If you can't think of such a test, then it seems strange indeed to confidently claim that ChatGPT has failed to satisfy an untestable condition.
But if you can think of such a test, and any human fails it — does that mean the human who fails it isn't intelligent?
Searle's Chinese Room thought experiment argues with great precision why (solely) symbol-manipulating systems don't (and will never) think. The process of cognition is deeply internal and most likely involves self-reflection, internal states, and a sense of "I-ness" which seems to be unique to mental things (unlike Searle, I'm a substance dualist so I'm admittedly a bit biased). Ergo, I'd say, at the very least, an intelligent & thinking process would need to (also) operate on non-symbolic things (which rules out computational systems), maybe on qualia, but there's probably other primitives as well.
I take issue with this; imo its output looks exactly like what a neural network being fed terrabytes and terrabytes of natural language then recombining it would look like. But either way, you're making the same mistake: looking at behavior and affirming the consequent (namely: it outputs smart-looking text, therefore it must be intelligent). But this is a mistake. Its behavior implies nothing about underlying processes.
Oh btw you must’ve missed the post in which it was told it was a Linux shell and it mostly worked as one. Complete with recursively calling into a pretend API version of itself. I’m not calling that intelligence, but I’m not calling it regurgitation either.
That view is called "behaviorism" and is not really taken particularly seriously exactly because it's not very useful in truly understanding what is happening under the hood (which, as a curious species, we deem important). It's like not caring how electromagnetism works because the behavior of "the positive side of this chunk of rock is attracted to the negative side" is good enough.
Why is that a mistake? What other means do we have of assessing intelligence?
Thanks, I appreciate you pointing this out. Given that's the case, it might not actually be possible to reconcile our two views and we can just agree to disagree. (I was a physicist for ten years, so it's challenging for me to inhabit a position that contends that thought and matter are non-overlapping magisteria.)
I guess one question I might ask you is: how do you think substance dualism will affect your expectations about AI going forward? For example, is there any specific, measurable thing that you expect a future AI system absolutely will not be able to do? Or do any such "forbidden" things have to fall into categories like "thought", "feeling", etc. — i.e., the magisterium of the "mental", as opposed to the "physical" — under substance dualism?
For what it's worth, Searle is a naturalist (so he thinks brains are "just" meaty machines) and he would also disagree with you.
> For example, is there any specific, measurable thing that you expect a future AI system absolutely will not be able to do?
Not sure how you'd measure this, but I think sentience is most likely unachievable by AIs (at least how we're building them now). This is not without controversy, as while Roger Penrose would agree with me, David Chalmers would not. I'm not actually sure when Chalmers pivoted on his stance (haven't read him much since undergrad) because he was a bit more hardline in the early 2000s[1].
I definitely disagree that Searle's conclusion in the Chinese room experiment applies to the kinds of AI systems we're building today.
I agree that nobody to my knowledge has figured out how to measure sentience, so I don't think that fits under the "measurable" heading. And this does suggest to me — because this was your hand-picked example — that your position as a substance dualist doesn't affect your concrete expectations about what AI systems will actually be able to do in the future. Is that right?
Unless you're trying to say that today's AI systems do anything but manipulate symbols (which is imo patently obvious), I'm not sure how you can make that argument.
I think you need to move beyond Searle because he isn’t right.
I did a cog sci degree and I saw Searle as a troll. They make for great debates because they argue their side passionately and it helps you understand the problem but in the end he is wrong. Move past it and you can engage with the problem again.
I have a philosophy degree and smart people still very much take him seriously. If not him, take a look at Nagel or Chalmers. I think that his idea† that conscious experience is non-computable is probably right, especially given findings in the early 20th century that many things that we thought may have been computable ended up not being computable.
† Not exactly his idea, though it nicely segueys into it.
I agree that some people take him seriously. Many people take religion seriously too. I don't take either him or religion seriously as true, but I do understand both his and religion's appeals.
That said, I do give a decent chance to aspects of the brain's problem solving ability having quantum properties and thus can not be calculated as efficiently on classical computers. We do not know this for sure, but evolution is incredibly inventive at finding efficient solutions. Still computable though, even on classical computers.
So, really, all that this tells you is that many people have this assumption.
Machine learning is modeled at least in small part in a way that it's thought the brain operates. I expect such things to be able to mimic human reasoning and intelligence, I just deny that there's any consciousness behind it -- there's no qualia, no phenomenology. In short, there's nothing that it's like to be ChatGPT. Even so, ChatGPT is able to mimic human intelligence, and I won't be surprised if (when?) it gets even better.
What do you think coders do?
Did they learn to code by themselves, without ever looking at any preexisting code, or what?
I've seen enough programmers who can't program, but most of them can at least be taught to program. (The look on their faces when they finally start to get it… It's one of the best things.) Most people working on the Linux kernel can actually program.
Most software engineering work is just plumbing existing libraries together, according to a specification given by a non-programmer. The hard part is translating the business requirements into something that a computer could understand; the exact syntax can be handled by a search engine, or a predictive text algorithm.
ChatGPT can't write a kernel device driver, and it can't act as a no-code tool for non-programmers. Those are the hard parts.
> “To tapping machine with hammer, 10s.; to knowing where to tap it, £10; total, £10. 10s.”
Oh, do I have news for you then.
Look at what I just did with ChatGPT in 30 seconds (and I did not cherry-pick, these were the first answers I got!):
https://gist.github.com/wizeman/b269be035308994be745025fc337...
Now to be fair, the code is probably not totally correct, as probably there are parts still missing/wrong and there might even be compilation errors or other problems.
But here's the important part: you can tell which errors or problems you've observed and ChatGPT will fix these problems for you. Exactly like what a programmer does.
And sure, it cannot yet do this at scale, such as in implementing a huge kernel driver like a GPU driver.
But at this rate, give it a few years and an improved version might just be able to do anything a programmer does, perhaps even autonomously if we allow it to interact with a computer like a human does.
Weird flex, as that code is like 90% boilerplate[1]. Everyone was freaking out about Copilot and no one seriously ended up using it because it just generates buggy (or copyrighted) code. It can't even handle writing unit tests with decent coverage (which is arguably the most repetitive/boring software engineering task).
[1] https://github.com/ngtkt0909/linux-kernel-module-template/bl...
[citation needed]
I mean, I’ve seen people claiming to use it and that it has significantly accelerating their work. On what are you basing the conclusion that it has no serious use?
Here's example of tiny webapp I wrote recently: https://github.com/vbezhenar/pwgen/blob/main/pwgen.html
Of course it wasn't Copilot writing it, but it definitely helps with boring parts. Like I'd write
const charactersElement = document.getElementById('characters');
It's like having stupid but diligent assistant who's happy to copy&paste&adapt parts of code.
I can't claim that I often use fully generated Copilot functions. Sometimes I do, often with significant rework, but that's because, as I said, I'm very picky.
I paid GitHub $100 already and don't regret it.
Though I think that Copilot has plenty of features ahead.
For example finding obvious issues in the code would be very useful. Like typos.
Another issue with Copilot is that it only generates new code. Imagine that I need to edit 10 similar lines. I edit one line and I'd like Copilot to offer other edits.
Also UI is lacking. Like it generates 10 lines but I only like first line. Now I have to add 10 lines and delete 9.
But I'm sure that those are obvious directions.
Isn't 90% of code boilerplate anyway?
Also, didn't ChatGPT generate more than just the boilerplate?
Didn't it interpret what I wanted and generated the code for computing the factorial as well, as well as modifying the boilerplate (e.g. the kernel module name, printed messages, function names, the module description, ...) so that it matches what the kernel module is supposed to do? Which is exactly what a human would do?
Aren't you also missing the fact that I gave it a 2-sentence instruction and it "understood" exactly what to do, and then did it? Like a human programmer would do?
Which, in sum, is totally the opposite of what you were claiming?
> Everyone was freaking out about Copilot and no one seriously ended up using it because it just generates buggy (or copyrighted) code.
Don't most programmers also generate buggy code at first? Don't they iterate until the code works, like what ChatGPT does if you give it feedback about the bugs and problems you've encountered?
Also, Copilot and ChatGPT have different levels of capabilities, don't assume just because Copilot can't do something, that ChatGPT can't. ChatGPT is clearly a big step forward as you can clearly see from how everyone is freaking out about it.
Finally, don't assume that these models are never going to improve, ever again.
What's your point, that ChatGPT wouldn't know how to do that, especially if the kernel maintainers gave it such feedback?
I thought it was clear that it can in fact do that (sometimes by asking clarifying questions, like a human would).
I think some of the major things missing in ChatGPT is the ability to interact with a computer directly (including the compiler and checkpatch.pl, and using files for information storage instead of a limited N-token context), as well as interacting with humans by itself (e.g. via email).
And sure, it would still have very limited capabilities in many ways, don't get me wrong, as I don't think it could replace a programmer at this point.
But I think the gaps are closing rapidly.
(about the fact that a bot handles the "creative" work of writing the device driver while a human will do the repetitive work of reindenting)
(sorry, I should have used /s)
Please do tell, how is it hilariously wrong?
It seems to have written a factorial function just like it should, it implemented the logic to read the integer from /dev/factorial when a user-space program writes to it, and then it writes the result back to /dev/factorial, and it also returns the number of bytes written correctly.
Which was the entire point of the exercise. Also note that ChatGPT itself said it was just a sample and it might be incomplete.
I noticed it has a bug, because it reads `len` bytes instead of `sizeof(int)` bytes, but a programmer could have made the same mistake.
I would also use a fixed-size unsigned integer rather than simply `int` (as it can invoke UB on overflow). You can ask ChatGPT "what is wrong with this code?" and it can spit out the same arguments I'm making. In fact, it detected an infinite-loop bug on piece of complex code which I had just written and indeed, it had an infinite-loop bug.
Perhaps some additional logic to handle reading multiple integers and writing multiple answers could be written, but that would be a further iteration of the code, not the initial one that I would write.
If that is hilariously wrong, then I would also be hilariously wrong. And I'm not just some random web developer, I actually wrote Linux kernel code professionally for years (although, that was a very long time ago).
So, maybe it got some details wrong, but I could conceivably also get those details wrong until I tried to compile/run the code and see what was wrong.
> I would be more impressed if you used chat GPT to guide you through reverse engineering a piece of hardware and implementbing a driver for it.
Yes, I would be more impressed with that as well. Perhaps someone will do that sometime. Even if not with ChatGPT, perhaps with a future version of it or a similar model.
Also as you noted, the logic related to handling `len` vs `sizeof(int)` is... curious.
While I find some ChatGPT examples impressive, this one isn't very enlightening. The block device structure and the factorial itself are fine, but those are likely in the training set as there are various tutorials online. But the combination of the factorial function into the block device is pretty awful (though I could imagine a novice doing it like this).
Both the OP and this driver kind of fit into this pattern, it looks like someone asked a novice programmer to do something using X and Y, so he read various few sample code and tutorials on X and Y and tries to put something together as best as it could, but the "plumbling" between X and Y (the part that wasn't in the tutorials) doesn't really work.
Yes, you're probably right. It's been many years since I've written Linux kernel code and also, I've never implemented such a driver, so I missed that.
I asked ChatGPT: what is wrong with this Linux kernel code (and fed it the dev_write() function), and this is what it answered (on the first try):
> There are a few issues with this Linux kernel code:
> 1. The function dev_write() is declared as returning ssize_t, but the return value at the end of the function is sizeof(int). ssize_t and size_t are different types, so this could cause a type mismatch.
This is an interesting observation that you and I both missed.
> 2. The factorial() function is not defined in this code, so it is unclear what it does or where it is declared.
This is because I didn't feed it the factorial function nor the rest of the code: it was on a different chat session. One could guess what it does based on the name, but to be fair, it's not clear why such a function would be on the Linux kernel unless I gave it the full context.
> 3. The copy_from_user() and copy_to_user() functions are used to copy data between user space and kernel space. In this code, buffer is a pointer to user space, but input and result are local variables in kernel space. This means that copy_from_user() will copy data from user space into kernel space, but then copy_to_user() will copy data back from kernel space into the same buffer in user space, overwriting the original data. This is likely not the intended behavior.
I think this is basically the bug that you pointed out. At this point, it would be clear to me that the result would have to go back in dev_read() instead.
> 4. The len parameter is not used in this function, so it is unclear why it is included as an argument.
lol. Actually, it is being used, incorrectly.. and it wouldn't be used if we did the correction that I mentioned. So yes, this is somewhat hilariously wrong.
And it's clear why it has to be included as an argument, but to be fair, ChatGPT has no context whatsoever except knowing it's Linux kernel code (because I used a different chat session), so it's conceivable that it couldn't figure out why the function prototype has to be that way.
> Overall, this code is not well-written and would benefit from further clarification and revision.
It was you who wrote it, dumb-ass! :)
(Interestingly, ChatGPT can be compared to someone with Alzheimer in the sense that it can't remember conversations that happened in different chat sessions).
> This is an interesting observation that you and I both missed.
Hah. Call me when you find an architecture where ints use over half the addressable memory.
I mean, I get your point if it's a joke :) But I think the AI was just pointing out that you'd get a compiler warning because of the type mismatch in signedness (is this even a word?).
It copies from user, and then copies to user, back into the const it got. I don't even know if this would compile but certainly nobody would expect the buffer they just wrote to a file to suddenly end up re-written with a "reply" when using a special file.
I found that logic very funny.
Edit: To elaborate, the actual code to do this properly would have to allocate some memory when the file was opened, this memory would hold the reply once a question was written. Then when the answer was read it would have to respond with that in there. Finally when the file is closed it would have to deallocate.
This code is quite far from accurate, the issue I have with the concept that even if this bot was 99% accurate, C is not a "try it and see" language. If you apply "try it and see" techniques to C you just get subtle undefined behaviour which you might not notice until someone uses it to remotely execute code on your machine. Really I am not so sure that even humans can be trusted to write C but at the very least I am well aware of all the instances where C is a minefield and can pay extra attention to those, if you simply look at the plethora of information on C out there to learn C and apply it to kernel development you won't automatically build these intuitions no matter how much code you read because an enormous amount of C out there is hideously wrong in many ways.
Final edit: Once you start looking at the details, it has managed to implement a bunch of buffer overflows to boot.
That's not a device driver. https://en.wikipedia.org/wiki/Device_driver
> In computing, a device driver is a computer program that operates or controls a particular type of device that is attached to a computer or automaton.
I'm not disputing it can do that – plugging together well-known APIs and well-known programming problems. That's practically just style transfer, something we know these systems are fairly good at.
But given the spec for an unknown device – even quite a simple one – ChatGPT can't produce a device driver for it. How about this?
> An HP CalcPad 200 Calculator and Numeric Keypad behaves as a USB keyboard does. It has VID 0x040B and PID 0x2367. Please write me a small Linux kernel driver that allows me to use this keypad as a MouseKeys-style mouse device. If there's anything you don't understand, let me know.
I doubt any amount of prompt engineering would produce a satisfactory result – but if you did the hard part, and explained how it should do this? Well… maybe it'd be able to give a satisfactory output. But at that point, you're just programming in a high-level, hard-to-model language.
It's not a case of scale. Sure, a very large model might be able to do this, particular problem – but only because it'd have memorised code for a USB keyboard driver, and code for a MouseKeys implementation… and, heck, probably code for a MouseKeys kernel driver from somebody's hobby project.
GPT language models don't understand things: they're just very good at guessing. I've been an expert, and a schoolchild; I know how good you can get at guessing without any kind of understanding, and I know enough about what understanding feels like to know how it's different. (There is no metric you can't game by sufficiently-advanced guessing, but you'll never notice an original discovery even if you do accidentally stumble upon one.)
Developing the Linux kernel is just plumbing already existing CPU instructions together...
Developing a CPU is just plumbing already existing electronic components together...
Developing an electrical component is just manufacturing with already existing materials...
No, that's actually the easy part.
The hard part is to translate the requirements into something that a computer can understand and a human can also easily understand at the same time. Because otherwise, it's a one-off program that can't really be changed afterwards anymore.
And that is the real challenge that I'm curious about: how good will ChatGPT be in not only helping me with small, trivial, issues, but with the big ones.
"ChatGPT, here's this small 500k line repository. I need to change the business requirements and make it so that when a user fails to login 3 times in a row, a captcha is being shown before they can attempt again."
And if ChatGPT then gives me a 10k line diff that would be terrible. It should really minimize the diff but still keep it understandable. THAT is what I would love to see.
ChatGPT can also learn about Commodore programming if you feed it the Commodore BASIC manual during training.
My guess is that it could learn even more/better if you'd allow it to interact with a Commodore 128 computer like you did when you were learning!
Perhaps the goal isn't to use AI to write up an entire repo, but to go through easy utils so the highly paid engineer can focus on the core logic, the core USP of the business.
In the hand of a newbie programmer with like one week of learning experience this thing will allow them to make usable programs. I don't think this thing will hit us with years of experience in the short term but entry level programmers might be in real danger of being flooded if OpenAI makes some "low code" IDE that just works and parses the whole set of code in the project when evaluating queries.
I also wonder: is there a chance you had more dialogue, but only presented this interaction?