AI News, Artificial intelligence is changing every aspect of war artificial intelligence

Battle algorithmArtificial intelligence is changing every aspect of war

AS THE NAVY plane swooped low over the jungle, it dropped a bundle of devices into the canopy below.

The idea of collecting data from sensors, processing them with algorithms fuelled by ever-more processing power and acting on the output more quickly than the enemy lies at the heart of military thinking across the world’s biggest powers.

similar flurry of activity is under way in China, which wants to lead the world in AI by 2030 (by what measure is unclear), and in Russia, where President Vladimir Putin famously predicted that “whoever becomes the leader in this sphere will become the ruler of the world”.

AI is a broad and blurry term, covering a range of techniques from rule-following systems, pioneered in the 1950s, to modern probability-based machine learning, in which computers teach themselves to carry out tasks.

Deep learning—a particularly fashionable and potent approach to machine learning, involving many layers of brain-inspired neural networks—has proved highly adept at tasks as diverse as translation, object recognition and game playing (see chart).

There is now more of that than ever before—in 2011 alone, the most recent year for which there are data, America’s 11,000-or-so drones sent back over 327,000 hours (37 years) of footage.

In lab-based tests, algorithms surpassed human performance in image classification by 2015 and nearly doubled their performance in a tougher task, object segmentation, which involves picking out multiple objects from single images, between 2015 and 2018, according to Stanford University’s annual index of AI progress.

Earth-i, a British company, can apply machine-learning algorithms from a range of satellites to identify different variants of military aircraft across dozens of bases with over 98% accuracy (see main picture), according to Sean Corbett, a retired air vice-marshal in the Royal Air Force (RAF) who now works for the firm.

“The clever bit”, he says, “is then developing methods to automatically identify what is normal and what is not normal.” By watching bases over time, the software can distinguish routine deployments from irregular movements, alerting analysts to significant changes.

In 2012 leaked documents from the NSA, America’s signals-intelligence agency, described a programme (reassuringly called Skynet), which applied machine learning to Pakistani mobile-phone data in order to pick out individuals who might be couriers for terrorist groups.

“It’s beginning to shift intelligence from the old world, where commanders asked a question and intelligence agencies used collection assets to find the answer, to a world where answers are in...the cloud,” says Sir Richard Barrons, a retired general who commanded Britain’s joint forces until 2016.

Air-force tests on command-and-control planes and transporters showed that such predictive maintenance could reduce unscheduled work by almost a third, which might allow big cuts in the $78bn that the Pentagon currently spends on maintenance.

Northern Arrow, a tool built by UNIQAI, an Israeli AI firm, is one of many products on the market that helps commanders plan missions by crunching large volumes of data on variables such as enemy positions, weapon ranges, terrain and weather—a process that would normally take 12 to 24 hours for soldiers the old-fashioned way by poring over maps and charts.

These “expert system” platforms, such as Northern Arrow and America’s similar CADET software, can work far quicker than human minds—two minutes for CADET compared with 16 person-hours for humans, in one test—but they tend to employ rule-following techniques that are algorithmically straightforward.

In the real world, randomness often gets in the way of making precise predictions, so many modern AI systems combine rule-following with added randomness as a stepping stone to more complex planning.

“For Chinese military strategists, among the lessons learned from AlphaGo’s victories was the fact that an AI could create tactics and stratagems superior to those of a human player in a game that can be compared to a war-game,” wrote Elsa Kania, an expert on Chinese military innovation.

In December 2018 another of DeepMind’s programs, AlphaStar, trounced one of the world’s strongest players in StarCraft II, a video game played in real-time, rather than turn-by-turn, with information hidden from players and with many more degrees of freedom (potential moves) than Go.

“What do we do when AI is applied to military strategy and has calculated the probabilistic inferences of multiple interactions many moves beyond that which we can consider,” asks wing-commander Keith Dear, an RAF intelligence officer, “and recommends a course of action that we don’t understand?” He gives the example of an AI that might propose funding an opera in Baku in response to a Russian military incursion in Moldova—a surreal manoeuvre liable to baffle one’s own forces, let alone the enemy.

Sir Richard Barrons points out that Britain’s defence ministry is already purchasing a technology demonstrator for a cloud-based virtual replication of a complex operating environment—known as a single synthetic environment—essentially a military version of the software that powers large-scale online video games such as “Fortnite”.

Artificial intelligence is changing every aspect of war

AI-enabled weapons could wage war with a speed and complexity that is beyond human understanding A

The Pentagon’s budget for 2020 has lavished almost $1bn on by 2030 (by what measure is unclear), and in Russia, where President Vladimir Putin famously predicted that “whoever becomes the leader in this sphere will become the ruler of the world”.

In February, the Defence Advanced Research Projects Agency ( For all that, most such systems embody intelligence that is narrow and brittle—good at one task in a well-defined environment, but liable to fail badly in unfamiliar settings.

So existing autonomous weapons are comprised of either loitering missiles that smash into radars or quick-firing guns that defend ships and bases.

Earth-i, a British company, can apply machine-learning algorithms from a range of satellites to identify different variants of military aircraft across dozens of bases with over 98% accuracy (see main picture), according to Sean Corbett, a retired air vice-marshal in the Royal Air Force ( Algorithms, of course, are omnivorous and can be fed any sort of data, not just images.

“Bulk data combined with modern analytics make the modern world transparent,” noted Sir Alex Younger, the head of The point of processing information, of course, is to act on it.

And the third way firm, is one of many products on the market that helps commanders plan missions by crunching large volumes of data on variables such as enemy positions, weapon ranges, terrain and weather—a process that would normally take 12 to 24 hours for soldiers the old-fashioned way by poring over maps and charts.

The retired colonels drafted to simulate Iraqi insurgents “got so scared” of the software, notes Boris Stilman, one of its designers, that “they stopped talking to each other and used hand signals instead”.

intelligence officer, “and recommends a course of action that we don’t understand?” He gives the example of an Western governments insist that humans will be “on the loop”, supervising things.

The prospect of accurate and rapid strikes “could erode stability by increasing the perceived risk of surprise attack”, writes Zachary Davis in a recent paper for the Lawrence Livermore National Laboratory.

“If data is the fuel of director General Jack Shanahan expressed his concerns on August 30th: “What I don’t want to see is a future where our potential adversaries have a fully alllibertynews Time is drawing near.

Doesnt change the nuclear war scenario one bit - just conventional Artificial intelligence and warThey may offer super-human speed and precision, but AI-enabled weapons are vulnerable to being hacked or tricked with misleading data I'm at war with my shorts...

With laser this drones are just mosquitoes Good to know my teenagers totally wasted their time building hand-eye coordination playing video games so much.

Zao, a face-swapping app, takes off in China — making AI-powered deepfakes for everyoneFor 30 seconds, anyone in China can now take the place of Leonardo DiCaprio in some of his most iconic roles — and all it takes is a smartphone and a bit of personal data.

Accenture's head of artificial intelligence shares the 4-step plan every company should consider before investing in AIAthina Kanioura says firms shouldn't pursue a tech upgrade as a solution for every problem.

What a waste, automation could save lives in the medical field yet here we have the usual suspect, the Pentagon, taking a concept as beautiful as AI, and believing it’s best apllication is what?

I feel like we're going to reach a point where we rely too much on automated defense systems/ai, and something is going to go wrong/get hacked and we're going to get screwed.

The next tech leap of solar panels will revolutionized markets the same way voice over IP changed the phone industry, subscription rather than meter consumption.

Neanderthals had a propensity for earache, nudging them to their doom

AS THE NAVY airplane swooped low over the jungle, it dropped a bundle of gadgets into the cover beneath.

The thought of gathering knowledge from sensors, processing them with algorithms fuelled by ever-more processing energy and performing on the output extra shortly than the enemy lies on the coronary heart of army pondering the world over’s largest powers.

The same flurry of exercise is underneath method in China, which needs to steer the world in AI by 2030 (by what measure is unclear), and in Russia, the place President Vladimir Putin famously predicted that “whoever turns into the chief on this sphere will change into the ruler of the world”.

AI is a broad and blurry time period, protecting a variety of strategies from rule-following programs, pioneered within the 1950s, to fashionable probability-based machine studying, during which computer systems train themselves to hold out duties.

Deep studying—a very trendy and potent strategy to machine studying, involving many layers of brain-inspired neural networks—has proved extremely adept at duties as numerous as translation, object recognition and sport taking part in (see chart).

In lab-based assessments, algorithms surpassed human efficiency in picture classification by 2015 and practically doubled their efficiency in a harder job, object segmentation, which entails choosing out a number of objects from single photos, between 2015 and 2018, in line with Stanford College’s annual index of AI progress.

Earth-i, a British firm, can apply machine-learning algorithms from a variety of satellites to establish totally different variants of army plane throughout dozens of bases with over 98% accuracy (see primary image), in line with Sean Corbett, a retired air vice-marshal within the Royal Air Pressure (RAF) who now works for the agency.

“The intelligent bit”, he says, “is then growing strategies to robotically establish what’s regular and what’s not regular.” By watching bases over time, the software program can distinguish routine deployments from irregular actions, alerting analysts to vital adjustments.

In 2012 leaked paperwork from the NSA, America’s signals-intelligence company, described a programme (reassuringly referred to as Skynet), which utilized machine studying to Pakistani mobile-phone knowledge with the intention to select people who could be couriers for terrorist teams.

“It’s starting to shift intelligence from the outdated world, the place commanders requested a query and intelligence businesses used assortment belongings to search out the reply, to a world the place solutions are in…the cloud,” says Sir Richard Barrons, a retired basic who commanded Britain’s joint forces till 2016.

Air-force assessments on command-and-control planes and transporters confirmed that such predictive upkeep may scale back unscheduled work by virtually a 3rd, which could permit massive cuts within the $78bn that the Pentagon at the moment spends on upkeep.

Northern Arrow, a instrument constructed by UNIQAI, an Israeli AI agency, is considered one of many merchandise in the marketplace that helps commanders plan missions by crunching giant volumes of information on variables comparable to enemy positions, weapon ranges, terrain and climate—a course of that might usually take 12 to 24 hours for troopers the old school method by poring over maps and charts.

These “skilled system” platforms, comparable to Northern Arrow and America’s comparable CADET software program, can work far faster than human minds—two minutes for CADET in contrast with 16 person-hours for people, in a single take a look at—however they have a tendency to make use of rule-following strategies which can be algorithmically simple.

In the true world, randomness typically will get in the way in which of creating exact predictions, so many fashionable AI programs mix rule-following with added randomness as a stepping stone to extra advanced planning.

The retired colonels drafted to simulate Iraqi insurgents “received so scared” of the software program, notes Boris Stilman, considered one of its designers, that “they stopped speaking to one another and used hand indicators as an alternative”.

“For Chinese language army strategists, among the many classes discovered from AlphaGo’s victories was the truth that an AI may create techniques and stratagems superior to these of a human participant in a sport that may be in comparison with a war-game,” wrote Elsa Kania, an skilled on Chinese language army innovation.

In December 2018 one other of DeepMind’s packages, AlphaStar, trounced one of many world’s strongest gamers in StarCraft II, a online game performed in real-time, relatively than turn-by-turn, with data hidden from gamers and with many extra levels of freedom (potential strikes) than Go.

“What will we do when AI is utilized to army technique and has calculated the probabilistic inferences of a number of interactions many strikes past that which we are able to take into account,” asks wing-commander Keith Pricey, an RAF intelligence officer, “and recommends a plan of action that we don’t perceive?” He provides the instance of an AI that may suggest funding an opera in Baku in response to a Russian army incursion in Moldova—a surreal manoeuvre liable to baffle one’s personal forces, not to mention the enemy.

Sir Richard Barrons factors out that Britain’s defence ministry is already buying a expertise demonstrator for a cloud-based digital replication of a posh working setting—often called a single artificial setting—basically a army model of the software program that powers large-scale on-line video video games comparable to “Fortnite”.

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