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Comment by dual_dingo | Hacker News Reader

The "I" in AI is just complete bullshit and I can't understand why so many people are in a awe of a bit of software that chains words to another based on some statistical model.

The sad truth is that ChatGPT is about as good an AI as ELIZA was in 1966, it's just better (granted: much better) at hiding its total lack of actual human understanding. It's nothing more than an expensive parlor trick, IMHO.

Github CoPilot? Great, now I have to perform the most mentally taxing part of developing software, namely understanding other people's code (or my own from 6 months ago...) while writing new code. I'm beyond thrilled ...

So, no, I don't have an AI fatigue, because we absolutely have no AI anywhere. But I have a massive bullshit and hype fatigue that is getting worse all the time.

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I'm more fatigued by people denying the obvious that ChatGPT and similar models are revolutionary. People have been fantasizing about the dawn of AI for almost a century and none managed to predict the rampant denialism of the past few months.

I suppose it makes sense though. Denial is the default response when we face threats to our identity and sense of self worth.

So, to you, ChatGPT is approaching AGI?

I do believe if we are going to get AGI without some random revolutionary breakthrough, to achieve it iteratively, It's going to come through language models.

Think about it.

What's the most expressive medium we have which is also absolutely inundated with data?

To broadly be able to predict human speech you need to broadly be able to predict the human mind. To broadly predict a human mind requires you build a model of it, and to have a model of a human mind? Welcome to general intelligence.

We won't realize we've created an AGI until someone makes a text model, starts throwing random problems at it, and discovers that it's able to solve them.

> I do believe if we are going to get AGI without some random revolutionary breakthrough, to achieve it iteratively, It's going to come through language models.

Language is way, way far removed from intelligence. This is well-known in cognitive psychology. You'll find plenty of examples of stroke victims who are still intelligent but have lost the ability to produce coherent sentences, and (though much rarer) examples of people who can produce clear, eloquent prose, yet are so learning and mentally challenged that they can't even tell the difference between fantasy and reality.

We don't judge AI by their ability to produce language, we judge them by their conference and ability to respond intelligently, to give us information we can use.

> To broadly be able to predict human speech you need to broadly be able to predict the human mind

This is a non sequitur. The human mind does a whole lot more than string words together. Being able to predict which word would logically follow another does not require the ability to predict anything other than just that.

I think what the commenter is saying is that, in time, language models too will do a lot more than string words together. If it's large enough, and you train it well enough to respond to “what's the best next move in this chess position?” prompts with good moves, it will inevitably learn chess.

I don't think that follows, necessarily. Chess has an unfathomable amount of states. While the LLM might be able to play chess competently, I would not say it has learned chess unless it is able to judge the relative strength of various moves. From my understanding, an LLM will not judge future states of a chess game when responding to such a prompt. Without that ability, it's no different than someone receiving anal bead communications from Magnus Carlsen.

An LLM could theoretically create a model with which to understand chess and predict a next move, you just need to adjust the training data and train the model until that behavior appears.

The expressiveness of language lets this be true of almost everything.

Exactly. Since language is a compressed and transmittable result of our thought, to predict text as accurately as possible requires you do the same. A model with understanding of the human mind will outperform one without.

> Being able to predict which word would logically follow another does not require the ability to predict anything other than just that.

Why? Wouldn't you expect that technique to generally fail if it isn't intelligent enough to know what's happening in the sentence?

"The ability to speak does not make you intelligent." — Qui-Gon Jinn, The Phantom Menace.

Why the obsession with AGI? The point is that ChatGPT is already useful.

Is it? I see it mostly generates BS much faster.

Brothers Grimm would like a word with you about what "BS" means.

ChatGPT is good at making up stories.

Perhaps a more interesting question is "how much better do we understand what characteristics AGI will have due to ChatGPT?"

We don't really understand what intelligence means -- in humans or our creations -- but ChatGPT gives us a little more insight (just like ELIZA, and the psychological research behind it, did).

At the very least, ChatGPT helps us build increasingly better Turing tests.

Yes. It is obviously already weak AGI (obvious to anyone if they saw it 20 years ago).

It is also obvious that we are in the middle of a shift of some kind. Very hard to see from within, but clearly we will look back at 2022 as the beginning of something…

> I suppose it makes sense though. Denial is the default response when we face threats to our identity and sense of self worth.

It's important to note that this is your assumption which I believe to be wrong (for most people here).

There's a fellow that kinda predicted it in 1950 [0]:

> These arguments take the form, "I grant you that you can make machines do all the things you have mentioned but you will never be able to make one to do X."

> [...]

> The criticisms that we are considering here are often disguised forms of the argument from consciousness, Usually if one maintains that a machine can do one of these things, and describes the kind of method that the machine could use, one will not make much of an impression.

Every time "learning machines" are able to do a new thing, there's a "wait, it is just mechanical, _real_ intelligence is the goalpost".

[0] https://www.espace-turing.fr/IMG/pdf/Computing_Machinery_and...

>Every time "learning machines" are able to do a new thing, there's a "wait, it is just mechanical, _real_ intelligence is the goalpost".

Just because people shift the goalposts doesn't mean that the new position of the goalposts isn't closer to being correct than the old position. You can criticise the people for being inconsistent or failing to anticipate certain developments, but that doesn't tell you anything about where the goalposts should be.

> ChatGPT and similar models are revolutionary

For _what purpose_, tho? It's a good party trick, but its tendency to be confidently wrong makes using it for anything important a bit fraught.

If you're the type of person that struggles to ramp up production of a knowledge product, but has great success in improving a knowledge product through an iterative review process, then these generative pre-trained transformers are fantastic tools in your toolbox.

That's about the only purpose I've found so far, but it seems a big one?

If you work at a computer, it will increase your productivity. Revolutionary is not the word I'd use, but finding use cases isn't hard.

I can buy that it's a better/worse search engine (better in that it's easier to formulate a query and you get the response right there without having to parse the results; worse in that there's a decent chance the response is nonsense, and it's very confident when it's being wrong about things).

I can't really imagine asking it a question about anything I cared about and not verifying via a second source, though, given its accuracy issues. This makes it feel a lot less useful.

But will it? After accounting for the time needed to fix all the bugs it introduces?

Humans introduce bugs too. ChatGPT is still new, so it probably makes more mistakes than a human at the moment, but it's only a matter of time until someone creates the first language model that will measurably outperform humans in this regard (and several other important regards).

>> it's only a matter of time

That reminds me how in my youth many were planning on vacations to Mars resorts and unlimited fusion energy) Stars looked so close, only a matter of time!

> it's only a matter of time until someone creates the first language model that will measurably outperform humans in this regard

This seems to have been the rallying cry of AI-ish stuff for the past 30 years, tho. At a certain point you have to ask "but how much time"? Like, a lot of people were confidently predicting speech recognition as good as a human's from the 90s on, for instance. It's 2023, and the state of the art in speech recognition is a fair bit better than Dragon Dictate in the 90s, but you still wouldn't trust it for anything important.

That's not to say AI is useless, but historically there's been a strong tendency to say, of AI-ish things "it's 95% of the way there, how hard could the last 5% be?" The answer appears to be "quite hard, actually", based on the last few decades.

As this AI hype cycle ramps up, we're actually simultaneously in the down ramp of _another_ AI hype cycle; the 5% for self-driving cars is going _very slowly indeed_, and people seem to have largely accepted that, while still predicting that the 5% for generative language models will be easy. It's odd.

(Though, also, I'm not convinced that it _is_ just a case of making a better ChatGPT; you could argue that if you want correct results, a generative language model just isn't the way to go at all, and that the future of these things mostly lies in being more convincingly wrong...)

Anyone still remembers the self-driving hype?

How will it do that?

One of major problems of modern computer-based work is that there are too many people already in those roles, doing work that isn't needed. Case in point: the culling of tens of thousands of software engineers, people who would consider themselves to be doing 'bullshit jobs'.

It seems to me that the tendency to be confidently wrong is entirely baked into intelligence of all kinds. In terms of actual philosophical rationality, human reasoning is also much closer to cargo cults than to cogito ergo sum, and I think we're better for it.

I cannot but think that this approach of "Strong Opinions, Weakly Held" is a much stronger path forward towards AGI than what we had before.

The problem is that ChatGPT is about as useful as all the other dilettantes claiming to be polymaths. Shallow, unreliable knowledge on lots of things only gets you so far. Might be impressive at parties, but once there's real, hard work to do, these things fall apart.

Even if ChatGPT could only make us 10% better at solving the "easy" things but on a global scale, that is already a colossal benefit to society.

> I suppose it makes sense though. Denial is the default response when we face threats to our identity and sense of self worth.

Respectfully, that reads as needlessly combative within the context. It sounds like the blockchain proponents who say that the only people who are against cryptocurrencies are the ones who are “bitter for having missed the boat”.¹

It is possible and perfectly reasonable to identify problems in ChatGPT and similar technologies without feeling threatened. Simple example: someone who is retired and monetarily well off, whose way of living and sense of self worth are in no way affected by developments in AI, can still be critical and express valid concerns when these models tell you that it’s safe to boil a baby² or give other confident but absurdly wrong answers to important questions.

¹ I’m not saying that’s your intention, but consider that type of rhetoric may be counterproductive if you’re trying to make another understand your point of view.

² I passed by that specific example on Mastodon but I’m not finding it now.

As much as I’m sick of AI products, I’m even more sick of the “ChatGPT is bullshit” argument.

I like this take. It has many clear applications already and LLM's are still only in their infancy. I both criticize and use ChatGPT at work. It has flaws and it has advantages. That it's bullshit or "ELIZA" is a short-sighted view that overvalues the importance of AGI and misses what we're already getting.

But yes indeed, there are many, many AI products launched during this era of rapid progress. Even kind of shoddy products can be monetized if they provide value over what we had before. I think the crowded market and all the bullshit and all the awesome, all at once, is a sign of very rapid progress in this space. It will probably not always be like this and who knows what we are approaching.

How are you using it at work?

I've used it to proof emails for grammar, and it's done ok.

I'll also throw random programming questions into it, and it's been hit and miss. SO is probably still faster, and I like seeing the discussion. The problem with chatGPT right now is it gives an answer like it's certainty when it's often wrong.

I can see the benefits of this interaction model (basically summarizing all the things from a search into what feels like a person talking back), but I don't see change the world level hype at the moment.

I also wonder if LLMs will get worse over time through propagation error as content is generated by other LLMs.

I’m not the person you replied to but I’ve been using OpenAI’s API a lot for work. Some examples:

- Embedding free text data on safety observations, clustering them together, using text completion to automatically label the clusters, and identifying trends

- Embedding free text data on equipment failures. Some of our equipment failures have been classified manually by humans into various categories. I use the embeddings to train a model to predict those categories for uncategorized failures.

- Analyzing employee development goals and locating common themes. Then using this to identify where there are gaps we can fill in training offerings.

The only problem with the “ChatGPT is bullshit” argument is that it is only half true.

ChatGPT, when provided with a synthetic prompt is reliably a synthesizer, or to use the loaded term, a bullshiter.

When provided with an analytic prompt, it is reliably a translator.

Terms, etc: https://www.williamcotton.com/articles/chatgpt-and-the-analy...

> ChatGPT, when provided with a synthetic prompt is reliably a synthesizer, or to use the loaded term, a bullshiter.

sounds like most people tbf

There are people who in many situations use as much critical though as ChatGPT does.

ChatGPT isn't as good as a human who puts in a lot of effort, but in many jobs it can easily outperform humans who don't care very much.

It can be both bullshit and utterly astounding.

In terms of closing the gap between AI hype and useful general purpose AI tools, no one can reasonably deny that it's an absolute quantum leap.

It's just not a daily driver for technical experts yet.

> quantum leap

Ironically accurate.

In normal English usage, a quantum leap is a step-change, a near-discrete rather than continuous improvement, a large singular advance.

Given we are not talking about state changes in electrons, there is nothing wrong with this description of ChatGPT - it truly does feel like a massive advance to anyone who has even cursorily played with it.

For example, you can ask it questions like "Who was born first, Margaret Thatcher or George Bush?" and "Who was born first, Tony Blair or George Bush?" and in each instance it infers which George Bush you are talking about.

I honestly couldn't imagine something like this being this good only three years ago.

(1) You are correct in that placing both of those questions into Google doesn't quite get you anywhere near the answer that I imagine ChatGPT gives you (as you point out). Although, Google does "infer" which Bush you are talking about, there isn't a clear "this person is older" answer, you have to dive into the wiki pages basically to get the answer.

(2) Counter. I asked it the other day "how many movies were Tom Hanks and Meg Ryan in together" and the answer ChatGPT gave was 2 ... not only is that wrong it is astonishingly wrong (IMO). You could be forgiven for forgetting Ithaca from 2015. I could forgive ChatGPT for forgetting that one. But You've Got Mail? That's a very odd omission. So much so I'm genuinely curious how it could possible get the answer wrong in that way. And for the record, Google presents the correct answer (4) in a cut out segment right at the top, a result and presentation very close to what one would expect from ChatGPT.

I don't know about other use cases like generating stories (or tangentially art of any kind) for inspiration, etc. But as a search engine things like ChatGPT NEED to have attributions. If I ask the question "Does a submarine appear in the movie Battlefield Earth?" it will confidently answer "no". I _think_ that answer is right, but I'm not really all that confident it is right. It needs to present the reasons it thinks that is right. Something like "No. I believe this because (1) the keyword submarine doesn't appear in the IMDb keywords (<source>), (2) the word submarine doesn't appear in the wikipedia plot synopsis (<source>), (3) the film takes place in Denver (<source>) which is landlocked making it unlikely a submarine would be found in that location during the course of the film."

The Tom Hanks / Meg Ryan question/answer would at least more interesting if it explained how it managed to be so uniquely incorrect. That question will haunt me though ... there's some rule about this right? Asking about something you have above average knowledge in and watching someone confidently answer it incorrectly. How am I supposed to ever trust ChatGPT again about movie queries?

The biggest thing I’ve learned from chatGPT is that real people struggle with the difference between intelligence, understanding, and consciousness / sentience.

Because they are all ill defined in the manner they are used in common language. Hell, we have trouble describing what they are, especially in a scientific fact based setting.

Before this point in history we accepted 'I am that I am' because there wasn't any challenger to the title. Now that we are putting this to question we realize our definitions may not work well.

Which should be no surprise, as people have been grappling with these ideas for centuries, and we still don't have any definitive idea of what consciousness/sentience truly is. What I find interesting is that at one point the Turing test seemed to be the gold standard for intelligence, but chatGPT could pass that with flying colors. So how exactly will we know if/when true intelligence does emerge?

Well, my point wasn’t that there is a good definition of consciousness.

My point was that “consciousness” and “intelligence” are very different things. One does not imply the other.

Consciousness is about self reflection. Intelligence is about insight and/or problem solving. The two are often correlated, especially in animals, especially in humans, but they’re not the same thing at all.

“Is chatgpt consciousness” is a totally different question than “is chatgpt intelligent”.

We will know chatgpt is intelligent when it passes our tests of intelligence, which are imperfect but at least directionally correct.

I have no idea if/when we we know whether chatgpt is conscious, because we don’t really have good definitions of consciousness, let along tests, as you note.

The most annoying thing to me is people thinking AI wants things and gets happy and sad. It doesn't have a mamailian or reptilian brain. It just holds a mirror up to humanity generally via matrix math and probability.

Well said. It is a mistake to anthropomorphize large language models; they really hate that.

>The biggest thing I’ve learned from chatGPT is that real people struggle with the difference between intelligence, understanding, and consciousness / sentience.

Well, I'm no fan of chatGPT. But it appears most people are worse than chatGPT, because just regurgitate what they hear with no thought or contemplation. So you can't really blame average folks who struggle with the concepts of intelligence/understanding that you mention.

I can say with a certain degree of confidence that you haven't actually used CoPilot daily.

This is such a bullshit answer. No, I don't use it daily because I tried it for a couple hours and it suggested nothing useful and several harmful. Why would I keep using it?

I've worked with teams that used Copilot. They claim it's great "Hey, now I don't have to actually spend any time writing all this boilerplate!" while for me, the person who has to review their code before releasing stuff, easier ways of writing boilerplate is not a positive, it's a negative.

If writing boilerplate becomes effortless, then you'll write more of it, instead of feeling the pain of writing it and then trying to reduce it, because you don't want to spend time writing it.

And since Copilot was accepted as a way to help the developers on the teams, the increase of boilerplate have been immersive.

I'm borderline pissed, but mostly at our own development processes, not at Copilot per se. But damn if I didn't wish it existed somehow, although it was inevitable it would at one point.

>the increase of boilerplate have been immersive

Has it really? Or are you worried that this is something that will happen?

Of course I don't know how other people use it but I find that it's very much like having a fairly skilled pair programmer on board. I still need to do a lot of work but I get genuine help. I don't find that I personally write more boilerplate code than before, every programming principle applies as it always has.

I wrote it in past tense, it's based on actual situations :) If you don't believe what I write, I guess it doesn't matter what I write now. Regardless.

One simple example that I've had to reject more than once.

- Function 1 does something

- Developer needs something like Function 1 but minor change

- Developer starts typing name of function which has a similar name to Function 1, but again, minor difference

- Copilot helpfully suggests copy-pasting Function 1 but with the small change incorporated

- Developer accepts it, commits and sends the patch my way

Rather than extracting the common behavior into it's own function and call that from both of them, refactors which Copilot doesn't suggest, the developers is fine with just copy-pasting the function.

Now we have to maintain two full slightly different functions, rather than 1 full functions + 2 minor ones.

Obviously a small example, and it wouldn't be worth extracting it the first time it happens or on a smaller scale. But once you have entire teams doing something like this, it becomes a bit harder to justify copy-paste approach, especially when you want the codebase not to evolve to complete spaghetti.

And finally, I'm not blaming the tool, it's not Copilots fault. But it does seem to have made developers who rely on it think less, compared to the ones that don't.

I feel ya. If your job is to kick back bad code, and now there is a tool that generates bad code, how does this not make your job more important?

Why not get some of the freed up, Copilot augmented developer labor budget moved to testing and do more there or build more tools to make your personal, boilerplate, repetitive tasks more efficient?

If the coders are truly just dumping bad code your way, that's an externality and the cost should be called out.

I use github copilot on a daily basis and it improves my time from thinking to code.

Often I have times where I'm think about a specific piece of code that I need and I have it partially in my head and github copilot "just completes" it. I press tab and that's it.

I'm not talking about writing entire functions where you have to mentally strain yourself to understand what it wrote.

But I've never seen any autocompleter do it so good then github copilot. Even for documentation purposes like JSdoc and related commenting system it's amazing.

It's a tool I pay for now since it's proven to be a tool that increases my productivity.

Is it gonna replace us? I hope not, but it does look promising as one of those tools people will talk about in the future.

It would be helpful if people could include in their assessment roughly how much time they've personally spent using these tools.

Helping write boilerplate is to Copilot what cropping is to Photoshop.

Some of the ways I've found Copilot a powerful tool in my toolbox: Writing missing comments (especially unfamiliar code bases), "translating" parts of unfamiliar code to a more familiar language, suggesting ideas for how to implement a feature (!) in comments.

you could stop doing code reviews and do something else

Yep. I’m personally skeptical of so many other use cases for LLMs but CoPilot is fantastic and basically just autocomplete on rocket fuel. If you can use autocomplete, you can use CoPilot super effectively.

I almost always turn autocomplete off except in circumstances where the API has bad documentation. I also found that copilot was an aggravation more than a help after using it for a couple weeks.

We programmers enjoy writing code. We derive satisfaction when a code is perfect and elegant. But its going to end very soon. Artists are freaking out because things that takes them days to create now only take 2 seconds. We are next.

The writing is on the wall. Programming as we know it is going to end. We should be embracing these tools and should start moving from software developers to software architects role.

I don't think you're getting what I'm saying. I'm _faster_ without autocomplete

I've used it quite a lot and I agree with the original post. It seemed really useful at first but then it started introducing several bugs in large blocks of code. I've stopped using it in the end since the small snippets on the one line size is trivial enough to write myself (with just vim proficiency) and the larger blocks on the order of a function autocomplete is too bug prone (and kills too much willpower budget to fix).

I haven't. Now you know for a fact :)

What I have seen about it ranged from things that can be nearly just as well handled by your $EDITOR's snippet functionality to things where my argument kicked in - I have to verify this generated code does what I want, ergo I have to read and understand something not written by me. Paired with the at least somewhat legally and ethical questionable source of the training data, this is not for me.

So stop evangelizing about stuff you haven’t used. Understanding code is easier than writing it from the scratch. That’s why code review doesn’t take as much time as writing code and you still need to prove your code works, even if you wrote it yourself.

I am not evangelizing, I am just stating why this is not for me and my way to write software.

Understanding code is only easier for simple tasks. I've definitely had copilot spit out complex algorithms that looked right at first glance but actually had major issues that required me to write it from scratch.

Thats why you test, this could also happen with code you wrote so it’s not an argument against copilot. Did you wrote your “complex algorithm” and then run and debugged it in your head? No, you’ve tested it. Do the same with Copilots code

If testing is the equalizer, there is no difference between black box code and something you fully understand. Which fair enough, is how ML works in general.

I contend understanding the semantics of code is harder than writing the syntax. Reading the syntax without thinking deeply (to the level needed to write it, or deeper) seldom helps you realize unexpected corner cases. This is why stochastic testing is so valuable.

ad.: Code review takes less time than writing code for the same reason reading a book takes less time than writing one. Distillation and organization of ideas requires expertise gained through experience and long thought. Reading a book requires reading ability.

Understanding a book (and the intricacies underlying it) takes effort on the order of the original writing, but most people don't seek that level of understanding. The same is true of code.

Code review often takes me longer than writing code. More generally, reading other people's code is more difficult for me than writing (or reading) my own.

I can say with a higher degree of confidence that you haven't actually used CoPilot daily for any respectably sized project.

oops, you are wrong :)

> The "I" in AI is just complete bullshit and I can't understand why so many people are in a awe

I agree.

And the worst thing is that the bullshit hype comes round every decade or so, and people run around like headless chickens insisting that "this time its different", and "this time its the REAL THING".

As you say, first(ish) there was ELIZA. Than this that and everything else. Then Autonomy and all that dot-com era jazz. Now with compute becoming more powerful and more compact, any man and his dog can stuff some AI bullshit where it doesn't belong.

I have seen comments below on this thread where people talk about "well, it's closing the gap". The thing you have to understand is that the gap will always exist. Ultimately you will always be asking a computer to do something. And computers are dumb. They are and will always be beholden to the humans that program them and the information that you feed them. The human will always have the upper hand at any tasks that require actual intelligence (i.e. thoughtful reasoning, adapting to rapidly changing events etc.).

This is not always true, see Chess.

AlphaGo as well. A few years back people were saying AI could never come close to beating a human at Go.

Man, if this were 1800 you'd be stating that man would never fly and the horse would never be supplanted by the engine. I honestly don't believe you have any scientific or rational reasoning for the point you are attempting to make in your post, because if you were you'd be stating that animal intelligence is magical.

> Man, if this were 1800 you'd be stating that man would never fly and the horse would never be supplanted by the engine.

I'm sorry, what sort of bullshit argument is that ?

Flight and engines are both natural evolution using natural physics and mechanics.

Artificial Intelligence is nothing but a square-peg-round-hole, when you have a sledgehammer everything looks like a nut scenario.

They are natural to you maybe with hindsight? Powered flight was most definitely not considered natural at the time. In fact, most attempts at flight were trying to mimic birds at first.

Flight and engines are natural evolution but intelligence is magic? Nature accomplished intelligence via random walk and it is a complicated mess because of it. To think that we cannot accomplish at least parts of intelligence is insane to me.

> And the worst thing is that the bullshit hype comes round every decade or so, and people run around like headless chickens insisting that "this time its different", and "this time its the REAL THING".

This. To answer the OPs question, this is what I'm fatigued about.

I'm glad we're making progress. It's a hell of a parlor trick. But the hype around it is astounding considering how often it's answers are completely wrong. People think computers are magic boxes, and so we must be just a few lever pulls away from making it correct all the time.

Or maybe my problem is that I've overestimated the average human's intelligence. If you can't tell ChatGPT apart from a good con-man, can we consider the Turing test passed? It's likely time for a redefinition of the Turing test.

Instead of AI making machines smarter, it seems that computers are making humans dumber. Perhaps the AI revolution is about dropping the level of average human intelligence to match the level of a computer. A mental race to the bottom?

I'm reminded of the old Rod Serling quote: We're developing a new citizenry. One that will be very selective about cereals and automobiles, but won't be able to think.

I'm having a really hard time following your argument. But absolutely agree we need to redefine the Turing test. Only problem is that I can no longer come up with any reasonable time-limited cognitive task that next year's AI would fail at, but a "typical human" would pass.

"Intelligence" is probably too nebulous a term for what it is we're trying to build. Like "pornography", its hard to rigidly define, but you know it when you see it.

I think "human level intelligence" is an emergent phenomenon arising from a variety of smaller cognitive subsystems working together to solve a problem. It does seem that ChatGPT and similar models have at least partially automated one of the subsystems in this model. Still, it can't reason, doesn't know it's wrong, and can't lie because it doesn't understand what a lie is. So it has a long way to go. But it's still real progress in the sense that it's allowing us to better see the dividing lines between the subsystems that make up general intelligence.

I think that we'll need to build a better systems level model of what general intelligence is and the pieces it's built out of. With a better defined model, we can come up with better tests for each subsystem. These tests will replace the Turing test.

>>Instead of AI making machines smarter, it seems that computers are making humans dumber. Perhaps the AI revolution is about dropping the level of average human intelligence to match the level of a computer. A mental race to the bottom?

I came here to make this comment. Thank you for doing it for me.

I remember feeling shocked when this article appeared in the Atlantic in 2008, "Is Google Making Us Stupid?": https://www.theatlantic.com/magazine/archive/2008/07/is-goog...

The existence of the article broke Betteridge's law for me. The fact that this phenomenon it is not more widely discussed describes the limit of human intelligence. Which brings me back around to the other side... perhaps we were never as intelligent as we suspected?

> perhaps we were never as intelligent as we suspected?

Yeah, I think you're right. Intelligence is just something our species has evolved as a strategy for survival. It isn't about intelligence, it's about survival.

The cognitive skills needed to survive/navigate/thrive in the digital era are very different than the cognitive skills required to survive in the pre-digital era.

We're biologically programmed through millions of years of evolution to survive in a world of scarcity. Intelligence used to be about tying together small bits of scarce information to find larger patterns so that we can better predict outcomes.

Those skills are being rendered more and more irrelevant in a world of information abundance. Perhaps the "best fit" humans of the future are those that possess new form of "intelligence", relying less on reason and more on the ability to quickly digest the firehose of data thrown at them 24-7.

If so, then the AI we were trying to build in the 1950s would necessarily be different than the AI that our grandchilden would find helpful.

You're dead on. Isn't it wild that despite our seemingly impressive intelligence, such insights never seem to rise to the level of... second nature.

I forgot to add something to my original post. >>"I remember feeling shocked when this article appeared in the Atlantic in 2008..."

At the time I was shocked that the question was even being asked!

“AI” isn’t bull shit, it’s correctly labeled. It’s intelligence which is artificial: i.e. fake, ersatz, specious, not genuine… It’s our fault for not just reading the label. (I absolutely agree with your post and your viewpoint, just to be clear!)

"Artificial" is not synonymous with "fake". "Fake" implies a level of deception.

Not necessarily true. People talk about “fake meat” all the time but it’s clear there’s no level of fraudulence implied by this usage. It’s meant in the sense of “artificial meat”. There are multiple ways the word “fake” is used, and one is as a synonym for “artificial”.

However, in this case, it does seem that there is a level of fraudulence and deception. Given that “fake” often is used exactly the way you say, maybe “fake intelligence” would indeed be a more appropriate term.

Fake meat is still a form of deception; it's something that's not meat pretending to be meat. If lab grown meat gets good enough to be indistinguishable from "real" meat, then it would no longer be fake, it would just be artificial.

Is “fake grass” (i.e. astroturf) a form of deception in your eyes?

When seen at a distance, it can trick people into thinking you have a perfectly manicured lawn. But you're just lazy/evil/it's a holiday house. Deception!

Haha. I give up. :-)

Have you been around people who say "fake meat?" Every time I've heard it, it was said derisively and implied fraudulent meat.

Yes. I’ve heard loads of people refer to it as fake without an implied pejorative meaning.

The people I know that like it use artificial and the people that won't try it use fake.

Fascinating. Not all people are the same. Who’d have thought?

Definitely. Quirks like this are also what makes AI difficult.

Superficial Intelligence may hit the mark there.

Actually it is [1] [2]

[1] Synonyms of artificial has "faked" : https://www.thesaurus.com/browse/artificial

[2] Synonyms of fake has "artificial": https://www.thesaurus.com/browse/fake

Artifical means "not human" in this context for me, but I understand "Intelligence" as the abiltiy to actual reason about something based on things you learned and/or experienced, and these "AI" tools don't do this at all.

But defining "intelligence" is a philosopical question that doesn't necessarily have one answer for everything and everyone.

Personally, I try to take a more inductive approach. We don’t know what intelligence is, but we assume it’s something we exhibit. We also clearly recognize other animals as possessing the same trait to varying degrees. Since we don’t know what it is, and since (I would argue) we can only convincingly claim that exists in other biological organisms without meeting a high burden of proof, to claim that it exists in an inorganic substrate requires a VERY large burden of proof to be met, similar to what would be met if you were claiming that magic existed. In my view, calling computers “intelligent” is in the same league as claiming that crystals are magic. Of course, this depends on my own philosophical interpretation of what intelligence is, as you say.

Intelligence is a capability not a mechanism, and therefore if you're able/willing to define what that capability is, there should be no problem measuring/gauging the intelligence of any system, biological or not. You don't need to look inside the black box - you only need to test if the black box has this capability.

Intelligence may be a fuzzily defined word in everyday usage, but I don't think it's the mystery you present it to be. Joe public may argue against any and all definitions of the word that they personally disagree with (maybe just dislike), but it's nonetheless quite easy to come up with a straightforward and reductive definition if you actually want to!

You and I are clearly referring to two different things when we use the word “intelligence”. It is also not nearly so easy to come up with a simple/mechanical/verifiable definition for the thing that you’re referring to. Unless you have a good definition—in which case, you might want to put your money where your mouth is and get busy revolutionizing multiple fields of human inquiry!

It’s also plain that many people are very interested in looking inside the black box and think the contents of the black box are relevant and important. This fact doesn’t change just by your saying so.

People are interested in looking inside the black box (our brain) for sure, partly for inspiration as to how to implement intelligence among other things, but implementing isn't the same as defining.

Being able to define what you want to achieve isn't generally the same as knowing HOW to achieve it (except in this case the definition of intelligence rather does suggest the right path).

The intention of the "artificial" in "AI" is not that particular meaning of "artificial", but the one for "constructed, man-made"—see meaning #1 in the Wiktionary definition[0]; the one you are using is #2.

It is often frustrating that English has words with such different (but clearly related) definitions, as it can make it far too easy to end up talking past each other.

[0] https://en.wiktionary.org/wiki/artificial

Uh oh, are you an AI? You seem to have missed my attempt at wry humor.

Let me introduce you to https://en.wikipedia.org/wiki/Poe%27s_law

I agree with you completely. I work in the field and I think your sentiment is way more common amongst people who know about the technology, vs the fair weather fans who have all jumped on the hype bandwagon recently. I actually posted the same thing (that it's no different than Eliza) a month or so ago, and got at least one hilarious dismissal, like the "I bet you make widgets" person that replied to you.

Thank you!

If you believe that ChatGPT is similar to Eliza, then I can guarantee that you have no rigorous no-wriggle-room definition of what intelligence is. Maybe you think you understand it, or have defined it, but I'm 100% certain any such definition is not 100% reductive and instead relies on other ill-defined works like "reasoning" etc etc.

“It’s just statistics” is an evergreen way to dismiss AI. The problem is you’re also just statistics.

Shh. The models don’t like hearing that.

Source for consciousness / intelligence to be "statistics"?

I don't think there is any because there is no functional model for what organic intelligence is or how it operates. There are plethora of fascinating attempts / models but only a subset implore that it is solely "statistical". And even if it was statistical, the implementation of the wet system is absolutely not like a gigantic list of vectorized (stripped of their essence) tokens

There’s a man who claims to have solved consciousness as a multilayered Bayesian prediction system.

See Scott Alexander for attempts to explain what is apparently impenetrable papers on it.

Sources for intelligence to be magic? I mean we know it's complicated but intelligence also spans the smallest creatures on the planet to humans. This points at intelligence being a reduceable problem that is layered. On top of that it's unlikely we need to model nerve behavior to get something intelligence like output.

That's like saying that airplanes aren't flying since they're not flapping their wings. Intelligence is a capability - not a specific mechanism.

Consciousness is a subjective experience (regardless of what you believe/understand to be responsible for that experience), so discussing "consciousness/intelligence" is rather like discussing "cabbages/automobiles".

Look at how Microsoft is instructing GPT to become "Sydney" and re-evaluate your opinions about what is intelligence:

https://twitter.com/marvinvonhagen/status/162365814434901197...

> Github CoPilot? Great, now I have to perform the most mentally taxing part of developing software, namely understanding other people's code (or my own from 6 months ago...) while writing new code. I'm beyond thrilled ...

I think there's an argument to be made that AI is being used here to help you tackle the more trivial tasks so you have more time to focus on the more important, and challenging tasks. Albeit I recognise GitHub CoPilot is legally questionable.

But yes, I agree with your overall point that AI has still not been able to 'think' like a human but rather can only still pretend to think like a human, and history has shown that users are often fooled by this.

I think the parent’s comment is probably referring to the fact if you use Copilot to write code then you have to go through and try to understand what it wrote and possibly debug it. And you don’t have the opportunity to ask it why it wrote it the way it did when reviewing its code.

I think you’re right, but that just means parent doesn’t understand copilot and is off tilting at windmills.

Copilot is amazing for reducing the tedium of typing obvious but lengthy code (and strings!). And it’s inline and passive; it’s not like you go edit -> insert -> copilot function and it dumps in 100 lines of code you have to debug. Which is what it sounds like parent is mistaking it for.

I’m reminded of 1995, when an elderly relative told me everything wrong with the internet based on TV news and not having ever actually seen the internet.

> Copilot is amazing for reducing the tedium of typing obvious but lengthy code (and strings!)

Which it occasionally mistypes. Then you're off to chase a small piece of error in a tub of boilerplate. Great stuff! For actual example, see [0]

[0] https://blog.ploeh.dk/2022/12/05/github-copilot-preliminary-...

You must be a much better programmer than I if those are examples you’d use copilot for. I was thinking more like:

   start_value = get_*start_value(user_input)*
   self.log.d*ebug(‘got start_value {start_value}’)*

. . . where the would-be italics are what copilot would likely suggest for completion.

And if it’s wrong, you just. . . keep typing. It’s autocomplete, just like IDEs have for other things. I’m kind of astounded that people have such an emotional reaction to an optional, low-key, passive, easily-ignored tool that sometimes saves a bunch of typing. Yes, if you always accept the suggestions you’ll have problems. Just like literally every other coding assistance tool.

That's not my blog, I just thought the example to be relevant.

> I was thinking more like:

That example is straight up from any of those "programming is not bound by typing speed" essays of yore.

> people have such an emotional reaction to an optional, low-key, passive, easily-ignored tool that sometimes saves a bunch of typing.

Maybe because it's not generally advertised by proponents as "an optional, low-key, passive, easily-ignored tool that sometimes saves a bunch of typing"? Just look at the rest of the thread, it's pronounced as a game-changer in productivity.

Different experiences, I guess. I’m a low end, part-time hobbyist programmer, and for me at least 75% of my time is spent essentially typing in obvious, easily-checked code. It has been a game changer for me. It’s also led me to write better comments, because rather than being a pure tax, they improve the generated code.

I can see how someone who’s always working on sophisticated, mentally challenging code would get less benefit and would see more frequent errors.

But it's trickling in small chunks at a time unless you are just smashing tab repeatedly and don't look at what it did until the very end. You can also not accept what it offers and just continue writing code for yourself. If a dev submitted a bunch of Copilot code they don't understand and can't answer questions about you reject the PR outright and they eventually realize it didn't save them any time or effort. Copilot isn't the employee.

As soon as I open a fresh IDE these days I immediately miss CoPilot and it's the first thing I install.

Hype or not, it's incredibly useful and has increased my productivity by at least 20%. Worth every penny.

I agree. I didn't understand the big deal that it passed a google interview either. IMO, that said more about the uselessness of the interview than the 'AI'.

Co-pilot has been semi-useful. It's faster than search SO, but like you said, I still have to review all the code and it's often wrong in subtle ways.

This is the meat of the issue - ChatGPT is exposing certain things a susceptible to bullshit attacks; humans have just been relatively bad at those.

It will turn out to be a useful tool for those who know what they’re asking about so they can check the answer quickly; but it will be USED by tons of people who don’t have a way of verifying the answers given.

ChatGPT is of actual help for me in various daily tasks, which was never the case with ELIZA or earlier chatbots which were only good as a curiosity or to have some fun.

Lack of actual human understanding? Of course, by definition a machine will always lack human understanding. Why does that matter so much if it's a helpful tool?

For what it's worth, I do agree that there is a lot of hype. But contrary to blockchain, NFTs, web3, etc., this is actually useful for many people in many everyday use cases.

I see it as more similar to the dot com hype - buying a domain and creating a silly generic website didn't really multiply the value of your company as some people thought in that era, but that doesn't mean that websites weren't a useful technology with staying power, as time has shown.

I'm sorry I don't want it to get much smarter.

It you ask it to go through and comment code it does a pretty good job of that.

some things better than others(not that great at CSS)

need a basic definition of something. got it.

tell it to write a function it's not bad.

As a BA just tell it what your trying to do and what questions it should ask users. It will get some good ideas for you.

Want it to be a PM have create a loop asking every 10 minutes if your done yet.

Is it a senior engineer? no. can it pass a senior engineering interview? quite possibly.

debug code hit or miss.

I think the big thing it's not that great at front end code. It can't see so that probably makes sense. a fine-tuned version of clip that interacted with a browser would probably be pretty scary.

What's the point of letting it comment code? The programmer who reads the code can run it as well.

I don't really think of ChatGPT as AI at this point, just an incredibly useful tool.

I wonder if we will look back at this comment (and others like it) as similar to the infamous “takedown” of Dropbox when it was first posted on HN.

Time will tell, I certainly can’t predict.

The "I" in AI is just complete bullshit

We're about six minutes away from "AI bros" becoming a thing.

The same kind of grifters who always latch onto the latest thing and hype it up in order to make a quick buck are already knocking on AI's door.

See also: Cryptocurrency, and Beanie Babies.

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