The United States is encouraging Ukraine to launch cross-border "terrorist" attacks, a Russian official alleged, after Moscow was hit by a series of drone strikes. The White House, meanwhile, said on Tuesday it did not support attacks inside Russia, and that it is still gathering information on the incident. "What are these attempts to hide behind the phrase they are'gathering information'?" Anatoly Antonov, Russia's ambassador to the US, said in remarks published on the Telegram messaging channel. "This is an encouragement for Ukrainian terrorists."

This paper solves the Watchman Route Problem (WRP) on a general discrete graph with Heuristic Search. Given a graph, a line-of-sight (LOS) function, and a start vertex, the task is to (offline) find a (shortest) path through the graph such that all vertices in the graph will be visually seen by at least one vertex on the path. WRP is reminiscent but different from graph covering and mapping problems, which are done online on an unknown graph. We formalize WRP as a heuristic search problem and solve it optimally with an A*-based algorithm. We develop a series of admissible heuristics with increasing difficulty and accuracy. Our heuristics abstract the underlying graph into a disjoint line-of-sight graph (GDLS) which is based on disjoint clusters of vertices such that vertices within the same cluster have LOS to the same specific vertex. We use solutions for the Minimum Spanning Tree (MST) and the Traveling Salesman Problem (TSP) of GDLS as admissible heuristics for WRP. We theoretically and empirically investigate these heuristics. Then, we show how the optimal methods can be modified (by intelligently pruning away large sub-trees) to obtain various suboptimal solvers with and without bound guarantees. These suboptimal solvers are much faster and expand fewer nodes than the optimal solver with only minor reduction in the quality of the solution.

Socially disadvantaged communities have often raised legitimate concerns about being over-policed and under-protected. Now, the rise of AI algorithms driving a myriad of "predictive policing" attempts has threatened to exacerbate the problem. The use of automated algorithms in policing does not do away with inequity; biases might be introduced through how such machines are trained. The black-box nature of state-of-the-art AI algorithms that do not consider the underlying social mechanics of crime, fosters little confidence that such schemes can ultimately thwart crime in any meaningful manner. To make things worse, AI algorithms are demonstrably an effective force-multiplier for the state, manifesting an evermore intrusive control and surveillance apparatus to monitor all aspects of our lives.

We are surrounded by surveillance cameras that record us at every turn. But for the most part, while those cameras are watching us, no one is watching what those cameras observe or record because no one will pay for the armies of security guards that would be required for such a time-consuming and monotonous task. But imagine that all that video were being watched -- that millions of security guards were monitoring them all 24/7. Imagine this army is made up of guards who don't need to be paid, who never get bored, who never sleep, who never miss a detail, and who have total recall for everything they've seen. Such an army of watchers could scrutinize every person they see for signs of "suspicious" behavior.

If you weren't already one of the 45 million people playing Fortnite, last week was when you heard about it. The game--which is really two games, one about building things while killing monsters and surviving as a group, the other about building things and killing each other--is now enough of a cultural smash that the nongaming press has decided it's time to explain it to its uninitiated readers. For the most part, it's identified why the game is such a hit--while overlooking its debt to the many, many similar games and industry practices that came before it. The free-to-play game, originally released in 2017 by Epic Games, has a kid-friendly color palette and (for a shooter game) a notable lack of graphic excess, making it a relatively safe entry point into the world of video games. In its more popular "battle royale" mode--the one you may have seen Drake playing--approximately 100 players travel via hot air balloon–lifted school bus to an island with quirky location names and a vaguely post-apocalyptic vibe.

When I came across a Reddit thread titled "Shower Network: people come in a shower and start shooting videos, streaming them live New AI application questions" there was no force in the universe that could have stopped me from clicking that link. The thread began inauspiciously with "Hi! I'd like to ask a few questions about possible implementation of AI technologies in a project that is going to rely on and invest in AI just to survive not to mention to do well." Most of the time this means the poster doesn't really know what AI is, as I'm sure fellow r/artificial readers would agree. Sometimes, it's just a boring series of questions about Python or AutoML or something else that only developers care about. But every once in a while something so strange happens you can't help but marvel at its oddity.

The Cloverfield Paradox would have been a theatrical failure. It's exactly the sort of B-grade sci-fi critics tend to eviscerate. So how do you generate hype for a movie that's practically doomed? If you're Netflix, you unveil a trailer during the Super Bowl with an unprecedented announcement: you'll be able to watch the film right after the game ends. It's the sort of "holy shit" moment you could only pull off if you're a global entertainment powerhouse.

Khosrow Hadavi, Wen-Ling Hsu, Tony Chen, and Cheoung-Nam Lee Industrial managers, engineers, and technologists have many expectations from artificial intelligence and its application to knowledge-based systems. Although the past decade has witnessed a number of innovative applications of AI in manufacturing, the field is still in its infancy and holds even greater promise for the future. The AAAI Press book Artificial Intelligence Applications in Manufacturing, (from which the following article was selected) presents a number of articles that relate to the enhancement of planning and decision making capabilities in today's automated production environments. Scheduling problems can generally be described as allocating resources to tasks while satisfying a set of constraints (Baker 1974; Conway et al. 1967). More often than not, the constraint sets are large and diverse, the objectives conflict with each other, and the scheduling problems quickly become NPhard.

Artificial intelligence (AI) and machine learning are causing excitement all over the world. Recent reports, such as one from Accenture, claim it has the potential to revolutionise the future of all businesses operations. For instance, research tasks that take hundreds of hours, such as candidate profiling, can now be performed by an AI within seconds. It's no wonder that many businesses are tapping into this trend – the potential savings, in both time and money, are extraordinary. However, what are the consequences of programming AI in today's environment?

How smart should a smart home be before it's worthy of the name? To date, perhaps the term has been too readily applied to homes that are merely high-tech. Automated systems, remote control of appliances from mobile devices, TV and phone over IP--these are all welcome breakthroughs. These technologies are almost synonymous with the smart home and so-called intelligent buildings in general, but there's little or no intelligence to them. For a home to be considered smart, it must in a sense become a robot--a machine capable of, if not true intelligence (and certainly not sentience), sensing data, processing it, drawing conclusions of its own accord, and then acting upon those conclusions.