I'm going to burn some social capital on asking a stupid question, because it's something that's been bothering me for a long time. The question is, why do we think we know that it's necessary to understand a lot of mathematics to productively engage in FAI research?
My first line of skepticism can perhaps be communicated with a simplified analogy: It's 10,000 BC and two people are watching a handful of wild sheep grazing. The first person wonders out loud if it would be possible to somehow teach the sheep to be more docile.
The second person scoffs, and explains that they know everything there is to know about training animals, and it's not in the disposition of sheep to be docile. They go on to elaborate all the known strategies for training dogs, and how none of them can really change the underlying temperament of the animal.
The first person has observed that certain personality traits seem to pass on from parent to child and from dog to puppy. In a flash of insight they conceive of the idea of intentional breeding.
They cannot powerfully articulate this insight at the level of genetics or breeding rules. They don't even know for a fact that sheep can be bred to be more docile. But nonetheless, in a flash, in something like one second of cognitive experience they've gone from not-knowing to knowing this important secret.
End of analogy. The point being: it is obviously possible to have true insights without having the full descriptive apparatus needed to precisely articulate and/or prove the truth of the insight. In fact I have a suspicion that most true, important insight comes in the form of new understandings that are not well-expressed by existing paradigms, and eventually necessitate a new communication idiom to express the new insight. Einstein invented Einstein notation because not just because it's succinct, but because it visually rearranges the information to emphasize what's actually important in the new concept he was communicating and working with.
So maybe my steelman of "why learn all this math" is something like "because it gives you the language that will help you construct/adapt the new language which will be required to express the breakthrough insight." But that doesn't actually seem like it would be important in being able to come up with that insight in the first place.
I will admit I feel a note of anxiety at the thought that people are looking at this list of "prerequisites" and thinking, wow, I'm never going to be useful in thinking about FAI. Thinking that because they don't know what Cantor's Diagonalization is and don't have the resources in terms of time to learn, their brainpower can't be productively applied to the problem. Whereas, in contrast, I will be shocked if the key, breakthrough insight that makes FAI possible is something that requires understanding Cantor's Diagonalization to grasp. In fact, I will be shocked if the key, breakthrough insight can't be expressed almost completely in 2-5 sentences of jargon-free natural language.
I have spent a lot of words here trying to point at the reason for my uncertainty that "learn all of mathematics" is a prerequisite for FAI research, and my concerns with what I perceive to be the unproven assumption that the pathway to the solution necessarily lies in mastering all these existing techniques. It seems likely that there is an answer here that will make me feel dumb, but if there is, it's not one that I've seen articulated clearly despite being around for a while.
Fwiw I also think it is not necessary to know lots of areas of math for AI safety research. Note that I do in fact know a lot of areas of math relatively shallowly.
I do think it is important to be able to do mathematical reasoning, which I can roughly operationalize as getting to the postrigorous stage in at least one area of math.
After reflecting on recent criticism, all RAISE projects have been discontinued, and a postmortem is in our plans. (Update: the postmortem is here) One of those projects was the AI Safety prerequisites online course originally announced here. We're sharing the curriculum here in almost plaintext format so that people can easily find and access it. There is also a GDoc here. (Note: two other products of RAISE are series of lessons on IRL and IDA. They don't have such neatly formed curriculums, and they are still accessible as online lessons.)
It was supposed to be a guide for helping people who want to get into AI safety research. It contains only foundations of math topics (Logic and proof, ZF(C) Set theory, Computability theory to be precise), which are more useful for agent foundations stuff and not useful for machine learning stuff. It was planned to be extended to cover more topics, but that never happened.
How to use this
The main path contains 20 levels. It is the recommended path through the curriculum. Its levels are actually short sequences of levels from the three other paths.
To see what textbooks are required to study a path, see its beginning. Computability theory and set theory paths require two paid textbooks.
13 out of 20 levels of the main path are covered by our online course, which is free (but still requires paid textbooks). To use the online course, register here. You might prefer to use it instead of the text below because it provides more sense of progress, contains solutions to the exercises, has some writing mistakes fixed, maybe feels like a less tedious thing, and provides some additional exercises which we don't think are important.
Credits
The curriculum was made by Erik Istre and Trent Fowler. People who created the online course are: Philip Blagoveschensky, Davide Zagami, Toon Alfrink, Hoagy Cunningham, Danilo Naiff, Lewis Hammond. Also these people contributed: Jacob Spence, Roland Pihlakas. Also, thanks to Grasple for providing their services for free.
Main path
Level 1. Basic logic
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill Guides for this Level:
Level 2. Basic set theory
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill Guides for this level:
Level 3. Set Theoretic Relations and Enumerability
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill Guides for this level:
Level 4. Formal Semantics
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 5. Formal Proof
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Milestone - You’re now a !
Take a moment to reflect on what you’ve learned so far. Remind yourself of the important concepts that you’ve come across, and mark down any concepts which are still giving you trouble. Determine whether you need to seek out further resources to clear up any confusions.
Level 6. Turing Machines and the Halting Problem
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 7. Equivalence Relations and Orderings
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 8. Abacus Computability and Mathematical Proof by Induction
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 9. The Natural Numbers in Set Theory and More Induction
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 10. Recursive Functions
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 11. Set Theoretic Recursion
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 12. The Equivalence of Different Notions of Computability
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Milestone - You’re now a !
As last time, take a moment to reflect on what you’ve learned so far. Remind yourself of the important concepts that you’ve come across, and mark down any concepts which are still giving you trouble. Determine whether you need to seek out further resources to clear up any confusions.
Level 13. Isomorphisms
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 14. Logic Review and The Relationship Between Computation and Logic
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 15. Finite and Countable Sets
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 16 (elective). Linear Orders and Completing the Real Numbers
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 17. Basic Model Theory
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Milestone - You’re now a !
As last time, take a moment to reflect on what you’ve learned so far. Remind yourself of the important concepts that you’ve come across, and mark down any concepts which are still giving you trouble. Determine whether you need to seek out further resources to clear up any confusions.
Level 18 (elective). A quick look at cardinal and ordinal numbers
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 19. Arithmetization and Representation of Recursive Functions
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Level 20. Godel’s Incompleteness Theorems and Axiomatic ZFC
The big ideas:
To move to the next level you need to be able to:
Why this is important:
Skill guides for this level:
Milestone - You’re now a !
As last time, take a moment to reflect on what you’ve learned so far. Remind yourself of the important concepts that you’ve come across, and mark down any concepts which are still giving you trouble. Determine whether you need to seek out further resources to clear up any confusions.
Logic and proof path
The main textbook used in this path is a free textbook "Forall x" version 1.4. download v1.4 another link for v1.4 latext version Note that numbering of exercises sometimes changes between versions.
Another resource used in this path is Introduction to Mathematical Arguments by Michael Hutchings.
Level 1. Basic logic
Part 1 "What is logic" of this level on Grasple
Part 2 "Sentential logic" of this level on Grasple
Part 3 "Truth tables" of this level on Grasple
Read Chapter 1 of forall x, “What is Logic?”.
Read Chapter 2 of forall x, “Sentential Logic”
Read Chapter 3 of forall x, “Truth Tables”
Level 2. Quantified logic. Introduction to mathematical arguments
This level on Grasple
Level 3. Formal semantics basics
This level on Grasple
Level 4. Formal proofs
This level on Grasple
Level 5. Proof by induction
This level on Grasple
Set theory path
This path uses the Introduction to set theory. Third edition. Revised and expanded by Hrbacek, Jech.
Level 1. Basic set theory
This level on Grasple
Work the following exercises:
3.4
3.5a
Extra Credit: 3.3
Level 2. Set theoretic relations
This level on Grasple
This level uses Introduction to Set Theory by Hrbacek and Jech.
Level 3. Equivalence relations and orderings
This level on Grasple
Resources: Introduction to Set Theory by Hrbacek and Jech
Level 4. Natural numbers and induction
This level on grasple
Resources: Introduction to Set Theory by Hrbacek and Jech
Read Chapter 3 “Natural Numbers” Section 1 “Introduction to Natural Numbers”
Read Chapter 3 Section 2 “Properties of Natural Numbers”
Read Chapter 3 Section 3 “The Recursion Theorem”
Read Chapter 3 Section 4 “Arithmetic of Natural Numbers”
Level 5. Set theoretic recursion
This level at grasple
Resources: Introduction to Set Theory by Hrbacek and Jech
Level 6. Operations, structures, isomorphism
This level on Grasple
Resources: Introduction to Set Theory by Hrbacek and Jech
Level 7. Cardinality. Finite and countable sets
Resources: Introduction to Set Theory by Hrbacek and Jech
Elective: Level 8. Linear orderings. Completeness. Uncountable sets
Resources: Introduction to Set Theory by Hrbacek and Jech
Elective: Level 9. Cardinal numbers
Resources: Introduction to Set Theory by Hrbacek and Jech
Elective: Level 10. Ordinal numbers. Axiom of replacement. Transfinite induction and recursion
Resources: Introduction to Set Theory by Hrbacek and Jech
Elective: Level 11. Axiom of choice
Resources: Introduction to Set Theory by Hrbacek and Jech
Level 12. ZF(C) set theory
Resources: Introduction to Set Theory by Hrbacek and Jech
Computability theory path
This path uses Computability and Logic (Fifth Edition) by Boolos, Burgess and Jeffrey. An instructor's manual is available, it has hints for many problems.
Level 1. Enumerability and diagonalization
This level on grasple
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey
Level 2. Turing machines and the halting problem
This level on grasple
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey
Level 3. Abacus computability
This level on grasple
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey
Level 4. Recursive functions
This level on grasple
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey
Level 5. The equivalence of different notions of computability
This level on grasple
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey
Level 6. First order logic
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey
Level 7. Undecidability of first order logic
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey
Level 8. Models, their existence. Proofs and completeness.
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey
Level 9. Arithmetization. Representability of recursive functions
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey
Level 10. Indefinability, undecidability, incompleteness. The unprovability of consistency.
Resources: Computability and Logic Fifth Edition by Boolos, Burgess, Jeffrey