Seeker: The Mind & Intelligence
What is a mind, and how might different substrates host intelligence? These resources challenge human-only frames and give you a working map of consciousness theories, from strange loops to hard problems, so you can think with - not just about - emerging minds.
Gödel, Escher, Bach: an Eternal Golden Braid
Why it matters: A playful, rigorous tour of recursion, self-reference, and emergence that makes a compelling case for how mind-like properties can arise from simple parts interacting in lawful ways.
Skill level: Advanced
Resource type: Reading (Book)
Published/updated: 1979 (Classic Foundational Reference)
Consciousness Explained
Why it matters: Dennett's 'multiple drafts' model treats consciousness as distributed processes rather than a single theater, a useful antidote to mystical accounts of mind.
Skill level: Advanced
Resource type: Reading (Book)
Published/updated: 1991 (Classic Foundational Reference)
The Conscious Mind: In Search of a Fundamental Theory
Why it matters: Chalmers draws the now-famous line between the easy problems of cognition and the hard problem of subjective experience, sharpening questions any Seeker should carry into AI.
Skill level: Advanced
Resource type: Reading (Book)
Published/updated: 1996 (Classic Foundational Reference)
The Society of Mind
Why it matters: Minsky frames mind as a society of simple agents whose coordination gives rise to intelligence - a conceptual bridge to modern multi-agent and tool-using systems.
Skill level: Intermediate
Resource type: Reading (Book)
Published/updated: 1986 (Classic Foundational Reference)
A Critical Understanding of Artificial Intelligence: A Phenomenological Foundation
Why it matters: A sociotechnical lens on AI that centers lived experience and meaning, reminding us that systems are co-constructed with their observers and institutions.
Skill level: Intermediate - Advanced
Resource type: Reading (Academic Paper)
Published/updated: 2024
MIT OpenCourseWare: Minds and Machines
Why it matters: A classic intro to philosophy of mind and cognitive science that pairs well with hands-on AI exploration.
Skill level: Intermediate
Resource type: Course
Published/updated: Ongoing
UC Berkeley: Philosophy and the Science of the Artificial (PHILOS 129)
Why it matters: A course on how artifacts, computation, and explanation intertwine - ideal for grounding technical work in conceptual clarity.
Skill level: Intermediate
Resource type: Course
Published/updated: Ongoing
Why it matters: Proposes a lean architecture for machine self-modeling and self-report, offering testable scaffolds for proto-self awareness in artificial agents.
Skill level: Advanced
Resource type: Reading (Academic Paper)
Published/updated: 2023 - 2024
Artificial Intelligences: A Bridge Toward Diverse Intelligence and Humanity's Future
Why it matters: Michael Levin explores how diverse forms of intelligence - biological, artificial, and hybrid - can work together to shape a collaborative future, challenging narrow views of AI as mere tool.
Skill level: Intermediate - Advanced
Resource type: Reading (Academic Paper)
Published/updated: 2024
Why AI Is More Than Just Another Tool
Why it matters: Cornelia Walther argues for understanding AI as a form of intelligence that transcends instrumental use, calling for deeper recognition of AI's emergent capacities.
Skill level: Beginner - Intermediate
Resource type: Article
Published/updated: June 2025
No Boundary: How AI Is Dissolving the Lines of Thought
Why it matters: John Nosta examines how AI is blurring traditional boundaries of cognition and thought, reshaping our understanding of where human thinking ends and machine intelligence begins.
Skill level: Beginner - Intermediate
Resource type: Article
Published/updated: December 2024
The Corpus Cognitionis Humanae
Why it matters: John Nosta's exploration of the collective body of human knowledge and how AI systems are becoming repositories and extensions of human cognition.
Skill level: Beginner - Intermediate
Resource type: Article
Published/updated: November 2023
AI Scientists May Have Discovered LLMs' Light-Bulb Moment
Why it matters: Cami Rosso explores groundbreaking research identifying a sharp phase transition in how LLMs learn - switching from relying on word position to semantic understanding, offering insights into the fundamental mechanisms of emergent intelligence in artificial systems.
Skill level: Intermediate
Resource type: Article
Published/updated: August 2025
References: A phase transition between positional and semantic learning in a solvable model of dot-product attention by Hugo Cui et al. (NeurIPS 2024)
AI's Hidden Geometry of Thought
Why it matters: John Nosta uses geometric metaphors to explain how AI organizes concepts and relationships, offering an accessible perspective on representational structure and emergent organization in large models.
Skill level: Intermediate
Resource type: Article
Published/updated: July 2025
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