[p4wnc6]

I think the reference to cellular automata is a bit misplaced. Yes, Rule 110 is Turing complete, but I don't think this has anything to do with the sort of lookup table that Searle is appealing to. You can write programs with Rule 110, by arranging an initial state and letting the rules mutate it. However, a lookup table that merely contains terminal endpoints can't do that. It doesn't have the necessary self-reference.

People always like to say this about Matthew Cook's result on Rule 110 and connect it to Searle's argument, but they are just totally different things. If Searle instead talked about encoding a general purpose AI program to translate sentences, and his substrate of computation happened to be a cellular automata, that's fine, but it would be no different than him postulating an imaginary C++ program that translates the sentences, meaning he would be assuming a solution to A.I. completeness from the start, whether it is via cellular automata or some typical programming language or whatever.

But the type of lookup table he is talking about is just an ordinary hash table, it's just a physical store of fixed immutable data which is not interpreted as self-referencing in a programmatic sense, but instead which simply holds onto, and is "unaware" of, translation replies for each possible input.

[makebelieve]

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.

[wsieroci]

"The approach I am taking is a kind of metabolic computing"

1. What exactly do you mean by "a kind of metabolic computing"?

2. What is first step you want to accomplish?

3. What do you think (feel) is actually happening in any sentient animal that leads to semantic content? How it is possible that this happens? We know that it happens because we are sentient animals. The question is: where is this difference because as animals we are also machines and it seems that everything what is happening in our cells is purly syntactical.

[makebelieve]

If we look at how organism manage semantic information, we know it is done with cells and cell clusters and making "connections" between cells in nervous systems. (it isn't all nervous system cells though). The cells exist and function because of the molecular activity that goes on in the cell and to a lesser degree the surrounding environment. (a hostile environment can destroy cells through molecular interactions). But there is not "cell" level phenomena the produces cells or their behavior. It's all molecular interactions.

Molecules are driven not by exterior phenomena, but by changes intrinsic to the molecules and atoms and other particles they interact with. We live in a particle universe. We do not live in a universe with outside "forces" or "laws" that cause the particles to behave in any way. Everything about the physics is from the inside out, and the interactions are always "local" to particles. Large scale phenomena are actually huge quantities of particle phenomena that we perceive as a single large phenomena. (this is a kind of illusion).

When we try to write programs that simulate physical phenomena, like atoms, or molecules we write the code from the outside in. It is the program which causes the data changes to simulate some chemistry. But in nature, there is no program determining how the molecules act. chemical changes occur because of features about the individual molecules interacting, not because of a rule. Simulations like this do not replicate what happens between individual molecules, the replicate what would happen if molecules were controlled by an external rule (which they are not).

any rule based simulation can only express the set of possible outcome conditions from the rules and data. but it cannot capture it's axioms, and it cannot capture conditions that in fact exist outside it's axiomatic boundary. (Aaronson and p4wnc6 both remark on this limitation by pointing out the complexity necessary to achieve a good Turing test result or sentient AI).

My approach is to treat this intrinsic nature of molecular interactions as a fact and accept it as a requirement for a kind of computer system that can do "molecular interactions" from the inside out. And my supposition is (not proved yet!) that a mixture of such interactions could be found that is stable, that would be homeostatic. And if such a mixture could be found, then could a mixture be found that can be encapsulated in a membrane like structure. And could such a mixture store in it's set of code/data like "molecules" it's internal program - eg. DNA.

I think the answer is yes.

There are three different steps that all have to work together.

One is understanding how representation works (see my email to you, it's outside the bounds of this thread). So understanding how semantic content and awareness works, in all situations and conditions, is a precondition to recognizing when we have code that can generate semantic content.

The next is finding a model of how representation is instantiated in organisms to use as a basis for a machine model.

The third is then coding the machine model, to do what organisms do so that the machine understands semantic content, and the machine should produce awareness and consciousness.

I believe metabolic functioning is the key feature to allow us to do representational processing. hence why I call the approach I am taking, metabolic computing. The step I am currently on is writing up an interpreter that I think can do "molecular" interactions between code/data elements. Meaning that the data/code elements determine all the interactions between data and code intrinsically. the interpreter processes those "atomic" interactions based on intrinsic features of that code. Essentially, every bit of code/data is a single function automata and they can all change each other so the system may or may not work dependent on the constituent "molecules" of the system. I call this "the soup".

previous prototypes required me to do all the addressing, which itself was a big leap forward for me. But now the code/data bits do the addressing themselves. (each function interacts "locally" but interactions can create data structures, which is the corollary to molecules forming into larger structures and encapsulating molecular interactions into things like membranes).

So the next step is finish the interpreter, then see if I can get the right soup to make functions (like dna and membranes. I've written out RNA like replication examples and steady state management as discussed in systems biology so I there is a path forward). Then see if I can get to homeostasis and a "cell" from datastructures and interacting "molecules". the step after that is multiple "cells" and then sets of cells that form structures between inputs and outputs. eg. a set of "retina" cells that respond to visual inputs, a set of cells that "process" signals from those retina cells, and motor cells that take their cues from "process" cells etc.

the cell level stuff and above is mostly straightforward. it's forming different kinds of networks that interact with each other. Nodes themselves are semantic content. but how do you make networks from the inside out? from meaningless (syntactic) molecular interactions? that is where the metabolic systems (and stigmergy) come into play. (actually, stigmergy comes into play at many levels)

In biology, the syntactic to semantic jump happens at the cell. the cell itself is a semantic thing. the syntactic processes maintain the cell. the cells underlying mechanims and interactions are all syntactic. and the cell doesn't "cause" anything, everything happens in the cellular processes for their own intrinsic reasons, but the cell, it is semantic content. (embodiment).

the embodiment path is how to get representation, awareness, and consciousness.

My apologies that this is somewhat all over the map, but the problem of making machine sentience actually work requires that theory, model, and implementation all work. And if any of them don't work, then the outcome of sentience becomes impossible. And that's just a lot of different stuff to try to compress into a comment!