this post was submitted on 29 Jul 2023
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Well mostly the flaw is people assigning the test abilities it was never intended. Like testing intelligence. Turing outright as first thing in the paper presenting "imitation game" noted moving away from testing intelligence, since he didn't know to do that. Even on the realm of "testing intelligent kind of behavior" well more like human like behavior and human being here proxy for intelligent, it was mostly an academic research idea. Not a concrete test meant to be some milestone.
Turing wanted a way to step away from stuff like "thinking" and "intelligence" directly and then proposed "imitation game" mostly to the rest of the academia as way to develop computer systemics more towards "intelligent behavior". It was mostly like "hey we need some goal to have as a goal to have something to move towards with these intelligence things. This isn't intelligence, but it might be usefull goal or tool for development work". Since without some goal/project/aim to have project don't advance. So it was "how about we try to develop a thing, that can beat this imitation game. Wouldn't that be good stepping stone. Then we can move to the actual serious stuff. Just an idea".
However since this academic "thinking out aloud spitballing ideas" was uttered by the Alan Turing, it became the Turing Test and everyone started taking it way too seriously. Specially outside academia. Who yes did play the imitation game with their programs as it was intended as research and development tool.
exemplified by for example this little exerpt of "not trying to do anything too complete and ground breaking here":
It is pretty literally "I had a thought". Turin makes no claims of machine beating the game having any significance other than "machine beat this game I came up with, neat". There is no argument of if machine beats imitation game, then X or then it means Y is reached.
Rest of the paper is actually about objections to the core idea of "it could ever be possible for machine to think" and even as such said imitation game is kinda lead in or introduction to Turing's treatise various objections of various "it would be impossible for machine to think" arguments. Starting with theological argument of "only human soul can think. Hence no animal or machine can think." .... since it was 1950's.
What is a Chinese room?
Imagine that you're locked in a room. You don't know any Chinese, but you have a huge instruction book written in English that tells you exactly how to respond to Chinese writing. Someone outside the room slides you a piece of paper with Chinese writing on it. You can't understand it, but you can look up the characters in your book and follow the instructions to write a response.
You slide your response back out to the person waiting outside. From their perspective, it seems like you understand Chinese because you're providing accurate responses, but actually, you don't understand a word. You're just following instructions in the book.
Its a thought experiment involving a room where people write letters and shove them under the door of the Chinese kid's dorm room. He doesn't understand what's in the letters so he just forwards the mail randomly to his Russian and Indian neighbours who sometimes react angrily or happily depending on the content. Over time the Chinese kid learns which symbols make the Russian happy and which symbols make the Indian kid happy, and so forwards the mail correspondingly until he starts dating and gets a girlfriend that tells him that people really shouldn't be shoving mail under his door, and he shouldn't be forwarding mail he doesnt understand for free.
https://en.wikipedia.org/wiki/Chinese_room
Wow, solid wiki article! It's very hard to say anything on the subject that hasn't been said.
I didn't see the simple phrasing:
"What if the human brain is a Chinese Room?"
but that seems to fall under eliminative materialism replies.
Part of the Chinese Room program (both in our heads and in an AI) could be dedicated to creating the experience of consciousness.
Searle has no substantial logical reply to this criticism. He openly takes it on faith that humans have consciousness, which is funny because an AI could say the same thing.
The whole point of the Chinese room is that it doesn't need anything "dedicated to creating the experience of consciousness". It can pass the Turing test perfectly well without such a component. Therefore passing the Turing test - or any similar test based solely on algorithmic output - is not the same as possessing consciousness.
Man, I love coming across terms like this.
Chinese Room, Chinese Walls, Dutch Treat, Dutch Uncle, Dutch Oven.
The Chinese room argument makes no sense to me. I cant see how its different from how young children understand and learn language.
My 2 year old sometimes unmistakable start counting when playing. (Countdown for lift off) Most numbers are gibberish but often he says a real number in the midst of it. He clearly is just copying and does not understand what counting is. At some point though he will not only count correctly but he will also be able to answer math questions. At what point does he “understand” at what point would you consider that chatgpt “understands”  There was this old tv programm where some then ai experts discussed the chinese room but they used a chinese restaurant for a more realistic setting. This ended with “So if i walk into a chinese restaurant, pick sm out on the chinese menu and can answer anything the waiter may ask, in chinese. Do i know or understand chinese? I remember the parties agreeing to disagree at that point.
For one thing, understanding implies that a word is linked to an abstract concept. So if you say "The car is red", you first need to compare the abstract concept of "red" to the car in question.
The Chinese room bypasses all of that, it can say "The car is red" without ever having seen a red object at all, much less consider the abstract concept of red.
Do you maintain this line of reasoning if it only says “the car is red” when the car is in fact red. And is capable of changing the answer to correctly mentioned a different color when the item In question is a different question.
Some ai demos show that programs like gpt-4 are already way passed this when provided with, it can not only accurate describe whats in the image but also the context.
Some examples, mind these where shown in an openAI demo for gpt4, Open ai has not yet made their version of this tech publicly available.
When i see these examples, i am not convinced that the ai truly understands everything it is saying. But it does seem to understand context, One of the theories on how it can do this (they are still a black box) is talked about in some papers that large language models may actually create an internal model of the world similar to humans and use that for logical reasoning and context.
It doesn't matter if the answer is right. If the AI does not have an abstract understanding of "red" then it is using a different process to get to the answer than humans. And according to Searle, a Turing machine cannot have an abstract understanding of "red", no matter how complex the question or how complex an internal model is used to determine its answers.
Going back to the Chinese Room, it is possible that the instructions carried out by the human are based on a complex model. In fact, it is possible that the human is literally calculating the output of a trained neural net by summing the weights of nodes, etc. You could even carry out these calculations yourself, if you could memorize the parameters.
Your use of "black box" gets to the heart of it. Memorizing all of the parameters of a trained NN allows you to calculate an answer, but they don't give you any understanding what the answer means. And if they don't tell you anything about the meaning, then they don't tell the CPU doing that calculation anything about meaning either.
I don’t think ai will ever use a process to derive an answer the same way as a human does. Maybe thats part of the goal for the original Turing test but i don’t think the biological human ways is the only way to intelligent understanding “on par” with human intelligence.
Does a blind person have an abstract understanding of “red”?
I can imagine an intelligent alien species, unable to perceive colors like us but yet having an sense to detect to what they call “surface temperature” which allow them to recognize specific wave lengths of the ligt reflecting on surfaces, this is sort of how humans see color but maybe for the alien they hear this as sound. They then go on and use this sensory input to make music. A song about the specific light wavelength that humans know as a deep bordeaux red color.
Do these biological Intelligent aliens not have an abstract understanding of the color red? I would say they do, its different then how we understand it for sure but both are valid. An even more supreme species might have both those understandings and combine them for an even deeper fuller sensory understanding of “red”.
I see ai similar to this, its a program contained in computer hardware. With no body of its own its depending on us to provide it with input. This is now mostly text so the ai obtains a text based understanding of the world, hence why its so decent at poetry. But when we attach more sensors like a camera then that will change.
I am not sure how to discuss “a human using instructions to calculate perfect answers, but not getting an understanding of what that answers means” wed might have to agree to disagree on that but i feel like thats all my brain has ever done. Were born in a complex place we do not comprehend, are given some instructions mostly by copying what others are doing. Then we find a personal meaning in those things, which as far as i am aware is unique for everyone. (Tbf: i am an autist, the fact that not all humans experience reality the same and that i had to find and learn my own personal understanding of the world has greatly shaped how i think about these systems)
Perhaps I should rephrase the argument as Searle did. He didn't actually discuss "abstract understanding", instead he made a distinction between "syntax" and "semantics". And he claimed that computers as we know them cannot have semantics, whereas humans can (even if we don't all have the same semantics).
Now consider a quadratic expression. If you want to solve it, you can insert the coefficients into the quadratic formula. There are other ways to solve it, but this will always give you the right answer.
If you remember your algebra class, you will recognize that the quadratic formula isn't just some random equation to compute. You use it with intention, because the answer is semantically meaningful. It describes things like cars accelerating or apples falling.
You can teach a three year old to identify the coefficients, you can show them the symbols that make up the quadratic formula: "-", second number, "+", "√", "(", etc. And you can teach them to copy those symbols into a calculator in order. So a three year old could probably solve a quadratic expression. But they almost certainly have no idea why they are doing what they are doing. It's just a series of symbols that they were told to copy into a calculator, their only intention was to copy them in order correctly. There are no semantics behind the equation.
For that matter, a three year old could equally well enter the symbols necessary to calculate relativistic time dilation, which is an even shorter equation. But if their parents proudly told you that their toddler can solve problems in special relativity, you might think, "Yes... but not really."
That three year old is every computer program. Sure, an AI can enter symbols into a calculator and report the answer. If you tell them to enter a different series of symbols, they will report a different answer. You can tell the AI that one answer scores 0.1 and another scores 0.8, and to calculate a different equation that is based partly on those scores. But to the AI, those scores and equations have no semantic meaning. At some point those scores might stop increasing, and you will declare that the AI is "trained". But at no point does the AI assign any semantic content behind those symbols or scores. It is pure syntax.
ChatGPT will never understand. LLMs have no capacity to do so.
To understand you need underlying models of real world truth to build your word salad on top of. LLMs have none of that.
What are your underlying models of the world built out of? Because I'm human, and mine are primarily built out of words.
How do you draw a line between knowing and understanding? Does a dog understand the commands it's been trained to obey?
Your underlying model is not made out of words, but out of concepts. You can have multiple words that all map to the same concept, i.e. cosmos, universe, space. Or a single word that map to different concepts.
No, they aren't. You represent them with words. But you sure as hell aren't responding to someone throwing you a football with words trying to figure out where it's going.
No, a dog (while many times more intelligent than chatGPT) doesn't understand anything.
Your brain understands concepts and can self-conceptualise, LLMs cannot do either. They can sound convincingly as if they understand concepts but that's because we fill in gaps due to how we understand language. The examples of broken or distorted sentences being understandable applies here. You and I can communicate in broken sentences because you and I understand the concepts beneath the conversation. LLMs play on that understanding but they do not understand its concepts.
Yes... the chinese experiment misses the point, because the Turing test was never really about figuring out whether or not an algorithm has "conscience" (what is that even?)... but about determining if an algorithm can exhibit inteligent behavior that's equivalent/indistinguishable from a human.
The chinese room is useless because the only thing it proves is that people don't know what conscience is, or what are they even are trying to test.
I mean, is there any test that can do significantly better?
That's what we need to figure out