AGI Curriculum

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:

  1. History of AI
  2. AI Algorithms, Structures and Methods
  3. Neuroscience & Cognitive Psychology
  4. Philosophy of Mind
  5. 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

On Intelligence by  Jeff Hawkins — presents a view of neuroscience and AGI that I have argued is very oversimplified, but still is worth knowing about

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 … Toward a Formal Model of Cognitive Synergy 

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

I wrote a nontechnical overview book on my own approach to AGI (also surveying a lot of other material), called The AGI Revolution

Way back in 2005 or so,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

Marcus Hutter’s book Universal AI  is pretty  mathematical but it’s also a work of art and has a great deal of conceptual value.  Shane Legg’s book Machine Superintelligence is along similar lines and shorter and easier to read.

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

And then of course there’s our tome on OpenCog, *Engineering General Intelligence, Volume 1 and Volume 2* … free versions are linked from here 

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