33:
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302:(Luong et al, 2015) compared the relative performance of global (that of (Bahdanau et al, 2014)) and local (sliding window) attention model architectures for machine translation, and found that a mixed attention architecture had higher quality than global attention, while the use of a local attention architecture reduced translation time.
234:. LSTM became the standard architecture for long sequence modelling until the 2017 publication of Transformers. However, LSTM still used sequential processing, like most other RNNs. Specifically, RNNs operate one token at a time from first to last; they cannot operate in parallel over all tokens in a sequence.
241:
controller (1992) learns to compute a weight matrix for further processing depending on the input. One of its two networks has "fast weights" or "dynamic links" (1981). A slow neural network learns by gradient descent to generate keys and values for computing the weight changes of the fast neural
288:-size output vector, which was then processed by another recurrent network into an output. If the input is long, then the output vector would not be able to contain all relevant information, and the output quality degrades. As evidence, reversing the input sentence improved seq2seq translation.
1666:
Dosovitskiy, Alexey; Beyer, Lucas; Kolesnikov, Alexander; Weissenborn, Dirk; Zhai, Xiaohua; Unterthiner, Thomas; Dehghani, Mostafa; Minderer, Matthias; Heigold, Georg; Gelly, Sylvain; Uszkoreit, Jakob (3 June 2021). "An Image is Worth 16x16 Words: Transformers for Image
Recognition at Scale".
317:. The new model was a seq2seq model where the encoder and the decoder were both 8 layers of bidirectional LSTM. It took nine months to develop, and it achieved a higher level of performance than the statistical approach, which took ten years to develop. In the same year, self-attention
392:
Already in spring 2017, even before the "Attention is all you need" preprint was published, one of the co-authors applied the "decoder-only" variation of the architecture to generate fictitious
Knowledge articles. Transformer architecture is now used in many
283:
word of the source text was processed. Although in theory such a vector retains the information about the whole original sentence, in practice the information is poorly preserved, since the input is processed sequentially by one recurrent network into a
384:, by removing its recurrence to process all tokens in parallel, but preserving its dot-product attention mechanism to keep its text processing performance. Its parallelizability was an important factor to its widespread use in large neural networks.
159:
Six of the eight authors were born outside the United States; the other two are children of two green-card-carrying
Germans who were temporarily in California and a first-generation American whose family had fled persecution, respectively.
256:
The idea of encoder-decoder sequence transduction had been developed in the early 2010s (see for previous papers). The papers most commonly cited as the originators that produced seq2seq are two concurrently published papers from 2014.
424:(2018), an encoder-only Transformer model. In 2019 October, Google started using BERT to process search queries. In 2020, Google Translate replaced the previous RNN-encoderâRNN-decoder model by a Transformer-encoderâRNN-decoder model.
344:
Seq2seq models with attention (including self-attention) still suffered from the same issue with recurrent networks, which is that they are hard to parallelize, which prevented them to be accelerated on GPUs. In 2016,
216:(1995), a RNN which used various innovations to overcome the vanishing gradient problem, allowing efficient learning of long-sequence modelling. One key innovation was the use of an
673:
129:
should be linearly scaled up from 0 to maximal value for the first part of the training (i.e. 2% of the total number of training steps), and to use dropout, to stabilize training.
1729:
Choromanski, Krzysztof; Likhosherstov, Valerii; Dohan, David; Song, Xingyou; Gane, Andreea; Sarlos, Tamas; Hawkins, Peter; Davis, Jared; Mohiuddin, Afroz (19 November 2022),
115:
Some early examples that the team tried their
Transformer architecture on included English-to-German translation, generating Knowledge articles on "The Transformer", and
585:
1516:
Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina (11 October 2018). "BERT: Pre-training of Deep
Bidirectional Transformers for Language Understanding".
1436:
106:
An early design document was titled "Transformers: Iterative Self-Attention and
Processing for Various Tasks", and included an illustration of six characters from the
809:
237:
Modern
Transformers overcome this problem, but unlike RNNs, they require computation time that is quadratic in the size of the context window. The linearly scaling
1692:
1382:
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Chen, Lili; Lu, Kevin; Rajeswaran, Aravind; Lee, Kimin; Grover, Aditya; Laskin, Michael; Abbeel, Pieter; Srinivas, Aravind; Mordatch, Igor (24 June 2021),
1458:
Peng, Bo; Alcaide, Eric; Anthony, Quentin; Albalak, Alon; Arcadinho, Samuel; Biderman, Stella; Cao, Huanqi; Cheng, Xin; Chung, Michael (10 December 2023),
1395:
Parikh, Ankur P.; TÀckström, Oscar; Das, Dipanjan; Uszkoreit, Jakob (25 September 2016). "A Decomposable
Attention Model for Natural Language Inference".
1142:
Chung, Junyoung; Gulcehre, Caglar; Cho, KyungHyun; Bengio, Yoshua (2014). "Empirical
Evaluation of Gated Recurrent Neural Networks on Sequence Modeling".
663:
1770:
Esser, Patrick; Kulal, Sumith; Blattmann, Andreas; Entezari, Rahim; MĂŒller, Jonas; Saini, Harry; Levi, Yam; Lorenz, Dominik; Sauer, Axel (5 March 2024),
1310:
2052:
177:
1288:
Wu, Yonghui; et al. (1 September 2016). "Google's Neural
Machine Translation System: Bridging the Gap between Human and Machine Translation".
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369:
you need". That hypothesis was against conventional wisdom of the time, and even his father, a well-known computational linguist, was skeptical.
950:
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Cho, Kyunghyun; van Merriënboer, Bart; Gulcehre, Caglar; Bahdanau, Dzmitry; Bougares, Fethi; Schwenk, Holger; Bengio, Yoshua (October 2014).
880:
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Bahdanau, Dzmitry; Cho, Kyunghyun; Bengio, Yoshua (19 May 2016). "Neural Machine Translation by Jointly Learning to Align and Translate".
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149:, Lukasz Kaiser, and Illia Polosukhin. All eight authors were "equal contributors" to the paper; the listed order was randomized. The
89:
73:
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Luong, Minh-Thang; Pham, Hieu; Manning, Christopher D. (2015). "Effective Approaches to Attention-based Neural Machine Translation".
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is another LSTM that converts the vector into a sequence of tokens. Similarly, (Cho et al, 2014) was 130M-parameter model that used
1218:
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Gruber, N.; Jockisch, A. (2020), "Are GRU cells more specific and LSTM cells more sensitive in motive classification of text?",
291:(Bahdanau et al, 2014) introduced an attention mechanism to seq2seq for machine translation to solve the bottleneck problem (of
1621:
314:
990:
Christoph von der Malsburg: The correlation theory of brain function. Internal Report 81-2, MPI Biophysical Chemistry, 1981.
1420:
736:
533:
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Jerome A. Feldman, "Dynamic connections in neural networks," Biological Cybernetics, vol. 46, no. 1, pp. 27-39, Dec. 1982.
1121:
Sutskever, Ilya; Vinyals, Oriol; Le, Quoc Viet (14 December 2014). "Sequence to sequence learning with neural networks".
242:
network which computes answers to queries. This was later shown to be equivalent to the unnormalized linear Transformer.
586:"'Attention is All You Need' creators look beyond Transformers for AI at Nvidia GTC: 'The world needs something better'"
455:
339:
209:
leaves the model's state at the end of a long sentence without precise, extractable information about preceding tokens.
187:
119:. These convinced the team that the Transformer is a general purpose language model, and not just good for translation.
205:(1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the
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with an order of magnitude less parameters than LSTMs. One of its authors, Jakob Uszkoreit, suspected that attention
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467:
350:
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869:
Parallel Distributed Processing, Volume 1: Explorations in the Microstructure of Cognition: Foundations, Chapter 2
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230:
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By 2023, all eight authors had left Google and founded their own AI start-ups (except Ćukasz Kaiser, who joined
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output vector), allowing the model to process long-distance dependencies more easily. They called their model
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276:(GRU) instead of LSTM. Later research showed that GRUs are neither better nor worse than LSTMs for seq2seq.
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These early seq2seq models had no attention mechanism, and the state vector is accessible only after the
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Liu, Zhuang; Mao, Hanzi; Wu, Chao-Yuan; Feichtenhofer, Christoph; Darrell, Trevor; Xie, Saining (2022).
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32:
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2006:
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84:, but the authors go further in the paper, foreseeing the technique's potential for other tasks like
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1081:"Learning Phrase Representations using RNN EncoderâDecoder for Statistical Machine Translation"
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Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
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876:
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744:
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See Reprint in Models of Neural Networks II, chapter 2, pages 95-119. Springer, Berlin, 1994.
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In 2017, the original (100M-sized) encoder-decoder transformer model was proposed in the "
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Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
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299:, as it "emulates searching through a source sentence during decoding a translation".
260:(Sutskever et al, 2014) was a 380M-parameter model for machine translation using two
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2018:
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202:
126:
53:
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Since 2020, Transformers have been applied in modalities beyond text, including the
103:. The name "Transformer" was picked because Uszkoreit liked the sound of that word.
64:
proposed in 2014 by Bahdanau et al. It is considered a foundational paper in modern
2284:
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Katharopoulos, Angelos; Vyas, Apoorv; Pappas, Nikolaos; Fleuret, François (2020).
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recurrence is sufficient for language translation, thus the title "attention is
146:
100:
1038:"Transformers are RNNs: Fast autoregressive Transformers with linear attention"
866:
Rumelhart, David E.; McClelland, James L.; Hinton, Geoffrey E. (29 July 1987).
2080:
1978:
1759:. Conference on Computer Vision and Pattern Recognition. pp. 11976â11986.
1356:. Austin, Texas: Association for Computational Linguistics. pp. 551â561.
1087:. Doha, Qatar: Association for Computational Linguistics. pp. 1724â1734.
969:
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is an LSTM that takes in a sequence of tokens and turns it into a vector. The
1799:"Transformer: A Novel Neural Network Architecture for Language Understanding"
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785:
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951:"Learning to control fast-weight memories: an alternative to recurrent nets"
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authored by eight scientists working at Google. The paper introduced a new
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For many years, sequence modelling and generation was done by using plain
36:
An illustration of main components of the transformer model from the paper
2012:
913:
417:
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which used neurons that multiply the outputs of other neurons, so-called
898:"Learning, invariance, and generalization in high-order neural networks"
631:
Shinde, Gitanjali; Wasatkar, Namrata; Mahalle, Parikshit (6 June 2024).
498:
Some architectures, such as RWKV or state space models, avoid the issue.
2046:
1899:
1772:
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
1589:"The inside story of how ChatGPT was built from the people who made it"
634:
Data-Centric Artificial Intelligence for Multidisciplinary Applications
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1059:(2021). "Linear Transformers Are Secretly Fast Weight Programmers".
697:"Transformers: the Google scientists who pioneered an AI revolution"
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Decision Transformer: Reinforcement Learning via Sequence Modeling
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Proceedings of the Annual Meeting of the Cognitive Science Society
616:
454:. The vision transformer, in turn, stimulated new developments in
68:, as the transformer approach has become the main architecture of
31:
1373:
1371:
560:"AI Researcher Who Helped Write Landmark Paper Is Leaving Google"
376:" paper. At the time, the focus of the research was on improving
2159:
1421:"8 Google Employees Invented Modern AI. Here's the Inside Story"
1083:. In Moschitti, Alessandro; Pang, Bo; Daelemans, Walter (eds.).
737:"8 Google Employees Invented Modern AI. Here's the Inside Story"
405:
1821:
224:. Neural networks using multiplicative units were later called
408:(2018) was a bi-directional LSTM that produces contextualized
122:
As of 2024, the paper has been cited more than 100,000 times.
1614:"Improving language understanding with unsupervised learning"
1817:
1348:
Cheng, Jianpeng; Dong, Li; Lapata, Mirella (November 2016).
125:
For their 100M-parameter Transformer model, they suggested
76:. At the time, the focus of the research was on improving
664:"Transformers Revolutionized AI. What Will Replace Them?"
439:, became unexpectedly popular, triggering a boom around
431:
of decoder-only Transformers became state of the art in
2357:
2300:
178:
Transformer (deep learning architecture) § History
1538:"Google: BERT now used on almost every English query"
1350:"Long Short-Term Memory-Networks for Machine Reading"
810:"Meet the $ 4 Billion AI Superstars That Google Lost"
1352:. In Su, Jian; Duh, Kevin; Carreras, Xavier (eds.).
1219:"Sequence to Sequence Learning with Neural Networks"
1217:
Sutskever, Ilya; Vinyals, Oriol; Le, Quoc V (2014).
264:(LSTM). The architecture consists of two parts. The
112:
animated show. The team was named Team Transformer.
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470:(2024), are based on the Transformer architecture.
1223:Advances in Neural Information Processing Systems
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939:
541:Advances in Neural Information Processing Systems
528:; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion;
1814:A concurrent blog post on Google Research blog.
992:http://cogprints.org/1380/1/vdM_correlation.pdf
896:Giles, C. Lee; Maxwell, Tom (1 December 1987).
1460:RWKV: Reinventing RNNs for the Transformer Era
827:Feldman, J. A.; Ballard, D. H. (1 July 1982).
353:, which are easy to parallelize, and achieved
95:The paper's title is a reference to the song "
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2320:
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1013:Hinton, Geoffrey E.; Plaut, David C. (1987).
816:. 13 July 2023 – via www.bloomberg.com.
313:, which replaced the previous model based on
145:, Niki Parmar, Jakob Uszkoreit, Llion Jones,
8:
804:
802:
532:; Kaiser, Ćukasz; Polosukhin, Illia (2017).
1116:
1114:
1112:
1015:"Using Fast Weights to Deblur Old Memories"
829:"Connectionist models and their properties"
412:, improving upon the line of research from
340:Attention (machine learning) § History
201:(RNNs). A well-cited early example was the
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1484:"Was Linguistic A.I. Created by Accident?"
774:"Was Linguistic A.I. Created by Accident?"
155:article highlights the group's diversity:
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1309:Lewis-Kraus, Gideon (14 December 2016).
1691:was invoked but never defined (see the
1381:was invoked but never defined (see the
1253:was invoked but never defined (see the
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676:from the original on 26 September 2023
489:(2014) further reduced its complexity.
349:applied a self-attention mechanism to
1564:"Recent Advances in Google Translate"
709:from the original on 28 December 2023
435:. In 2022, a chatbot based on GPT-3,
7:
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1731:Rethinking Attention with Performers
1165:Frontiers in Artificial Intelligence
767:
765:
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450:, speech recognition, robotics, and
2076:Quantum Artificial Intelligence Lab
1797:Uszkoreit, Jakob (31 August 2017).
1686:
1376:
1248:
875:. Cambridge, Mass: Bradford Books.
2222:Generative pre-trained transformer
1624:from the original on 18 March 2023
1482:Marche, Stephen (23 August 2024).
1439:from the original on 20 March 2024
772:Marche, Stephen (23 August 2024).
695:Murgia, Madhumita (23 July 2023).
458:. Image and video generators like
25:
584:Goldman, Sharon (20 March 2024).
311:Google Neural Machine Translation
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1687:Cite error: The named reference
1377:Cite error: The named reference
1249:Cite error: The named reference
1063:. Springer. pp. 9355â9366.
662:Toews, Rob (3 September 2023).
397:that contribute to the ongoing
315:statistical machine translation
2408:Artificial intelligence papers
1379:2017_Attention_Is_All_You_Need
1055:Schlag, Imanol; Irie, Kazuki;
137:The authors of the paper are:
1:
2423:Artificial intelligence stubs
845:10.1016/S0364-0213(82)80001-3
456:convolutional neural networks
427:Starting in 2018, the OpenAI
27:2017 research paper by Google
2356:. You can help Knowledge by
2299:. You can help Knowledge by
558:Love, Julia (10 July 2023).
188:Timeline of machine learning
1044:. PMLR. pp. 5156â5165.
534:"Attention is All you Need"
433:natural language generation
2439:
2335:
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1311:"The Great A.I. Awakening"
1229:. Curran Associates, Inc.
337:
330:, was proposed for LSTMs.
249:
207:vanishing-gradient problem
185:
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56:architecture known as the
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2211:Attention Is All You Need
1855:
970:10.1162/neco.1992.4.1.131
547:. Curran Associates, Inc.
374:Attention is all you need
199:recurrent neural networks
88:and what is now known as
42:Attention Is All You Need
18:Attention is all you need
328:intra-sentence attention
90:multimodal Generative AI
2350:artificial intelligence
1756:A ConvNet for the 2020s
1645:finetune-transformer-lm
1178:10.3389/frai.2020.00040
404:In language modelling,
334:Parallelizing attention
212:A key breakthrough was
66:artificial intelligence
2352:-related article is a
2295:-related article is a
2086:Tensor Processing Unit
1648:, OpenAI, 11 June 2018
347:decomposable attention
262:long short-term memory
252:Seq2seq § History
246:Attention with seq2seq
182:Seq2seq § History
162:
37:
1593:MIT Technology Review
487:Gated recurrent units
441:large language models
420:. It was followed by
274:gated recurrent units
231:higher-order networks
157:
70:large language models
44:" is a 2017 landmark
35:
1362:10.18653/v1/D16-1053
914:10.1364/AO.26.004972
351:feedforward networks
323:, originally called
222:multiplicative units
97:All You Need Is Love
72:like those based on
2217:Future of Go Summit
1103:10.3115/v1/D14-1179
1057:Schmidhuber, JĂŒrgen
947:Schmidhuber, JĂŒrgen
382:machine translation
218:attention mechanism
82:machine translation
62:attention mechanism
1963:In popular culture
1542:Search Engine Land
1315:The New York Times
958:Neural Computation
464:Stable Diffusion 3
448:vision transformer
359:textual entailment
325:intra-attention or
172:Historical context
86:question answering
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1544:. 15 October 2020
908:(23): 4972â4978.
882:978-0-262-68053-0
833:Cognitive Science
395:generative models
226:sigma-pi networks
16:(Redirected from
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1934:Competitions
1912:AlphaGo Zero
1865:Google Brain
1806:. Retrieved
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109:Transformers
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2227:Google Labs
2053:Transformer
1548:24 November
590:VentureBeat
388:AI boom era
239:fast weight
147:Aidan Gomez
101:the Beatles
58:transformer
2397:Categories
2154:Chinchilla
2081:TensorFlow
1979:The MANIAC
1781:2403.03206
1740:2009.14794
1716:2106.01345
1689:Gulati2020
1674:2010.11929
1618:openai.com
1469:2305.13048
1402:1606.01933
1295:1609.08144
1274:1508.04025
680:3 December
506:References
452:multimodal
429:GPT series
357:result in
293:fixed-size
186:See also:
2172:VideoPoet
2113:Assistant
2007:AlphaStar
2001:AlphaFold
1947:Lee Sedol
1918:AlphaZero
1849:Google AI
1693:help page
1501:27 August
1496:0028-792X
1433:1059-1028
1383:help page
1323:0362-4331
1255:help page
1251:inventors
1236:1409.3215
1205:220252321
1149:1412.3555
1128:1409.3215
1094:1406.1078
1061:ICML 2021
1042:ICML 2020
922:0003-6935
853:0364-0213
814:Bloomberg
791:24 August
786:0028-792X
749:1059-1028
639:CRC Press
617:1409.0473
305:In 2016,
297:RNNsearch
2255:Category
2203:See also
2106:Chatbots
2013:AlphaDev
1893:Versions
1808:9 August
1628:18 March
1622:Archived
1598:6 August
1443:6 August
1437:Archived
1197:33733157
978:16683347
949:(1992).
930:20523475
754:20 March
713:22 March
707:Archived
674:Archived
462:(2021),
418:word2vec
2265:Commons
2119:Sparrow
2047:WaveNet
1971:AlphaGo
1941:Fan Hui
1900:AlphaGo
1886:AlphaGo
1333:22 June
1188:7861254
595:1 April
569:1 April
437:ChatGPT
399:AI boom
378:seq2seq
363:without
270:decoder
266:encoder
133:Authors
117:parsing
78:Seq2seq
2413:Google
2293:Google
2191:(2024)
2174:(2024)
2168:(2023)
2166:Gemini
2162:(2022)
2156:(2022)
2150:(2021)
2144:(2018)
2127:(2023)
2125:Gemini
2121:(2022)
2115:(2016)
2061:(2022)
2055:(2017)
2049:(2016)
2021:(2024)
2015:(2023)
2009:(2019)
2003:(2018)
1982:(2023)
1974:(2017)
1955:(2017)
1953:Ke Jie
1949:(2016)
1943:(2015)
1926:(2019)
1924:MuZero
1920:(2017)
1914:(2017)
1908:(2016)
1906:Master
1902:(2015)
1860:Google
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669:Forbes
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460:DALL-E
180:, and
166:OpenAI
2348:This
2291:This
2182:Other
2148:LaMDA
2069:Other
1994:Other
1776:arXiv
1735:arXiv
1711:arXiv
1669:arXiv
1652:1 May
1573:8 May
1518:arXiv
1464:arXiv
1425:Wired
1397:arXiv
1290:arXiv
1269:arXiv
1231:arXiv
1201:S2CID
1144:arXiv
1123:arXiv
1089:arXiv
974:S2CID
954:(PDF)
873:(PDF)
741:Wired
612:arXiv
537:(PDF)
474:Notes
286:fixed
152:Wired
99:" by
2354:stub
2297:stub
2189:Vids
2160:PaLM
2142:BERT
2059:Gato
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1600:2024
1575:2024
1550:2020
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1492:ISSN
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1429:ISSN
1335:2023
1319:ISSN
1193:PMID
926:PMID
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643:ISBN
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571:2024
468:Sora
422:BERT
416:and
406:ELMo
380:for
355:SOTA
281:last
214:LSTM
1358:doi
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