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2020 | OriginalPaper | Chapter

9. Kreative Künstliche Intelligenz und Emotionen

Authors : Gerhard Paaß, Dirk Hecker

Published in: Künstliche Intelligenz

Publisher: Springer Fachmedien Wiesbaden

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Zusammenfassung

Dieses Kapitel zeigt, dass tiefe neuronale Netze (TNN) auf kreative Art neuartige Bilder, Texte, Musik und Dialoge erzeugen können. Bei Bildern sind generative adversariale Netze (GAN) in der Lage, Bilder mit bestimmten Eigenschaften oder Stilmerkmalen zu generieren. Zudem können sie Bilder eines Typs in einen anderen Typ übertragen, z.B. ein Foto in ein Gemälde. Für die Erzeugung von Texten gibt es mittlerweile Sprachmodelle, welche neue komplexe Geschichten erfinden und in flüssiger Sprache formulieren können. Musik-TNN werden mit den Noten von Musikstücken trainiert und können neue Musikstücke „komponieren“, die nach Ansicht von Fachleuten eine gute Qualität erreichen. Zum Ende des Kapitels werden noch intelligente Sprachassistenten diskutiert, die in der Lage sind, die emotionale Befindlichkeit des Gesprächspartners in seinen Dialogbeiträgen zu erkennen. Sie können darauf adäquat reagieren und kreative und fokussierte Antworten geben, sodass sich der Gegenüber verstanden fühlt und motiviert ist, das Gespräch weiter zu führen. Im asiatischen Raum gibt es derartige Chatbots mit Hunderten von Millionen Nutzern.

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Adiwardana, D., et al. 2020. Towards a Human-like Open-Domain Chatbot. [Online] 2020. [Zitat vom: 10. 02. 2020.] arXiv preprint arXiv:2001.09977.

Ba, J. L., Kiros, J. R., and Hinton, G. E. 2016. Layer normalization. [Online] 2016. [Zitat vom: 16. 05. 2019.] arXiv preprint arXiv:1607.06450.

BayLearn. 2019. Music Transformer: Generating Music with Long-Term Structure. [Online] 2019. [Zitat vom: 19. 05. 2019.] https://www.reddit.com/r/MachineLearning/comments/a604mb/p_music_transformer_generating_music_with/.

Brilee, A. 2019. Assessment of MuseNet. [Online] 2019. [Zitat vom: 18. 05. 2019.] https://news.ycombinator.com/item?id=19749767.

Brock, A., Donahue, J. und Simonyan, K. 2019. Large scale gan training for high fidelity natural image synthesis. ICLR. 2019.

Brown, Tom B.; Mann, Benjamin; Ryder, Nick; Subbiah, Melanie; Kaplan, Jared; Dhariwal, Prafulla; Neelakantan, Arvind; Shyam, Pranav; Sastry, Girish; Askell, Amanda; Agarwal, Sandhini; Herbert-Voss, Ariel; Krueger, Gretchen; Henighan, Tom; Child, Rewon; Ramesh, Aditya; Ziegler, Daniel M.; Wu, Jeffrey; Winter, Clemens; Hesse, Christopher; Chen, Mark; Sigler, Eric; Litwin, Mateusz; Gray, Scott; Chess, Benjamin; Clark, Jack; Berner, Christopher; McCandlish, Sam; Radford, Alec; Sutskever, Ilya; Amodei, Dario (July 22, 2020). „Language Models are Few-Shot Learners“. arXiv:2005.14165.

Cheng, W. F., et al. 2018. Image inspired poetry generation in xiaoice. [Online] 2018. [Zitat vom: 29. 12. 2019.] arXiv preprint arXiv:1808.03090

Child, R., et al. 2019. Generating Long Sequences with Sparse Transformers. [Online] 2019. [Zitat vom: 18. 05. 2019.] arXiv preprint arXiv:1904.10509

Chintala, S., et al. 2018. How to Train a GAN? Tips and tricks to make GANs work. [Online] 2018. https://cloud.tencent.com/developer/article/1164396

Code_GPT-2. 2019. Github. [Online] 2019. [Zitat vom: 14. 05. 2019.] https://github.com/openai/gpt-2.

Cohen, Noam. 2019. Will California’s New Bot Law Strengthen Democracy? The New Yorker. 2019, 02.07.2019.

Dathathri, S., et al. 2020. Plug and play language models: a simple approach to controlled text generation. [Online] 2020. arXiv preprint arXiv:1912.02164

di Nuovo, Alessandro. 2018. How robot carers could be the future for lonely elderly people. Independent. [Online] 06. 12. 2018. [Zitat vom: 01. 09. 2019.] https://www.independent.co.uk/life-style/gadgets-and-tech/features/robot-carer-elderly-people-loneliness-ageing-population-care-homes-a8659801.html.

Dredge, S. 2019. Music Created by Artificial Intelligence is better than you Think. [Online] 2019. [Zitat vom: 19. 05. 2019.] https://medium.com/s/story/music-created-by-artificial-intelligence-is-better-than-you-think-ce73631e2ec5.

Elgammal, A., et al. 2017. Can: Creative adversarial networks, generating „art“ by learning about styles and deviating from style norms. [Online] 2017. [Zitat vom: 10. 01. 2020.] arXiv preprint arXiv:1706.07068

Fan, A., Lewis, M. und Dauphin, Y. 2018. Hierarchical neural story generation. [Online] 2018. arXiv preprint arXiv:1805.04833

Fernández, J. D. und Vico, F. 2013. AI methods in algorithmic composition: A comprehensive survey. Journal of Artificial Intelligence Research, 48, 513–582. 2013.

Gehrmann, S., et al. 2018. End-to-end content and plan selection for natural language generation. E2E NLG Challenge System Descriptions. 2018.

Glaser, April. 2016. Pepper, the emotional Robot, learns how to Feel like an American. Wired. [Online] 06. 07. 2016. [Zitat vom: 01. 09. 2019.] https://www.wired.com/2016/06/pepper-emotional-robot-learns-feel-like-american/.

Goodfellow, I., et al. 2014. Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680). 2014.

Guo, X., et al. 2018. Group-Level Emotion Recognition using Hybrid Deep Models based on Faces, Scenes, Skeletons and Visual Attentions. Proceedings of the 2018 on International Conference on Multimodal Interaction (pp. 635–639). ACM. 2018.

Hindupur, A. 2018. The GAN Zoo. [Online] 2018. https://github.com/hindupuravinash/the-gan-zoo.

Huang, C. Z. A., et al. 2018. Music Transformer: Generating Music with Long-Term Structure. [Online] 2018. [Zitat vom: 19. 05. 2019.] https://openreview.net/pdf?id=rJe4ShAcF7.

Huang, C.-Z., Simon, I. und Dinculescu, M. 2019. Music Transformer: Generating Music with Long-Term Structure. [Online] 2019. [Zitat vom: 19. 05. 2019.] https://magenta.tensorflow.org/music-transformer.

Isola, P., et al. 2017. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125–1134). 2017.

Karras, T., et al. 2018. Progressive growing of gans for improved quality, stability, and variation. ICLR 2018. 2018.

King, Adam. 2019. OpenAI’s new machine learning model. [Online] 2019. [Zitat vom: 13. 11. 2019.] https://talktotransformer.com/.

Köchel, Ludwig Ritter von. 1862. Chronologisch-thematisches Verzeichnis sämtlicher Tonwerke Wolfgang Amade Mozarts. Nebst Angabe der verloren gegangenen, angefangenen, übertragenen, zweifelhaften und unterschobenen Compositionen desselben. s.l. : Breitkopf & Härtel., 1862.

Kuyda, Eugenia. 2019. Replika. [Online] 2019. [Zitat vom: 01. 09. 2019.] https://replika.ai/.

Ledig, C., et al. 2017. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681–4690). 2017.

Miroshnichenko, A. 2018. AI to Bypass Creativity. Will Robots Replace Journalists? (The Answer Is „Yes“). Information, 9(7), 183. 2018.

Molteni, Megan. 2017. The Chatbot Therapist will see you now. Wired. [Online] 06. 07 2017. [Zitat vom: 01. 09. 2019.] https://www.wired.com/2017/06/facebook-messenger-woebot-chatbot-therapist/.

Pasquarelli, Adrianne. 2019. Chase commits to AI after machines outperform humans in copywriting trials. Adage. [Online] 30. 07. 2019. [Zitat vom: 10. 01. 2020.] https://adage.com/article/cmo-strategy/chase-commits-ai-after-machines-outperform-humans-copywriting-trials/2187606.

Payne, C. 2019. MuseNet. [Online] 2019. [Zitat vom: 18. 05. 2019.] https://openai.com/blog/musenet/.

Peiser, Jacyln. 2019. The Rise of the Robot Reporter. The New York Times. 5. Feb. 2019.

Radford, A., et al. 2019. Better Language Models and their Implications. OpenAI. [Online] 2019. [Zitat vom: 14. 05. 2019.] https://openai.com/blog/better-language-models/.

Radford, A., et al. 2019. Language Models are Unsupervised Multitask Learners. [Online] 18. 02. 2019. https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf.

Radford, A., Metz, L. und Chintala, S. 2016. Unsupervised representation learning with deep convolutional generative adversarial networks. ICLR 2016. 2016.

Roller, S., et al. 2020. Recipes for building an open-domain chatbot. [Online] 28. 04. 2020. [Zitat vom: 01. 05. 2020.] https://arxiv.org/pdf/2004.13637.

Rosset, Corby. 2020. Turing-NLG: A 17-billion-parameter language model by Microsoft. Microsoft Research Blog. [Online] 13. 02. 2020. [Zitat vom: 10. 04. 2020.] https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/.

Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. 2019. Megatron-lm: Training multi-billion parameter language models using gpu model parallelism. [Online] 2019. [Zitat vom: 27. 01. 2020.] arXiv preprint arXiv:1909.08053

Shum, H. Y., He, X. D. und Li, D. 2018. From Eliza to XiaoIce: challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering, 19(1), 10–26. 2018.

Vardaan. 2019. AI to Steal Fashion Model Jobs? New AI Able to Generate Entire Bodies of People Who Don’t Exist. [Online] 2019. [Zitat vom: 02. 09. 2019.] https://www.indianweb2.com/2019/05/05/ai-to-steal-fashion-model-jobs-new-ai-able-to-generate-entire-bodies-of-people-who-dont-exist/.

Vig, J. 2019. OpenAI GPT-2: Understanding Language Generation through Visualization. [Online] 2019. [Zitat vom: 17. 05. 2019.] https://towardsdatascience.com/openai-gpt-2-understanding-language-generation-through-visualization-8252f683b2f8.

Vincent, J. 2019. The first AI-generated textbook shows what robot writers are actually good at. [Online] 2019. [Zitat vom: 10. 01. 2020.] https://www.theverge.com/2019/4/10/18304558/ai-writing-academic-research-book-springer-nature-artificial-intelligence.

Vincent, James. 2019. OpenAI has published the text-generating AI it said was too dangerous to share. The Verge. [Online] 13. 11. 2019. https://www.theverge.com/2019/11/7/20953040/openai-text-generation-ai-gpt-2-full-model-release-1-5b-parameters.

Wang, C., Li, M. und Smola, A. J. 2019. Language Models with Transformers. [Online] 2019. [Zitat vom: 27. 05. 2019.] arXiv preprint arXiv:1904.09408

Wolf, S. 2018. CycleGAN: Learning to Translate Images (Without Paired Training Data). [Online] 2018. https://towardsdatascience.com/cyclegan-learning-to-translate-images-without-paired-training-data-5b4e93862c8d.

Zhang, H., et al. 2017. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision (pp. 5907–5915) 2017.

Zhou, L., et al. 2018. The design and implementation of XiaoIce, an empathetic social chatbot. [Online] 2018. arXiv preprint arXiv:1812.08989

Zhu, H., et al. 2018. XiaoIce Band:A Melody and Arrangement Generation Framework for Pop Music. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2837–2846). ACM. 2018.

Zhu, J. Y., et al. 2017. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2223–2232). 2017.

Title: Kreative Künstliche Intelligenz und Emotionen
Authors: Gerhard Paaß
Dirk Hecker
Publisher: Springer Fachmedien Wiesbaden
Book: Künstliche Intelligenz
Print ISBN: 978-3-658-30210-8

Electronic ISBN: 978-3-658-30211-5

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-658-30211-5_9

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