I was not trying to connect rule 110 to Searle's argument per se, but rather to the critique of Searle's argument. Namely, that criticisms of the lookup table are not criticisms of Searle's argument or the point he makes. a C++ program, brainfk, a CA, a one instruction set computer, or whatever computational process is used doesn't matter. The lookup table is just one component of the Rooms operation. I agree Searle is talking about a hash table, but he is also talking about the rules to interface an input value to a set of possible output values via some mechanical process, and the man in the room acts as a kind of stack machine.

You are right, Searle isn't making an argument about the translation of sentences. (translating them to what?)

He is making an argument about how the mechanism of computation cannot capture semantic content. He explains this in the google video very well: https://www.youtube.com/watch?v=rHKwIYsPXLg

And all of the... let's call them "structural" critiques are moot. Searle's point is that computer systems cannot understand semantic content because they are syntactic processing machines. And he shows this with his argument.

The opposite view is that computers can understand semantic content. (so there is understanding and there is meaning understood by the computer) and the reason Searle doesn't believe computers can do this is because his argument is flawed.

Which leaves us with a small set of options:

1) That the structure Searle proposes can in fact understand semantic content and Searle just doesn't understand that it does.

I don't think anyone believes this. My iphone is certainly more capable, a better machine, with better software than Searle's room, and no one believes my iphone understands semantic content. so the belief the Room does understand semantic content but not my iphone is plainly false.

2) Searle's Room is simply the wrong kind of structure, or the Room is not a computer, or not a computer of sufficient complexity and therefore it cannot understand semantic content

I think this is the point you are making, but correct me if I'm wrong. This is not an objection against Searle's point. It's a critque of the structure of the argument, but not the argument itself. Searle could rewrite his argument to satisfy this objection, but it wouldn't change his conclusion.

Which bring us to the generalized objection:

3) that sufficient complex computer would understand semantic content.

Aaronson's paper is about the complexity problem and how a sufficiently complex system would APPEAR to understand semantic content by passing a Turing test within some limited time.

There are many arguments to this line of reasoning. One of them is that all such limitations are irrelevant. You yourself are not engaged in a limited time turing test, no person is. The issue is not passing turing tests, it instantiating sentience.

But thinking about complexity gets us off the root of the objection. You intuit that increasing or decreasing complexity should give us some kind of gradient of sentience. So an insufficiently complex system would not be sentient and would not understand semantic content, but this isn't what Searle is arguing.

Searle is demonstrating that no syntactic processing mechanism can understand semantic content. Understanding semantic content is a necessary condition for sentience, therefore no computer which does syntactic processing can be sentient. A gradient of complexity related to sentience is irrelevant.

In the one case: our computers become so complex it becomes sentient -> because it is sentient it can understand semantic content. Vs. understand semantic content and that leads to sentience.

The gradient of complexity to sentience is an intuition. Understanding of semantic content can be atomic. Even if a computer only understands the meaning of one thing, that would disprove Searle's argument. A gradient of complexity isn't necessary. Searle is saying there is a threshold of understanding semantic content that a computer system must pass to even have a discussion about actual sentience. And if a computer is categorically incapable of understanding semantic content, it is therefore incapable of becoming sentient.

Said another way, sentience is a by-product of understanding semantic content. Sentience is not a by-product of passing turing tests. The complexity required to pass a turing test, even of finite or infinite length, says nothing about whether a machine does or does not understand semantic content.

All the structural critiques of Searle fail because they do not offer up a program or system that understands semantic content.

Show me the code that runs a system that understands semantic content. Even something simple, like true/false. or cat/not a cat. If Searle's structure of the room is insuffiently complex, then write a program that is sufficiently complex. And if you can't, then it stands to reason that Searle at least might be correct: computers, categorically, cannot understand semantic content BECAUSE they do syntactic processing.

Google's awesome image processing that can identify cats does not know what a cat is at all. It simply provides results to people who recognize what cats are, and recognize that the google machine is very accurate at getting the right pictures. but even when google gets it wrong, it does not know the picture does not have a cat in it. In fact, the google machine does not know if what it serves up is a cat picture even if there is a cat in the picture.

The Searle Google talk covers this very well: https://www.youtube.com/watch?v=rHKwIYsPXLg

If you fed googles cat NN a training corpus of penguin pictures and ranked the pictures of penguins as successes, it would serve up penguins as if they were cats. But no person would ever tell you a cat is a penguin. Because penguins and cats are different things, they have different semantic content.

I would love to see that Searle is wrong. I'm sure he would be just as pleased. So I am curious if you do have or know of a machine that does do, even the smallest amount, of semantic processing. Because solving that problem with symbolic computation would save me a ton of effort.