Four short links: 4 Dec 2020
NAND Game, Game AI, In-Database Machine Learning, and Datastores at Scale
- NAND Game — You start with a single component, the nand gate. Using this as the fundamental building block, you will build all other components necessary. (See also NAND to Tetris)
- Facebook’s Game AI — today we are unveiling Recursive Belief-based Learning (ReBeL), a general RL+Search algorithm that can work in all two-player zero-sum games, including imperfect-information games. ReBeL builds on the RL+Search algorithms like AlphaZero that have proved successful in perfect-information games. Unlike those previous AIs, however, ReBeL makes decisions by factoring in the probability distribution of different beliefs each player might have about the current state of the game, which we call a public belief state (PBS). In other words, ReBeL can assess the chances that its poker opponent thinks it has, for example, a pair of aces.
- In-Database Machine Learning — We demonstrate our claim by implementing tensor algebra and stochastic gradient descent using lambda expressions for loss functions as a pipelined operator in a main memory database system. Our approach enables common machine learning tasks to be performed faster than by extended disk-based database systems or as well as dedicated tools by eliminating the time needed for data extraction. This work aims to incorporate gradient descent and tensor data types into database systems, allowing them to handle a wider range of computational tasks.
- Scaling Datastores at Slack with Vitess — Vitess is YouTube’s MySQL horizontal-scaling solution. This article is a really good write-up of what they were doing, why it didn’t work, how they tested the waters with Vitess, and how it’s working for them so far.
