Sketch of an AGI Curriculum
I often get asked questions like: “If I want to work on AGI, what should I study first to get up to speed?”
This page gives a rough stab at an answer.
It is a list of some books that would be useful reading for anyone wanting to seriously get into AGI. Some are more critical than others; and of course, omission of a book from this list does not imply its irrelevance or unimportance.
Sorry that most of these books cost money to legally obtain. I would have liked to assemble a comparable list consisting only of legally freely available materials, but that would have required a lot more effort.
If I were going to structure a degree program on AGI, I would use these books as part of the core. At a first stab, I might divide the curriculum into five main courses, such as:
- History of AI
- AI Algorithms, Structures and Methods
- Neuroscience & Cognitive Psychology
- Philosophy of Mind
- AGI Theories & Architectures
The books listed below would give raw materials for all the above courses (to be supplemented by various papers and assignments, etc.). I have divided them into 5 categories corresponding to the above list, though this is somewhat crude as some of the books really cross-cut the categories.
History of AI
These two classic books will give you a feeling for the early history of the AI field:
Computers and Thought, an edited book from 1963
What Computers Still Can’t Do, by Hubert Dreyfus
AI Algorithms, Structures and Methods
The books in this category are not mainly about AGI, but present ideas that are worth knowing about if you’re going to work on AGI.
Artificial Intelligence by Russell and Norvig. This is not an AGI book, it’s a narrow AI book. But it’s excellent for what it is. Many of the ideas and methods described here have a role to play in various AGI architectures.
The Design of Innovation by David Goldberg — a deep, wide-ranging and readable discussion on evolutionary learning
Introduction to Evolutionary Computing, by Eiben and Smith — a competent, current overview of genetic algorithms and genetic programming
Neural Networks and Learning Machines, by Simon Haykin — a good (though long) review of work on neural nets and related methods for machine learning, including recurrent neural networks
Foundations of Language by Ray Jackendoff — by far the most thorough and deep treatment of traditional linguistics (as opposed to statistical linguistics) I’ve seen. A great way to understand the nature of all the various linguistic phenomena that an AGI will have to deal with.
Speech and Language Processing, by Jurafsky and Martin — an excellent review of the field of “statistical language processing.” This is certainly not an AGI-ish approach to linguistics, yet it does teach us a lot about the nature of language, since any AGI that learns language is going to rely on similar statistical phenomena to a certain extent.
Probabilistic Robotics, by Thrun, Burgard and Fox — a narrow-AI approach to robotics, but it’s useful to know how this stuff is done, and what difficulties they run up against
Neuroscience and Cognitive Psychology
Some solid textbooks…
Neuroscience: Exploring the Brain, Bear, Connors and Paradiso — a remarkably comprehensible textbook summarizing our current knowledge on the complex system that is the human brain
Fundamentals of Cognitive Psychology by Robert Kellogg — a straightforward review of cognitive psychology, a bit dry but well worth understanding if you want to build a human-like AGI
Developmental Cognitive Neuroscience, by Johnson and de Hann — a straightforward text reviewing how the child’s mind/brain develops. Useful to know if you want to build an AGI that develops in some vaguely similar way.
Some lighter-weight books presenting individual scientists’ relevant ideas:
Constructing a Language, by Michael Tomasello — a masterful review of language learning as a social and embodied process
Action in Perception by Alva Noe — on the connection between seeing and acting
The Vision Revolution by Mark Changizi — explaining vision as prediction from a neuroscience and cognitive science perspective
Some Philosophy of Mind Relevant to AGI
My book The Hidden Pattern presents a philosophy of mind specifically oriented toward AGI, though also dealing with many other topics. Some more recent ideas along similar lines are given in these papers: Toward a General Theory of General Intelligence, A Mind-World Correspondence Principle
Neural Correlates of Consciousness, an edited volume by Thomas Metzinger
Being No One, by Thomas Metzinger — the best book I know about how the mind creates the self
The Radiance of Being, by Allan Combs — a fantastic review and analysis of the various states of consciousness humans get into
The Embodied Mind, by Varela, Thompson and Rosch — a classic on the relation between mind and body
Supersizing the Mind by Andy Clark — an up-to-date overview of work on the “extended mind”; the way mind extends into environment and body, rather than just residing in brain
Erik Jantsch, The Self-Organizing Universe … a “big picture” systems-theory view of the mind and world, putting human intelligence in perspective. Out of print and hard to find, but a rather good read
Gregory Bateson, Mind & Nature: a Necessary Unity — describing mind as a cybernetic system among many others
The Neurophilosophy of Free Will, by Henrik Walter — a take on “Free will” that is scientifically sound and relevant to AGI
Some Readings about AGI Itself
Cassio Pennachin and I edited a book on Artificial General Intelligence
Pei Wang and I edited a book on the Theoretical Foundations of Artificial General Intelligence
Shane Legg’s PhD thesis “Machine Superintelligence” gives a relatively readable (though still somewhat technical) overview of the abstract theory of general intelligence pursued by his PhD supervisor Marcus Hutter and others
Eric Baum’s book What Is Thought? reviews a variety of interesting issues related to AGI
Erik Mueller’s book Commonsense Reasoning presents a logic-based approach to AGI in some depth, drawing directly on the ideas of AI pioneer John McCarthy
Pei Wang’s book Rigid Flexibility outlines Pei’s unique view on AGI and the underlying logical and control mechanisms
The proceedings volumes of the Artificial General Intelligence conference series contain a host of papers related to AGI, and the conference websites contain links to free PDFs of the papers
The AGI Journal contains papers relevant to AGI, primarily at this point of a theoretical nature
My former colleague Moshe Looks’ PhD thesis is excellent and explains how to combine evolutionary learning and probabilistic modeling.
This book summarizes work on SOAR, perhaps the most thorough and successful of the “Good Old Fashiond AI Systems” (and which is still under development, incorporating a number of modern features):
The Soar Cognitive Architecture