For example it cannot identify musical chords because despite (I presume) ample training material including explanations of how exactly this works, it cannot reasonable represent this as an abstract rigorous rule, as humans do. So I ask what is C E G and it tells me C major correctly, as it presumably appears many times throughout the training set, yet I ask F Ab Db and it does not tell me Db major, because it did not understand the rules at all.
In a sense though, this is just a logic problem.
The reason that chatGPT can write quantum computer programs to in any domain (despite the lack of existing programs!) is because it can deal with the concepts of quantum computing and the concepts in a domain (eg, predicting housing prices) and align them.
Not everything is logic and math; very little of human reasoning is. And this despite the fact that, as a Pythagorean at heart, I believe the world is made of math.
Can you be more specific? I literally don't know what you mean. What can you say about quantum mechanics that is not mathematical or logical in nature? Barring metaphysical issues of interpretation, which I assume is not what you mean.
1. Superposition: In quantum mechanics, particles can exist in multiple states simultaneously, until they are measured. This is called superposition. It's like a coin spinning in the air, being both heads and tails at the same time, until it lands and shows one face.
2. Wave-particle duality: Particles like electrons, photons, and others exhibit both wave-like and particle-like properties. This means they can sometimes behave as particles, and at other times, as waves. This dual nature has been experimentally demonstrated through phenomena like the double-slit experiment.
3. Quantum entanglement: When two particles become entangled, their properties become correlated, regardless of the distance between them. If you measure one of the entangled particles, you'll immediately know the state of the other, even if they are light-years apart. This phenomenon is often referred to as "spooky action at a distance."
4. Heisenberg's uncertainty principle: This principle states that we cannot simultaneously know the exact position and momentum of a particle. The more precisely we know one of these properties, the less precisely we can know the other. This inherent uncertainty is a fundamental aspect of quantum mechanics.
5. Quantum tunneling: In quantum mechanics, particles can "tunnel" through barriers that would be insurmountable in classical physics. This is because the particle's wave function, which describes its probable location, can extend beyond the barrier, allowing the particle to appear on the other side.
6. Quantum superposition of states: Quantum systems can exist in multiple states at once, and when you measure a property of the system, it "collapses" into one of the possible states. This is a fundamental difference between quantum and classical mechanics, where systems have definite properties even before measurement.
These concepts can be discussed and reasoned about without delving into the complex mathematical equations that govern quantum mechanics. While a mathematical understanding is necessary for rigorous study and application of the theory, non-mathematical discussions can still provide valuable insights into the strange and fascinating world of quantum mechanics.*
QED
Claiming these concepts are not mathalematical is like saying addition is not mathematics because you can explain it with words or diagrams to a child!
I hate to break it to you, but humans aren't thinking logically, or exclusively logically. In fact I would say that humans are not using logic most of the time, and we go by intuition most of our lives (intuition is shorthand for experience, pattern matching and extrapolation). There is a reason of why we teach formal logic in certain schools....
It even told me about the role of each in a chord progression and how even though they share the same notes they resolve differently
Humans clearly don't think logically anyhow. Thats why we need things like abacus to help us concretely store things, in our head everything is relative in importance to other things in the moment
So it gave you a wrong answer and when you spelled out the correct answer it said "OK" x) Was that it? Or am I missing something.
In music theory a set of the same notes can be one of several chords, the correct answer as to which one it is depends on the "key" of the song and context which wasnt provided, so the AI decided to define the root note of the chord as the bottom one which is a good and pretty standard assumption.
It's kind of like asking the AI about two equivalent mathematical functions expressed in different notation, and it saying a bunch of correct facts about the functions like their derivative, x intercept and stuff and how it can be used, but needing a prod to explicitly say the fact that they are interchangable. It's the kind of trivial barely-qualifies-as-an "oversight" I would expect actual human people who fully understand the material to make.
All the time. It often fails to understand simple concepts. It doesn't really seem to understand anything.
For example, try to get it to write some code for a program in a moderately obscure programming language. It's terrible: it will confidently produce stuff, but make errors all over the place.
It's unable to understand that it doesn't know the language, and it doesn't know how to ask the right questions to improve. It doesn't have a good model of what it's trying to do, or what you're trying to do. If you point out problems it'll happily try again and repeat the same errors over and over again.
What it does is intuit an answer based on the data it's already seen. It's amazingly good at identifying, matching, and combining abstractions that it's already been trained on. This is often good enough for simple tasks, because it has been trained on so much of the world's output that it can frequently map a request to learned concepts, but it's basically a glorified Markov model when it comes to genuinely new or obscure stuff.
It's a big step forward, but I think the current approach has a ceiling.
I don’t think its ability to program in an obscure program is really a great test. That’s a matter of syntax more than semantics, no?
Novel conceptual blends are where it excels. Yes, it needs to understand the concepts involved to blend them —but humans need that too.
It doesn't seem to have any "meta" understanding. It's subconscious thought only.
If I asked a human to program in a language they didn't understand, they'd say they couldn't, or they'd ask for further instructions, or some reference to the documentation, or they'd suggest asking someone else to do it, or they'd eventually figure out how to write in the language by experimenting on small programs and gradually writing more complex ones.
GPT4 and friends "just" take an input that seems like it could plausibly answer the request. If it gets it wrong then it just has another go using the same generative technique as before with whatever extra direction the human decides to give it. It doesn't think about the problem.
("just" doing a lot of work in the above sentence: what it does is seriously impressive! But it still seems to be well behind humans in capability.)
I think artifacts can easily reflect the understanding of the designer (Socrates claims an etymology of Technology from Echo-Nous [1])
But for an artifact to understand — this is entirely dependent on how you operationalize and measure it. Same as with people—we don’t expect people to understand things unless we assess them.
And, obviously we need to assess the understanding of machines. It is vitally important to have an assessment of how well it performs on different evals of understanding in different domains.
But I have a really interesting supposition about AI understanding that involves it’s ability to access the Platonic world of mathematical forms.
I recently read a popular 2016 article on the philosophy of scientific progress. They define scientific progress as increased understanding — and call it the “noetic account.” [2] Thats a bit of theoretical support for the idea that human understanding consists of our ability to conceptualize the world in terms of the Platonic forms.
Plato ftw!
[1] see his dialogue Cratylus
[2] Dellsén, F. (2016). Scientific progress: Knowledge versus understanding. Studies in History and Philosophy of Science Part A, 56, 72-83.
Is that really any different than asking me to attempt to program in a moderately obscure programming language without a runtime to test my code on? I wouldn't be able to figure out what I don't know without a feedback loop incorporating data.
>If you point out problems it'll happily try again and repeat the same errors over and over again.
And quite often if you incorporate the correct documentation, it will stop repeating the errors and give a correct answer.
It's not a continuous learning model either. It has small token windows where it begins forgetting things. So yea, it has limits far below most humans, but far beyond any we've seen in the past.
To the extent that humans have encoded the concepts into words, and that text is in the training set, to that degree ChatGPT can work with the words in a way that is at least somewhat true to the concepts encoded in them. But it doesn't actually understand any of the concepts - just words and their relationships.
But I suspect your notion of understanding is not measurable, is it? For you, chatGPT lacks something essential such that it is incapable of understanding, no matter the test. Or do you have a way to measure this without appeal to consciousness or essentialism?
Does understanding require consciousness? Maybe yes, for the kind of understanding I'm thinking of, but I'm not certain of that.
How do you measure understanding? You step a bit outside the training set, and see if whoever (or whatever) being tested can apply what it has learned in that somewhat novel situation. That's hard when ChatGPT has been trained on the entire internet. But to the degree we can test it, ChatGPT often falls down horribly. (It even falls down on things that should be within its training set.) So we conclude that it doesn't actually understand.
How come humans are so efficient when these computers are using enormous amounts of energy? When will we have a computer that only requires a few slices of pizza worth of energy to get to the next step?
I don't think this is the case at all. Language is how we encode and communicate ideas/concepts/practicalities; with sufficient data, the links are extractable just from the text.
What GPT does is not "text" - although it centers that as the interface - but "symbols". The billions of parameters express different syntaxes and how they relate to each other. That's why GPT can translate languages and explain things using different words or as different personas.
So when we ask it to solve a spatial problem, we aren't getting a result based on muscle memory and visual estimation like "oh, it's about 1/3rd of the way down the number line". Instead, GPT has devised some internal syntax that frames a spatial problem in symbolic terms. It doesn't use words as we know them to achieve the solution, but has grasped some deeper underlying symbolic pattern in how we talk about a subject like a physics problem.
And this often works! But it also accounts for why its mathematical reasoning is limited in seemingly elementary ways and it quickly deviates into an illogical solution, because it is drawing on an alien means of "intuiting" answers.
We can definitely call it intelligent in some ways, but not in the same ways we are.
It doesn’t work programmatically—that’s why it fails at logic. But it can reason inductively very very well. Do you have an example besides logic/math where it doesn’t understand simple concepts?