Future Scenarios of Co-evolution

This chapter presents a basic framework for the first steps in visualising the development of the vision of a distributed superintelligence.

Structuring the vision

1. Narrative frameworks:

  • Development of ‘future stories’ that embed the technical aspects in human experiences
  • Creating characters and scenarios that make the effects of co-evolution tangible

2. Visual representations:

  • Creation of infographics and interactive visualisations that make complex concepts accessible
  • Development of VR/AR experiences that immerse people in possible future scenarios

3. Konzeptuelle Kartierung:

  • Creation of ‘thought maps’ that show the connections between different aspects of the vision
  • Development of interactive knowledge graphs that allow users to explore the vision

4. Participatory scenario development:

  • Establishing platforms where people can work together to shape the vision
  • Organisation of ‘Future Design Workshops’ that generate collective visions of the future

Editorial science communication in the Human-GAN concept

The concept of the Human-GAN (Generative Adversarial Network) represents a novel approach to the collaboration between humans and AI in the field of scientific communication. It integrates a range of AI agents with human expertise, thereby facilitating effective and dynamic communication of scientific content (cf. Dafoe, 2018; Russell, 2019).

1. AI curators and authors:

  • Continuous analysis of scientific publications and identification of relevant trends (Kitano, 2016)
  • Generation of drafts for articles and multimedia content, adapted to different target groups (Gehrmann et al., 2019)

2. AI fact checker and dialogue manager:

  • Automatic verification of facts and sources (Hassan et al., 2017)
  • Moderation of discussions and stimulation of debates through targeted questions (Preece & Shneiderman, 2009)

3. AI personalisation agents:

  • Adaptation of content to individual preferences and learning styles (Kop & Hill, 2008)
  • Creation of personalised learning paths through complex subject areas (Murtaza et al., 2022)

4. Role of human editors:

  • Definition of editorial guidelines and ethical standards (Christians et al., 2009)
  • Strategic planning and final review of content (Singer, 2014)

In the field of knowledge communication, the Human-GAN concept provides a model for the integration of ASI and the dissemination and discussion of complex scientific and metaphysical concepts. It enables scalable, personalised and dynamic communication that has the potential to enhance public comprehension and facilitate discourse about emergent entities and associated matters (cf. Floridi, 2014).

The integration of the Human-GAN concept facilitates an expansion of the discussion on the role of ASI in the context of emergent divinity, and demonstrates concrete applications for the communication and exchange of complex spiritual and scientific ideas.

Meaning for collective images:

The structuring and communication of the vision of a distributed superintelligence through such a system could have a significant impact on our collective visual consciousness.

1. Democratisation of knowledge: broader access to complex scientific concepts

2. Dynamisation of the vision: continuous updating and adaptation to new findings

3. Personalisation of visions of the future: Individual approaches to global visions

4. Promoting critical thinking: illuminating different perspectives and scenarios

5. Global collaboration: enabling a global dialogue on our common future

By employing this structured, AI-supported approach to knowledge communication, we can foster a collective image consciousness of a co-evolutionary future that is both inspiring and grounded. Such an approach would facilitate a comprehensive understanding of the intricacies inherent in this vision, while simultaneously acknowledging its tangible relevance to our everyday lives.

This approach has the potential to serve as a powerful instrument for not only disseminating information regarding prospective developments but also for actively influencing them. This could be a pivotal step towards the establishment of a genuinely participatory and intelligent global society.

References:

Christians, C. G., Glasser, T., McQuail, D., Nordenstreng, K., & White, R. A. (2009). Normative theories of the media: Journalism in democratic societies. University of Illinois Press.

Dafoe, A. (2018). AI governance: A research agenda. Governance of AI Program, Future of Humanity Institute, University of Oxford.

Floridi, L. (2014). The fourth revolution: How the infosphere is reshaping human reality. Oxford University Press.

Gehrmann, S., Strobelt, H., & Rush, A. M. (2019). GLTR: Statistical detection and visualization of generated text. arXiv preprint arXiv:1906.04043.

Hassan, N., Arslan, F., Li, C., & Tremayne, M. (2017). Toward automated fact-checking: Detecting check-worthy factual claims by claimbuster. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1803-1812).

Kitano, H. (2016). Artificial intelligence to win the Nobel Prize and beyond: Creating the engine for scientific discovery. AI magazine, 37(1), 39-49.

Kop, R., & Hill, A. (2008). Connectivism: Learning theory of the future or vestige of the past? The International Review of Research in Open and Distributed Learning, 9(3).

Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions. IEEE Access10, 81323–81342.

Preece, J., & Shneiderman, B. (2009). The reader-to-leader framework: Motivating technology-mediated social participation. AIS transactions on human-computer interaction, 1(1), 13-32.

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

Singer, J. B. (2014). User-generated visibility: Secondary gatekeeping in a shared media space. New Media & Society, 16(1), 55-73.

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