../../MikeMacBook.jpg

Plain Ben On Twitter I Stuck All Those Doodlestoguns Together

I stuck all those #DoodlesToGuns together and put them all into a Twitter Moment for easier viewing! I stopped counting but there's like 50 of 'em... Thank you to those who took part, supported with kind words, or simply clicked like 💙https://t.co/HkvUz4t0mt pic.twitter.com/v1A5cZGSdy — Ben (@PlainBen) July 14, 2019

RT @JohnHemmings2: Excellent report on the technical vulnerabilities found in #Huawei’s devices, code, and #5G systems. A superb and highly…

(original)

Humans Fold AI Conquers Pokers Final Milestone

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.