Quus, Redux

[updated 3/20/2022 to add footnote 2]

Philip Goff
explores Kripke's quss function, defined as:

(defparameter N 100)

(defun quss (a b)
(if (and (< a N) (< b N))
(+ a b)

In English, if the two inputs are both less than 100, the inputs are added, otherwise the result is 5.
Goff then claims:

Rather, it’s indeterminate whether it’s adding or quadding.

This statement rests on some unstated assumptions. The calculator is a finite state machine. For simplicity, suppose the calculator has 10 digits, a function key (labelled "?" for mystery function) and "=" for result. There is a three character screen, so that any three digit numbers can be "quadded". The calculator can then produce 1000x1000 different results. A larger finite state machine can query the calculator for all one million possible inputs then collect and analyze the results. Given the definition of
quss, the analyzer can then feed all one million possible inputs to quss, and show that the output of quss matches the output of the calculator.

Goff then tries to extend this result by making N larger than what the calculator can "handle". But this attempt fails, because if the calculator cannot handle
bigN, then the conditionals (< a bigN) and (< b bigN) cannot be expressed, so the calculator can't implement quss on bigN. Since the function cannot even be implemented with bigN, it's pointless to ask what it's doing. Questions can only be asked about what the actual implementation is doing; not what an imagined unimplementable implementation is doing.

Goff then tries to apply this to brains and this is where the sleight of hand occurs. The supposed dichotomy between brains and calculators is that brains can know they are adding or
quadding with numbers that are too big for the brain to handle. Therefore, brains are not calculators.

The sleight of hand is that our brains can work with the descriptions of the behavior, most calculators are built with only the behavior. With calculators, and much software, the descriptions are stripped away so that only the behavior remains. But there exists software that works with descriptions to generate behavior. This technique is known as "symbolic computation". Programs such as
Maxima, Mathematica, and Maple can know that they are adding or quadding because they can work from the symbolic description of the behavior. Like humans, they deal with short descriptions of large things1. We can't write out all of the digits in the number 10^120. But because we can manipulate short descriptions of big things, we can answer what quss would do if bigN were 10^80. 10^80 is less than 10^120, so quss would return 5. Symbolic computation would give the same answer. But if we tried to do that with the actual numbers, we couldn't. When the thing described doesn't fit, it can't be worked on. Or, if the attempt is made, the old programming adage, Garbage In - Garbage Out, applies to humans and machines alike.

[1] We deal with infinity via short descriptions, e.g. "10 goto 10". We don't actually try to evaluate this, because we know we would get stuck if we did. We tag it "don't evaluate". If we actually need a result with these kinds of objects, we get rid of the infinity by various finite techniques.
[2] This post title refers to a prior brief mention of
quss here. In that post, it suggested looking at the wiring of a device to determine what it does. In this post, we look at the behavior of the device across all of its inputs to determine what it does. But we only do that because we don't embed a rich description of each behavior in most devices. If we did, we could simply ask the device what it is doing. Then, just as with people, we'd have to correlate their behavior with their description of their behavior to see if they are acting as advertised.


Truth, Redux

In response to the claim on Twitter that "truth is metaphysical", I claimed the opposite, that "truth is actually physical (it's the behavior of recognition)". Being unhappy with my previous demonstration of this (it's clumsy, IMO), I want to see if there is a simpler demonstration.

Logic is mechanical operations on distinct objects. At it simplest, logic is the selection of one object from a set of two (see "
The road to logic", or "Boolean Logic"). Consider the logic operation "equivalence". If the two input objects are the same, the output is the first symbol in the 0th row ("lefty"). If the two input objects are different, the output is the first symbol in the 3rd row ("righty").


If this were a class in logic, the meaningless symbols "lefty" and "righty" would be replaced by "true" and "false".


But we can't do this. Yet. We have to show how to go from the meaningless symbols "lefty" and "righty" to the meaningful symbols "T" and "F". The lambda calculus shows us how. The lambda calculus describes a universal computing device using an alphabet of meaningless symbols and a set of symbols that describe behaviors. And this is just what we need, because we live in a universe where atoms do things. "T" and "F" need to be symbols that stand for behaviors.

We look at these symbols, we recognize that they are distinct, and we see how to combine them in ways that make sense to our intuitions. But we don't know we do it. And that's because we're "outside" these systems of symbols looking in.

Put yourself inside the system and ask, "what behaviors are needed to produce these results?" For this particular logic operation, the behavior is "if the other symbol is me, output T, otherwise output F". So you need a behavior where a symbol can positively recognize itself and negatively recognize the other symbol. Note that the behavior of T is symmetric with F. "T positively recognizes T and negatively recognizes F. F positively recognizes F and negatively recognizes T." You could swap T and F in the output column if desired. But once this arbitrary choice is made, it fixes the behavior of the other 15 logic combinations.

In addition, the lambda calculus defines true and false as behaviors.
1 It just does it at a higher level of abstraction which obscures the lower level.

In any case, nature gives us this behavior of recognition with electric charge. And with this ability to distinguish between two distinct things, we can construct systems that can reason.

[1] Electric Charge, Truth, and Self-Awareness. This was an earlier attempt to say what this post says. YMMV.


On Rasmussen's "Against non-reductive physicalism"

I'm re-reading Feser's "Philosophy of Mind" and becoming increasingly cranky as I do so. It's not a good book. So when this tweet appeared in my timeline, I responded with my usual grace and charm.


A day later, a similar tweet appeared. This time, the tweet was advertising Dr. Josh Rasmussen as the speaker. Rasmussen is an associate professor of philosophy at Azusa Pacific University. His CV lists the paper "Against Non-reductive Physicalism" which attempts to argue that "thoughts are ... not physical nor grounded in the physical."

His argument is examined below the fold.