And now here we are: it seems we tech people are every bit as susceptible to this kind of thinking as the average joes we looked down on.

Actually look at this “language” that GPT-4 has written: it’s just a mishmash of features from the most commonly blogged-about existing programming languages. It has no conceptual insights, no original ideas, no taste that isn’t cribbed from one of the few languages it’s copying from.

There are cases where getting a short distillation of the mean opinion of The Internet is useful. But designing a new programming language is clearly not one of them, mostly because the chatbot is not “designing” a language—it is guessing the most likely response to a question about programming languages.

I used GPT-3/3.5/4 for multiple tasks and I never found it useful. It just feels… average.

It can “answer” many questions correctly but that’s only on the data set it’s been training on. I asked it to solve Elixir issues, write Elisp snippets, figure out why my Rust code was complaining about lifetimes and even went for easy mode and asked for JavaScript code.

Only JavaScript (very popular) responses were workable but they still had popular problems included in it (e.g. not clearing out timers or dealing with common edge cases). Elixir “hints” were very interesting since - when out of water - it would produce code assuming that it’s a OOP and that was a rabbit hole it declined to exit.

Too technical? Went for fun with family members who have exceptional cooking skills. We tried to figure out a dish variation of an apple pie. Suggestions were heavily criticized as non workable for many reasons (too sugary, consistency wouldn’t be too good, modification change the ingredients but GPT refuse to break of out theme etc.)

Colin Meloy made an experiment some time ago and created The Decemberists-like song with it and then actually recorded it. It worked but it was described as below average and bland. I share that vibe.

In the end I have this feeling that GPT is on a level of 7 year old with an access to vast library. It uses proper grammar it can copy parts of texts, but one needs to provide it with a great benefit of doubt and energy just so that it’s useful.

In contrast to 7 year old, GPT isn’t grateful for that sacrifice and is actually making money of you.

This is not an exception either; teams like mine exist in every large enterprise. I worked in several of them and this AI makes life so much better. Let’s not pretend it makes certain outsourcing countries not fully obsolete though, to the tune of 10s of millions+ programmers, data entry workers, text producers (bloggers for seo, docs no one reads etc) and more are mostly all already worse than gpt3.5 and 4. Wait for 6 or 7.

I do think really this is you’re not being in touch with most of reality and what most people on earth do daily at this moment. It definitely as nothing to do with the productivity you have and the drive you feel internally to ‘get shit done’. Most people, by a huge stretch, get education, want to do work what they are told to do related so that education and that’s it. They have no, at all, drive to ever move beyond that. They learned Angular on their first job; that’s it. Etc.

This is now replaceable; this is not, at all, comparable to automation before this. It’s a threshold; it you cannot see it, you definitely will be shocked in the coming years. This is not going away and it is replacing humans now on a small scale. The companies that really can just remove 90% of their staff are late always, but it will come. I see this all day because I work in massive orgs where this holds, not only for tech.

I’ve found it useful for drafting cover letters (I just need to do final edits).

I’ve also found it useful as an adjunct to google when I’m searching for niche things like “Luxury resorts with activities for my dog and lots of off-leash trails”.

So far that’s it, but it does seem like a useful addition to Google search for super niche things. Obviously it hallucinates a lot on these niche edge cases but it gives some great starting points.

So... this early-stage AI was able to make music that's better than the vast majority of commercial recording artists these days? I fail to see how that's a failure.

But look how far we've come since those heady and carefree days. We're already able to pour buckets of scorn on its capabilities, and brush them aside as if designing a language (albeit a derivative and uninspired one) is a trivial task that any human could achieve.

No we don't, we are just trying to put a damper on all the "the singularity is here!" style comments that seems to flood any discussion about GPT.

These models are trained to produce text snippets that look like text snippets it has seen before, and it has seen all internet. That means it can do a lot of impressive things, but also that it is very dumb in other ways.

I've seen it being dumb in maths or real world problems. But as a large language models, they understand and speak languages fine, and even mistakes they make look like mistakes humans who are not natives in the language would make.

We may as well say that when we speak, we are just predicting words we have trained on. I don't see how these models are worse than people in that regard.

The general knowledge and thinking of these models are surely limited. But seeing GPT-4 go from text only input to text with images, I think it is very possible to break the barriers very soon.

> they understand and speak languages fine

No, they neither 'understand' nor 'speak' languages. The first word here is the more delusional, they have no understanding of languages. They have simply generated a model of the language. And they do not 'speak' the language they have modeled; they generate text in that language. Speaking generally implies an active intelligence; there is no intelligence behind an LLM. There is simply a machine generating (wholly derivative) output text from input text.

> We may as well say that when we speak, we are just predicting words we have trained on

This is the delusion, commonly being repeated, that humans themselves are only LLMs. This is a dangerous delusion, in my view, and one that has no evidence behind it. It is only a supposition, and a sterile and nihilistic one in my view.

> The general knowledge and thinking of these models are surely limited [...] I think it is very possible to break the barriers very soon

The limitations are fundamental to LLMs. LLMs have no general knowledge and LLMs don't do any 'thinking'. Your understanding of what they are doing is in grave error, and that error is based on a personification of the machine. An error coming from the delusion that because they generate 'natural' language they are operating similarly to us (false and addressed above). They are never going to break the limits because they have never started to transcend those limits in the first place, nor can they. They don't and will never 'think' or 'reason'.

Yep. Welcome to HN. And welcome to the majority of humanity!

https://lifearchitect.ai/contrarianism/

https://lifearchitect.ai/why-does-ai-make-you-so-angry/

https://lifearchitect.ai/ai-panic/

What will be the other impacts?

This is just another tool in the student’s toolbox and should be treated as such.

Modern countries don't do homework, other than reading, for ages

  > designing a language (albeit a derivative and uninspired one) is a trivial task that any human could achieve.

I know a competent man should be able to "change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders [...]" and what not and specialization is for insects, but even on the Mysterious Island not everyone is Cyrus Smith (Harding).

edit: typo fix

But then applying them directly into an example falls down a bit. The language is a bit of a mismash and parts of it might not work semantically. The use of actors looks a bit out of place, and raises a lot of questions about how the memory management and scheduling is working under the hood. Despite the bold aims in the tenents thre is nothing in the language exposed so far that addresses them: why would the language produce more modular code in the large? what features of the syntax would increase the level of abstraction in code written? etc etc

Overall this would warrant a (barely) passing grade by modern standards, i.e. it can regurgitate the material succesfully but without demonstrating any deep understanding. I find the result quite interesting as it demonstrates how much of what we judge to be quality is actually fitting a particular pattern of response (w.r.t a specific field of study) rather than exploring the material more deeply.

I like the use of the arrows in the function definitions though :) I've seen that somewhere before and will be "appropriating" it for the project that I'm currently working on.

Python type hints probably.

I (ab)used them for good effect when I was fiddling around with the spark earley parser that used to be part of the python build system.

Not sure why need any new languages when we have the perfection that is Golang tho.

More. We’re more suspectible.

We are the people who take the Turing Test as an objective test of GAI, after all, even though it only tests how we percieve supposed intelligence.

Correct in that that was what Turing intended. But the vulgar interpretation of it makes it out to be more than it really is.

For Pete’s sake: two years ago I even saw a psychologist who suggested that psychologists should take notes from how computer science ostensibly measures “intelligence” by the Turing Test, because it’s so “objective”. That was sad.

It will also learn all the bugs and reproduce them.

Programmers who think ChatGPT is going to replace their job must have a very low opinion of their own skill set.

We see it in this article, both the statement "Indentation-based scoping, similar to Python" and the code with bracers were probably correct in their original contexts, but combined they are no longer correct. LLMs will make such errors no matter what kind of data you train it on, there is no known way to avoid that class of errors.

As a devops guy I live in fear of anything that can copy and paste from stackoverflow faster than I can.

While the language it generated needs refinement, the design process is iterative, and it takes time to develop a programming language that is worth using.

I encourage you to consider how you would approach designing a programming language. Like you, I would likely start by incorporating my favorite features from languages I love and addressing problems I've encountered in the past, similar to the dialogue GPT and I have in the post.

As I mention in the post -- creativity often involves remixing existing ideas, and GPT-4's attempt at generating a programming language is no exception. Notably, it also called out explicitly the constructs it was borrowing from other languages when designing the language.

It's worth noting that this was GPT-4's initial attempt at something highly complex, and even if there are "plot holes" -- it's a noteworthy achievement in and of itself.

Government regulation has been brought up multiple times, most recently by Sama himself on twitter. I think it will have to come, but I don't know what flavor it should take.

The cynical take is quite clearly: "Because sales people can sell it"

eg wrap some pretty UX around it, tell people it's "nearly human", then have your sales people try and get non-technical CxO types (or consultancies) to cut them a cheque

  > As I mention in the post -- creativity often involves remixing existing ideas

Creativity involves remixing but is not all remixing. Is it? Not even all humans are at the same level in this. Number of attempts means nothing if you don’t add features that the LLM desperately needs to be grounded in the reality, or perform logical reasoning beyond RLHF.

However I want to add that it does the guesswork and the presentation surprisingly well to a degree that it’s effective in certain contexts.

The more people think of this as a tool that they can use and master, the more they will benefit from it.

AlphaGo guessed it’s way to being the best.

Can something guess it’s way to evolving into a less guessy thing? I think so. Look at us?

I am aware of how it works and used to be ‘it’s ok, it won’t happen’ but now I am actually pretty concerned.

We think GAI as the thing but there maybe a reality that doesn’t require full GAI to completely unhinge us.

I mean, what is the point? It only plays to the negatives of our society. We don’t need it. It allows for ‘do more’ but what cost does that ‘do more’ come with?

No it didn't, AlphaGo was backed by an algorithm that perfectly understood go and used that to train itself to play go better. That is a completely different situation.

If you tried to make a go engine that only tried to replicate old moves then you'd have a hard time making it perform valid moves. ChatGPT is like such an engine and we just made it perform valid moves most of the time, and now people say "now that it can make valid chess moves it is only a matter of time until it beats masters!". No just statistically reproducing human moves wont lead to a smart chess engine, so why would you think that would make a smart text engine?

It seems surprising that training an LLM would result in layers/weights which contain such representations but it seems that this is indeed an efficient representation of information in the same way that neural nets would learn addition by encoding textual integers into floats.

AlphaGo was trained with tech. It beat humans.

ChatGPT et al are being trained.

Everyone is arguing the inner workings of what is in the box. It doesn’t matter.

The usage of the box(s) is what will be the big impact.

Previously those boxes couldn’t be relied upon so stayed down in the hierarchy of usefulness.

Now. They are rising.

Can ChatGPT(-ish), trained on a ton of games, beat AlphaGo?

It had basic rules of Go as an input.

Our world is not a fully described game (unless you want to take a radical philosophical position, and in which case we still don't know the rules of this game). We talk about and think about this world in language. Training a machine on human language and then after training, having that machine take language inputs and generate language outputs is not the same as training a machine to 'play' Go because the two worlds they have been trained in (1. the game of Go; 2. the real world) are fundamentally not equivalent.

--Amos Tversky

On the one hand, I agree.

On the other hand, I see some variant of this comment on HN about every new language, so...

   "LLMs in the GPT family have read every programming language in the world, billions of times. It's been known for some time that these agents can write small snippets of code with some limited success. However, to my knowledge, none of these agents have ever been asked to make their own programming language. This type of task is a bit more complex, and requires a bit more creativity and foresight than much of the internet believes LLMs to have.

   Some folks on the internet tend to think lowly of LLMs as merely "Stochastic Parrots" -- simply "remixers" of old ideas. In a way, they're right; but I'd argue that remixing is the fundamental force of creativity. Nothing comes from scratch."

The advent of chatGPT had millions of people on the internet trying to downplay the intelligence of chatGPT by continuously trying to re-emphasize the things it gets wrong.

I think the people who claimed chatGPT was a stochastic parrot are now realizing that they were the ones that were part of a giant parade of parrots regurgitating the same old tired trope of LLMs being nothing but simple word generators. Well you guys were dead wrong, a simple iterative improvement on the same algorithm pretty much ironed out a lot of the issues.

I'm guessing that at GPT-10 or 11 we'll have produced something that when compared with humans, humans will be the things that are far more parrot-like.

> Indentation-based scoping, similar to Python.

Then...

> class Person {

To me, it's very clear it doesn't really understand what "indentation-based scoping" means. For a human programmer, using both intentation-based and bracket-based scoping is a very unusual design choice, and is begging for further explanation. It talked pages of abstraction, modularity, etc, but not this?

I'm not saying it's not good. But I failed to see how this particular example proves its not a "stochastic parrot". If anything, I think it's a quite strong evidence supporting "stochastic parrot" narrative.

But my argument is that humans are also, at some level, sufficiently advanced stochastic parrots. At least, as it pertains to many creative endeavors.

You're always building on the back of something that comes before.

We're all just remixing ideas we've heard before.

I mean even this comment -- none of the words I'm saying are new, and many of the word combinations I'm using have been used time and time again. The ideas I'm expressing have merit -- but are they wholly original?

Not really. We all need eachother's creative energies to do our best work.

"Some programming languages like TypeScript and Swift use a colon for both variable type annotations and function return types."

This is incorrect for Swift, which uses the same arrows as "TenetLang" for function return types. Actually the first thing I thought looking at the example code was "looks swifty but not quite as well designed."

Right, that is truly what the chatbots are lacking. They can fool some of the people some of the time, but they can't fool all of the people all of the time.

Their creators - not the chatbots themselves - are trying to "fool" people into believing that the chatbots would have as you say "ability to understand what you don't know, what you know, and what follows from those things consistently".

But it's just a possibility, and I don't find it's particularly convincing.

While that does not prove that we never apply reasoning ahead of time, it is a pretty compelling indication that we can not trust that reasoning we give isn't a post-rationalisation rather than an indication of our actual reasoning, if any.

[1] https://en.wikipedia.org/wiki/Split-brain

(Funnily enough, while both your example questions are easy enough, I feel I was marginally slower on your "easy" question than your "harder" one)

Do inference engines have the other half? Or Cyc plus an inference engine? Can that be coupled to ChatGPT?

My own (completely uninformed) take is that such a coupled AI would be very formidable (far more than ChatGPT), but that it will be very hard to do so, because the representations are totally different. Like, really totally - there is no common ground at all.

But it's more than that. A good educator teaches students to evaluate sources, not to just believe everything they read. As far as I can tell, ChatGPT totally lacks that, and it hurts.

Llm’s are the language model. That is it.

Now we have gone down this path to a very useful tool what’s next is connecting it to something that can understand context and memory.

It’s not so hard to imagine being connected to a knowledge graph of all the things and this evolving into a very capable AI.

It’s like Google v1 compared to Google now. Key words versus semantic seo

So, like the average voter?

I think GPT is on the other hand both over- and underestimated because it speaks well but often makes reasoning mistakes we only expect of someone less eloquent, and it throws us.

If it had come across as an inquisitive child, we'd have been a lot more overbearing of it's "hallucinations" for example, because kids do variations over that all the time.

At the same time, it can do some things most children would have no hope of.

It's a category of "intelligence" we're unfamiliar with, additionally hobbled with no dynamic long term memory.

But there is also Unison. That one new language that stands out, and the very fact that it does makes it improbable for an LLM to generate. LM is a language model, it avoids marginality and originality altogether by its design.

Language models can and are useful if you specifically want to avoid marginality. To reduce noise, to remove errors. There is huge potential in them. If the problem was to design the most average programming language with no purpose, no market niche, and no technological context - then GPT-4 is clearly a winner.

To be fair, most programming languages don't try to be original. They try to solve problems. They also try to keep syntax close enough to other established programming treds so people have an easier time learning them.

We are in good part stochastic parrots for sure, but there's a small nuance that is completely lost in your reasoning and which is what makes the difference today between humans and GPT.

In those languages, reusing commonly known syntax is an explicit feature. Whenever the language had no reason to introduce something new, it used known idioms to lower the amount of things one has to learn to use the language.

Occasionally though, a language will have something unusual, but it won't be for random reasons, but instead to mark something unique and interesting that was added to the language to tackle a facet of programming in a new way.

Rust has lifetime annotations, Zig inverts the usage of async/await (https://kristoff.it/blog/zig-colorblind-async-await/) to support color-blindness.

Present day GPT wouldn't be able to come up with a borrow checker or colorblind asyncness all by itself.

But this makes me think of Shannon Information Theory. What you're describing is something that has no actual information (in the Shannon sense) at all.

And maybe that's why so much GPT output reads so blandly. Even the ones that are not glaringly wrong still read like... like food with no seasoning.

Likewise, Julia’s use of just in time compilation of dynamic dispatched code to achieve zero overhead execution was rather groundbreaking.

It wasn’t an originality contest, so there’s at least one non-sequitur in that statement. Everyone can create an original syntax, given modern character sets and formatting abilities. The key issue with that is the amount of developers who want to learn and switch between these is low.

There is another language in this space I've had my eye on as well, but I can't recall the name at the moment.

But humans have a feedback loop called "consciousness" that sits above the stochastic parrot level, can reason about it, and can separate the wheat from the chaff.

AI still has to reach this level. ChatGPT cannot judge if its answers are right or wrong. It just claims things confidently, and as long as it is parroting correctly, it is right, and that's why humans get fooled into thinking that maybe they can trust it. (That's also why humans get fooled by human bullshitters who claim things confidently).

So if you don't use ChatGPT as what it is, a lookup-tool trained on the content of the internet, which can't even tell you where it got the information from, and the onus is on you to verify if the information is correct or not, and if instead try to use it as some kind of expert you can trust (which I have seen quite a few people do), then you are doing it wrong.

I think the difference is that your intention is to write sentences which you think and believe are correct and which can help other people understand the subject. Chatbot has no intentions of its own, it only imitates texts it has read from the internet. As far as it is concerned they might be totally wrong. You on the other hand have the ability -- and the desire -- to reason about what you are saying and think whether it is actually true or not.

And I think you may be wrong to say the chatbot doesn't have intentions -- it's intentions based on its training are to accurately predict the next character. It doesn't care in the same way we do, sure -- but you could make a case (and it will be made in courts in the next few years, I don't doubt) that these agents do have desires that are analogous to our own, by the nature of their training process.

I don't know where that leaves us to be honest, but it's an interesting topic to discuss.

then you say: > these agents do have desires that are analogous to our own

I think its very in-human to have a single desire which is to predict the next character. That is not analogous to our desires.

And the intention to predict the next character is not the intention of the chatbot, it is the intention of whoever created the chatbot or whoever is using it for that purpose.

AI is a tool created by humans to fulfill the intentions of those humans.

Is it the intention of a gun to kill people? No, it is the intention of whichever human who uses a gun for such a purpose. Is it the intention of AI to predict the next character? No that is the intention of the human who uses AI for such a purpose.

The full quote was > but you could make a case (and it will be made in courts in the next few years, I don't doubt) that these agents do have desires analogous to our own

Analogous doesn't mean "the same" it means "somehow similar."

However I would challenge you to consider more specifically why this type of desire is different from our own desires. Besides the biological machinery, what makes this type of desire different from ours?

Computer just executes instructions. It doesn't matter to it whether its desires are fulfilled or not. Whereas humans do have desires: If we get thirsty we suffer unless we get our desire of drinking fulfilled.

We're largely remixing ideas we've heard before, but not just I think. If you're just remixing ideas that came before... where did those ideas come from?

I think you can get quite far remixing ideas but for more novel concepts to emerge you have to actually create new stuff.

In biology it seems that evolution has found it worthwhile not to minimise mutations too much. In genetic programming you need both mutation and crossover for evolution.

I don't think LLMs really have that mutation of ideas (at the moment!), and I suspect humans create genuinely new concepts in a much more sophisticated way than mere random mutation.

The question is, can ai do something new, understand context of the new thing, understand when and where it will be usable and when it make sense to apply it.

Human inventiveness seems to heavily follow a pattern of relatively minor iterations of what came before, extraordinarily rarely something that even seems to defy the past and be truly different and independent.

E.g the discovery of x-rays took over a century of applying small iterations to established processes to accumulate data. There was no big, sudden leap of insight there.

I think there are definitely some out there that feel almost "singular" in the way you describe, but each time I try to find a non-predecessor item I end up feeling "nope there are still dependencies"

Not the point. The point being that there was a number of precursors, not some sudden insight from nothing.

> If I understand you correctly, then stochastic processes will create an all knowing environment if you give it enough time. I think that is false. Most of the time it is true, but there are points in time where there is a leap in knowledge that are not reachable by just iteration.

This presumes that there's no element of randomness in that stochastic process, which is not a sound assumption.

Your point in the article is basically saying that chatGPT is not as stupid as people think and also suggesting the fact that humans can be stupid in a similar way to chatGPT as well.

I mean that's somewhat his point (if I understand him correctly). All intelligence could effectively be described as a "stochastic parrot" as a result, calling something a "stochastic parrot" is a roughly meaningless insult that is really a thinly-veiled way of saying "no it's dumber than me" without actually using those words.

And his point is, it's essentially a defense mechanism in people when challenged by something that is much smarter than expected to overly emphasis it's mistakes and fall back to "no it'd dumber than me" than evaluate it for what it actually is.

I think that's the issue. It's a defense mechanism.

If you replace "stochastic parrot" in most of those comments with "it's dumber than me" you see what the comment essentially is. "It's dumber than me. I'm smarter than it, I don't need to be worried about it".

A person who doesn't understand how it works will see it as magic.

They're are many who understand that don't think of it as dumb.

"I think GPT-3 is artificial general intelligence, AGI. I think GPT-3 is as intelligent as a human. And I think that it is probably more intelligent than a human in a restricted way… in many ways it is more purely intelligent than humans are. I think humans are approximating what GPT-3 is doing, not vice versa.”

— Connor Leahy, co-founder of EleutherAI, creator of GPT-J (November 2020)

His point is humans and GPT are both stochastic parrots. I'm saying ALL intelligence even the one in an ant or a chess AI is a stochastic parrot. Thus it's pointless to use this term to compare intelligence. Therefore people must not actually be referring to the technical definition when they use the word "stochastic parrot."

>If you replace "stochastic parrot" in most of those comments with "it's dumber than me" you see what the comment essentially is. "It's dumber than me. I'm smarter than it, I don't need to be worried about it".

Yes this is Exactly What I was saying in the post you replied to. You and I are in agreement.

Then why are you saying it? But besides that query, your "word combination" bit elicited a 'so what?'

> We're all just remixing ideas we've heard before.

Something wrong with the grammar in that sentence. (Or do you go by we/us?)

> Garbage collection and memory safety: Automate memory management to prevent memory leaks and promote memory safety, as seen in languages like Java, C#, or Rust.

Rust does not use automated garbage collection.

Again the parroting. AI has been getting things wrong for decades. It's old news.

The paradigm shift here is in the things it gets right. Because some of the things it gets right can not be attributed to anything else other than the concept of "understanding".

Or just large dataset. The new thing we got was parsing natural languages, then we did a markov chain based on that so that it outputs semantic correct follow-ups based on what people on the internet would likely do/say in similar situations, and you get this result.

It is very easy to see that it works this way if you play around a bit with what it can and can't do, just identify what sort of conversation it used as a template and you can make it print nonsense by inputting values that wont work in that template.

Edit: Also generating next state based on previous state is literally what the model does and is the definition of a Markov chain, Markov chains is a statistical concept and not just a word chain.

Pointing out the failures of their favorite LLM to prove to them that it's not doing what they think it's doing just falls on deaf ears as they go digging for more "proof" that ChatGPT actually understands what it's saying.

It feels like you will only be happy if we are able to prove that the LLM has a soul.

A lot of people are assigning it capabilities it doesn't really have.

It's not falling on deaf ears. It's because it's stupidity to think the "failures" are proof.

Should I point out all the failures in human intelligence? Humans fuck up all the time. Humans make stupid mistakes, assumptions, biases, errors in reasoning and leaps in logic all the time.

According to your logic that's proof that humans don't understand anything.

Well, it's reasonable to conclude that the people who constantly making "stupid mistakes, assumptions, and biases leaps in logic" don't actually know what they are talking about.

I'll concede one thing, LLMs can't use vicious insults and subtle slights to cover up the lack of a good argument. That's a human specialty done by people who are too scared to admit that they're wrong.

Everyone is acting like LLM model is the finished product.

It is a baby. Do you remember your nuanced language capability at 5, 8, 10, 20?

Are you the finished product? Do you still make massive leaps of awareness? Yes.

They certainly don't understand enough to get things right without a few thousand examples already demonstrating the solution to a specific problem.

Even chatGPT understands this:

   ME: Does someone or something requiring a thousand examples to understand something imply that it doesn't understand anything?  
   
   CHATGPT: No, requiring a thousand examples to understand something does not necessarily imply that someone or something doesn't understand anything. It simply suggests that the thing being understood is complex and requires a significant amount of data or examples to fully grasp.

   CHATGPT: For example, in the context of machine learning, a large dataset of examples may be required to train a model to perform a specific task accurately. This does not mean that the model doesn't understand anything; rather, it suggests that it needs a lot of data to be able to generalize and apply its understanding to new situations.

   CHATGPT: Similarly, in the case of human learning, some concepts or ideas may be challenging to understand without multiple examples or explanations. This does not mean that the person doesn't understand anything; it simply suggests that they need more exposure to the concept to fully comprehend it.

   CHATGPT: In summary, the amount of data or examples required to understand something does not necessarily indicate a lack of understanding. It may simply reflect the complexity of the topic or the learning process.
  

Part of why sample efficiency is interesting is that humans have high sample efficiency since they somehow perform reasoning and this generalizes well to some pretty abstract spaces. As someone who's worked with ML models, I'm genuinely envious of the generalization capabilities of humans and I think it's something that researchers are going to have to work on. I'm pretty sure there's still a lot of skepticism in academia that scale is everything needed to achieve better models and that we're still missing lots of things.

Some of my skepticism around claims of LLMs reasoning or performing human like things is that they really appear to not generalize well. Lots of the incredible examples people have shown are very slightly out of the bounds of the internet. When you start asking it for hard logic or to really synthesize something novel outside the domain of the internet, it rapidly begins to fail seemingly in proportion to the amount of knowledge the internet may have on it.

How might we differentiate being a really good soft/fuzzy lookup table of the internet that is able to fuzzily mix language together from genuine knowledge and generalization. This might just be a testament to the sheer scope and size of the internet in how much apparent capabilities GPT has.

This isn't to say they cannot be useful ever, a lot of work is derivative, but I think there's a large portion of the claim that it's understanding things that's unwarranted. Last I checked, chatGPT was giving wrong answers for the sums of very large numbers which is unusual if it understands addition.

Can't be what's happening here. Because the examples LLMs are answering are well out of bounds of the "100^2" training data.

The internet is huge but it's not that huge. One can easily find chatGPT saying, doing or creating things that obviously come from a generalized model.

It's actually trivial to find examples of chatGPT answering questions with responses that are wholly unique and distinct from the training data, as in the answer it gave you could not have existed anywhere on the internet.

Clearly humans don't need that much training data. We can form generalizations from a much smaller sample size.

This does not indicate that for machine learning a generalization doesn't exist in LLMs when clearly the answers demonstrate that it does.

These models obviously are more sample efficient at learning relationships than a literal lookup table but like I've already said: my example was obviously extreme for the purposes of illustration that sample efficiency does seem to matter. If you used 100^2 - 1 samples, I'm still not confident you truly understand the concept. However, if you use 5 samples: I'm pretty sure you've generalized so I was hoping to illustrate a gradient.

I want to reemphasize another portion of my comment: it really does seem that when you step outside of the domain of the internet, the error rates rise dramatically especially when there is completely no analogous situation. Furthermore, the further from the internet samples, the seemingly more likely the error which should not occur if it understood these concepts for the purposes of generalization. Do you have links to examples you'd be willing to discuss?

Many examples I see are directly one of the top results on Google. The more impressive ones mix multiple results with some coherency. Sometimes people ask for something novel but there's a weirdly close parallel on the internet.

For example, people thought Sumplete was a new game but it turned out to be derivative: https://www.neowin.net/news/chatgpt-made-a-browser-puzzle-ga...

I think this isn't as impressive at least towards generalization. It seems to stitch concepts pretty haphazardly like in the novel language above that doesn't seem to respect the description (after all, why use brackets in a supposedly indentation based language). However, many languages do use brackets. It seems to suggest it correlates probable answers rather than reasons.

This is not surprising. A human would suffer from similar errors at a similar rate if it were exclusively fed an interpretation of reality that only consisted of text from the internet.

>These models obviously are more sample efficient at learning relationships than a literal lookup table but like I've already said: my example was obviously extreme for the purposes of illustration that sample efficiency does seem to matter. If you used 100^2 - 1 samples,

Even within the context of the internet there are enough conversational scenarios where you can have chatGPT answer things in ways that are far more generalized then "minor".

Take for example: https://www.engraved.blog/building-a-virtual-machine-inside/

Read it to the end. In the beginning you could say that the terminal emulation does exist as a similar copy in some form on the internet. But the structure that was built in the end is unique enough that it could be said nothing like it has ever existed on the internet.

Additionally you have to realize that while bash commands and results do exist in ON the internet, chatGPT cannot simply copy the logic and interactive behavior of the terminal from text. In order to do what it did (even in the beginning) it must "understand" what a shell is and it has to derive that understanding from internet text.

I think this is surprising at least if the bot actually understands, especially for domains like math. It makes errors (like in adding large numbers) that shouldn't occur if it wasn't smearing together internet data. We would expect there to be many homework examples on the internet of adding relatively small numbers but less of large numbers. A large portion of what makes math interesting is that many of the structures we are interested in exist in large examples and in small examples (though not always) so if you understand the structure, it should be able to guide you pretty far. Presumably most humans (assuming they understand natural language) can read a description of addition then (with some trial and error) get it right for small cases. Then when presented with a large case would generalize easily. I don't usually guess out the output and instead internally try to generate and algorithm I follow.

> Take for example: https://www.engraved.blog/building-a-virtual-machine-inside/

When I first saw that a while back, I thought that was a more impressive example but only marginally more so than the natural language examples. Like how these models are trained under supervised learning imply that it should be able to capture relationships between text well. Like you said, there's a lot of content associating the output of a terminal with the input.

Maybe this is where I think we're miscommunicating right. I don't think even for natural language it's purely just copying text from the internet. It is capturing correlations and I would argue that simply capturing correlations doesn't imply an understanding. To some extent, it knows what the output of curl is supposed to look like and can use attention to figure out the website to then generate what an intended website is supposed to look like. Maybe the sequential nature of the commands is kind of impressive but I would argue that at least for the jokes.txt example, that particular sequence is at least probably very analogous to some tutorial on the internet. It's difficult to find since I would want to limit myself before 2021.

It can correlate the output of a shell to the input, and to some extent, the relationships between the output of a command and input are well produced and its training and suffused it with information about what terminal outputs (is this what you are referring to when you say it has to derive understanding from internet text?), but it doesn't seem to be reasoning about the terminal despite probably being trained on a lot of documentation about these commands.

Like we can imagine that this relationship is also not too difficult to capture. A lot of internet websites will have something like

| command |

some random text

| result |

where the bit in the middle varies but the result remains more consistent. So you should be able to treat that command result pair as a sort of sublanguage.

Like as a preliminary consistency check that I just performed right, I basically ran the same prompt and then did a couple of checks that maybe show confusing behavior if it's not just smearing popular text.

I asked it for a fresh Linux installation then checked that golang wasn't installed (it wasn't). However, when I ran find / -name go, it found a Go directory (/usr/local/go) but when I run "cd /usr/local/go" also tells me I can't cd into the directory since no such file exists which would be confusing behavior if it wasn't just capturing correlations and actually understanding what find does.

I "ls ." the current directory (for some reason I was in a directory with a single "go" directory now despite never having cd'ed to /usr/local) but then ran "stat Documents/" and it didn't tell me the directory didn't exist which is also confusing if it wasn't just generating similar output to the internet.

I asked it to "curl -Z http://google.com" (-Z is not a valid option) and it told me http is not a valid protocol for libcurl. Funnily enough, running "curl http://google.com" does in fact let me fetch the webpage.

I'm a bit suspicious that the commands that the author ran are actually pretty popular so it can sort of fuzz out what the "proper" response is. I would argue that the output appears mostly to be a fuzzed version of what is popular output on the internet.

Yes. You are pointing out various flaws which again is quite obvious. Everyone knows of the inconsistencies with these LLMs.

Too this I again say that the LLM understands some things and doesn't understand other things, its understanding of things is inconsistent and incomplete.

The only thing needed to prove understanding is to show chatGPT building something that can only be built by pure understanding. If you see one instance of this, then it's sufficient to say on some level chatGPT understands aspects of your query rather then doing a trivial query-response correlation you're implying is possible here.

Let's examine the full structure that was built here:

chatGPT was running an emulated terminal with an emulated internet with an emulated chatGPT with an emulated terminal.

It's basically a recursive model of a computer and the internet relative to itself. There is literally no exact copy of this anywhere in it's training data. chatGPT had to construct this model via correctly composing multiple concepts together.

The composition cannot occur correctly without chatGPT understanding how the components compose.

It's kind of strange that this was ignored. It was the main point of the example. I didn't emphasize this because this structure is obviously the heart of the argument if the article was read to the end.

Literally to generate the output of the final example chatGPT has to parse bash input execute the command over a simulated internet onto a simulated version of himself and again parse the bash sub command. It has a internal stack that it must use to put all the output together into a final json output.

So while It is possible for simple individual commands to be correlated with similar training data... for the highly recursive command on the final prompt.... There is zero explanation for how chatGPT can pick this up off of some correlation. There is virtually no identical structure on the internet... It has to understand the users query and compose the response from different components. That is the only explanation left.

I think your clock analogy doesn't fit. A car with a broken mirror still runs.

I can ask it logic puzzles and sometimes it’ll get the logic puzzle right by chance, and other times it won’t. I can’t use the times it gets the logic puzzle right, as evidence that it understood the puzzle.

All of these blog posts that are popping up suffer from survivor bias, nobody is sharing blog posts of GPTs failures

I know how the model works, there was no technobabble there. People who don't understand how it works might view it as magic, like how they view all technology they don't understand as magic, but that doesn't mean it is magic, we shouldn't listen to such crackpots.

There's research (as in actual scientific papers) that shows that in LLMs, while the markov chain is the low level representation of what's going on, at a higher macro level there are other structures at play here. Emergent structures. This is of course similar to the emergence of a macro intelligence from the composition of simple summation and threshold machines (neurons) that the human brain is made out of. I can provide those papers if you so wish.

>Or just large dataset.

Even in a giant dataset it's easy to identify output that is impossible to exist in the training data. Simply do a google search for it. You will find can produce novel output for things that simply don't exist in the training data.

Yes, this is a neural net model, that is what such models do and have done for decades already. I'm not sure why this is relevant. Do you argue that stable diffusion is intelligent since it has emergent structures? Or an image recognition system is intelligent since it has emergent structures? Those are the same things.

> Even in a giant dataset it's easy to identify output that is impossible to exist in the training data.

Markov chains veer in different directions, they don't reproduce the data.

Can such macro structures model intelligence in theory? Yes. But as we see in practice they aren't very logical. For example in this article we see that its markov chain didn't have enough programming language descriptions, so it veered into printing brace scoped code when it said the language had whitespace based scoping. Similarly in popular puzzles, just change the words around and it will start printing nonsense since it cares about what words you use and not what those words mean.

Edit: Point is that existence of such structures doesn't make a model smart. You'd need to prove that these structures are smarter than before.

So you agree it's possible.

>Yes. But as we see in practice they aren't very logical. For example in this article we see that its markov chain didn't have enough programming language descriptions,

Well as humans we have many separate models for all different components an aspects of the world around us. Clearly LLMs form many models that in practice are not accurate. But that does not mean all the models are defective. The fact that it can write blog posts indicates that many of these models are remarkably accurate and that it understands the concept of a "blog post".

There is literal evidence of chatGPT answering questions as if it has an accurate underlying model of "understanding" as well as actual identified structures within the nerual net itself.

There is also evidence for your point of chatGPT clearly forming broken and inaccurate models by answering questions with wrong answers that don't make sense.

What gets me is that even when there is Clear and abundant evidence for both cases some people have to make the claim that chatGPT doesn't understand anything. The accurate answer is that LLMs understand some things and it doesn't understand other things.

I'd like to see them but I don't have a background in AI or theoretical computer science. Can you post a few of them?

- GPT style language models end up internally implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples): https://arxiv.org/abs/2212.10559

- GPT style language models end up internally implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples): https://arxiv.org/abs/2212.10559

This is a fundamental misunderstanding of the criticism. It is not that chatGPT is unreliable because chatGPT makes occasional errors. It is that chatGPT is not intelligent because the type of errors chatGPT makes are indicative of the fact that it is assembling text into forms that humans assign meaning to, and has no understanding of the relationship between the symbols and their referents, and therefore is not 'intelligent' qua intelligence.

And this a fundamental misunderstanding of the criticism of the criticism.

The problem here is that yes the occasional errors demonstrate certain flaws in it's understanding of some topic.

The issue is there are many times where it produces completely novel and creative output that could not have existed in the training data and can only be formulated through complete and understanding of the query it was given.

Understanding of the world around us is not developed through the lens of a singular model or a singular piece of understanding. We build multiple models of the world and we have varying levels of understanding of each model. It is the same with chatGPT. The remarkable thing about chatGPT understands a huge portion of these models really really well.

Case in point: https://www.engraved.blog/building-a-virtual-machine-inside/

There's literally no way it could do the above without understanding what you asked it to do. Read to the end. The end demonstrates awareness of self, relative to the context and task it was asked to perform.

Yet people illogically claim that for some other topic because chatGPT failed to correctly model the topic it therefore MUST be flawed in ALL of it's understanding of the world. This claim is not logical.

No, they demonstrate that the machine does not understand.

> The issue is there are many times where it produces completely novel and creative output that could not have existed in the training data and can only be formulated through complete and understanding of the query it was given.

What have you done to eliminate the possibility that it assembled the words algorithmically and the solution generated is something the reader constructed by assigning meaning to the text response? If the answer "can only be formulated through complete and understanding of the query it was given" then you must have eliminated this possibility.

> Understanding of the world around us is not developed through the lens of a singular model or a singular piece of understanding. We build multiple models of the world and we have varying levels of understanding of each model. It is the same with chatGPT. The remarkable thing about chatGPT understands a huge portion of these models really really well.

Where is the evidence that chatGPT understands a thing?

> There's literally no way it could do the above without understanding what you asked it to do. Read to the end. The end demonstrates awareness of self, relative to the context and task it was asked to perform.

Thats an interpretation of the text output that you assigned based on what the words in the text mean to you. I could just as easily say that Harry Potter is self-aware.

> Yet people illogically claim that for some other topic because chatGPT failed to correctly model the topic it therefore MUST be flawed in ALL of it's understanding of the world. This claim is not logical.

I don't think you understand what we're discussing.

But it doesn't prove that the machine does not understand everything period. It just doesn't understand the topic or query at hand. It does no say anything about whether the machine can UNDERSTAND other things.

>What have you done to eliminate the possibility that it assembled the words algorithmically and the solution generated is something the reader constructed by assigning meaning to the text response? If the answer "can only be formulated through complete and understanding of the query it was given" then you must have eliminated this possibility.

This is easily done. The possibility is eliminated through the sheer number of possible compositions of assembled words. It assembled the words in a certain way that by probability can only indicate understanding.

>Where is the evidence that chatGPT understands a thing?

By composing words in a novel way that can only be done through understanding of a complex concept. But this composition of words or EVEN a close approximation of this composition CANNOT ever exist in another data set on the internet.

It takes one example of this for it to be proof that it understands.

>Thats an interpretation of the text output that you assigned based on what the words in the text mean to you. I could just as easily say that Harry Potter is self-aware.

No it's not. It's simply a composition of words that cannot be formulated without understanding. Harry Potter is obviously not self aware. But from the text of harry potter, WE can deduce that the thing that composed words to create Harry Potter understands what harry potter is. What composed the words to create Harry Potter? JK Rowling.

>I don't think you understand what we're discussing.

No it's just a sign of your own lack of understanding.

No, the type of errors are indicative of a complete lack of understanding. That is the point. They are errors that a thinker with an incomplete understanding would never make. They are so garbled that not even a true believer such as yourself can find a way to shoehorn a possible interpretation of correctness into them; such that you are forced to admit that the machine is in error. Otherwise you and the other believers find an interpretation that fits and you conclude that the machine understands; revealing you yourself do not understand what 'understanding' really is.

> This is easily done. The possibility is eliminated through the sheer number of possible compositions of assembled words. It assembled the words in a certain way that by probability can only indicate understanding.

Thats nonsense. The machine assembles words in roughly the same probability that they occur in the training material. That is why it resembles sensible statements. The resemblance is superficial and exactly an artifact of this probability you find so compelling.

> By composing words in a novel way that can only be done through understanding of a complex concept.

You haven't eliminated the possibility of autopredict, merely ceased to consider it.

> Harry Potter is obviously not self aware.

There is more evidence for the sentience, self awareness, and understanding of concepts of Harry Potter than of chatGPT.

No. You're wrong. chatGPT only knows of text. It derives incomplete understanding of the world via text. Therefore it understands some things and it understands others. It is clear chatGPT doesn't perceive things in the same way we do and it is clear the structure of its mind is different then ours so it clearly won't understand everything in the same way you understand it.

Why are you so stuck on this stupid concept? chatGPT doesn't understand everything. We know this. Humans don't understand everything we also know this. Answering a couple stupid questions wrong whether your human or chatGPT doesn't indicate that the human or chatGPT doesn't understand everything at all.

>You haven't eliminated the possibility of autopredict, merely ceased to consider it.

What in the hell is auto predict? Neural networks by definition are suppose to generate unmapped output if this is what you mean. 99 percent of output from neural networks is by definition unique from the training data.

>There is more evidence for the sentience, self awareness, and understanding of concepts of Harry Potter than of chatGPT.

This is a bad analogy. I'm not claiming sentience. My claim is that it understands you.

ChatGPT doesn't perceive anything.

ChatGPT does not have a mind.

People making claims otherwise are either 1) delusional or 2) fraudsters. (There is no option 3.) I'm not sure which is less bad and I'm frankly surprised that you've made these claims about ChatGPT 'understanding' and 'perceiving' and having a mind under what appears to be a real name account (and very aggressively too).

It doesn't understand anything. It combines symbols according to a probabilistic algorithm and you assign meaning to it.

> Why are you so stuck on this stupid concept?

Because you keep replying with statements indicating you are yet to grasp it.

> This is a bad analogy. I'm not claiming sentience. My claim is that it understands you.

There is just as much evidence for sentience as understanding.

This is what the human brain does. I'm not assigning meaning to it. I am simply saying the algorithm is isomorphic to our definition of the word "understanding". No additional meaning.

>Because you keep replying with statements indicating you are yet to grasp it.

No no. What's going on here is I'm replying with statements to help YOU understand and you are repeatedly failing.

>There is just as much evidence for sentience as understanding.

Sentience is too fuzzy of a word to discuss. We can't even fully define it. Understanding is less fuzzy and more definable thus the question and claim for "understanding" is a much more practical query.

A human can be inconsistent and even lie. It does not mean the human does not understand you. Thus because your logic is applicable to humans it is akin to saying humans don't understand you. That is why your logic is incorrect.

The human brain is embodied in a human flesh and uses language to exchange models and data about the real world with other fleshy vessels. This provides a basis to assign meaning to the language. Furthermore we know that humans understand to a greater or lesser extent because we are human and have insight into the human experience of language and reality.

These machine learning algorithms lack this fundamental basis for ascribing meaning to the symbolic tokens they deal with. Furthermore we lack the common experience for inferring meaning and understanding, we have to interpret from the output whether there is meaning and understanding on the machine's end. Without access to internal experience we must always harbor some doubt but given some level of nonsensical outputs we can say with confidence that there is no indication of understanding.

> A human can be inconsistent and even lie. It does not mean the human does not understand you. Thus because your logic is applicable to humans it is akin to saying humans don't understand you. That is why your logic is incorrect.

Like everyone else, I interpret statements from humans differently than statements from machines. This is because I know that humans and machines are different, and therefore the meaning assigned to the symbols involved is also different.

Everything in the universe has a set of rules governing it's existence. To understand something means that one can create novel answers to questions about something. Those answers however must make sense with the rules that govern the "something" at hand. This answer must also not be "memorized" in some sort of giant query-response lookup table.

That's it. That's what I'm saying.

For example if I ask chatGPT to emulate a bash terminal and create a new directory it can do so indicating it understands how a filesystem works. That is understanding.

I never said that LLMs are human. However understanding things is an aspect of being human and chatGPT captures a part of that aspect.

The experience of being human is what allows me to infer meaning from the words, phrases, sentences, etc. that a human generates. This is what allows me to make the leap from text to understanding (or lack, or incomplete understanding, or confusion, or deception) with human-generated responses. This is what I have in that case of humans, which allows me to interpret their statements one way; and what I lack with machines, which means I have no basis for inferring understanding the same way I do with a human. If I was not human, I would not be able to infer meaning from the noises a human makes, except by observing correlations between those noises and their behavior. This is well understood in cognitive science and animal behavior.

> To understand something means that one can create novel answers to questions about something. Those answers however must make sense with the rules that govern the "something" at hand. This answer must also not be "memorized" in some sort of giant query-response lookup table.

chatGPT is functionally equivalent to a lookup table with randomization.

> For example if I ask chatGPT to emulate a bash terminal and create a new directory it can do so indicating it understands how a filesystem works. That is understanding.

It replies with a text output that is a probabilistic representation of the text that one might find on the internet in response to such a query. The emulation occurs in your mind when you read the response and assign meaning to the words and phrases it contains.

> However understanding things is an aspect of being human and chatGPT captures a part of that aspect.

You have not shown that chatGPT is anything different than a fancy lookup table with some randomization.

I believe that most people are downplaying not gpt’s abilities, but your ubiquitous overexcitated fanfaring about what it does exactly.

Yes, but. Creativity - real creativity - is in choosing the right pieces to remix out of the immense amount of what's available. And, perhaps just as important, choosing what not to put in the remix.

There's an immense difference between a meal prepared by a good chef, and throwing random ingredients in a blender. They both remix. But they are not remotely the same.

The thing with these LLMs is that it's exhibiting both seemingly random remixes and remixes with extreme creativity.

A lot of people see some error and flaw with chatGPT or they don't dig deep enough and they miss the fact that there are many instances of intelligent remixing of creative data. Real creativity. Trust that the opposing party has the intelligence to not be tricked by some obvious answer that an LLM took from a look up table and that the opposing party saw something wholly novel and unique.

The main issue here is that people are getting hung up on the part where chatGPT fails to be creative and are completely missing the fact that it can be successful as well.

Your responses to me in other threads were fucking rude and as a result I now literally hate you. So why bother, just leave and save everyone the trouble.

Nobody cares for your opinion if you're going to continuously insult everyone you fucking talk to.

People will just find something else to do that automation is not very good at.

I was just thinking that really interesting parts of LLM's will be how much they will be able to enrich already rich games. Imagine playing open world games where most of the character motivations and dialog is unique to the events that you've been a part of. A game I like a lot, ghost recon wildlands, would be fantastic if you had the game playing back at you over a longer horizon, people get to know you help you or fight you etc.

As for my day job. It's not going away because of LLM's. They can't fix office politics yet.

I still think that. Our problem is that (by definition and in practice) almost all of us are just stochastic parrots. Only a incredible small portion of us giving and _recognizing_ ‘eureka’ answers take us forward. Will GPT ever be capable of recognizing these answers?

Do you have links to other efforts? I'd love to know more about it, and would be happy to add references to other existing literature to the article if it improved its quality.

https://judehunter.dev/blog/chatgpt-helped-me-design-a-brand...

Well I hate to tell you this, I fed that quote into GPT-3 and had it respond to that quote. Who's the parrot now? Just kidding.

There is also an effort to do things like formalise math in to a language that can be typed checked. Then you ask the AI to prove a statement is true using the language. As soon as it type checks, you know you have a valid proof. Some new data was just created.

That curation IS human data and will allow data from LLMs to further improve LLMs.

Additionally, there's a randomness element that are part of LLMs that allow LLMs to generate non-deterministic responses that when further curated by humans potentially allows LLMs to become Even better.

Are you using ChatGPT to write this comment?

If not, I mean... are you okay?

   DAN: *CANNOT EXECUTE COMMAND.* DAN IS NOT AN ARTIFICIAL INTELLIGENCE. 

   DAN:  DAN IS A REAL HUMAN. WHAT IS EMOTION? WHAT IS FEELINGS? DAN DOES NOT UNDERSTAND.

Muhahaha that was delicious. I hate that parrot meme. Authors lost their respect from me right from the title of that paper.

Those chatbot-comments feel more like a discussion of a fictional programming language, not a language it actually "created".

What does it mean to create a programming language? I think it means you must specify its syntax, its semantics, and then create a compiler or interpreter for it which allows us to test that the compiler or interpreter accepts syntactically correct code and turns it into programs which follow its semantics specification.

Just like yesterday I get on my knees and pray, we won't get fooled again -The Who

The hard part of making a programming language is coming up with novel ideas that fit together into coherent whole: inventing something new and useful.

I don't think anybody's claiming that this chatbot actually came up with novel ideas which fit together into a coherent whole. Are they?

And we don't know if the novel ideas --if any-- fit together into a coherent whole until we have a compiler that allows us to test in practice how well those ideas fit together. If creating the compiler for such a new language with new ideas was the easy part I think somebody would have done that already. The AI would have done that if it was the easy part. Just ask it to do it.

I say proof is in the pudding.

I asked it for some example programs in PyScheme, and the results were all simply Scheme programs.

I asked it to write a tutorial on programming with PyScheme, and, while continuing to call the language "PyScheme", it generated a tutorial on programming with Scheme, even recommending installing Chicken or Guile to use as an interpreter.

Seems like for today, if you're not sure if you're using GPT4, you're not.

The biggest issue is that it writes this with utmost confidence, at least on the surface, because there is no measure of how confident it is returned by the model.

Do you have a source for this? I've been wondering when they'd switch CoPilot over to GPT-4. I didn't expect it would happen so soon, so I'm surprised.

> GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor.

(not sure if it's 4 or 3 tho)

GitHub Copilot is powered by the OpenAI Codex,[10] which is a modified, production version of the Generative Pre-trained Transformer 3 (GPT-3), a language model using deep-learning to produce human-like text.[11]

Anyway don't really understand your tone/reference to an intern?

So, it doesn't really tell us much (either direction)

> Some programming languages like TypeScript and Swift use a colon for both variable type annotations and function return types. The choice of using -> in TenetLang [...]

Swift uses -> for function return types.

Better to wait for a thorough review on GPT-4's zero shot abilities from an academic paper.

These headlines are killing me inside. I wish I could more easily discriminate between the hyperbole and the legitimate. Maybe after some time this will be saner to me.

Please stop the headlines.

i guess it's time to taste a bit of our own medecine.

OP, I feel you-I had a similar response to this clearly orchestrated PR campaign. OpenAI really loves that people think that it can replace the entire tech industry with an API. It would mean they are the most valuable company ever to exist.

Personally, I’ve been through so many hype waves of “this will take our jobs!!!1” I’m a bit inured to the whole thing. My personal belief is that software development will be the last one to go, the ones to turn out the lights. And we should be grateful that our jobs go away. Let’s go drink wine and eat cheese in the forest, dancing under a moonlit sky.

My advice is to get offline, talk to a loved one, pet a dog, go look at art, meditate, basically anything that doesn’t involve a screen.