The final round of DARPA's AlphaDogfight Trial is complete, and once again, the winning AI pilot celebrated its victory against a field of virtual contenders by going on to defeat a human F-16 pilot. An AI pilot developed by Heron Systems won the shootout, defeating a fellow AI from Lockheed. All of the simulated fighter battles were restricted to allow use of the nose cannon only, and after the AI vs. AI matches, an anonymous human pilot entered the competition, wearing a VR helmet. The Heron Systems AI defeated him 5 - 0, although he was able to change tactics and survive significantly longer in the final round. Commentators noted the AI's "superhuman" aiming ability produced an edge even as the simulated F-16s circled low to the ground at high speed and would've experienced extreme G forces.

If a ML system uses gender information in credit scoring, then gender information is probably relevant for credit scoring. We all know that women, for example, are more risk averse than men on average and that there are more men with very low IQ's; and more men take part in dangerous activities than can maim them. All those things contribute to credit risk. I looked at some actuarial motorcycle accident data from a Swedish insurance company a couple of years ago, and the accident rate of young men (18-25 maybe) was something like 40 times higher than women in the same age interval. Of course, EU law requires us to offer the same rate to men and women, so we have to ignore this; and thus the women pay more than they should if things were fair.

Editor's Note: The following is a guest post from Arnaud Legrand, head of public sector marketing at Nokia. A virtuous circle exists in hyperconnected cities where economic prosperity, business growth and social well-being combine to become increasingly efficient and effective. Many of the factors that feed into this iterative growth, such as social, health, environmental and business advantages, are difficult to calculate. However, the statistics that prove qualitative benefits – creating new business opportunities, filling talent gaps, improving public health, reducing crime, boosting productivity and addressing income inequality – are more measurable. The ability to establish a measurable framework through which to accelerate transformation is critical in order to justify the time and resources required to take a smart city to the next level.

From a numerical perspective, resolving the wide range of spatiotemporal scales within such physical systems is challenging since extremely small spatial and temporal numerical We propose MeshfreeFlowNet, a novel deep learningbased stencils would be required. In order to alleviate the super-resolution framework to generate continuous computational burden of fully resolving such a wide range (grid-free) spatiotemporal solutions from the low-resolution of spatial and temporal scales, multiscale computational approaches inputs. While being computationally efficient, MeshfreeFlowNet have been developed. For instance, in the subsurface accurately recovers the fine-scale quantities flow problem, the main idea of the multiscale approach of interest. MeshfreeFlowNet allows for: (i) the output is to build a set of operators that map between the unknowns to be sampled at all spatiotemporal resolutions, (ii) a set associated with the computational cells in a fine-grid and the of Partial Differential Equation (PDE) constraints to be imposed, unknowns on a coarser grid. The operators are computed and (iii) training on fixed-size inputs on arbitrarily numerically by solving localized flow problems. The multiscale sized spatiotemporal domains owing to its fully convolutional basis functions have subgrid-scale resolutions, ensuring encoder.

We present an implementation of SOTER, a run-time assurance framework for building safe distributed mobile robotic (DMR) systems, on top of the Robot Operating System (ROS). The safety of DMR systems cannot always be guaranteed at design time, especially when complex, off-the-shelf components are used that cannot be verified easily. SOTER addresses this by providing a language-based approach for run-time assurance for DMR systems. SOTER implements the reactive robotic software using the language P, a domain-specific language designed for implementing asynchronous event-driven systems, along with an integrated run-time assurance system that allows programmers to use unfortified components but still provide safety guarantees. We describe an implementation of SOTER for ROS and demonstrate its efficacy using a multi-robot surveillance case study, with multiple run-time assurance modules. Through rigorous simulation, we show that SOTER enabled systems ensure safety, even when using unknown and untrusted components.

We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which is made available by smart city infrastructure such as bike-sharing systems. The key decision is where to build bike lanes in the existing road network. As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. Traditional bike lane planning approaches often rely on surveys and heuristics. We develop a general and novel optimization framework to guide the bike lane planning from bike trajectories. We formalize the bike lane planning problem in view of the cyclists' utility functions and derive an integer optimization model to maximize the utility. To capture cyclists' route choices, we develop a bilevel program based on the Multinomial Logit model. We derive structural properties about the base model and prove that the Lagrangian dual of the bike lane planning model is polynomial-time solvable. Furthermore, we reformulate the route choice based planning model as a mixed integer linear program using a linear approximation scheme. We develop tractable formulations and efficient algorithms to solve the large-scale optimization problem. Via a real-world case study with a city government, we demonstrate the efficiency of the proposed algorithms and quantify the trade-off between the coverage of bike trips and continuity of bike lanes. We show how the network topology evolves according to the utility functions and highlight the importance of understanding cyclists' route choices. The proposed framework drives the data-driven urban planning scheme in smart city operations management.

We consider a stochastic multi-arm bandit problem where rewards are subject to adversarial corruption. We propose a novel attack strategy that manipulates a UCB principle into pulling some non-optimal target arm $T - o(T)$ times with a cumulative cost that scales as $\sqrt{\log T}$, where $T$ is the number of rounds. We also prove the first lower bound on the cumulative attack cost. Our lower bound matches our upper bound up to $\log \log T$ factors, showing our attack to be near optimal.

Microsoft's latest Flight Simulator entry doesn't do anything small. It's a title that comes on 10 DVDs and allows you to explore the world in almost its entirety. It turns out that scale even extends to its accidental inclusions. Flight Simulator users recently found an unusual landmark: a 212-story monolith towering over an otherwise nondescript suburb in Melbourne, Australia. In Microsoft Flight Simulator a bizarrely eldritch, impossibly narrow skyscraper pierces the skies of Melbourne's North like a suburban Australian version of Half-Life 2's Citadel, and I am -all for it- pic.twitter.com/6AH4xgIAWg

IMAGE: Cover for "What is Artificial Intelligence: A Conversation between an AI Engineer and a Humanities Researcher " view more What do the words'artificial' and'intelligence' mean? And what are the consequences of developing AI? Instead of reiterating received definitions or surveying the field from a disciplinary perspective, Peter and Suman put two differing standpoints into conversation in their new book What is Artificial Intelligence? to engage with these questions and more. Peter is an AI engineer: with his applied approach, he focuses on how to make AI work. Suman is a humanities researcher: his approach is conceptual and so he concentrates on what people and academics mean when they say'AI'. Covering issues such as the meaning of'automation' and'language', What is Artificial Intelligence?

The US Department of Energy (DOE) has announced it will deploy $21 million (£16m) of funding for fusion energy research projects. The finance will enable scientists to take advantage of new artificial intelligence (AI) and machine learning technologies to speed up progress in fusion energy research. Part of the funding will also be allocated to improve operations at the Office of Science fusion facilities by automating data analysis and enabling algorithms. In a statement, DOE said: "AI and machine learning will help us to accelerate progress in fusion and keep American scientists at the forefront of fusion research."