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- NAND Recreation — You begin with a single element, the nand gate. Utilizing this as the elemental constructing block, you’ll construct all different parts crucial. (See additionally NAND to Tetris)
- Fb’s Recreation AI — as we speak we’re unveiling Recursive Perception-based Studying (ReBeL), a basic RL+Search algorithm that may work in all two-player zero-sum video games, together with imperfect-information video games. ReBeL builds on the RL+Search algorithms like AlphaZero which have proved profitable in perfect-information video games. In contrast to these earlier AIs, nonetheless, ReBeL makes selections by factoring within the chance distribution of various beliefs every participant may need in regards to the present state of the sport, which we name a public perception state (PBS). In different phrases, ReBeL can assess the probabilities that its poker opponent thinks it has, for instance, a pair of aces.
- In-Database Machine Studying — We display our declare by implementing tensor algebra and stochastic gradient descent utilizing lambda expressions for loss features as a pipelined operator in a primary reminiscence database system. Our method permits frequent machine studying duties to be carried out sooner than by prolonged disk-based database programs or in addition to devoted instruments by eliminating the time wanted for information extraction. This work goals to include gradient descent and tensor information sorts into database programs, permitting them to deal with a wider vary of computational duties.
- Scaling Datastores at Slack with Vitess — Vitess is YouTube’s MySQL horizontal-scaling resolution. This text is a very good write-up of what they had been doing, why it didn’t work, how they examined the waters with Vitess, and the way it’s working for them to date.
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