参考文献

本作は史実に基づくフィクションです(based on historical events)。実在の人物・団体の描写には創作を含み、内面や未公開の発言は作者の解釈です。出典は「参考文献」に記載しています。
本作に登場する実在の研究者・出来事は、下記の公開された論文・提案書に基づいています。作中の会話・心理描写は、これらの公開事実をもとにした創作です。

Arc 1「黎明」

A. M. Turing (1950) Computing Machinery and IntelligenceMind, 59(236), 433–460
J. Weizenbaum (1966) ELIZA — A Computer Program For the Study of Natural Language Communication Between Man and MachineCACM, 9(1), 36–45
K. M. Colby ほか (1972) Turing-like Indistinguishability Tests for the Validation of a Computer Simulation of Paranoid ProcessesArtificial Intelligence, 3, 199–221
C. R. Jones, B. K. Bergen (2024) Does GPT-4 pass the Turing test?NAACL 2024
C. R. Jones, B. K. Bergen (2025) Large Language Models Pass the Turing TestarXiv:2503.23674
J. McCarthy, M. L. Minsky, N. Rochester, C. E. Shannon (1955) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
F. Rosenblatt (1958) The Perceptron: A Probabilistic Model for Information Storage and Organization in the BrainPsychological Review, 65(6), 386–408
M. Minsky, S. Papert (1969) Perceptrons: An Introduction to Computational GeometryMIT Press
E. A. Feigenbaum ほか (1971) On Generality and Problem Solving: A Case Study Using the DENDRAL ProgramMachine Intelligence 6
E. A. Feigenbaum, P. McCorduck (1983) The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the WorldAddison-Wesley
B. G. Buchanan, E. H. Shortliffe (1984) Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming ProjectAddison-Wesley
V. L. Yu ほか (1979) Antimicrobial Selection by a Computer: A Blinded Evaluation by Infectious Diseases ExpertsJAMA, 242(12), 1279–1282
R. Sutton (2019) The Bitter Lessonincompleteideas.net(本人によるエッセイ)
D. E. Rumelhart, G. E. Hinton, R. J. Williams (1986) Learning representations by back-propagating errorsNature, 323, 533–536

Arc 2「覚醒」

K. Fukushima 福島邦彦 (1980) Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in positionBiological Cybernetics, 36(4), 193–202
Y. LeCun ほか (1989) Backpropagation Applied to Handwritten Zip Code RecognitionNeural Computation, 1(4), 541–551
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner (1998) Gradient-Based Learning Applied to Document RecognitionProceedings of the IEEE, 86(11)
G. E. Hinton, S. Osindero, Y.-W. Teh (2006) A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 18(7), 1527–1554
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei 李飛飛 (2009) ImageNet: A Large-Scale Hierarchical Image DatabaseCVPR 2009
A. Krizhevsky, I. Sutskever, G. E. Hinton (2012) ImageNet Classification with Deep Convolutional Neural NetworksNIPS 2012
M. D. Zeiler, R. Fergus (2013) Visualizing and Understanding Convolutional NetworksarXiv:1311.2901
J. Hu, L. Shen, G. Sun (2017) Squeeze-and-Excitation NetworksarXiv:1709.01507
N. Srivastava, G. Hinton ほか (2014) Dropout: A Simple Way to Prevent Neural Networks from OverfittingJMLR, 15, 1929–1958
K. Simonyan, A. Zisserman (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition (VGG)arXiv:1409.1556
C. Szegedy ほか (2014) Going Deeper with Convolutions (GoogLeNet / Inception)arXiv:1409.4842
S. Ioffe, C. Szegedy (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftarXiv:1502.03167
K. He 何愷明, X. Zhang, S. Ren, J. Sun (2015) Deep Residual Learning for Image Recognition (ResNet)arXiv:1512.03385
O. Russakovsky ほか (2015) ImageNet Large Scale Visual Recognition Challenge (ILSVRC)IJCV, 115(3), 211–252
T. Mikolov ほか (2013) Efficient Estimation of Word Representations in Vector Space (word2vec)arXiv:1301.3781
S. Hochreiter, J. Schmidhuber (1997) Long Short-Term MemoryNeural Computation, 9(8), 1735–1780
I. Sutskever, O. Vinyals, Q. V. Le (2014) Sequence to Sequence Learning with Neural NetworksNIPS 2014
D. Bahdanau, K. Cho, Y. Bengio (2014) Neural Machine Translation by Jointly Learning to Align and TranslatearXiv:1409.0473
V. Mnih ほか (2015) Human-level control through deep reinforcement learning (DQN)Nature, 518, 529–533
D. Silver ほか (2016) Mastering the game of Go with deep neural networks and tree searchNature, 529, 484–489
D. Silver ほか (2017) Mastering the game of Go without human knowledge (AlphaGo Zero)Nature, 550, 354–359
A. Vaswani ほか (2017) Attention Is All You NeedNIPS 2017

図解に使ったデータ

Polo Club of Data Science, Georgia Tech Transformer Explainer (github.com/poloclub/transformer-explainer)Self-Attention の図解に出る数値は、同プロジェクトが公開している GPT-2 small の事前計算データから、第 1 ブロックの 12 ヘッド分だけを取り出して使っています。同プロジェクトは MIT License で公開されており、以下がその全文です。
MIT License

Copyright (c) 2022 Polo Club of Data Science

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
OpenAI GPT-2 small (github.com/openai/gpt-2)上記の数値は、GPT-2 small が実際に計算した出力です。GPT-2 は Modified MIT License(Software Copyright (c) 2019 OpenAI)で公開されており、同ライセンスは「本ソフトウェアが生成した内容には著作権表示を含めなくてよい」と定め、GPT-2 を使って作ったものであることを明示するよう求めています。本作はこれに従います。
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