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Approaching AI: A Practical Guide to Understanding and Using AI for HCI

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Artificial Intelligence in HCI (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14050))

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Abstract

Artificial intelligence (AI), is an important evolution in computer science that is only beginning to take hold within the HCI communities. But while the science of AI was twice pronounced dead, it continues to evolve, so do the number of definitions, concepts, applications, and theories that are included in the study of AI. Understanding the definitions and concepts, the functions, terms and relationships associated with this sub-discipline of computer science can be challenging. Today, HCI researchers have an opportunity to begin to apply AI concepts to designing interactions and interfaces that will represent an evolution to the way humans use computers. However, because of the complexities and seemingly disconnected research efforts, HCI researchers must develop a clear and practical understanding of AI — the discipline, concepts, technologies, and terminology — to effectively develop the safe and trusted AI applications of the future. Towards this goal, this paper presents a high-level overview of AI, its history, and the key components, terms, and technologies that currently represent the constantly evolving science. Our goal is to motivate and support the adoption of AI as a safe and trusted layer of computer interactions towards the development of a new paradigm for HCI research.

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References

  1. Asimov, I., Robot, I.: Doubleday science fiction, Doubleday, Garden City, NY (1950)

    Google Scholar 

  2. Beaudouin-Lafon, M.: Designing interaction, not interfaces. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 15–22. AVI 2004, Association for Computing Machinery, New York, NY, USA (2004)

    Google Scholar 

  3. Bengio, Y., Lecun, Y., Hinton, G.: Deep learning for AI. Commun. ACM 64(7), 58–65 (2021)

    Article  Google Scholar 

  4. Black, E., et al.: Reasoning and interaction for social artificial intelligence. AI Commun. 35(4), 309–325 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  5. Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1), 245–271 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  6. Brandão, M., Mansouri, M., Magnusson, M.: Editorial: responsible robotics. Front. Robot. AI 9, 937612 (2022). https://doi.org/10.3389/frobt.2022.937612

    Article  Google Scholar 

  7. Brooks, R.A.: Elephants don’t play chess. Robot. Auton. Syst. 6(1), 3–15 (1990). https://doi.org/10.1016/S0921-8890(05)80025-9. https://www.sciencedirect.com/science/article/pii/S0921889005800259. Designing Autonomous Agents

  8. Brooks, R.A.: Intelligence without representation. Artif. Intell. 47(1), 139–159 (1991). https://doi.org/10.1016/0004-3702(91)90053-M. https://www.sciencedirect.com/science/article/pii/000437029190053M

  9. Cambria, E., White, B.: Jumping NLP curves: a review of natural language processing research. IEEE Comput. Intell. Mag. 9(2), 48–57 (2014). https://doi.org/10.1109/MCI.2014.2307227

    Article  Google Scholar 

  10. Cheng, X., Lin, X., Shen, X., Zarifis, A., Mou, J.: The dark sides of AI. Electron. Mark. 32, 11–15 (2022)

    Article  Google Scholar 

  11. Cimiano, P., Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web 8(3), 489–508 (2017)

    Google Scholar 

  12. Civit, M., Civit-Masot, J., Cuadrado, F., Escalona, M.J.: A systematic review of artificial intelligence-based music generation: scope, applications, and future trends. Exp. Syst. Appl. 209, 118190 (2022). https://doi.org/10.1016/j.eswa.2022.118190. https://www.sciencedirect.com/science/article/pii/S0957417422013537

  13. Google Cloud. https://console.cloud.google.com/getting-started

  14. Collings, E., Ghahramani, Z.: LaMDA: our breakthrough conversation technology (2021). https://blog.google/technology/ai/lamda/

  15. Computer History Museum. https://www.computerhistory.org/chess/first-tests/

  16. Craven, M., et al.: Learning to construct knowledge bases from the world wide web. Artif. Intell. 118(1), 69–113 (2000)

    Article  MATH  Google Scholar 

  17. Ehsan, U., Liao, Q.V., Muller, M., Riedl, M.O., Weisz, J.D.: Expanding explainability: towards social transparency in AI systems. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. CHI 2021, Association for Computing Machinery, New York, NY, USA (2021)

    Google Scholar 

  18. Endriss, U., et al.: Autonomous agents and multiagent systems: perspectives on 20 years of AAMAS. AI Matt. 7(3), 29–37 (2022)

    Article  Google Scholar 

  19. Grandinetti, J.: Examining embedded apparatuses of AI in Facebook and Tiktok. AI and Society, pp. 1–14 (2021)

    Google Scholar 

  20. Haenlein, M., Kaplan, A.: A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. California Manag. Rev. 61(4), 5–14 (2019). https://doi.org/10.1177/0008125619864925. http://journals.sagepub.com/doi/10.1177/0008125619864925

  21. Hinton, G.: The next generation of neural networks. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 1. SIGIR 2020, Association for Computing Machinery, New York, NY, USA (2020)

    Google Scholar 

  22. IBM: What is artificial intelligence (AI)? https://www.ibm.com/topics/artificial-intelligence. Accessed 10 Feb 2023

  23. IBM: (2012). http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/

  24. Ji, S., Pan, S., Cambria, E., Marttinen, P., Yu, P.S.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494–514 (2022)

    Article  MathSciNet  Google Scholar 

  25. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  26. Macpherson, T., et al.: Natural and artificial intelligence: a brief introduction to the interplay between AI and neuroscience research. Neural Netw. Off. J. Int. Neural Netw. Soc. 144, 603–613 (2021)

    Google Scholar 

  27. McLean, S., Read, G.J.M., Thompson, J., Baber, C., Stanton, N.A., Salmon, P.M.: The risks associated with artificial general intelligence: a systematic review. J. Exper. Theoret. Artif. Intell. 35(4), 1–15 (2021). https://doi.org/10.1080/0952813X.2021.1964003

  28. Mehdi, Y.: Reinventing search with a new AI-powered Microsoft Bing and Edge, your copilot for the web. https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/ (2023)

  29. Midjourney. https://www.midjourney.com/home/

  30. Mirsky, Y., Lee, W.: The creation and detection of deepfakes: a survey. ACM Comput. Surv. 54(1), 1–41 (2021)

    Google Scholar 

  31. Moor, J.: The Dartmouth college artificial intelligence conference: the next fifty years. AI Mag. 27(4), 87 (2006)

    Google Scholar 

  32. Murphy, R.R.: Introduction to AI Robotics, second edition. MIT Press (2019)

    Google Scholar 

  33. Nader, K., Toprac, P., Scott, S., Baker, S.: Public understanding of artificial intelligence through entertainment media. AI ’I &’ Society (2022)

    Google Scholar 

  34. Newell, A., Simon, H.: The logic theory machine-a complex information processing system. IRE Trans. Inf. Theory 2(3), 61–79 (1956)

    Article  Google Scholar 

  35. OpenAI. https://openai.com/

  36. OpenAI. https://openai.com/dall-e-2/

  37. Oxford English Dictionary. https://www.oed.com/view/Entry/271625?

  38. PyTorch. https://pytorch.org

  39. Radanliev, P., De Roure, D., Maple, C., Ani, U.: Super-forecasting the ‘technological singularity’ risks from artificial intelligence. Evol. Syst. 13, 747–757 (2022)

    Article  Google Scholar 

  40. Read, G.J., O’Brien, A., Stanton, N.A., Salmon, P.M.: Learning lessons for automated vehicle design: Using systems thinking to analyse and compare automation-related accidents across transport domains. Saf. Sci. 153, 105822 (2022)

    Article  Google Scholar 

  41. Russell, S., Norvig, P.: Artificial intelligence: a modern approach. Prentice Hall, 3 edn. (2010)

    Google Scholar 

  42. Shipman, F.M., Marshall, C.C.: Ownership, privacy, and control in the wake of cambridge analytica: the relationship between attitudes and awareness. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–12. CHI 2020, Association for Computing Machinery, New York, NY, USA (2020)

    Google Scholar 

  43. Thagard, P.: Parallel computation and the mind-body problem. Cogn. Sci. 10(3), 301–318 (1986)

    Article  Google Scholar 

  44. Trummer, I.: From BERT to GPT-3 codex: harnessing the potential of very large language models for data management. Proc. VLDB Endow. 15(12), 3770–3773 (2022). https://doi.org/10.14778/3554821.3554896

  45. Vinyals, O., Le, Q.V.: A neural conversational model. CoRR abs/1506.05869 (2015)

    Google Scholar 

  46. Weiser, M.: The computer for the 21st century. Sci. Am. 265(3), 94–104 (1991)

    Article  Google Scholar 

  47. Wertheimer, T.: Blake Lemoine: Google fires engineer who said AI tech has feelings. BBC News (2022). https://www.bbc.com/news/technology-62275326

  48. Wikipedia. https://en.wikipedia.org/w/index.php?title=List_of_datasets_for_machine-learning_research &oldid=1135282041 (2023). Page Version ID: 1135282041

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Correspondence to Maria Karam .

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Karam, M., Luck, M. (2023). Approaching AI: A Practical Guide to Understanding and Using AI for HCI. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14050. Springer, Cham. https://doi.org/10.1007/978-3-031-35891-3_32

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  • DOI: https://doi.org/10.1007/978-3-031-35891-3_32

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