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  2. AlphaZero - Wikipedia

    en.wikipedia.org/wiki/AlphaZero

    After four hours of training, DeepMind estimated AlphaZero was playing chess at a higher Elo rating than Stockfish 8; after nine hours of training, the algorithm defeated Stockfish 8 in a time-controlled 100-game tournament (28 wins, 0 losses, and 72 draws). [2] [3] [4] The trained algorithm played on a single machine with four TPUs.

  3. Largest differencing method - Wikipedia

    en.wikipedia.org/wiki/Largest_differencing_method

    LDM always returns a partition in which the largest sum is at most 7/6 times the optimum. [4] This is tight when there are 5 or more items. [2] On random instances, this approximate algorithm performs much better than greedy number partitioning. However, it is still bad for instances where the numbers are exponential in the size of the set. [5]

  4. Leela Chess Zero - Wikipedia

    en.wikipedia.org/wiki/Leela_Chess_Zero

    Leela Chess Zero (abbreviated as LCZero, lc0) is a free, open-source chess engine and volunteer computing project based on Google's AlphaZero engine. It was spearheaded by Gary Linscott, a developer for the Stockfish chess engine, and adapted from the Leela Zero Go engine.

  5. Algorithmic radicalization - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_radicalization

    Algorithmic radicalization is the concept that recommender algorithms on popular social media sites such as YouTube and Facebook drive users toward progressively more extreme content over time, leading to them developing radicalized extremist political views. Algorithms record user interactions, from likes/dislikes to amount of time spent on ...

  6. Incremental learning - Wikipedia

    en.wikipedia.org/wiki/Incremental_learning

    Other algorithms can be adapted to facilitate incremental learning. Examples of incremental algorithms include decision trees (IDE4, [ 1 ] ID5R [ 2 ] and gaenari ), decision rules , [ 3 ] artificial neural networks ( RBF networks , [ 4 ] Learn++, [ 5 ] Fuzzy ARTMAP, [ 6 ] TopoART, [ 7 ] and IGNG [ 8 ] ) or the incremental SVM .

  7. Strongly-polynomial time - Wikipedia

    en.wikipedia.org/wiki/Strongly-polynomial_time

    An algorithm that runs in polynomial time but that is not strongly polynomial is said to run in weakly polynomial time. [2] A well-known example of a problem for which a weakly polynomial-time algorithm is known, but is not known to admit a strongly polynomial-time algorithm, is linear programming.

  8. YouTube's algorithm pushes right-wing, explicit videos ... - AOL

    www.aol.com/news/youtubes-algorithm-pushes-wing...

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  9. Amdahl's law - Wikipedia

    en.wikipedia.org/wiki/Amdahl's_law

    Then we are told that the 1st part is not sped up, so s1 = 1, while the 2nd part is sped up 5 times, so s2 = 5, the 3rd part is sped up 20 times, so s3 = 20, and the 4th part is sped up 1.6 times, so s4 = 1.6. By using Amdahl's law, the overall speedup is