The White Paper on Artificial Intelligence (the "AI White Paper"), recently released by the European Commission, provides the clearest indication yet that the EU is seriously considering regulating the development and deployment of artificial intelligence ("AI"). If adopted, the Commission's proposals would likely increase the already significant compliance burden imposed on technology-focused and technology-dependent businesses operating in the EU, and may lead to significantly divergent practices between the EU and the rest of the world. It is evident from the text of the AI White Paper that the Commission considers that its proposals would help to establish the EU at the centre of global AI technology development and deployment, and to encourage investment in the EU in this space, although it is not entirely clear that a lack of additional regulation is currently holding the EU back. The AI White Paper charts a rough course for the potential future regulation of AI technologies in the EU. It will require much greater detail and refinement before it can realistically progress to full-blown legislation.

A mobility map, which provides maximum achievable speed on a given terrain, is essential for path planning of autonomous ground vehicles in off-road settings. While physics-based simulations play a central role in creating next-generation, high-fidelity mobility maps, they are cumbersome and expensive. For instance, a typical simulation can take weeks to run on a supercomputer and each map requires thousands of such simulations. Recent work at the U.S. Army CCDC Ground Vehicle Systems Center has shown that trained machine learning classifiers can greatly improve the efficiency of this process. However, deciding which simulations to run in order to train the classifier efficiently is still an open problem. According to PAC learning theory, data that can be separated by a classifier is expected to require $\mathcal{O}(1/\epsilon)$ randomly selected points (simulations) to train the classifier with error less than $\epsilon$. In this paper, building on existing algorithms, we introduce an active learning paradigm that substantially reduces the number of simulations needed to train a machine learning classifier without sacrificing accuracy. Experimental results suggest that our sampling algorithm can train a neural network, with higher accuracy, using less than half the number of simulations when compared to random sampling.

The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional spaces (operators). The key innovation in our work is that a single set of network parameters, within a carefully designed network architecture, may be used to describe mappings between infinite-dimensional spaces and between different finite-dimensional approximations of those spaces. We formulate approximation of the infinite-dimensional mapping by composing nonlinear activation functions and a class of integral operators. The kernel integration is computed by message passing on graph networks. This approach has substantial practical consequences which we will illustrate in the context of mappings between input data to partial differential equations (PDEs) and their solutions. In this context, such learned networks can generalize among different approximation methods for the PDE (such as finite difference or finite element methods) and among approximations corresponding to different underlying levels of resolution and discretization. Experiments confirm that the proposed graph kernel network does have the desired properties and show competitive performance compared to the state of the art solvers.

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of pitting and stress corrosion cracking, with a focus on dry storage canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion cracks via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

NASA has equipped its Mars 2020 rover with everything it needs to explore the Red planet, except for a name – until now. Called Perseverance, the rover's title was picked from a'Name the Rover' essay contest that received 28,000 entries from children ranging from kindergartners to high school. The name was revealed on Thursday during a live streaming and was chosen by seventh grader Alex Mathers who's winning essay compared the rover to the human race. 'If you think about it, all of these names of past Mars rovers are qualities we possess as humans.' 'We are always curious, and seek opportunity. We have the spirit and insight to explore the Moon, Mars, and beyond. But, if rovers are to be the qualities of us as a race, we missed the most important thing.

A decade ago in the California high desert, 11 finalists competed in an unprecedented 60-mile race. Robot cars needed to safely and swiftly complete the mission without any human intervention -- while also interacting with human-driven vehicles -- in under six hours. It was the 2007 DARPA Urban Challenge, an autonomous vehicle competition that unofficially kicked off today's self-driving technology initiatives. The vehicles were considered incredible at the time, and looking back, this marked the beginning of a long journey. DARPA ensured a certain level of success by carefully managing scope: Participants agreed to a set of rigorously defined traffic rules, and DARPA eliminated pedestrian and cyclist traffic from the challenge.

The spread of the COVID-19 coronavirus is a fluid situation changing by the day, and even by the hour. The growing worldwide public health emergency is threatening lives, but it's also impacting businesses and disrupting travel around the world. The OECD warns that coronavirus could cut global economic growth in half, and the Federal Reserve will cut the federal interest rates following the worst week for the stock market since 2008. Just how the COVID-19 coronavirus will affect the way we live and work is unclear because it's a novel disease spreading around the world for the first time, but it appears that AI may help fight the virus and its economic impact. A World Health Organization report released last month said that AI and big data are a key part of the response to the disease in China.

With a tank-like continuous track and an angular arm reminiscent of the Pixar lamp, the lightweight PackBot robot was designed to seek out, defuse and dispose of the improvised explosive devices, or IEDs, that killed and injured thousands of coalition soldiers during the wars in Iraq and Afghanistan. Bomb disposal was and is highly dangerous work, but the robot could take on the riskiest parts while its human team controlled it remotely from a safer distance. US Army explosive ordinance disposal technician Phillip Herndon was assigned a PackBot during his first tour in Iraq. Herndon's team named their robot Duncan, after a mission when the robot glitched and began spinning in circles, or doughnuts (doughnuts led to Dunkin Donuts, hence Duncan). His fellow bomb disposal techs named theirs too, and snapped photos of themselves next to robots holding Xbox controllers, dressed in improvised costumes or posing with a drink in their claws.

The writing is on the wall. Every major global governmental agency is warning of the imminent tipping point towards catastrophic climate change, even the world's largest oil company Saudi Aramco is now talking about reaching peak oil within the next 20 years, and the International Energy Agency projects that it will happen in more like 10. Solar and wind are cheaper than ever, and large-scale solar mega-projects are quickly becoming the norm. It makes sense, then, that even the supermajor oil companies are diversifying their portfolios and investing in their own demise--also known as the renewable energy sector. Way back in July, 2017 Oilprice reported that France's Total S.A. was "leading the charge on renewables". At the time, Total's website boasted: "For Total, contributing to the development of renewable energies is as much a strategic choice as an industrial responsibility. We are doing our part to diversify the global energy mix by investing in renewables, with a strategic focus on solar energy and bioenergies."

Here's the thing: All the top-performing Medicare Advantage plans are using, today, right now, some form of advanced analytics and artificial intelligence (AI) to risk-stratify their populations and predict which members will, without intervention, become high cost in the near term. The idea is then to intervene to mitigate risk and stop bad things from happening--bad things that stink if you're the patient and also cost a lot if you're the plan. That's what population health management is all about, after all. Others using AI, right now, to do the kind of predictive analytics that you need to excel at pop health include PCP groups and other providers, mainly those at risk to manage populations or readmissions. In this health care podcast, I talk with Andrew Eye about AI.