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this post was submitted on 29 Jul 2023
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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.