"Top Gun" producer Jerry Bruckheimer sees the overall benefit of artificial intelligence. "Anything that makes our lives easier that doesn't take jobs away from people that we work with every day is good for everybody. It gives them a better movie experience. We can make things look more real and things like that," he told Fox News Digital. However, he didn't see the technology eliminating important jobs in the industry.

One of two firefighting helicopters that collided in midair over a Southern California brush fire lacked an electronic warning device that alerts pilots to approaching aircraft -- a critical deficiency, according to at least one former wildland fire pilot. As the National Traffic Safety Board continues to investigate the fatal, Aug. 6 crash of two contract California Department of Forestry and Fire Protection helicopters, a career pilot and advocate for collision avoidance systems is calling attention to the fact that one of the choppers lacked a traffic collision-avoidance system, or TCAS, which audibly alerts pilots when another aircraft is nearby. "I'm frankly shocked that this is not required on contract helicopters to this day," said Juan Browne, a former U.S. Forest Service lead plane pilot who now flies Boeing 777s out of Los Angeles for a major airline. "That's the one last piece of safety equipment that could have prevented this accident," he said. The helicopter crash, which killed three, marks a rare instance in which an aviation battle of a California fire has resulted in a midair collision.

The most interesting TV I've watched recently did not come from a conventional television channel, nor even from Netflix, but from TV coverage of parliament. It was a recording of a meeting of the AI in weapons systems select committee of the House of Lords, which was set up to inquire into "how should autonomous weapons be developed, used and regulated". The particular session I was interested in was the one held on 20 April, during which the committee heard from four expert witnesses – Kenneth Payne, who is professor of strategy at King's College London; Keith Dear, director of artificial intelligence innovation at the computer company Fujitsu; James Black from the defence and security research group of Rand Europe; and Courtney Bowman, global director of privacy and civil liberties engineering at Palantir UK. An interesting mix, I thought – and so it turned out to be. Autonomous weapons systems are ones that can select and attack a target without human intervention. It is believed (and not just by their boosters) that these systems could revolutionise warfare, and may be faster, more accurate and more resilient than existing weapons systems.

This paper presents a general approach for measuring distances between board games within the Ludii general game system. These distances are calculated using a previously published set of general board game concepts, each of which represents a common game idea or shared property. Our results compare and contrast two different measures of distance, highlighting the subjective nature of such metrics and discussing the different ways that they can be interpreted.

A team of scientists has used GPU-accelerated deep learning to show how color can be brought to night-vision systems. In a paper published this week in the journal PLOS One, a team of researchers at the University of California, Irvine led by Professor Pierre Baldi and Dr. Andrew Browne, describes how they reconstructed color images of photos of faces using an infrared camera. The study is a step toward predicting and reconstructing what humans would see using cameras that collect light using imperceptible near-infrared illumination. The study's authors explain that humans see light in the so-called "visible spectrum," or light with wavelengths of between 400 and 700 nanometers. Typical night vision systems rely on cameras that collect infrared light outside this spectrum that we can't see.

Night vision is typically monotone--everything the wearer can see is colored in the same hue, which is mostly shades of green. But by using varying wavelengths of infrared light and a relatively simple AI algorithm, scientists from the University of California, Irvine have been able to bring back some color into these desaturated images. Their findings are published in the journal PLOS ONE this week. Light in the visible spectrum, similar to an FM radio, consists of many different frequencies. Both light and radio are part of the electromagnetic spectrum.

Night-vision cameras convert infrared light – outside the spectrum visible to humans – into visible light so we can "see in the dark". But this infrared information only allows a black-and-white image to be constructed. Now, AI can colourise these images for a more natural feel. Andrew Browne at the University of California, Irvine, and his colleagues used a camera that can detect both visible light and part of the infrared spectrum to take 140 images of different faces. The team then trained a neural network to spot correlations between the way objects appeared in infrared and their colour in the visible spectrum. Once trained, this AI could predict the visible colouring from pure infrared images, even those originally taken in total darkness.

In many board games and other abstract games, patterns have been used as features that can guide automated game-playing agents. Such patterns or features often represent particular configurations of pieces, empty positions, etc., which may be relevant for a game's strategies. Their use has been particularly prevalent in the game of Go, but also many other games used as benchmarks for AI research. Simple, linear policies of such features are unlikely to produce state-of-the-art playing strength like the deep neural networks that have been more commonly used in recent years do. However, they typically require significantly fewer resources to train, which is paramount for large-scale studies of hundreds to thousands of distinct games. In this paper, we formulate a design and efficient implementation of spatial state-action features for general games. These are patterns that can be trained to incentivise or disincentivise actions based on whether or not they match variables of the state in a local area around action variables. We provide extensive details on several design and implementation choices, with a primary focus on achieving a high degree of generality to support a wide variety of different games using different board geometries or other graphs. Secondly, we propose an efficient approach for evaluating active features for any given set of features. In this approach, we take inspiration from heuristics used in problems such as SAT to optimise the order in which parts of patterns are matched and prune unnecessary evaluations. An empirical evaluation on 33 distinct games in the Ludii general game system demonstrates the efficiency of this approach in comparison to a naive baseline, as well as a baseline based on prefix trees.

Game boards are described in the Ludii general game system by their underlying graphs, based on tiling, shape and graph operators, with the automatic detection of important properties such as topological relationships between graph elements, directions and radial step sequences. This approach allows most conceivable game boards to be described simply and succinctly.

In this paper we present a process for automatically generating manuals for board games within the Ludii general game system. This process requires many different sub-tasks to be addressed, such as English translation of Ludii game descriptions, move visualisation, highlighting winning moves, strategy explanation, among others. These aspects are then combined to create a full manual for any given game. This manual is intended to provide a more intuitive explanation of a game's rules and mechanics, particularly for players who are less familiar with the Ludii game description language and grammar.