Gödel, Escher, Bach

A metaphorical fugue on minds and machines in the spirit of Lewis Carroll.

Gödel, Escher, Bach is one of those books that many of us try to read through and far fewer finish. In an effort to actually get through the book, I've been taking notes on my way through; at the end of each chapter, I'd like to write up a chapter summary, to put my notes in a more narrative form.

Introduction: A musico-logical offering

This book deals with strange loops, in which moving through the various layers of some hierarchical system unexpectedly brings us right back to the beginning. Strange loops embody the ideas of recursion and infinity, and as we look through the lens of paradox we find highlighted a conflict between the finite and the infinite. One such paradox that forms a cornerstone of strange loops is Gödel’s incompleteness theorem, paraphrased as all consistent axiomatic formulations of number theory include undecidable propositions - that is, while proofs are demonstrations of propositions, they occur in a fixed system.

Reasoning is a powerful tool, partly because it is a patterned process that is governed to some extent by clearly communicable laws. Strange loops can defy reason, though, particularly through self-reference. We could try to ban self-reference, but the ability to reference oneself directly (e.g. this sentence is false) as well indirectly (the following sentence is false. The previous sentence is true.) makes this a difficult, if not impossible, task. Bertrand and Russell set out to do just this in the Principia Mathematica, deriving all of known mathematics from logic, eventually defining a theory of types via an artificial hierarchy. This theory of types was only sufficient to handle Russell’s paradox, but not the Epimenides or Grelling paradoxes. The idea of an object language at the bottom of a hierarchy where references could only be to the specific domain of the object arose, with metalanguages that described object languages (or lower metalanguages). What Gödel’s paper noted was that axiomatic system could derive all of the number-theoretic truths and remain fully consistent.

If no axiomatic system can remain fully consistent, how can we program intelligent behaviour? We have to build a hierarchy of rules; at the bottom are simple rules with increasing layers of metarules. A core thesis of the book is that strange loops involving self-modifying rules (whether that self-modification is direct or indirect) are at the core of intelligence.

Chapter I: The MU-puzzle

We will explore these ideas using post-production systems, which are those in which we are given some initial form, some rules for manipulating these forms, and perhaps a desired end state. For example, the MU-puzzle gives us an initial form (MI), and a set of rules of inference (or rules of production) that can be applied to a given state:

  1. Given a string with the last letter I, a U may be appended (ex. MI -> MIU).
  2. Given a string Mx, we can obtain Mxx (ex. MIU -> MIUIU).
  3. Any sequence of III may be replaced with U.
  4. Any occurrence of UU may be dropped.

In a formal system, we can think of a theorem as a string of symbols; a proof becomes the sequence of steps (or derivation) in which repeated application of the inference rules for the system produce the proof. The initial theorems (the free theorems) are called axioms, and a requirement of formal systems is the set of axioms must be characterised by a decision procedure, which is some test for theoremhood that always terminates in a finite amount of time.

As we start to dig into formal systems, we’ll need to be able to distinguish work that is done within the system from statements or observations about the system. Intelligence has the inherent property of being able to pop out of the current task to see what’s going on and identify patterns. The ability to recognise patterns might be another core property of intelligence; the unobservance of machines is generally thought to be a defining characteristic of machines to many people. As such, we’ll have three modes for dealing with formal systems:

  • The mechanical mode (M-mode), in which inference rules are mechanically applied to produce theorems.
  • The intelligent mode (I-mode), in which we look for patterns.
  • The un-mode (U-mode), which is yet to be described.

Chapter II: Meaning and Form in Mathematics

What we’re looking for is more than just a description of the axioms: not all grammatically correct forms are valid axioms, necessitating a test for axiomhood. Sometimes, a formal system will include axiom schemas, a literal expression that provides such a test. For example, in the pq-system, there is an axiom scheme such that xp-qx- is considered an axiom if x is composed of only hyphens. This early in the book, our formal systems only cover axioms described as strings; in the future, there will be a more nuanced notion of “form.”

A key distinction between the pq-system and MU-puzzle is that the pq-system only has lengthening rules, and lacks shortening rules. Systems with this property have a decision procedure for their axioms.

Now we have consider meaning and form: the string --p---q----- might symbolise 2+3=5, but does it actually mean that? There is an isomorphism between pq-theorems and additions: these two complex structures can be mapped onto each other such that each part of each structure maps to a corresponding part in the other. Isomorphism are also a cornerstone of intelligence; Hofstadter particularly notes that “The perception of an isomorphism between two known structures is a significant advance in knowledge… such perceptions of isomorphism which create meanings in the minds of people.” Similarly, a correspondence between symbols and words is called an interpretation - but note that this correspondence couldn’t be perceived without a prior choice of an interpretation of the symbols. Interpretation does not imply any meaning; there are meaningless interpretations devoid of isomorphic connections between theorems in the system and reality.

On a cautionary note, the symbols in a formal system will inevitably take on meaning once an isomorphism is found; this meaning must remain passive - we can’t create new theorems in the system by translating theorems from an isomorphic system.

There is some natural uncertainty as to whether our model of a formal system based on some part of mathematics is accurate - unless we have perfect information about the formal system and the domain of the interpretation, how can we know that theorems express truths? The answer lies in trusting the symbolic process where digits are treated as symbols and we have simple rules for manipulating them. It’s important to remember, however, that we often deal in “ideal” numbers that don’t actually reflect reality - for example, how do you count ideas? This requires an abstraction of the physical counting process.

Reasoning is the basis for our trust in mathematics: simple, beautiful, and compelling proofs. What makes the proof compelling is that even though each step in the process seems obvious, the end result isn’t - and we can’t check directly whether the end is true or not. Yet we are compelled to believe in this because acceptance of reason provides no alternative: if we agree that the step is reasonable, there’s no other outcome other than the conclusion. Such is the goal of mathematics: derivation of ironclad proofs of some non-obvious statement.

Chapter III: Figure and Ground

Our human nature causes the natural tendency to blur the distinction between strings and their interpretation as we work in the I-mode. It’s somewhat akin to the artistic ideas of figure and ground: figure being the primary, foreground subject of an artwork and ground being the negative space that traditionally has been incidental to the figure. This leads to two types of figures: cursively-drawn figures, where the ground is an accidental by-product of the figure, and recursive figures, where the ground is a figure in its own right. From this, we see that there exist recognisable forms whose ground is not any kind of recognisable form - that is, that there are cursively-drawn figures that are not recursive.

Let’s consider figure and ground in the context of formal systems: the figure is comprised of the set of theorems in the formal system. There exist formal systems where the negative space (the non-theorems) is not the positive space (set of theorems) of any other formal system. That is, there exist recursively enumerable sets (the mathematical equivalent of cursively-drawn figures) that are not recursive. From this, it follows that there are formal systems for which there are no typographical decision procedures.

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