South Korea will soon roll out a pilot project to use artificial intelligence, facial recognition and thousands of CCTV cameras to track the movement of people infected with the coronavirus, despite concerns about the invasion of privacy. Reuters reports: The nationally funded project in Bucheon, one of the country’s most densely populated cities on the outskirts of Seoul, is due to become operational in January, a city official told Reuters. The system uses an AI algorithms and facial recognition technology to analyze footage gathered by more than 10,820 CCTV cameras and track an infected person’s movements, anyone they had close contact with, and whether they were wearing a mask, according to a 110-page business plan from the city submitted to the Ministry of Science and ICT (Information and Communications Technology), and provided to Reuters by a parliamentary lawmaker critical of the project.

The Bucheon official said the system should reduce the strain on overworked tracing teams in a city with a population of more than 800,000 people, and help use the teams more efficiently and accurately. […] The Ministry of Science and ICT said it has no current plans to expand the project to the national level. It said the purpose of the system was to digitize some of the manual labour that contact tracers currently have to carry out. The Bucheon system can simultaneously track up to ten people in five to ten minutes, cutting the time spent on manual work that takes around half an hour to one hour to trace one person, the plan said.

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The American military news site Defense One shares a cautionary tale from top U.S. Air Force Major General Daniel Simpson (assistant deputy chief of staff for intelligence, surveillance, and reconnaissance). Simpson describes their experience with an experimental AI-based target recognition program that had seemed to be performing well:

Initially, the AI was fed data from a sensor that looked for a single surface-to-surface missile at an oblique angle, Simpson said. Then it was fed data from another sensor that looked for multiple missiles at a near-vertical angle. “What a surprise: the algorithm did not perform well. It actually was accurate maybe about 25 percent of the time,” he said.

That’s an example of what’s sometimes called brittle AI, which “occurs when any algorithm cannot generalize or adapt to conditions outside a narrow set of assumptions,” according to a 2020 report by researcher and former Navy aviator Missy Cummings. When the data used to train the algorithm consists of too much of one type of image or sensor data from a unique vantage point, and not enough from other vantages, distances, or conditions, you get brittleness, Cummings said. In settings like driverless-car experiments, researchers will just collect more data for training. But that can be very difficult in military settings where there might be a whole lot of data of one type — say overhead satellite or drone imagery — but very little of any other type because it wasn’t useful on the battlefield…

Simpson said the low accuracy rate of the algorithm wasn’t the most worrying part of the exercise. While the algorithm was only right 25 percent of the time, he said, “It was confident that it was right 90 percent of the time, so it was confidently wrong. And that’s not the algorithm’s fault. It’s because we fed it the wrong training data.”

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The artificial intelligence (AI) program DeepMind has gotten closer to proving a math conjecture that’s bedeviled mathematicians for decades and revealed another new conjecture that may unravel how mathematicians understand knots. Live Science reports: The two pure math conjectures are the first-ever important advances in pure mathematics (or math not directly linked to any non-math application) generated by artificial intelligence, the researchers reported Dec. 1 in the journal Nature. […] The first challenge was setting DeepMind onto a useful path. […] They focused on two fields: knot theory, which is the mathematical study of knots; and representation theory, which is a field that focuses on abstract algebraic structures, such as rings and lattices, and relates those abstract structures to linear algebraic equations, or the familiar equations with Xs, Ys, pluses and minuses that might be found in a high-school math class.

In understanding knots, mathematicians rely on something called invariants, which are algebraic, geometric or numerical quantities that are the same. In this case, they looked at invariants that were the same in equivalent knots; equivalence can be defined in several ways, but knots can be considered equivalent if you can distort one into another without breaking the knot. Geometric invariants are essentially measurements of a knot’s overall shape, whereas algebraic invariants describe how the knots twist in and around each other. “Up until now, there was no proven connection between those two things,” [said Alex Davies, a machine-learning specialist at DeepMind and one of the authors of the new paper], referring to geometric and algebraic invariants. But mathematicians thought there might be some kind of relationship between the two, so the researchers decided to use DeepMind to find it. With the help of the AI program, they were able to identify a new geometric measurement, which they dubbed the “natural slope” of a knot. This measurement was mathematically related to a known algebraic invariant called the signature, which describes certain surfaces on knots.

In the second case, DeepMind took a conjecture generated by mathematicians in the late 1970s and helped reveal why that conjecture works. For 40 years, mathematicians have conjectured that it’s possible to look at a specific kind of very complex, multidimensional graph and figure out a particular kind of equation to represent it. But they haven’t quite worked out how to do it. Now, DeepMind has come closer by linking specific features of the graphs to predictions about these equations, which are called Kazhdan-Lusztig (KL) polynomials, named after the mathematicians who first proposed them. “What we were able to do is train some machine-learning models that were able to predict what the polynomial was, very accurately, from the graph,” Davies said. The team also analyzed what features of the graph DeepMind was using to make those predictions, which got them closer to a general rule about how the two map to each other. This means DeepMind has made significant progress on solving this conjecture, known as the combinatorial invariance conjecture.

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FIFA will trial its new automatic offside technology next week at the Arab Cup as a test to potentially use it at the World Cup in Qatar next year. From a report: As per the Times, the technology relies on an artificial intelligence (AI) system sending an instant message to VAR when a player is offside, with the official then left to determine if a player has interfered with the passage of play or not. The technology will be used at all six stadiums used in the Arab Cup — which also takes place in Qatar — and comes after several trials behind closed doors took place at the likes of the Etihad Stadium and the Allianz Arena. Despite being able to relay an instant message to VAR, the technology will only be classed as semi-automated as the verdict will be sent to VAR and not the referee himself.

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