Skip to main content

Recognizing Artificial Intelligence: The Key to Unlocking Human AI Teams

Abstract

This chapter covers work and corresponding insights gained while building an artificially intelligent coworker, named Charlie. Over the past year, Charlie first participated in a panel discussion and then advanced to speak during multiple podcast interviews, contribute to a rap battle, catalyze a brainstorming workshop, and even write collaboratively (see the author list above). To explore the concepts and overcome the challenges when engineering human–AI teams, Charlie was built on cutting-edge language models, strong sense of embodiment, deep learning speech synthesis, and powerful visuals. However, the real differentiator in our approach is that of recognizing artificial intelligence (AI). The act of “recognizing” Charlie can be seen when we give her a voice and expect her to be heard, in a way that shows we acknowledge and appreciate her contributions; and when our repeated interactions create a comfortable awareness between her and her teammates. In this chapter, we present our approach to recognizing AI, discussing our goals, and describe how we developed Charlie’s capabilities. We also present some initial results from an innovative brainstorming workshop in which Charlie participated with four humans that showed that she could not only participate in a brainstorming exercise but also contribute and influence the brainstorming discussion covering a space of ideas. Furthermore, Charlie helped us formulate ideas for, and even wrote sections of, this chapter.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-77283-3_2
  • Chapter length: 23 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-77283-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   169.99
Price excludes VAT (USA)
Hardcover Book
USD   169.99
Price excludes VAT (USA)
Fig. 2.1
Fig. 2.2
Fig. 2.3
Fig. 2.4
Fig. 2.5

Notes

  1. 1.

    https://www.kaggle.com/naortedgi/twitter-twitts-from-news-providers.

  2. 2.

    https://www.kaggle.com/kazanova/sentiment140.

  3. 3.

    Wang et al. 2012. Harnessing Twitter “big data” for automatic emotion identification.

  4. 4.

    https://federalnewsnetwork.com/federal-tech-talk/2020/03/artificial-intelligence-it-gives-you-possibilities/.

  5. 5.

    https://www.com/mindworks-episode-2/.aptima.com/mindworks-episode-2/.

References

  • Black, S., Gardner, D. G., Pierce, J. L., & Steers, R. (2019). Design thinking, Organizational Behavior.

    Google Scholar 

  • Branwen, G. (2019). Gpt-2 neural network poetry.

    Google Scholar 

  • Brown T, et al. (2020). Language models are few-shot learners, in NeurIPS Proceedings.

    Google Scholar 

  • Brown, V., & Paulus, P. (1996). A simple dynamic model of social factors in group brainstorming. Small Group Research, 27(10), 91–114.

    CrossRef  Google Scholar 

  • Brown, V., Tumeo, M., Larey, T., & Paulus, P. (1998). Modeling cognitive interactions during group brainstorming. Small Group Research, 29(4), 495–526.

    CrossRef  Google Scholar 

  • Camacho, L., & Paulus, P. (1995). The role of social anxiousness in group brainstorming. Journal of Personality and Social Psychology, 68(106), 1071–1080.

    CrossRef  Google Scholar 

  • Cannon-Bowers, J., Tannenbaum, S., Salas, E., & Volpe, C. (1995). Defining competencies and establishing team training requirements. Team Effectiveness and Decision Making in Organizations, 16, 333–380.

    Google Scholar 

  • Case, N. (2018) How to become a centaur. Journal of Design and Science.

    Google Scholar 

  • Chen, M. X., et al. (2019) Gmail smart compose: Real-time assisted writing, in SIGKDD Conference on Knowledge Discovery and Data Mining.

    Google Scholar 

  • Cummings, P., Mullins, R., Moquete, M., & Schurr, N. (2021). HelloWorld! I am Charlie, an artificially intelligent conference panelist. HICSS.

    Google Scholar 

  • Feng, S., & Buxmann, P. (2020). My virtual colleague: A state-of-the-art analysis of conversational agents for the workplace, in Proceedings of the 53rd Hawaii International Conference on System Sciences.

    Google Scholar 

  • Fowler, M., Highsmith, J., et al. (2001). The agile manifesto. Software Development, 9(8), 28–35.

    Google Scholar 

  • Gnewuch, U., Morana, S., Adam, M., & Maedche, A. (2018). Faster is not always better: Understanding the effect of dynamic response delays in human-chatbot interaction. ECIS.

    Google Scholar 

  • Kepuska, V., & Bohouta, G. (2018). Next-generation of virtual personal assistants (microsoft cortana, apple siri, amazon alexa and google home), in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 99–103). IEEE.

    Google Scholar 

  • Keskar, N., McCann, B., Varshney, L., Xiong, C., & Socher, R. (2019). Ctrl: A conditional transformer language model for controllable generation. ArXiv, abs/1909.05858.

    Google Scholar 

  • Larson, J. R. (2010). In search of synergy in small group performance. Psychology Press.

    Google Scholar 

  • Luqi L., & Steigerwald, R. (1992). Rapid software prototyping, in Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences (vol. 2, pp. 470–479). IEEE.

    Google Scholar 

  • von Wolff, R. M., Hobert, S., & Schumann, M. (2019). How may i help you?–state of the art and open research questions for chatbots at the digital workplace, in Proceedings of the 52nd Hawaii International Conference on System Sciences.

    Google Scholar 

  • Nielsen, J. (1994). Guerrilla HCI: Using discount usability engineering to penetrate the intimidation barrier. Cost-Justifying Usability 245–272.

    Google Scholar 

  • Paulus, P., & Dzindolet, M. (1993). Social influence processes in group brainstorming. Journal of Personality and Social Psychology, 64(104), 575–586.

    CrossRef  Google Scholar 

  • Peng, X., Li, S., Frazier, S., & Riedl, M. (2020). Fine-tuning a transformer-based language model to avoid generating non-normative text. arXiv:2001.08764.

  • Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners.

    Google Scholar 

  • Ram, A., Prasad, R., Khatri, C., Venkatesh, A., Gabriel, R., Liu, Q., Nunn, J., Hedayatnia, B., Cheng, M., & Nagar, A., et al. (2018). Conversational AI: The science behind the alexa prize. arXiv:1801.03604.

  • Russet, C. (2020). Turing-NLG: A 17-billion-parameter language model by microsoft.

    Google Scholar 

  • Qin, L., Bosselut, A., Holtzman, A., Bhagavatula, C., Clark, E., & Choi, Y. (2019). Counterfactual story reasoning and generation. arXiv:1909.04076.

  • Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020). Pre-trained models for natural language processing: A survey. arXiv:2003.08271.

  • Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21.

    Google Scholar 

  • Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Penguin.

    Google Scholar 

  • Scherer, K. R. (2013). The functions of nonverbal signs in conversation, in The social and psychological contexts of language (pp. 237–256), Psychology Press.

    Google Scholar 

  • Schuetzler, R. M., Grimes, M., Giboney, J. S., & Buckman, J. (2014). Facilitating natural conversational agent interactions: lessons from a deception experiment.

    Google Scholar 

  • Serfaty, D. (2019). Imagine 2030: Ai-empowered learning. I/ITSEC.

    Google Scholar 

  • Shneiderman, B. (2020). Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), 1–31.

    CrossRef  Google Scholar 

  • Stroebe, W., & Diehl, M. (1994). Why groups are less effective than their members: On productivity losses in idea-generating groups. European Review of Social Psychology, 5(1), 271–303.

    CrossRef  Google Scholar 

  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need 06.

    Google Scholar 

  • Vincent, J. (2019). Openai’s new multitalented AI writes, translates, and slanders. The Verge, 14.

    Google Scholar 

  • Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018). Glue: A multi-task benchmark and analysis platform for natural language understanding 04.

    Google Scholar 

  • Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. (2019). Xlnet: Generalized autoregressive pretraining for language understanding 06.

    Google Scholar 

  • Zhang, Y., Sun, S., Galley, M., Chen, Y. -C., Brockett, C., Gao, X., Gao, J., Liu, J., & Dolan, B. (2019). Dialogpt: Large-scale generative pre-training for conversational response generation.

    Google Scholar 

  • Zhou, C., Sun, C., Liu, Z., & Lau, F. (2015). A c-lstm neural network for text classification 11.

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the larger team that has helped make Charlie a reality through design, development, deployment, testing and analysis. This team includes Laura Cassani, Peter Cinibulk, Will Dupree, Deirdre Kelliher, Manuel Moquete, Ryan Mullins, Louis Penafiel.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Patrick Cummings .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Cummings, P., Schurr, N., Naber, A., Charlie, Serfaty, D. (2021). Recognizing Artificial Intelligence: The Key to Unlocking Human AI Teams. In: Lawless, W.F., Mittu, R., Sofge, D.A., Shortell, T., McDermott, T.A. (eds) Systems Engineering and Artificial Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-77283-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77283-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77282-6

  • Online ISBN: 978-3-030-77283-3

  • eBook Packages: Computer ScienceComputer Science (R0)