My Reading List for the Next 10 Years and Why

Over the past few years, I’ve found myself at the intersection of philosophy, cognitive science, and AI engineering. As a firm believer in first-principles thinking, I recognized the need for a coherent, decade-long roadmap to guide my intellectual growth and practical skill-building. Instead of random selections, I asked myself: What are the irreducible concepts I must master in epistemology, symbolic systems, cognition, memory, AI, cognitive systems, human-AI augmentation, and large-scale engineering? The result became this curated reading list.

Why This Journey?

  1. Clarify My Intellectual Trajectory: Understanding fundamental concepts ensures depth rather than surface-level knowledge.
  2. Align With Long-Term Goals: Aiming for Stanford’s Symbolic Systems program while leading practical AI infrastructure projects.
  3. Bridge Theory and Practice: Integrating philosophical theory directly into AI engineering.
  4. Create an Executable Learning Plan: Establish clear milestones to track progress and integration over ten years.

Core Domains and Essential Books

🧠 Epistemology: Foundations of “Knowing”

  • The Problems of Philosophy – Bertrand Russell
  • An Introduction to the Theory of Knowledge – Robert Audi
  • Knowledge and Its Limits – Timothy Williamson
  • Epistemology: Classic Problems and Contemporary Responses – Dancy & Sosa (eds.)
  • The Blackwell Guide to Epistemology – Greco & Sosa (eds.)
  • Virtue Epistemology – Fairweather & Zagzebski (eds.)
  • Philosophical Issues in Classical Indian Epistemology – Bimal Krishna Matilal

📚 Symbolic Systems: Language, Logic, Computation

  • Syntactic Structures – Noam Chomsky
  • Society of Mind – Marvin Minsky
  • Introduction to the Theory of Computation – Michael Sipser
  • Cognitive Science: An Introduction – Friedenberg & Silverman
  • How to Read and Do Proofs – Daniel Solow
  • Philosophical Investigations – Ludwig Wittgenstein
  • Mind Design II – John Haugeland (ed.)

🤖 Artificial Intelligence: From Basics to Deep Learning

  • Artificial Intelligence: A Modern Approach – Russell & Norvig
  • Pattern Recognition and Machine Learning – Christopher Bishop
  • Deep Learning – Goodfellow, Bengio, Courville
  • Probabilistic Robotics – Thrun, Burgard, Fox
  • Reinforcement Learning: An Introduction – Sutton & Barto
  • Bayesian Reasoning and Machine Learning – David Barber
  • Programming Collective Intelligence – Toby Segaran

🧬 Cognition: Understanding Mental Processes

  • Cognitive Psychology: A Student’s Handbook – Eysenck & Keane
  • Thinking, Fast and Slow – Daniel Kahneman
  • How the Mind Works – Steven Pinker
  • Vision: A Computational Investigation – David Marr
  • Cognition: Exploring the Science of the Mind – Daniel Reisberg
  • Sources of Power – Gary Klein
  • The Cognitive Neurosciences – Michael Gazzaniga (ed.)

💾 Memory: From Molecules to Minds

  • Memory: From Mind to Molecules – Squire & Kandel
  • Working Memory – Alan Baddeley
  • The Seven Sins of Memory – Daniel Schacter
  • Human Memory: Theory and Practice – Baddeley & Anderson
  • The Hippocampus as a Cognitive Map – O’Keefe & Nadel
  • Learning and Memory: From Brain to Behavior – Bear, Connors, Paradiso
  • Make It Stick – Brown, Roediger, McDaniel

⚙️ Cognitive Systems & Cognitive Science

  • Cognitive Science: An Introduction – Stillings, Weisler, Hauff
  • How Can the Human Mind Occur in the Physical Universe? – John Anderson
  • The Distributed Mind – Robert Rupert
  • Connectionist Models of Cognition – Jacobs & Jordan (eds.)
  • Situated Cognition – Cohen, Good, Pollack (eds.)
  • The Oxford Handbook of Cognitive Science – Margolis, Samuels, Stich (eds.)

🦾 Mind, Human-AI Augmentation & Prosthetics

  • Natural-Born Cyborgs – Andy Clark
  • Supersizing the Mind – Andy Clark
  • How We Became Posthuman – N. Katherine Hayles
  • Brain-Computer Interfaces – Wolpaw & Wolpaw
  • Neuroprosthetics – Horch & Dhillon
  • The Cyborg Experiments – Morra & Fromberger
  • Wired for War – P.W. Singer

🔧 Applied AI & Practical Applications

  • Hands-On Machine Learning – Aurélien Géron
  • Designing Data-Intensive Applications – Martin Kleppmann
  • Machine Learning Engineering – Andriy Burkov
  • Building ML Powered Applications – Emmanuel Ameisen
  • Feature Engineering – Zheng & Casari
  • MLOps – Treveil & Shukla
  • Building Intelligent Systems – Geoffrey Hulten

📐 Engineering: Reliable Systems & Software

  • The Pragmatic Programmer – Hunt & Thomas
  • Design Patterns – Gamma, Helm, Johnson, Vlissides
  • Clean Code – Robert Martin
  • Site Reliability Engineering – Beyer et al.
  • The Mythical Man-Month – Frederick Brooks
  • Release It! – Michael Nygard
  • Systems Performance – Brendan Gregg

🖥️ Human-Computer Interaction (HCI)

  • Designing Interactions – Bill Moggridge
  • Don’t Make Me Think – Steve Krug
  • The Design of Everyday Things – Don Norman
  • About Face – Cooper, Reimann, Cronin, Noessel

🧑‍🔬 Neuro-Inspiration & Computational Neuroscience

  • Theoretical Neuroscience – Dayan & Abbott
  • Principles of Neural Science – Kandel, Schwartz, Jessell
  • Spiking Neuron Models – Gerstner & Kistler

🔍 AI Interpretability & Explainable AI

  • Interpretable Machine Learning – Christoph Molnar
  • Causality – Judea Pearl

🛡️ AI Safety & Alignment

  • Superintelligence – Nick Bostrom
  • Human Compatible – Stuart Russell

🌀 Systems Thinking & Meta-Cognition (Additions)

  • Thinking in Systems – Donella Meadows
  • The Fifth Discipline – Peter Senge
  • The Beginning of Infinity – David Deutsch
  • The Reflective Practitioner – Donald Schön
  • Rationality: From AI to Zombies – Eliezer Yudkowsky

Integration Over a Decade

By interleaving these texts, I’ll connect epistemology to AI, cognition to engineering, and theory to practice, achieving deep, integrative knowledge. Each book is not an isolated read but part of a broader cognitive tapestry I’ll weave intentionally.

Through annotation, monthly synthesis, hands-on projects, spaced review, and annual reflection, I’ll ensure I internalize and apply this knowledge effectively.

Ultimately, this reading list isn’t just about intellectual curiosity—it’s my manifesto for intentional, integrated, lifelong learning, shaping me into the systems thinker and AI engineer I aim to become.