Quellen & Bildnachweis

Quellen

• Azevedo, F. A. C. et al. (2009): Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology 513 (5), 532–541. https://doi.org/10.1002/cne.21974
• Balasubramanian, V. (2021): Brain power. Proceedings of the National Academy of Sciences 118 (32), e2107022118. https://doi.org/10.1073/pnas.2107022118
• Brown, T. et al. (2020): Language Models are Few-Shot Learners. NeurIPS 2020. https://arxiv.org/abs/2005.14165
• Campbell, M., Hoane, A. J. & Hsu, F. (2002): Deep Blue. Artificial Intelligence 134 (1–2), 57–83. https://doi.org/10.1016/S0004-3702(01)00129-1
• CERN: Web@30 – A short history of the Web (abgerufen November 2026). https://web30.web.cern.ch/web-history.html
• Deng, J. et al. (2009): ImageNet – A Large-Scale Hierarchical Image Database. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 248–255. https://doi.org/10.1109/CVPR.2009.5206848
• Deutsches Patent- und Markenamt: Konrad Zuse. Reihe „Meilensteine – Computerpioniere“ (abgerufen November 2026). https://www.dpma.de/english/our_office/publications/milestones/computerpioneers/konradzuse/index.html
• Google DeepMind (2016): AlphaGo – Match gegen Lee Sedol, Seoul, März 2016. https://deepmind.google/research/breakthroughs/alphago/
• Hu, K. (2023): ChatGPT sets record for fastest-growing user base – analyst note. Reuters, 2. Februar 2023 (zitiert eine UBS-Analyse). https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
• IBM: The IBM punched card. IBM Heritage (abgerufen November 2026). https://www.ibm.com/history/punched-card
• IEEE / Engineering and Technology History Wiki: Milestones – RAMAC, 1956. https://ethw.org/Milestones:RAMAC,_1956
• IEEE Computer Society (2011): Compatible Time-Sharing System (1961–1973). Fiftieth Anniversary Commemorative Overview. https://history.computer.org/pubs/2011-06-ctss.pdf
• Intel: The Story of the Intel 4004 – Intel’s First Microprocessor. Intel Museum (abgerufen November 2026). https://www.intel.com/content/www/us/en/history/museum-story-of-intel-4004.html
• Jain, S. & Wallace, B. C. (2019): Attention is not Explanation. Proceedings of NAACL-HLT 2019. https://arxiv.org/abs/1902.10186
• Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012): ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25 (NIPS 2012). https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
• Lapuschkin, S. et al. (2019): Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications 10, 1096. https://doi.org/10.1038/s41467-019-08987-4
• Lefkowitz, M. (2019): Professor’s perceptron paved the way for AI – 60 years too soon. Cornell Chronicle, 25. September 2019. https://news.cornell.edu/stories/2019/09/professors-perceptron-paved-way-ai-60-years-too-soon
• Lighthill, J. (1973): Artificial Intelligence – A General Survey. In: Artificial Intelligence: a paper symposium. London: Science Research Council.
• McCarthy, J., Minsky, M. L., Rochester, N. & Shannon, C. E. (1955): A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Nachdruck in AI Magazine 27 (4), 2006. https://ojs.aaai.org/index.php/aimagazine/article/view/1904
• McDermott, J. (1982): R1 – A Rule-Based Configurer of Computer Systems. Artificial Intelligence 19 (1), 39–88. https://doi.org/10.1016/0004-3702(82)90021-2
• Meta (2024): Introducing Meta Llama 3 – The most capable openly available LLM to date. Blogbeitrag, 18. April 2024. https://ai.meta.com/blog/meta-llama-3/
• Minsky, M. & Papert, S. (1969): Perceptrons – An Introduction to Computational Geometry. Cambridge, MA: MIT Press.
• Nvidia (2024): NVIDIA Blackwell Platform Arrives to Power a New Era of Computing. Pressemitteilung, 18. März 2024. https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing
• OpenAI (2022): Introducing ChatGPT. Blogbeitrag, 30. November 2022. https://openai.com/index/chatgpt/
• OpenAI (2024): Learning to reason with LLMs. Blogbeitrag, 12. September 2024. https://openai.com/index/learning-to-reason-with-llms/
• OpenAI Help Center: What are tokens and how to count them? (Faustregel für englische Texte: ein Token entspricht etwa vier Zeichen; abgerufen November 2026). https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them
• Radford, A. et al. (2019): Language Models are Unsupervised Multitask Learners. OpenAI.
• Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986): Learning representations by back-propagating errors. Nature 323, 533–536. https://doi.org/10.1038/323533a0
• Russakovsky, O. et al. (2015): ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115 (3), 211–252. https://arxiv.org/abs/1409.0575
• Searle, J. R. (1980): Minds, Brains, and Programs. Behavioral and Brain Sciences 3 (3), 417–424.
• Silver, D. et al. (2016): Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489. https://doi.org/10.1038/nature16961
• Simon, H. A. (1960): The New Science of Management Decision. New York: Harper & Row; nachgedruckt in: The Shape of Automation for Men and Management (1965). Zur Herkunft des Zitats: Quote Investigator, 11. November 2020. https://quoteinvestigator.com/2020/11/11/ai-can-do/
• Turing, A. M. (1950): Computing Machinery and Intelligence. Mind 59 (236), 433–460.
• U.S. Census Bureau (2016): History and the Census – Herman Hollerith and Mechanical Tabulation. Census History Stories, Januar 2016. https://www.census.gov/about/history/stories/monthly/2016/january-2016.html
• Vaswani, A. et al. (2017): Attention Is All You Need. Advances in Neural Information Processing Systems 30 (NeurIPS). https://arxiv.org/abs/1706.03762
• Weizenbaum, J. (1966): ELIZA – A Computer Program for the Study of Natural Language Communication Between Man and Machine. Communications of the ACM 9 (1), 36–45. https://doi.org/10.1145/365153.365168
• Weizenbaum, J. (1976): Computer Power and Human Reason – From Judgment to Calculation. San Francisco: W. H. Freeman. Deutsch: Die Macht der Computer und die Ohnmacht der Vernunft (1977).

Bildnachweis

• „IBM System/360 Mainframe“ – Foto: Erik Pitti, Lizenz: CC BY 2.0. Quelle: Wikimedia Commons
• „Nachbau einer Hollerith-Zählmaschine (Computer History Museum)“ – Foto: Adam Schuster, Lizenz: CC BY 2.0. Quelle: Wikimedia Commons
• „Lochkarte mit einer Zeile eines Fortran-Programms“ – Foto: Arnold Reinhold, Lizenz: CC BY-SA 2.5. Quelle: Wikimedia Commons
• „Nachbau der Zuse Z3 im Deutschen Museum, München“ – Foto: Venusianer (Wikipedia), Lizenz: CC BY-SA 3.0. Quelle: Wikimedia Commons
• „Glen Beck und Betty Snyder programmieren den ENIAC“ – Foto: U.S. Army, Lizenz: gemeinfrei. Quelle: Wikimedia Commons
• „Plattenstapel der IBM 350 (RAMAC) im Computer History Museum“ – Foto: Arnold Reinhold, Lizenz: CC BY-SA 3.0. Quelle: Wikimedia Commons
• „Terminal DEC VT100 (Living Computer Museum)“ – Foto: Jason Scott, Lizenz: CC BY 2.0. Quelle: Wikimedia Commons
• „IBM PC von 1981 (Computer History Museum)“ – Foto: The wub, Lizenz: CC BY-SA 4.0. Quelle: Wikimedia Commons
• „Der erste Webserver: NeXT-Rechner von Tim Berners-Lee am CERN“ – Foto: Coolcaesar (Wikipedia), Lizenz: CC BY-SA 3.0. Quelle: Wikimedia Commons
• „Deep Blue im Computer History Museum“ – Foto: Erik Pitti, Lizenz: CC BY 2.0. Quelle: Wikimedia Commons
• „Mark I Perceptron, Abbildung aus dem Bedienhandbuch von 1960“ – Foto: John C. Hay, Albert E. Murray, Lizenz: gemeinfrei. Quelle: Wikimedia Commons
• „Joseph Weizenbaum, 1982“ – Foto: Rochester Institute of Technology, Lizenz: gemeinfrei. Quelle: Wikimedia Commons
• „Rechenzentrum NERSC (National Energy Research Scientific Computing Center), Kalifornien“ – Foto: Derrick Coetzee, Lizenz: CC0. Quelle: Wikimedia Commons

Alle Grafiken und Diagramme sind eigens für dieses Buch gezeichnet.