Several techniques have been proposed over the years for automatic hypocenter localization. While those techniques have pros and cons that trade-off computational efficiency and the susceptibility of getting trapped in local minima, an alternate approach is needed that allows robust localization performance and holds the potential to make the elusive goal of real-time microseismic monitoring possible. Physics-informed neural networks (PINNs) have appeared on the scene as a flexible and versatile framework for solving partial differential equations (PDEs) along with the associated initial or boundary conditions. We develop HypoPINN -- a PINN-based inversion framework for hypocenter localization and introduce an approximate Bayesian framework for estimating its predictive uncertainties. This work focuses on predicting the hypocenter locations using HypoPINN and investigates the propagation of uncertainties from the random realizations of HypoPINN's weights and biases using the Laplace approximation. We train HypoPINN to obtain the optimized weights for predicting hypocenter location. Next, we approximate the covariance matrix at the optimized HypoPINN's weights for posterior sampling with the Laplace approximation. The posterior samples represent various realizations of HypoPINN's weights. Finally, we predict the locations of the hypocenter associated with those weights' realizations to investigate the uncertainty propagation that comes from those realisations. We demonstrate the features of this methodology through several numerical examples, including using the Otway velocity model based on the Otway project in Australia.

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.

Alessandra Rossi is a member of both the technical and organising committees for the RoboCup Humanoid League. We spoke to her about the Humanoid League Virtual Season, which concluded with the grand final of the virtual soccer competition, and a three day workshop. The Humanoid League Virtual Season (HLVS) has been driven by two main core motivations: firstly to allow teams to have support for continuous testing while making progresses and changes to their software, and secondly, to keep the teams connected throughout the year, thus strengthening the community and collaboration between teams. We wanted to let teams use the longer periods between games, and the continuous games throughout the year to test novel approaches, with less risk, and to aid their success in the overall tournament. In addition, this way, teams can thoroughly analyse the collected data between games, and make informed decisions on how to improve and implement their approaches for the following match.

Technology has completely infiltrated the built environment. Between IoT connectivity in buildings, indoor wayfinding and virtual tours, real estate is no longer the tech-averse industry it once was. In the context of digitization, there are new uses for Augmented Reality (AR). The technology has evolved from a marketing gimmick to a solid strategy for asset managers to improve their built environments. The premise of AR technology is the real-time integration of digital information to "augment" the user's physical experience.

The Indian Ministry of Science and Technology said this unique approach uses the predictive power of AI to identify road hazards and a collision warning system to communicate timely alerts to drivers, to make various safety-related improvements. Artificial intelligence (AI)-powered solutions may soon make roads in India a safer place to drive. The Indian government announced on Tuesday that an AI-powered technology could reduce the risk of road accidents in the country, which have killed more than a lakh people in 2020. In a bid to prevent this from happening, the Indian government said the AI approach will use a first-of-its-kind dataset consisting of 10,000 images. He said that this dataset is finely annotated with 34 classes collected from 182 driving sequences on Indian roads obtained from a front-facing camera attached to a car driving through the cities of Hyderabad, Bangalore and their outskirts.

By a News Reporter-Staff News Editor at Insurance Daily News -- New research on artificial intelligence is the subject of a new report. According to news reporting originating from Canberra, Australia, by NewsRx correspondents, research stated, "The Australian National Disability Insurance Scheme (NDIS) allocates funds to participants for purchase of services." Our news reporters obtained a quote from the research from University of New South Wales: "Only one percent of the 89,299 participants spent all of their allocated funds with 85 participants having failed to spend any, meaning that most of the participants were left with unspent funds. The gap between the allocated budget and realised expenditure reflects misallocation of funds. Thus we employ alternative machine learning techniques to estimate budget and close the gap while maintaining the aggregate level of spending. Three experiments are conducted to test the machine learning models in estimating the budget, expenditure and the resulting gap; compare the learning rate between machines and humans; and identify the significant explanatory variables."

This book was designed around major building blocks of the Python ecosystem that are useful to machine learning projects. There are a lot of things you could learn about Python, from language mechanics to the various libraries. Our goal is to take you straight to developing an intuition for the elements you can use in Python projects with laser-focused tutorials. We designed the tutorials to focus on how to get things done with Python. They give you the tools to both rapidly understand and apply each technique or operation. Each tutorial is designed to take you about one hour to read through and complete, excluding the extensions and further reading. You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.

"Data is the new oil." Originally coined in 2006 by the British mathematician Clive Humby, this phrase is arguably more apt today than it was then, as smartphones rival automobiles for relevance and the technology giants know more about us than we would like to admit. Just as it does for the financial services industry, the hyper-digitization of the economy presents both opportunity and potential peril for financial regulators. On the upside, reams of information are newly within their reach, filled with signals about financial system risks that regulators spend their days trying to understand. The explosion of data sheds light on global money movement, economic trends, customer onboarding decisions, quality of loan underwriting, noncompliance with regulations, financial institutions' efforts to reach the underserved, and much more. Importantly, it also contains the answers to regulators' questions about the risks of new technology itself. Digitization of finance generates novel kinds of hazards and accelerates their development. Problems can flare up between scheduled regulatory examinations and can accumulate imperceptibly beneath the surface of information reflected in traditional reports. Thanks to digitization, regulators today have a chance to gather and analyze much more data and to see much of it in something close to real time. The potential for peril arises from the concern that the regulators' current technology framework lacks the capacity to synthesize the data. The irony is that this flood of information is too much for them to handle.

As artificial intelligence and machine learning technologies continue to be developed, they may become powerful tools in many fields, including that of medicine. AI, complementing human experience and judgement, has already shown promise as a prognostic tool. Recent research using an AI program to help identify, from the results of chest scans, the risk of lung cancer is an example of the technique in action. Lung cancer is the second most common form of cancer worldwide, according to the World Cancer Research Fund. In Australia, it is the leading cause of cancer deaths and Cancer Australia estimates lung cancer accounted for 17.7% of all deaths from cancer in 2021.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Today is a big day for AI announcements from Microsoft, both from this week's Build conference and beyond. But one common theme bubbles over consistently: For AI to become more useful for business applications, it needs to be easier, simpler, more explainable, more accessible and, most of all, responsible. Responsible AI is actually at the heart of a lot of today's Build news, John Montgomery, corporate vice president of Azure AI, told VentureBeat. Most notable is Azure Machine Learning's preview of a responsible AI dashboard, which brings together capabilities in use over the past 18 months, such as data explorer, model interpretability, error analysis, counterfactual and causal inference analysis, into a single view.