What evidence do we have that this is how intelligence works?
Lol, this is precisely what chatgpt doesn't do well at all! It fails to understand simple concepts, logical inference, simple math, etc.
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?
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.
Ask a random person stuff like whats 465*42/12 and you'll get ridiculous answers, and LLM can use tools now.
I'd argue that it's actually superior to humans in that regard
Does chatGPT need a way to verify reality for itself to become truly intelligent ?
1) My confidence was around 80%, without any sense of absolute certainty. 2) I had no idea why I knew that, and where did I originally learned about what a house centipede looks like.
There's a good argument to be made you learned about potentially dangerous insects as a child, from books aimed at children. A reasonable person would make this argument. It's also likely that you retained that information because your would-be peers who aren't around, aren't around because their ascendants inserted their genetic material in to the humanity's descent's to a lesser extent than your extant peers.
You might not be able to immediately recall where and when you learned something, and that shouldn't be equated with genuinely having no idea.
I'd settle for a poor fascimillie, and argue strongly against human-like.
Human-like would be, let's say, a Cylon skin-job. ChatGPT is just a fraking toaster, at best.
But I agree with GP's eager-anthropomorphization complaint. When algorithms produce verifiably wrong output we call them errors.
Hallucinations are a mistake in perception.
The key difference appears a combination of two factors: We appear to be more likely to have learnt which subjects we're not very knowledgeable about through extensive feedback, be it through school or conversations where we're told we're wrong, and which also would appear to teach us to be more cautious in general. We also appear to have a (somewhat; far from perfect) better ability to separate memory from thoughts about our knowledge. We certainly can go off on wild tangents and make stuff up about any subject, but we get reinforced from very young that there's a time and a place for making stuff up vs. drawing on memory.
Both goes back simply to extensive (many years of) reinforcement telling us it has negative effects to make stuff up and/or believe things that aren't true, and yet we still do both of those, just not usually as blatantly as current LLMs without being aware.
So I'd expect one missing component is to add a training step that subjects the model to batteries of tests of the limits of their knowledge and incorporating the results of that in the training.
Agreed. I'd like to add another point to the discussion. It seems to me, as if LLMs are held to a higher standard regarding telling the truth than humans are. In my opinion the reason for this is that computers have been traditionally used to solve deterministic tasks and that people are not used to them making wrong claims.
Imagine subjecting a random human to the same battery of conversations and judging the truthfulness of their answers.
Now, imagine doing the same to a child too young to have had many years of reinforcement of the social consequences of not clearly distinguishing fantasy from perceived truth.
I do think a human adult would (still) be likely to be overall better at distinguishing truth from fiction when replying, but I'm not at all confident that a human child would.
I think LLMs will need more reinforcement from probing the limits of their knowledge to make it easier to rely on their responses, but I also think one of the reasons people hold LLMs to the standard they do is also that they "sound" knowledgeable. If ChatGPT spoke like a 7 year old, nobody would take issue with it making a lot of stuff up. But since ChatGPT is more eloquent than most adults, it's easy to expect it to behave like a human adult. LLMs have gaps that are confusing to us because the signs we tend to go by to judge someones intelligence are not reliable with LLMs.
Paradoxically, it seems as if the people who are pushing this the hardest are the same people who flat out deny even the slightest flicker of what could be considered intelligence.
Saying “most humans make dumb statements” is not a defense to build a machine that makes dumb statements imo
If we made a ChatGPT that has some “senses” it can use to verify its perceptions, and it does as well with this as humans generally do, I’m sure we’ll get interesting results but I’m not sure we’ll be any closer to solving the problem.
In other words, the human has a concept of truth or facts and simply had a memory lapse. This thing has no concept of truth at all.
People don't need the gigantic amount of input data that ChatGPT needs to learn. However I'm not sure what exactly "this" is you and GP are referring to, and it may be possible to improve existing ideas so that it works with less input data.
The training data isn't the input. It's a part of the algorithm.
Sure they do. Humans rely on tons of audio and video before they can even read (or walk).
>Let us consider the GPT-3 model with 𝑃 =175 billion parameters as an example. This model was trained on 𝑇 = 300 billion tokens. On 𝑛 = 1024 A100 GPUs using batch-size 1536, we achieve 𝑋 = 140 teraFLOP/s per GPU. As a result, the time required to train this model is 34 days.
https://arxiv.org/pdf/2104.04473.pdf
I'm not sure expressing brain capacity in FLOPs makes much sense, but I'm sure if it can be expressed in FLOPs, the amount of FLOPs going to learning for a normal human is less than that.
For reading, the same applies. Our brains are equipped with many of the foundational aspects required for reading, and we only _learn_ a part what is necessary for the skill of reading.
Unlike computer models, brains are no tabula rasa. So we don't need the same input as computer models to learn.
You are not storing it all, but you are finding patterns and correlations in it from the day you were born. This all forms a base where after a decade+ you can learn things fast from books, but that’s comparable to an LLM’s in context learning. It’s fast, but it depends on a deep base of prior knowledge.
And even if I did, most of it would not be of the same category as what LLMs are learning; information about how a fabric feels or the sound of an ambulance doesn't teach me anything about programming or other things GPT can do. So when comparing the inputs, it makes no sense to count all the information that our senses are getting, to all the input for an LLM.
>This all forms a base where after a decade+ you can learn things fast from books, but that’s comparable to an LLM’s in context learning
Not really comparable
I think you discount general knowledge acquired non-verbally too easily. The sound of an ambulance and how it moves through space and how it correlates with visual information represents an astronomical amount of information that can be generalized from handsomely. All these data streams have to be connected, they correlate and intertwine in fantastically complex ways.
I think the sound of an ambulance does teach you things that eventually help you “program”. You have witnessed similar (non-)events thousands if not millions of times. Each time it was accompanied with shitloads of sensory data from all modalities, both external and internal.
The base of general patterns you start out from once you are ready for language is staggering.
Again not saying LLMs work like that, because they do not. All I mean to do is put their information requirements in perspective. We ingest a lot more than a bunch of books.
The common perception has been that children aren't exposed to enough data to arrive at their grammatical language skills, implying there's some proto language built in. Comparative analysis of languages has looked for what aspects are truly universal but there's actually not a lot of concrete true universals to ascribe to our genetic innate language.
But if it is genetic that doesn't really mean it's fundamentally different than ChatGPT, it just took a different and drastically longer training period and then transfer learning when children learn their mother tongue.
It doesn't necessarily mean it's fundamentally different, but it doesn't mean it is comparable either. Geoff Hinton doesn't think the brain does backpropagation. Training a neural net uses backpropagation. So if Hinton is correct then saying "it just took a longer training period" while brains doesn't learn like our current neural nets is glossing over a lot of things.
Animals have bodies and have to survive in an environment. They're not just sitting there waiting on a prompt to generate text. They do things in the real world. Language is later invention by one particular social animal which serves our needs to communicate, which is different than waiting on prompts.
There are certainly some missing elements, such as ongoing training and memory, but it's not at all clear that there's any major conceptual differences. There could be, we just don't really know.
If you conclude, after watching this, that this is all intelligence is - namely cascading optimization and data structure problems chained in a row, then you and a fair number of people here wont ever find common ground. That is not to say that you're wrong, it just seems as if there is a missing component here that we haven't yet discovered.
They won't find common ground with who?
I think it is easy to distracted by the specific mechanisms by which these models work, but most of the technological detail is because we want something to happen on systems at the scale of what we can actually build. We simply can't build a human brain scale neural net yet. We build what we can and besides maybe with all this research we will figure something significant about what intelligence actually is.
The notion "This can't be all thought is" is as old as the idea of AI. I think it informed Turing when he proposed the Imitation Game. The insight is that people would be resistant to the idea of a bunch of simple things stuck together becoming thinking until they were faced with something that behaves sufficiently indistinguishable from themselves that to doubt that they were thinking would be akin to doubting everyone you meet.
In the end some people won't even accept an AI that does everything a human does as actually thinking, but then again some people are actually solipsistic.
Older still:
>It must be confessed, moreover, that perception, and that which depends on it, are inexplicable by mechanical causes, that is, by figures and motions, And, supposing that there were a mechanism so constructed as to think, feel and have perception, we might enter it as into a mill. And this granted, we should only find on visiting it, pieces which push one against another, but never anything by which to explain a perception. This must be sought, therefore, in the simple substance, and not in the composite or in the machine.
- Leibniz, 1714
For extraordinary claims ('intelligence'), the burden of proof is on those making the claim, not on others to prove the negative.
The sine function is nonlinear.
You give sprinkle in a tiny nonlinearity (e.g. x^2 instead of x) and suddenly you can get infinite complexity by weighted composition - which is also the reason why we're so helpless with nonlinear functions and reach for linear approximations immediately (cf. gradient descent).
Except you can "compose" affine functions using Horner's schema to get any polynomial... No need to sprinkle tiny non linearities. It's a buffet. Grab as much as you need.
y = b + x1 + x1^2 + x1^3 + ... + x1 * x2 + (x1 * x2)^2 + ... + x2 + x2^2 + ...
By that point you're making a Taylor approximation of the latent function through linear space, which is also a universal approximator.
So the commenter above is wrong -- neural networks are indeed just glorified linear regression from this point of view.
The main difference is that this kitchen sink regression is computationally inefficient which neural nets are extremely efficient computationally.*
I'm not an expert, but the motivation seems more like this:
- Linear regression and SVM sometimes work. But they apply to very few problems.
- We can fit those models using gradient descent. Alternatives to gradient descent do exist, but they become less useful as the above models get varied and generalised.
- Empirically, if we compose with some simple non-linearities, we get very good results on otherwise seemingly intractable problems like OCR. See Kernel SVM and Krieging.
- Initially, one might choose this non-linearity from a known list. And then fit the model using specialised optimisation algorithms. But gradient descent still works fine.
- To improve results, the choice of non-linearity must itself be optimised. Call the non-linearity F. We break F into three parts: F' o L o F'', where L is linear, and F' and F'' are "simpler" non-linearities. We recursively factorise the F' and F'' in a similar way. Eventually, we get a deep feedforward neural network. We cannot use fancy algorithms to fit such a model anymore.
- Somehow, gradient descent, despite being a very generic optimisation algorithm, works much better than expected at successfully fitting the above model. We have derived Deep Learning.
Deep learning has been a series of *engineering* successes stacking over each other rather than theory being applied rigorously.
It's hard to scale training on the "dumb" approximators like a kitchen sink regression, and balancing overfitting becomes a nightmare.
(Edit: this whole thread feels like a setup for this punchline)
ChatGPT points to us possibly going in the right direction. It is so good that people need to write long articles about why it is not as good as a human. Contrast to older efforts, which were pretty clearly not so great. I find this pretty compelling, and GOTCHA examples that show that ChatGPT isn't as good as humans in everything miss the point.
Birds fly and drones don't look like birds - but they fly. If the goal is flying, it's okay that we achieve it through something that doesn't exactly mimic what inspired it. Do we need a "full human" for all we do? Our billions of machines, many performing work previously done by humans, show that we don't.
If we can largely replicate intelligence, it's not super important whether "this is how human intelligence works".
Edit: the important bit is that the subject already has some qualities in common with the proposed classification which is a significantly different proposition than a purely hypothetical orbital teapot
The fact that ChatGPT doesn’t behave like an intelligence. (Though it converses like one with radical deficiencies in certain areas, which highlights the narrowness of the Turing Test, which itself is a big step.)
OTOH, it gets less bad at this when you wrap it in a loop that provides recall and other capacities with carefully constructed prompting as to how (“thought” process-wise, not just interaction mechanics) to integrate those capacities, so there’s maybe a decent argument that it models an important component of general intelligence.
1) Given an infinite series of nonlinearities, one can model any mathematical function to an arbitrary degree of precision. This has been proven and is accepted by any working mathematician.
2) The human brain / intelligence can be simulated by a sufficiently complex mathematical function. It is somewhat accepted that simulating the human brain is sufficient for intelligence. Disagreements usually boil down to: It won't have a soul, you can't simulate biological / quantum processes on digital computers, or something abut Qualia I don't really understand,
3) How big / complex is the function that we need to get to AGI / simulate human or above intelligence? TBD, it seems like experts disagree but many working in the field have been surprised by the capabilities that GPT has and we are likely closer to AGI than anyone thought 2 years ago.
> Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits
No, we haven't pushed linear regression to its limits. If it was only linear regression, it wouldn't work. Neural networks need a non-linearity to model complex things.
The beauty is that given an infinite series of nonlinearities, one can model any mathematical function. In practice we found it takes much less than "infinite", a handful will already get you a long way.
A stack of this basic building block, as you describe it, is really all it takes - we know that mathematically already. The interesting question is: how complex are the functions we need to model? So if we create a neural network of a certain size, is that size large enough to model the problem space?
> However we are only near AGI if this is in fact how intelligence works (or can work) and I don't believe we've seen any evidence of this. And there are some very big assumptions baked into this approach.
I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works?