Editing Computer chess (section)

=== Neural network revolution ===
While [[Artificial neural network|neural networks]] have been used in the [[evaluation function]]s of chess engines since the late 1980s, with programs such as NeuroChess, Morph, Blondie25, Giraffe, [[AlphaZero]], and [[MuZero]],<ref>{{citation|title=Learning to Play the Game of Chess|last=Thurn|first=Sebastian|year=1995|publisher=MIT Press|url=https://proceedings.neurips.cc/paper/1994/file/d7322ed717dedf1eb4e6e52a37ea7bcd-Paper.pdf|access-date=12 December 2021}}</ref><ref>{{citation|title=A Self-Learning, Pattern-Oriented Chess Program|last=Levinson|first=Robert|year=1989|publisher=ICCA Journal|volume=12|issue=4}}</ref><ref name="Giraffe">{{citation|title=Giraffe: Using Deep Reinforcement Learning to Play Chess|last=Lai|first=Matthew|date=4 September 2015|arxiv=1509.01549v1}}</ref><ref>{{Cite arXiv|eprint = 1712.01815|last1 = Silver|first1 = David|last2 = Hubert|first2 = Thomas|last3 = Schrittwieser|first3 = Julian|last4 = Antonoglou|first4 = Ioannis|last5 = Lai|first5 = Matthew|last6 = Guez|first6 = Arthur|last7 = Lanctot|first7 = Marc|last8 = Sifre|first8 = Laurent|last9 = Kumaran|first9 = Dharshan|last10 = Graepel|first10 = Thore|last11 = Lillicrap|first11 = Timothy|last12 = Simonyan|first12 = Karen|last13 = Hassabis|first13 = Demis|title = Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm|year = 2017|class = cs.AI}}</ref><ref name="MuZero">{{cite journal|arxiv=1911.08265|first1=Julian|last1=Schrittwieser|first2=Ioannis|last2=Antonoglou|title=Mastering Atari, Go, chess and shogi by planning with a learned model|last3=Hubert|last9=Hassabis|first11=Timothy|last11=Lillicrap|first10=Thore|last10=Graepel|first9=Demis|first8=Edward|first3=Thomas|last8=Lockhart|last7=Guez|first6=Simon|last6=Schmitt|first5=Laurent|last5=Sifre|first4=Karen|last4=Simonyan|first7=Arthur|journal=Nature|year=2020|volume=588|issue=7839|pages=604–609|doi=10.1038/s41586-020-03051-4|pmid=33361790|bibcode=2020Natur.588..604S|s2cid=208158225}}</ref> neural networks did not become widely adopted by chess engines until the arrival of [[efficiently updatable neural network]]s in the summer of 2020. Efficiently updatable neural networks were originally developed in [[computer shogi]] in 2018 by Yu Nasu,<ref name="Nasu">{{cite web|title=Efficiently Updatable Neural-Network-based Evaluation Function for computer Shogi|author=Yu Nasu|url=https://www.apply.computer-shogi.org/wcsc28/appeal/the_end_of_genesis_T.N.K.evolution_turbo_type_D/nnue.pdf|date=April 28, 2018|language=Japanese}}</ref><ref name="Nasu2">{{cite web|title=Efficiently Updatable Neural-Network-based Evaluation Function for computer Shogi (Unofficial English Translation)|author=Yu Nasu|website=[[GitHub]]|url=https://github.com/asdfjkl/nnue/blob/main/nnue_en.pdf|date=April 28, 2018|language=English}}</ref> and had to be first ported to a derivative of Stockfish called Stockfish NNUE on 31 May 2020,<ref>{{cite web|url=https://github.com/nodchip/Stockfish/releases/tag/stockfish-nnue-2020-05-30|title=Release stockfish-nnue-2020-05-30|website=Github|last=Noda|first=Hisayori|date=30 May 2020|access-date=12 December 2021}}</ref> and integrated into the official Stockfish engine on 6 August 2020,<ref name="Introducing NNUE Evaluation">{{cite web|title=Introducing NNUE Evaluation|url=https://blog.stockfishchess.org/post/625828091343896577/introducing-nnue-evaluation|date=6 August 2020}}</ref><ref name="Joost VandeVondele">{{cite web|title= official-stockfish / Stockfish, NNUE merge|url=https://github.com/official-stockfish/Stockfish/issues/2823#issue-665540175|author=Joost VandeVondele|website=[[GitHub]]|date=July 25, 2020}}</ref> before other chess programmers began to adopt neural networks into their engines.

Some people, such as the [[Royal Society]]'s [[Venki Ramakrishnan]], believe that [[AlphaZero]] led to the widespread adoption of neural networks in chess engines.<ref>{{cite book |title=[[Possible Minds: Twenty-five Ways of Looking at AI]] |date=2019 |publisher=Penguin Press |isbn=978-0525557999 |edition=Kindle| page=174| chapter=[[Venki Ramakrishnan]]: Will Computers Become Our Overlords?}}</ref> However, AlphaZero influenced very few engines to begin using neural networks, and those tended to be new experimental engines such as [[Leela Chess Zero]], which began specifically to replicate the AlphaZero paper. The [[deep neural network]]s used in AlphaZero's evaluation function required expensive [[graphics processing units]], which were not compatible with existing chess engines. The vast majority of chess engines only use [[central processing units]], and computing and processing information on the GPUs require special [[Library (computing)|libraries]] in the backend such as [[Nvidia]]'s [[CUDA]], which none of the engines had access to. Thus the vast majority of chess engines such as [[Komodo (chess)|Komodo]] and [[Stockfish (chess)|Stockfish]] continued to use handcrafted evaluation functions until [[efficiently updatable neural network]]s were ported to computer chess from [[computer shogi]] in 2020, which did not require either the use of GPUs or libraries like CUDA at all. Even then, the neural networks used in computer chess are fairly shallow, and the [[deep reinforcement learning]] methods pioneered by AlphaZero are still extremely rare in computer chess.