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'Siri, Find Me A Russian Submarine,' U.S. Navy Asks

#artificialintelligence

Virtual assistants such as Apple's Siri or Amazon's AMZN Alexa have become popular technological helpers. Ask a virtual assistant to find a restaurant or tell you today's weather, a soothing AI voice obligingly responds. So why not a virtual assistant to help the U.S. Navy find Russian and Chinese submarines? The Navy wants a virtual assistant -- like the ones found on consumer smartphones -- to help overloaded sonar operators manage multiple anti-submarine warfare (ASW) systems. In particular, active sonar on Navy cruisers and destroyers come with a variety of settings.


This Tenet Shows Time Travel May Be Possible - Issue 98: Mind

Nautilus

Time travel has been a beloved science-fiction idea at least since H.G. Wells wrote The Time Machine in 1895. The concept continues to fascinate and fictional approaches keep coming, prodding us to wonder whether time travel is physically possible and, for that matter, makes logical sense in the face of its inscrutable paradoxes. Remarkably, last year saw both a science-fiction film that illuminates these questions, and a real scientific result, spelled out in the journal, Classical and Quantum Gravity,1 that may point to answers. The film is writer-director Christopher Nolan's attention-getting Tenet. Like other time travel stories, Tenet uses a time machine.


Opinion/Middendorf: Military risks and potential of artificial intelligence

#artificialintelligence

Former Secretary of the Navy J. William Middendorf II, of Little Compton, lays out the threat posed by the Chinese Communist Party in his recent book, "The Great Nightfall." With the emerging priority of artificial intelligence (AI), China is shifting away from a strategy of neutralizing or destroying an enemy's conventional military assets -- its planes, ships and army units. AI strategy is now evolving into dominating what are termed adversaries' "systems-of-systems" -- the combinations of all their intelligence and conventional military assets. What China would attempt first is to disable all of its adversaries' information networks that bind their military systems and assets. It would destroy individual elements of these now-disaggregated forces, probably with missiles and naval strikes.


Opinion/Middendorf: Military risks and potential of artificial intelligence

#artificialintelligence

Former Secretary of the Navy J. William Middendorf II, of Little Compton, lays out the threat posed by the Chinese Communist Party in his recent book, "The Great Nightfall." With the emerging priority of artificial intelligence (AI), China is shifting away from a strategy of neutralizing or destroying an enemy's conventional military assets -- its planes, ships and army units. AI strategy is now evolving into dominating what are termed adversaries' "systems-of-systems" -- the combinations of all their intelligence and conventional military assets. What China would attempt first is to disable all of its adversaries' information networks that bind their military systems and assets. It would destroy individual elements of these now-disaggregated forces, probably with missiles and naval strikes.


Opinion: Artificial Intelligence's Military Risks, Potential

#artificialintelligence

Former Secretary of the Navy J. William Middendorf II, of Little Compton, lays out the threat posed by the Chinese Communist Party in his recent book, "The Great Nightfall." With the emerging priority of artificial intelligence (AI), China is shifting away from a strategy of neutralizing or destroying an enemy's conventional military assets -- its planes, ships and army units. AI strategy is now evolving into dominating what are termed adversaries' "systems-of-systems" -- the combinations of all their intelligence and conventional military assets. What China would attempt first is to disable all of its adversaries' information networks that bind their military systems and assets. It would destroy individual elements of these now-disaggregated forces, probably with missiles and naval strikes.


Reinforcement Learning For Constraint Satisfaction Game Agents (15-Puzzle, Minesweeper, 2048, and Sudoku)

arXiv.org Artificial Intelligence

In recent years, reinforcement learning has seen interest because of deep Q-Learning, where the model is a convolutional neural network. Deep Q-Learning has shown promising results in games such as Atari and AlphaGo. Instead of learning the entire Q-table, it learns an estimate of the Q function that determines a state's policy action. We use Q-Learning and deep Q-learning, to learn control policies of four constraint satisfaction games (15-Puzzle, Minesweeper, 2048, and Sudoku). 15-Puzzle is a sliding permutation puzzle and provides a challenge in addressing its large state space. Minesweeper and Sudoku involve partially observable states and guessing. 2048 is also a sliding puzzle but allows for easier state representation (compared to 15-Puzzle) and uses interesting reward shaping to solve the game. These games offer unique insights into the potential and limits of reinforcement learning. The Q agent is trained with no rules of the game, with only the reward corresponding to each state's action. Our unique contribution is in choosing the reward structure, state representation, and formulation of the deep neural network. For low shuffle, 15-Puzzle, achieves a 100% win rate, the medium and high shuffle achieve about 43% and 22% win rates respectively. On a standard 16x16 Minesweeper board, both low and high-density boards achieve close to 45% win rate, whereas medium density boards have a low win rate of 15%. For 2048, the 1024 win rate was achieved with significant ease (100%) with high win rates for 2048, 4096, 8192 and 16384 as 40%, 0.05%, 0.01% and 0.004% , respectively. The easy Sudoku games had a win rate of 7%, while medium and hard games had 2.1% and 1.2% win rates, respectively. This paper explores the environment complexity and behavior of a subset of constraint games using reward structures which can get us closer to understanding how humans learn.


New Navy destroyer-fired laser will change maritime war

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. When the Navy is ready to deploy a new 60kw ship-fired laser weapon from a destroyer later this year, maritime attack strategy and tactics will enter new dimensions of massive warfare on the open seas. Later this year, the Navy reports, the emerging High-Energy Laser with Optical-dazzler and Surveillance (HELIOS) will arm an Arleigh Burke Flight IIA DDG 51 destroyer, following additional land and ocean testing and assessments. This means that Navy destroyers will operate with the ability to incinerate enemy drones with great precision at the speed of light, stunning, burning or simply disabling them.


Navy wants 21 new large undersea and surface attack drones in 5 years

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Navy is getting into drones in a big way, with new plans to add 21 unmanned surface and underwater vessels over the next five years. The Navy just released its 30-year shipbuilding plan, which reflects a growing emphasis on the use of drones in maritime combat. Between now and 2026, the Navy aims to acquire 12 large unmanned surface vessels, one medium unmanned surface vessel and 8 extra-large unmanned underwater vessels, according to the plan.


Navy arms destroyers with new drone, aircraft and missile defenses

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Attacking enemy cruise missiles, fighter jets, helicopters and longer-range high altitude ballistic missiles all present substantial threats to Navy surface ships, especially when multiple attacks arrive simultaneously. By and large, defending against incoming ballistic missiles and air and cruise missiles requires separate defensive systems … until now. A new family of SPY-6 radar systems is being quickly expanded by the U.S. Navy to incorporate a much wider swath of the fleet.


LOREN: Logic Enhanced Neural Reasoning for Fact Verification

arXiv.org Artificial Intelligence

Given a natural language statement, how to verify whether it is supported, refuted, or unknown according to a large-scale knowledge source like Wikipedia? Existing neural-network-based methods often regard a sentence as a whole. While we argue that it is beneficial to decompose a statement into multiple verifiable logical points. In this paper, we propose LOREN, a novel approach for fact verification that integrates both Logic guided Reasoning and Neural inference. The key insight of LOREN is that it decomposes a statement into multiple reasoning units around the central phrases. Instead of directly validating a single reasoning unit, LOREN turns it into a question-answering task and calculates the confidence of every single hypothesis using neural networks in the embedding space. They are aggregated to make a final prediction using a neural joint reasoner guided by a set of three-valued logic rules. LOREN enjoys the additional merit of interpretability -- it is easy to explain how it reaches certain results with intermediate results and why it makes mistakes. We evaluate LOREN on FEVER, a public benchmark for fact verification. Experiments show that our proposed LOREN outperforms other previously published methods and achieves 73.43% of the FEVER score.