-term return, as long as the learned policy stays within the support of the behavior policy. To instantiate this principle, we reweight the data distribution in conservative Q-learning (CQL) to obtain an approximate support constraint formulation. The reweighted distribution is a mixture of the current policy and an additional policy trained to mine poor actions that are likely under the behavior policy. Our method, CQL (ReDS), is simple, theoretically motivated, and improves performance across a wide range of offline RL problems in Atari games, navigation, and pixel-based manipulation.

The security convoy turned on to Tehran's Imam Khomeini Boulevard at around 3:30pm on 27 November 2020. The VIP was the Iranian scientist Mohsen Fakhrizadeh, widely regarded as the head of Iran's secret nuclear weapons programme. He was driving his wife to their country property, flanked by bodyguards in other vehicles. They were close to home when the assassin struck. A number of shots rang out, smashing into Fakhrizadeh's black Nissan and bringing it to a halt.

India will take over the chair of the Global Partnership on Artificial Intelligence (GPAI) from France, the outgoing Council Chair on November 21, 2022 at a meeting to be hold in Tokyo. The Minister of State for Electronics & Information Technology and Skill Development & Entrepreneurship, Rajeev Chandrasekhar will represent India at the GPAI meeting. GPAI is an international initiative to support responsible and human-centric development and use of Artificial Intelligence (AI). This development comes on the heels of assuming the presidency of G20, a league of world's largest economies. GPAI is a congregation of 25 member countries, including the US, the UK, EU, Australia, Canada, France, Germany, Italy, Japan, Mexico, New Zealand, Republic of Korea, and Singapore.

Most of us don't think of power-hungry killer robots as an imminent threat to humanity, especially when poverty and the climate crisis are already ravaging the Earth. This wasn't the case for Sam Bankman-Fried and his followers, powerful actors who have embraced a school of thought within the effective altruism movement called "longtermism". In February, the Future Fund, a philanthropic organization endowed by the now-disgraced cryptocurrency entrepreneur, announced that it would be disbursing more than $100m – and possibly up to $1bn – this year on projects to "improve humanity's long-term prospects". The slightly cryptic reference might have been a bit puzzling to those who think of philanthropy as funding homelessness charities and medical NGOs in the developing world. In fact, the Future Fund's particular areas of interest include artificial intelligence, biological weapons and "space governance", a mysterious term referring to settling humans in space as a potential "watershed moment in human history".

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.