Michael Runcieman

Jeremy Hsu writing for Scientific American

So Pluribus instead deployed its depth-limited search, which considers how opponents might choose among only four general betting strategies: the precomputed blueprint, one biased toward folding, another biased toward calling and a fourth biased toward raising. This modified search helps explain why Pluribus’s success in six-player poker required relatively minimal computing resources and memory in comparison with past superhuman achievements in gaming AIs. Specifically, during live poker play, Pluribus ran on a machine with just two central CPUs and 128 gigabytes of memory. “It’s amazing this can be done at all, and second, that it can be done with no [graphics processing units] and no extreme hardware,” Sandholm says. By comparison, DeepMind’s famous AlphaGo program used 1,920 CPUs and 280 GPUs during its 2016 matches against top professional Go player Lee Sedol.

Sean Hollister writing for The Verge

Even if you say “no” to one app when it asks for permission to see those personally identifying bits of data, it might not be enough: a second app with permissions you have approved can share those bits with the other one or leave them in shared storage where another app — potentially even a malicious one — can read it. The two apps might not seem related, but researchers say that because they’re built using the same software development kits (SDK), they can access that data, and there’s evidence that the SDK owners are receiving it. It’s like a kid asking for dessert who gets told “no” by one parent, so they ask the other parent.