Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training // https://arxiv.org/abs/2108.06209

1941

Radford A., Kim J. W., Xu T., Brockman G., McLeavey C., Sutskever I. (2022). Robust Speech Recognition via Large-Scale Weak Supervision // https://arxiv.org/abs/2212.04356

1942

Dean J. (2022). 3 ways AI is scaling helpful technologies worldwide / Google blog, Nov 02, 2022 // https://blog.google/technology/ai/ways-ai-is-scaling-helpful/

1943

Zhang Y., Han W., Qin J., Wang Y., Bapna A., Chen Z., Chen N., Li B., Axelrod V., Wang G., Meng Z., Hu K., Rosenberg A., Prabhavalkar R., Park D. S., Haghani P., Riesa J., Perng G., Soltau H., Strohman T., Ramabhadran B., Sainath T., Moreno P., Chiu C., Schalkwyk J., Beaufays F., Wu Y. (2022). Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages // https://arxiv.org/abs/2303.01037

1944

Silver D., Huang A., Maddison C. J., Guez A., Sifre L., van den Driessche G., Schrittwieser J., Antonoglou I., Panneershelvam V., Lanctot M., Dieleman S., Grewe D., Nham J., Kalchbrenner N., Sutskever I., Lillicrap T., Leach M., Kavukcuoglu K., Graepel T., Hassabis D. (2016). Mastering the game of Go with deep neural networks and tree search / Nature, Vol. 529(7587), pp. 484—489 // https://doi.org/10.1038/nature16961

1945

Gibney E. (2016). Go players react to computer defeat / Nature // https://doi.org/10.1038/nature.2016.19255

1946

Mets C. (2016). The Sadness and Beauty of Watching Google's AI Play Go / Wired, 11.03.2016 // https://www.wired.com/2016/03/sadness-beauty-watching-googles-ai-play-go/

1947

Zastrow M. (2016). South Korea trumpets $860-million AI fund after AlphaGo 'shock' / Nature // https://doi.org/10.1038/nature.2016.19595

1948

International Joint Conferences on Artificial Intelligence Organization (2017). Deepmind Alphago Team Receives Inaugural Ijcai Marvin Minsky Medal For Outstanding Achievements in AI / Communications of the ACM, October 20, 2017 // https://cacm.acm.org/news/222067-deepmind-alphago-team-receives-inaugural-ijcai-marvin-minsky-medal-for-outstanding-achievements-in-ai/fulltext

1949

Jaap van den Herik H., Iida H., Plaat A. (2014). 8th International Conference, CG 2013, Yokohama, Japan, August 13–15, 2013. Revised Selected Papers. Computers and Games. Lecture Notes in Computer Science 8427 Theoretical Computer Science and General Issues. Springer International Publishing // https://books.google.ru/books?id=52kqBAAAQBAJ

1950

Коровски Ю. (2017). Искусственный интеллект безжалостно обыгрывает в го всех подряд / XX2 век // https://22century.ru/computer-it/41584

1951

Silver D., Schrittwieser J., Simonyan K., Antonoglou I., Huang A., Guez A., Hubert T., Baker L., Lai M., Bolton A., Chen Y., Lillicrap T., Fan H., Sifre L., Driessche G., Graepel T., Hassabis D. (2017). Mastering the game of Go without human knowledge / Nature, Vol. 550 (7676), pp. 354—359 // https://doi.org/10.1038/nature24270

1952

Silver D., Hubert T., Schrittwieser J., Antonoglou I., Lai M., Guez A., Lanctot M., Sifre L., Kumaran D., Graepel T., Lillicrap T., Simonyan K., Hassabis D. (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm // https://arxiv.org/abs/1712.01815

1953

Silver D., Hubert T., Schrittwieser J., Antonoglou I., Lai M., Guez A., Lanctot M., Sifre L., Kumaran D., Graepel T., Lillicrap T., Simonyan K., Hassabis D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play / Science, Vol. 362, Iss. 6419, pp. 1140—1144 // https://doi.org/10.1126/science.aar6404

1954

Lai M. (2015). Giraffe: Using Deep Reinforcement Learning to Play Chess. M. Sc. thesis, Imperial College London // https://arxiv.org/abs/1509.01549v1

1955

* Эта история стала одной из причин того, почему я занялся популяризацией науки в области ИИ. Честно говоря, было больно читать и слушать откровенную ерунду вроде того, что сотни программистов, огромные команды, которые занимались шахматами, теперь не нужны, они теперь уволены. Проблема заключалась в том, что команды из сотен наёмных программистов, занимающиеся компьютерными шахматами, существовали только в воображении автора высказывания, да и сила игры Giraffe была на тот момент далека от силы игры лучших шахматных программ.

1956

Baxter J., Tridgell A., Weaver L. (1998). KnightCap: A chess program that learns by combining TD(λ) with game-tree search / Proc. 15th International Conf. on Machine Learning, pp. 28—36 // https://arxiv.org/abs/cs/9901002

1957

Lüscher M. (2000). Automatic Generation of an Evaluation Function for Chess Endgames // http://www.datacomm.ch/m.luescher/evaluation_function_en.pdf

1958

Stoofvlees / Chess Programming Wiki // https://www.chessprogramming.org/Stoofvlees

1959

Kumar C., Fogel D. B. (1999). Evolution, Neural Networks, Games, and Intelligence / Proceedings of the IEEE, Vol. 87, Iss. 9, pp. 1471—1496 // https://doi.org/10.1109/5.784222

1960

Kumar C., Fogel D. B. (1999). Evolving Neural Networks to Play Checkers without Expert Knowledge / IEEE Transactions on Neural Networks, Vol. 10, Iss. 6, pp. 1382—1391 // https://doi.org/10.1109/72.809083

1961

Kumar C., Fogel D. B. (2001). Evolving an Expert Checkers Playing Program without Using Human Expertise / IEEE Transactions on Evolutionary Computation, Vol. 5, Iss. 4, pp. 422—428 // https://doi.org/10.1109/4235.942536

1962

Sapunov G. (2021). Hardware for Deep Learning. Part 4: ASIC // https://blog.inten.to/hardware-for-deep-learning-part-4-asic-96a542fe6a81

1963

Sapunov G. (2021). Hardware for Deep Learning. Part 4: ASIC // https://blog.inten.to/hardware-for-deep-learning-part-4-asic-96a542fe6a81

1964

Schrittwieser J., Antonoglou I., Hubert T., Simonyan K., Sifre L., Schmitt S., Guez A., Lockhart E., Hassabis D., Graepel T., Lillicrap T., Silver D. (2020). Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model // https://arxiv.org/abs/1911.08265

1965

Ye W., Liu S., Kurutach T., Abbeel P., Gao Y. (2021). Mastering Atari Games with Limited Data // https://arxiv.org/abs/2111.00210

1966

Tian Y., Zitnick L. (2018). Facebook Open Sources ELF OpenGo / facebook research // https://research.fb.com/blog/2018/05/facebook-open-sources-elf-opengo/

1967

Wu D. (2019). Accelerating Self-Play Learning in Go / Jane Street Tech Blog, Feb 28, 2019 //