An artificial intelligence algorithm has defeated a human F-16 fighter pilot in a virtual dogfight simulation. The Aug. 20 event was the finale of the Pentagon research agency's AI air combat competition. The algorithm, developed by Heron Systems, easily defeated the fighter pilot in all five rounds that capped off a yearlong competition hosted by the Defense Advanced Research Projects Agency. The competition, called the AlphaDogfight Trials, was part of DARPA's Air Combat Evolution program, which is exploring automation in air-to-air combat and looking to improve human trust in AI systems. "It's easy to go down the wrong path of thinking that that is either A) definitive in some way as to what the future of [basic fighter maneuvers will be]; or B) that it is a bad outcome," said Justin Mock of DARPA, a fighter pilot and commentator for the trials.

Jaynarayan H. Lala (jay.lala@rtx.com) is a Senior Principal Engineering Fellow at Raytheon Technologies, San Diego, CA, USA. Carl E. Landwehr (carl.landwehr@gmail.com) is a Research Scientist at George Washington University and a Visiting Professor at University of Michigan, Ann Arbor, MI, USA. John F. Meyer (jfm@umich.edu) is a Professor Emeritus of Computer Science and Engineering at University of Michigan, Ann Arbor, MI, USA. This Viewpoint is derived from material produced as part of the Intelligent Vehicle Dependability and Security (IVDS) project of IFIP Working Group 10.4.

Shifting artificial intelligence to the "edge" of the network could transform computing . . . and everyday life.

Automated machine learning is likely to introduce two critical problems. Fortunately, vendors are introducing tools to tackle both of them. Adoption of automated machine learning -- tools that help data scientists and business analysts (and even business users) automate the construction of machine learning models -- is expected to increase over the next few years because these tools simplify model building. For example, in some of the tools, all the user needs to do is specify the outcome or target variable of interest along with the attributes believed to be predictive. The automated machine learning (autoML) platform picks the best model.

It is one of the most important sites in the history of computing and of Britain's victory against Nazism. But now, for the second time in little more than a decade, the future of Bletchley Park hangs in the balance. Over the weekend, it emerged that the museum at the wartime code-breaking centre was facing a financial crisis because of the coronavirus - and that meant it was preparing to lay off 35 people, a third of its workforce. After being forced to close for over three months, it's now opened with reduced capacity due to social distancing regulations and the museum is on course to lose £2m ($2.6m). Iain Standen, the chief executive of Bletchley Park, said the principal strength of the successful museum and visitor attraction that had been built over recent years was its people: "However, the economic impact of the current crisis is having a profound effect on the Trust's ability to survive.

This is the first in a three-part series. In the already fast-changing world of HR, the ongoing COVID-19 pandemic is creating unimagined twists and turns as 2020 progresses, leading to unprecedented attention on HR technology to help employers manage these new challenges. No emerging technology arguably has had more impact on the evolution and refinement of the pandemic workplace than artificial intelligence--which is expected to continue in the months and years ahead. One HR area that has benefited the most from AI-based solutions is workforce management, mainly in recruiting for employers whose business sectors continued to thrive, or in managing challenges such as furloughs and layoffs for the sectors hit hardest by COVID-19. According to Greg Moran, CEO at OutMatch, a SaaS-based talent intelligence platform, the movement toward HR digitization, with the use of AI and machine learning, was already well underway at the start of the year.

Imagine the following situation: A company wants to teach an artificial intelligence (AI) to recognise a horse on photos. To this end, it uses several thousand images of horses to train the AI until it is able to reliably identify the animal even on unknown images. The AI learns quickly – it is not clear to the company how it is making its decisions but this is not really an issue for the company. It is simply impressed by how reliably the process works. Researchers talk in these cases about confounders – which are confounding factors that should actually have nothing to do with the identification process.

The algorithms build on FAIR's work in January of this year, when an agent was trained in Habitat to navigate unfamiliar environments without a map. Using just a depth-sensing camera, GPS, and compass data, it learned to enter a space much as a human would, and find the shortest possible path to its destination without wrong turns, backtracking, or exploration. The first of these new algorithms can now build a map of the space at the same time, allowing it to remember the environment and navigate through it faster if it returns. The second improves the agent's ability to map the space without needing to visit every part of it. Having been trained on enough virtual environments, it is able to anticipate certain features in a new one; it can know, for example, that there is likely to be empty floor space behind a kitchen island without navigating to the other side to look.

It is an increasingly common modern annoyance: arriving at the front of the queue to pay in a shop, pulling out a smartphone for a hygienic contact-free payment, and staring down at an error message because your phone fails to recognise your masked face. As more and more nations mandate masks to prevent the spread of coronavirus, technology companies are scrambling to keep up with the changing world. But some experts are warning that the change may have to start with users themselves. Apple's Face ID is the most well-known example of a consumer facial verification system. The technology, which uses a grid of infrared dots to measure the physical shape of a user's face, secures access to the company's iPhones and iPads, as well as other features such as Apple Pay.

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies. To optimize operating conditions of large scale semiconductor quantum devices, a large parameter space has to be explored. Here, the authors report a machine learning algorithm to navigate the entire parameter space of gate-defined quantum dot devices, showing about 180 times faster than a pure random search.