Stacking Scenarios: a layered approach to futures planning
I rewatched Inception this weekend and it got me thinking about dreams within dreams. I was also thinking about ARL’s AI-Influenced Futures in the Research Environment and their previous set: Envisioning Research Library Futures. I was curious: could I stack together both of these scenarios and generate an enriched new set? By blending the scenarios could uncover new insights or directions? It also gave me a chance to experiment with GPT-4o.
The combination of the two ARL sets resulted in 16 new scenarios:
Entrepreneurial Democracy: empowering research through open AI and collaborative innovation.
Technocratic Entrepreneurs: harnessing cutting-edge technology to drive entrepreneurial research initiatives.
Divisive Entrepreneurs: entrepreneurial spirit in a world of biased and fragmented AI applications.
Autonomous Entrepreneurs: autonomous AI partnerships fostering entrepreneurial research breakthroughs.
Sustainable Democracy: integrating AI in sustainable practices for a fair and equitable future.
Technocratic Sustainability: balancing high-tech advancements with environmental stewardship.
Divisive Sustainability: sustainable practices challenged by biased and divisive AI technologies.
Autonomous Sustainability: AI autonomy driving innovative solutions in sustainability.
Disciplined Democracy: structured research with inclusive, democratized AI applications.
Technocratic Disciplines: traditional academic control enhanced by technocratic AI integration.
Divisive Disciplines: navigating power struggles and biases in AI-led research environments.
Autonomous Disciplines: academic disciplines partnering with autonomous AI for research advancement.
Global Democracy: global research collaboration with democratized AI technologies.
Technocratic Globalism: global influence of tech giants driving AI advancements and research.
Divisive Globalism: global research landscape fragmented by biased and divisive AI applications.
Autonomous Globalism: AI-driven global research advancements with autonomous collaboration.
And you can go deeper into each of these. Here are a few quick examples to paint these worlds a little further:
Entrepreneurial Democracy
(Combination of Research Entrepreneurs + Democratizing AI)
In this future, research is revolutionized by the seamless integration of open AI technologies and a collaborative spirit. Universities, startups, and industry players work together to democratize access to AI tools, empowering researchers worldwide. This scenario highlights the transformative impact of inclusive AI, driving innovation and addressing global challenges through collective efforts. Libraries play a pivotal role as knowledge hubs, facilitating open access to data and fostering an environment of shared learning and discovery.
Divisive Disciplines
(Combination of Disciplines in Charge + Divisive AI)
In this fragmented world, biased and flawed AI applications are rampant, yet traditional academic disciplines retain significant control over research. Conflicts arise between AI developers and academic institutions, leading to power struggles and ethical dilemmas. Researchers navigate a landscape fraught with bias and misinformation, with libraries stepping in to mitigate these challenges. This scenario explores the societal impact of divisive AI technologies and the role of libraries in maintaining research integrity and ethical standards.
Autonomous Globalism
(Combination of Global Followers + Autonomous AI)
AI has become an autonomous partner in research and learning, leading to rapid global advancements and widespread adoption of AI-driven innovations. Different cultures and countries adapt to and interact with autonomous AI in unique ways, fostering cross-cultural collaborations. This scenario delves into the ethical and societal implications of such widespread AI integration, emphasizing the transformative impact on global research practices. Libraries evolve into dynamic hubs that support AI-human partnerships, ensuring equitable access to knowledge and resources.
Sample profile of: Autonomous Globalism
And still even deeper. Here is the a synthetic profile based on one of these possible blended scenarios via GPT 4o
In the scenario of Autonomous Globalism, AI has evolved into an autonomous partner in research and learning. This has led to rapid global advancements and widespread adoption of AI-driven innovations. Different cultures and countries have adapted to and interact with autonomous AI in unique ways, fostering cross-cultural collaborations. This scenario emphasizes the ethical and societal implications of widespread AI integration and highlights the transformative impact on global research practices. Libraries evolve into dynamic hubs that support AI-human partnerships, ensuring equitable access to knowledge and resources.
Characteristics of Research
AI Collaboration: Researchers work alongside autonomous AI systems that can independently generate hypotheses, design experiments, and analyze data. These AI systems are capable of learning and improving continuously, leading to faster and more accurate research outcomes.
Cross-Disciplinary Teams: Research teams are composed of human and AI researchers from various disciplines and cultural backgrounds, promoting diverse perspectives and innovative solutions.
Global Research Networks: Enhanced connectivity and collaboration tools enable seamless interaction among researchers worldwide, breaking down geographical barriers and fostering global partnerships.
Data-Driven Insights: Autonomous AI systems handle vast amounts of data, uncovering patterns and insights that humans might miss. This leads to more informed decision-making and groundbreaking discoveries.
Ethical Oversight: Research practices include robust ethical guidelines to ensure the responsible use of AI, addressing issues such as bias, privacy, and data security.
Role of Libraries
Knowledge Hubs: Libraries serve as central repositories for data, research findings, and AI algorithms, ensuring that information is accessible to all researchers. They provide platforms for data sharing and collaboration.
AI Support Centers: Libraries offer resources and training for researchers to effectively use autonomous AI systems. They also provide AI tools and software as part of their services.
Ethical Guardians: Libraries play a crucial role in monitoring the ethical use of AI in research, offering guidance and frameworks to address potential issues.
Cultural Integrators: Libraries support cross-cultural research initiatives by providing resources that respect and incorporate diverse cultural perspectives and knowledge systems.
Innovation Incubators: Libraries host innovation labs where researchers and AI systems can experiment with new ideas and technologies in a collaborative environment.
Role of Universities
AI-Enhanced Curriculum: Universities integrate AI into their curricula, ensuring that students are well-versed in using AI tools and understanding their implications. AI systems also assist in personalized learning experiences.
Research and Development Centers: Universities establish advanced R&D centers focused on AI and its applications across various fields. These centers collaborate with global institutions and industries.
Global Collaboration Hubs: Universities act as nodes in a global network of research institutions, facilitating international partnerships and knowledge exchange.
Ethical Leadership: Universities lead the way in developing and promoting ethical standards for AI use in research and education, ensuring that technological advancements align with societal values.
Community Engagement: Universities engage with local and global communities to address societal challenges through research, fostering a culture of innovation and responsibility.
Drivers of Change
AI Maturation: Continuous advancements in AI capabilities, including machine learning, natural language processing, and autonomous systems, drive the integration of AI into research and education.
Connectivity: Improved global connectivity and collaboration tools facilitate seamless communication and data sharing among researchers worldwide.
Globalization: Increasing interconnectedness among countries and cultures promotes cross-cultural collaborations and the sharing of diverse perspectives.
Ethical Awareness: Growing awareness of ethical issues related to AI drives the development of robust guidelines and frameworks for responsible AI use.
Policy and Regulation: Governments and international organizations implement policies and regulations that support the ethical and equitable use of AI in research.
Investment in Education: Increased funding and resources are directed toward AI education and training, ensuring that the workforce is prepared for an AI-driven future.
Funding for Innovation: Governments, industries, and institutions invest in AI research and development, recognizing its potential to drive economic growth and solve complex problems.
Collaborative Economies: The rise of collaborative economies encourages institutions to share resources and knowledge, fostering innovation and reducing duplication of efforts.
Pathway:
Current State:
AI technologies are rapidly advancing, but their integration into research and education is still in the early stages. Researchers and institutions are exploring the potential of AI, and ethical considerations are becoming increasingly important.
Short-Term Changes (1-3 years):
Investment in AI: Significant investments are made in AI research and development, focusing on creating advanced AI systems capable of autonomous decision-making.
Policy Development: Governments and international bodies develop policies and regulations to ensure the ethical use of AI in research and education.
Curriculum Integration: Universities begin integrating AI into their curricula, providing students with the skills needed to work with AI systems.
Medium-Term Changes (3-7 years):
AI Adoption: Researchers increasingly adopt autonomous AI systems, leading to faster and more efficient research processes.
Global Networks: Enhanced connectivity and collaboration tools enable the formation of global research networks, fostering international partnerships.
Library Evolution: Libraries evolve into dynamic knowledge hubs, offering AI support and ethical guidance.
Long-Term Changes (7-10 years):
Widespread AI Integration: AI becomes a ubiquitous partner in research and education, driving significant advancements and innovations.
Cross-Cultural Collaboration: Researchers from different cultures and backgrounds collaborate seamlessly, leveraging diverse perspectives to solve global challenges.
Ethical Research Practices: Robust ethical frameworks ensure the responsible use of AI, addressing issues such as bias, privacy, and data security.
Future State:
AI is an autonomous partner in research and education, leading to rapid global advancements and fostering a culture of innovation, collaboration, and ethical responsibility.
Obviously you can probe and refine, but this provides a taste of what’s possible in this blended world-building.
A Dream Within a Dream
Another interesting possibility is using all 16 scenarios as a collection and being able to explore themes across all them together. Here is an example:
Control vs. Autonomy
In the "Technocratic Reuse and Recycle" scenario, human experts manage AI applications, ensuring ethical and sustainable practices. Conversely, "Autonomous Reuse and Recycle" envisions AI systems operating independently, raising questions about oversight and ethics. The balance between human control and AI autonomy is crucial for effective and ethical AI-driven sustainability.
Inclusivity vs. Fragmentation
"Democratizing Research Entrepreneurs" presents a future where AI democratizes access to research tools, fostering collaboration and equity. In contrast, "Divisive Research Entrepreneurs" depicts a world where biased AI applications create fragmented and unequal opportunities, highlighting the importance of inclusivity in AI.
Collaboration vs. Competition
"Democratizing Disciplines in Charge" shows inclusive AI applications integrating with academic disciplines for collective progress. On the other hand, "Divisive Disciplines in Charge" features resistance to AI integration, leading to competition and disparities in research outcomes. Collaboration is key to maximizing AI's benefits.
Ethical Oversight vs. Rapid Innovation
"Technocratic Global Followers" emphasizes structured AI innovation with ethical oversight, while "Autonomous Global Followers" prioritizes rapid progress with less oversight, raising accountability concerns. Balancing innovation and ethics is vital.
Practical Applications vs. Fundamental Research
Eastern leadership focuses on practical AI applications like smart cities and healthcare, addressing immediate needs. Western leadership prioritizes fundamental research and theoretical advancements, laying the groundwork for future innovations. Both approaches are essential for AI progress.
Regional Leadership vs. Global Synergy
"Global Research Synergy" envisions a unified global approach to AI research, promoting collaboration and equity. In contrast, "Divisive Global Followers" depicts a fragmented world with disparate AI outcomes, emphasizing the need for global collaboration and unified standards.
Exploring these contrasts and paradoxes provides even more nuance as we consider the potential impact of AI. From a perspective-building standpoint, it can be helpful to take the scenarios that ARL developed about AI and then place them into different global contexts to see how different variations unfold.
Closing thought
Blending or stacking different scenarios does seem to open new conversations. While I see this as a proof-of-concept thought experiment, it’s clear that this layered approach has potential. As we navigate the evolving landscape of AI and its impact on research, higher education, and libraries, these 16 “enriched” scenarios provide some new strategic scaffolding for discussions. This approach stretches the imagination a bit, but I definitely think stacking broadens our understanding of potential futures.
I am going to keep experimenting with this set and also stacking it with other sets \too, but I wanted to share my tinkering with others who might be interested.
