LECTURE DETAILS

LECTURE ONE

• Topic: Demystifying super intelligence – Origins, Trajectories, and Preparedness
• Date: Thursday, 27th November, 2025
• Venue: Great Hall, KNUST
• Time: 1000 hours
• Chairperson: Chairman of Council/Vice-Chancellor
• Abstract of Lecture One: Demystifying Super Intelligence - Origins, Trajectories, and Preparedness.

  Artificial Intelligence (AI) is rapidly transforming our world, yet the concept of Superintelligence (SI) - an intellect vastly exceeding that of the brightest human minds - remains the most profound theoretical challenge of our time.

  This presentation demystifies Superintelligence, moving beyond science fiction to explore its conceptual basis, potential pathways to creation (such as through recursive self-improvement), and the immense, irreversible changes it could bring. Specifically, we will critically examine on a broad scale the dual-edged nature of SI. On one hand, it offers unprecedented opportunities to solve intractable global challenges relevant to Africa, including climate resilience, pandemic prevention, and resource-efficient development. On the other hand, it introduces risks related to the "control problem" - ensuring that a hyper-capable intelligence remains aligned with fundamental human values and goals. But whose values and goals should intelligence align with?

  In this talk I aim to foster an informed and proactive dialogue among students, faculty, researchers and the general public on the technical, ethical, and geopolitical imperatives of safe SI development critical to the local and global conversation with clearly outlined recommendations to ensure contextualised approaches for responsible use.

LECTURE TWO

• Topic: Case Studies of AI Applications and the Potential of Superintelligence: Benefits, Risks, and Challenges
• Date: Thursday, 27th November, 2025
• Venue: Great Hall, KNUST
• Time: 1600 hours
• Chairperson: Chairman of Council/Vice-Chancellor
• Abstract of Lecture Two: AI's Hippocratic Oath: Is "Do No Harm" Possible in a World of Biased Data?

  Continuing from lecture 1, I will focus on the potential of artificial intelligence (AI) and Superintelligence (SI) in healthcare, an area where AI applications are accelerating exponentially yet barriers remain. While AI has the potential to improve healthcare for the better, like other advancements in medical technology, it also has the potential to increase health disparities. Historically, medical technology has not been created with the needs of underserved settings in mind, and this has led to limited access in low and middle income countries. Reduced access to these healthcare tools leads to downstream gaps in data used to train machine learning models, and by extension, the potential for machine learning biases, should these models be used on individuals from historically excluded settings.

  In this talk I will provide an overview of our work on a health equity toolbox for evaluating large language models for surfacing model harms and bias, the development of the AfriMed-QA and TRINDS -  African-focussed health datasets for LLM evaluation and tuning, and the Nteasee Study, a mixed methods study to understand general population and expert perspectives on AI for health in Africa, including a deep dive on algorithmic fairness and colonialism. I will end with brief examples of considerations for beneficial applications of AI for health equity.

  Overall, I hope to make a case for considering geo-contextual perspectives for the ecosystem of AI development and evaluation for health.

LECTURE THREE

• Topic: Superintelligence: History, Current State, and Future for a Diverse World
• Date: Saturday, 29th November, 2025
• Venue: Great Hall, KNUST
• Time: 0900 hours
• Chairperson: Chancellor of the University
• Abstract of Lecture Three: Superintelligence, Health Equity, and the Imperative for Geo-Contextual AI Development

  This culminating lecture synthesizes the core themes from Day 1 (Demystifying Superintelligence) and Day 2 (AI's Hippocratic Oath), presenting a consolidated view of challenges and opportunities at the frontier of Artificial Intelligence (AI).

  We begin by establishing the conceptual basis of Superintelligence (SI) - an intellect vastly exceeding human capability - and critically examining its dual-edged nature. SI holds unprecedented potential to solve intractable global issues relevant to the African continent, such as climate resilience and pandemic prevention. However, it potentially introduces risks centered on the alignment problem: ensuring hyper-capable AI systems are engineered to adhere to fundamental human values.

  The lecture then pivots to the urgent, practical challenge of AI bias and health equity. Accelerating AI applications in healthcare risks exacerbating existing health disparities, particularly in underserved settings, due to historical patterns of exclusion that have resulted in gaps in training data.

  We will summarize key work in addressing this challenge, including the development of health equity toolboxes for large language models, the creation of African-focused health datasets (like AfriMed-QA and TRINDS), and insights from the Nteasee Study on African public and expert perspectives. This leads to a deep-dive on algorithmic fairness and the considerations necessary to move past data colonialism.

  Ultimately, this talk makes a case for an informed, geo-contextual approach to the entire AI ecosystem—from development to evaluation. I will outline practical recommendations for the safe and beneficial realization of Superintelligence and the creation of equitable AI healthcare tools both depend on proactively embedding local perspectives, ethical frameworks, and robust technical controls. The conversation shifts from "if" to "how" we can responsibly navigate this transformative future to ensure AI benefits all of humanity, presenting a cautiously optimistic viewpoint with clearly outlined recommendations for moving forward.

The Lectures would be climaxed with the 59th Congregation of the University on Saturday, 29th November, 2025 at the Great Hall of the University at 1000 Hours.