The Alignment Problem in Artificial General Intelligence

John Vervaeke and guest Sam Tideman delve into the intricate world of artificial general intelligence (AGI) and its intersection with healthcare. Sam, an expert in biostatistics, machine learning, and AI, shares valuable insights from his professional experiences, particularly in healthcare system optimization. The conversation navigates the ethical and moral challenges of applying AI in complex environments like emergency departments, the intricacies of predictive modeling, and the broader societal implications of AI, including its energy consumption and public perception. This episode is essential listening for anyone interested in understanding the nuanced interplay between technology, healthcare, and ethics, offering a comprehensive perspective on the current and future potential of AI to transform lives and systems.

Sam Tideman, an accomplished healthcare data scientist with an MS in Biostatistics, blends his analytical acumen with a passion for theology in his podcast, "Transfigured." The podcast features long-form discussions exploring the identity of Jesus, reflecting Sam's unique intersection of scientific expertise and spiritual inquiry.

Glossary of Terms

AGI (Artificial General Intelligence): An AI that has the ability to understand, learn, and apply its intelligence to a wide range of problems, much like human intelligence.

Biostatistics: The application of statistics to a wide range of topics in biology.

Resources and References:

Dr. John Vervaeke: Website | YouTube | Patreon | X | Facebook

Sam Tideman: YouTube

The Vervaeke Foundation

John Vervaeke YouTube

Awakening from the Meaning Crisis - series

Artificial Intelligence - series

The Crossroads of Predictive Processing and Relevance Realization | Leiden Symposium

Books, Articles, Publications, and Videos

Mentoring the Machines: Orientation - Part One: Surviving the Deep Impact of the Artificially Intelligent Tomorrow - John Vervaeke, Shawn Coyne 

Mentoring the Machines: Origins - Part 2: Surviving the Deep Impact of the Artificially Intelligent Tomorrow - John Vervaeke, Shawn Coyne 

Predictive processing and relevance realization: Exploring convergent solutions to the frame problem. Phenomenology and the Cognitive Sciences. Andersen, B., Miller, M., & Vervaeke, J. (2022).

Related Resources

Chicagoland Bridges of Meaning Meetup

Chapters with Timestamps

[00:00:00] Introduction of Sam Tiedemann and Episode Overview 

[00:01:15] Sam’s Background and Intersection with AI 

[00:04:11] The Role of AI in Healthcare and Emergency Departments 

[00:14:26] The Limitations of AI in Morally Complex Environments 

[00:24:34] Discussion on AI's Capability to Predict vs. Normative Decision-Making 

[00:53:06] The Energy Consumption and Environmental Impact of Training AI Models 

Timestamped Highlights

[00:00:00] John opens the discussion by welcoming Sam and introducing the topic of artificial general intelligence (AGI).

[00:01:15] Sam shares his diverse background, which spans theology, philosophy, and artificial intelligence.

[00:06:15] The conversation focuses on AI's potential and dangers, setting the stage for the day's discussion.

[00:09:28] Sam reflects on the complexities he faced while trying to implement AI in emergency department forecasting.

[00:14:53] Sam points out the practical limitations of AI in real-world applications.

[00:21:38] Sam criticizes the inflated expectations surrounding AI in healthcare projects.

[00:26:26] John and Sam discuss how predictive processing and relevance realization can be integrated into AI.

[00:29:37] They delve into the potential of AI to emulate human qualities like intentionality and care.

[00:34:11] John emphasizes the need to recognize the limitations of AI in solving complex real-world problems.

[00:38:30] Sam's parable features an AI model in healthcare that prescribes drugs probabilistically and learns from outcomes, hinting at AI's emerging agency.

[00:42:10] The feasibility of AI replicating human intuition and judgment in complex scenarios is questioned.

[00:46:15] John highlights the importance of a multidisciplinary approach to understanding and developing AI.

[00:49:57] Philosophical aspects of AI, such as intentionality and consciousness, are explored in-depth.

[00:53:30] Sustainability concerns in AI development, especially compared to the human brain's efficiency, are discussed.

[01:06:40] The episode concludes with a discussion on AI's inability to align with human normativity and the limitations of its social, cultural, and biological understanding.