The Department of Energy is teaming up with Microsoft to build AI tools that will help prevent and manage natural disasters, the agency announced today. Together, they'll co-chair the "First Five Consortium," a group that will focus on using predictive technology in areas like anticipating wildfires, managing fire lines, assessing overall damage, as well as handling search and rescue. The group is named after the critical first five minutes of a natural disaster -- the better first responders are prepared early on, the better they can contain issues and potentially save people. According to the DOE, its Pacific Northwest lab is already building on a prototype deep learning model that can help first responders make disaster-related decisions in near real time. We're still waiting on specific details, but the agency says that the model was originally developed by the Department of Defense's Joint Artificial Intelligence Center (JAIC).

Congressional speeches made by US politicians have become simpler since the 1970s and only require the reading age of a 13-year-old to be followed, study found. Computer scientists from Kansas State University analysed two million congressional speeches from Republican and Democrat politicians made between 1873 and 2010. Text analysis algorithms were used to examine how congressional speeches changed in terms of complexity, emotion and divisiveness over 138 years. More recent speeches use a smaller vocabulary, simpler language and talk about'the other party' more than speeches made even a decade ago, the authors found. Researchers put the drop in the reading level down to the rise of broadcast media in congress that started in the mid-1970s - with politicians'playing to the camera'.

For the first time in 2021, a major Machine Learning conference will have a track devoted to disaster response. The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021) has a track on "NLP Applications for Emergency Situations and Crisis Management". I am delighted to be the Senior Area Chair for this track! I've worked in machine learning and disaster response for 20 years and I'm glad that more people are now looking into how machine learning can help people at the most critical times. The majority of what goes into a paper on machine learning for disaster response should be the same as any other paper in applied science: reproducible methods that clearly advance our knowledge of how to deploy and evaluate machine learning technologies. However, there are aspects of disaster response that make some aspects of the science more important and a few aspects that are unique to disaster response.

Artificial intelligence (AI) is the most disruptive innovation of our lifetime. Its adoption has grown 60 percent in the last year, according to an April 2020 report by Narrative Science. The report's authors say the technology is having a "significant and imminent impact on everything from company strategy, to business operations, to job functions." So what are some of AI's implications in the new normal, one in which American entrepreneurs find themselves saving cash, working from home and wearing masks everywhere they go? Currently, for entrepreneurs, the most popular AI-powered solutions deal with predictive analytics (24 percent), machine learning (21 percent), language processing (14 percent) and voice recognition and response (14 percent), according to the same Narrative Science report.

Boston, United States - In 2015, I fell 25 feet (7.6 metres) from a Redwood tree and was in a coma for 10 days. I spent the rest of that year using an arm crutch and went through four months of outpatient rehabilitation. Nine months later, I had eye muscle surgery to correct double vision that resulted from damage to my occipital lobe. Five years later, I still suffer from fine motor deficits, balance issues, and have trouble with my memory and speech. My first application for Social Security Disability Insurance (SSDI) - a government grant which provides health insurance and a monthly allotment of money for people with disabilities to live on - was filled out on my behalf by my parents. I have no recollection of it and my short-term memory is still impaired.

However, attackers are now utilizing new tools and carrying out more detailed campaigns to breach defenses. This calls for more sophisticated defense mechanisms that make use of Artificial Intelligence (AI) and Machine Learning (ML) to protect your technology assets. Click here to view original webpage at www.itproportal.com

An asteroid has flown past Earth closer than any seen before. The tiny object, known as asteroid 2020 QG, came just 1,830 miles over the southern Indian Ocean on Sunday, the space agency said. As it did so, it was spotted by the Zwicky Transient Facility, a robotic camera that scans the sky in search of a variety of objects, from the smallest asteroids to the largest supernova. The asteroid 2020 QG is particularly small. It is about three to six meters across, scientists said, roughly the size of a large car.

Context: Demonstrating high reliability and safety for safety-critical systems (SCSs) remains a hard problem. Diverse evidence needs to be combined in a rigorous way: in particular, results of operational testing with other evidence from design and verification. Growing use of machine learning in SCSs, by precluding most established methods for gaining assurance, makes operational testing even more important for supporting safety and reliability claims. Objective: We use Autonomous Vehicles (AVs) as a current example to revisit the problem of demonstrating high reliability. AVs are making their debut on public roads: methods for assessing whether an AV is safe enough are urgently needed. We demonstrate how to answer 5 questions that would arise in assessing an AV type, starting with those proposed by a highly-cited study. Method: We apply new theorems extending Conservative Bayesian Inference (CBI), which exploit the rigour of Bayesian methods while reducing the risk of involuntary misuse associated with now-common applications of Bayesian inference; we define additional conditions needed for applying these methods to AVs. Results: Prior knowledge can bring substantial advantages if the AV design allows strong expectations of safety before road testing. We also show how naive attempts at conservative assessment may lead to over-optimism instead; why extrapolating the trend of disengagements is not suitable for safety claims; use of knowledge that an AV has moved to a less stressful environment. Conclusion: While some reliability targets will remain too high to be practically verifiable, CBI removes a major source of doubt: it allows use of prior knowledge without inducing dangerously optimistic biases. For certain ranges of required reliability and prior beliefs, CBI thus supports feasible, sound arguments. Useful conservative claims can be derived from limited prior knowledge.

Advances in Artificial Intelligence (AI) and Machine Learning (ML) that are being applied to legal efforts have raised controversial questions about the existent restrictions imposed on the practice-of-law. Generally, the legal field has sought to define Authorized Practices of Law (APL) versus Unauthorized Practices of Law (UPL), though the boundaries are at times amorphous and some contend capricious and self-serving, rather than being devised holistically for the benefit of society all told. A missing ingredient in these arguments is the realization that impending legal profession disruptions due to AI can be more robustly discerned by examining the matter through the lens of a framework utilizing the autonomous levels of AI Legal Reasoning (AILR). This paper explores a newly derived instrumental grid depicting the key characteristics underlying APL and UPL as they apply to the AILR autonomous levels and offers key insights for the furtherance of these crucial practice-of-law debates.

This paper investigates the use of Reinforcement Learning for the robust design of low-thrust interplanetary trajectories in presence of severe disturbances, modeled alternatively as Gaussian additive process noise, observation noise, control actuation errors on thrust magnitude and direction, and possibly multiple missed thrust events. The optimal control problem is recast as a time-discrete Markov Decision Process to comply with the standard formulation of reinforcement learning. An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted to carry out the training process of a deep neural network, used to map the spacecraft (observed) states to the optimal control policy. The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law. Numerical results are presented for a typical Earth-Mars mission. First, in order to validate the proposed approach, the solution found in a (deterministic) unperturbed scenario is compared with the optimal one provided by an indirect technique. Then, the robustness and optimality of the obtained closed-loop guidance laws is assessed by means of Monte Carlo campaigns performed in the considered uncertain scenarios.