Episode 14 · · 1h 18m
Alignment Problem Part 2 - Taking it Seriously
Play Episode 14 on YouTube
Can the AI "alignment problem" actually be defined, or is it fundamentally flawed from the start? In this episode of Philosophy, Programs, and Prompts, Casey Hart(@OntologyExplained) and Carl Brown(@InternetOfBugs) set aside bad-faith doomer rhetoric and corporate marketing moats to examine the core arguments behind AI alignment in good faith.
Casey argues that grappling with alignment acts as a conceptual gateway into foundational questions of ethics, paternalism, and what constitutes the "good life". Meanwhile, Carl pushes back from an engineering and practical standpoint, arguing that true alignment with humanity—which is notoriously misaligned with itself—is a distraction from pressing real-world issues like environmental degradation, social media algorithms, legal accountability, and building practical systems we can control.
Together, they dissect the mechanics of Coherent Extrapolated Volition (CEV), Isaac Asimov's Three Laws of Robotics, Nick Bostrom's orthogonality thesis, and the practical limits of guardrails, kill switches, and machine interpretability.
Chapters
- 0:00Cold Open: Why Computer Science Ignores History
- 0:28Welcome to Philosophy, Programs, and Prompts
- 1:53The Thesis: Alignment as a Gateway Drug to Philosophy
- 3:59Four Ways to Frame the Alignment Problem
- 6:05Paternalism, Ethics, and Defining "The Good Life"
- 9:50The Computer Science Habit of Working in a Vacuum
- 15:06Coherent Extrapolated Volition & Paternalistic Genies
- 20:20Eternal Recurrence and Avoiding an Algorithmic Rut
- 25:30Defining Alignment: Goals, Intentions, and Needs
- 31:40Can We Be Aligned with Non-Living Artifacts?
- 36:00Internal Self-Misalignment and Humanity's Fractured Goals
- 47:20A Taxonomy of Proposed Solutions: Moratoriums & Off Switches
- 54:20Asimov's Three Laws: Control Constraints vs. True Alignment
- 1:00:50Encoding Morality: Utilitarian Calculators & Perfect Parents
- 1:05:14Eliminativism, Orthogonality, and Decision Theory Flaws
- 1:09:30Machine Interpretability: What Science Can Actually Do
- 1:13:00Marketing Benchmarks vs. Real-World Harm