AI News, AI: More than Human

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”.

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When steering wheels are removed from vehicles and some jurisdictions begin to legislate against human driving in some regions, it will not be because the proofs of safety in a million scenarios were written out.

It will be because the distributions of recorded accidental deaths, dismemberments, and destructions of property resulting from an AI driver installed in a particular vehicle type, over a sufficiently convincing period of time, indicates its safety over those distributions for human drivers.

If we don't outlaw human driving under these specified conditions for the region under discussion, we are sentencing X number of men, women, children, and elderly pedestrians and passengers per year to a premature death.

Those that insist the systems operate in a way that can be explained will probably, consciously or subconsciously, be motivated by an interest in a perception that the dominance of human beings is manifest destiny.

Art Artificial Intelligence — Exploring AI’s Past, Present And Future

Michalis Michaelides, Consulting Data Scientist, Lorena Bălan, Software Engineer, QuantumBlack This week marked the end of AI: More Than Human exhibition, a three-month celebration of the creative and scientific developments in AI.

Held at London’s Barbican Centre, the exhibition provided the opportunity for artists and scientists to reflect on how artificial intelligence has impacted our day-to-day lives — and what further developments are still to come.

These ideas develop from clumsy self-portraits of humanity, such as the golem — a mythical creature created by man from inanimate objects — into multifaceted images of human intelligence.

The spring of symbolic logic and knowledge classification methods lasted through the 19th and early 20th centuries, before it was soon realised that what it had to offer was quite short of human-level sentience.

In a world where AI is becoming so embedded in our day-to-day lives, should we examine who is creating different models — and which safeguards are put in place to ensure they are deployed fairly and ethically?

What priorities and boundaries should we set for putting these new tools to use, and how can we best protect from undesirable outcomes — for all the prosperity of the Industrial Evolution, it cannot be denied that suffering was also a result.

Far more focused on the subjective, creative possibilities, this section explored how AI could be deployed to compose music, write complex literature pieces, or moderate comments, alongside even more subtle activities such as lip-reading or assigning meaning to text.

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