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OpenAI's GPT-3 may be the biggest thing since Bitcoin (maraoz.com)
1079 points by maraoz on July 18, 2020 | hide | past | favorite | 526 comments

I am deeply enjoying this comment thread - it's a bit of a Barium Meal [0] for determining how many people read (a) the headline, (b) the first paragraph, or (c) the whole thing before jumping straight into the compose box.

Having read to the bottom, the quality of text generation there absolutely blew me away. GPT-2 texts have a somewhat disconnected quality - "it only makes sense if you're not really paying attention" - that this article lacks entirely. Adjacent sentences and even paragraphs are plausible neighbours. Even on re-reading more closely, it doesn't feel like the world's best writing, but I don't notice major loss of coherence until the last couple of paragraphs. I am now really curious about the other 9 attempts that were thrown away. Are they always this good?!

[0] https://en.wikipedia.org/wiki/Canary_trap#Barium_meal_test

I've started working on a version of GPT-2 which generates English text. The purpose of this is to improve its ability to predict the next character in a text, by having it learn 'grammatical rules' for English. It already works well for predicting the next character when it has seen only a small amount of text, but becomes less accurate as the amount of training text increases. I have managed to improve this by having it generate text. That is, it creates an 'original' piece of text about 'topic x', then a slightly altered version of this text where one sentence has a single word changed, and this process is repeated many times (about a million). It seems to quickly learn how to vary sentences in a way that seems natural and realistic. I think the reason this works is because it reduces the chance that the grammar it has learned for one specific topic (e.g. snow) will accidentally be transferred to another topic (e.g. dogs). Of course, this all means nothing unless it actually learns something from the process of generating text. I haven't tried this yet, but the plan is to have it generate text about a topic, then have a second GPT-2 system try to guess what that topic is. If the resulting system is noticeably better at this task, then we know the process has increased its ability to generalize.

One potential issue with this approach is that the text it generates is 'nonsensical', in that it is almost like a word-salad. Although this is a standard problem with neural nets (and other machine learning algorithms), in this case the text actually is a word-salad. It seems that it has learned the rules of grammar, but not the meaning of words. It is able to string words together in a way that sounds right, but the words don't actually mean anything.

Plot twist: This comment was generated by GPT-3 prompted with some of the comments in this thread.

The thing that kills me is that to the vast majority of human beings the nonsensical technobabble above is probably indistinguishable from real, honest, logically consistent technobabble.[a]

Soon enough, someone will replicate the Sokal hoax[b] with GPT-3 or another state-of-the-art language-generation model. It's not hard to imagine GPT-3 writing a fake paper that gets published in certain academic journals in the social sciences.

[a] https://en.wikipedia.org/wiki/Technobabble

[b] https://en.wikipedia.org/wiki/Sokal_affair -- here's a copy of Sokal's hoax paper, "Transgressing the Boundaries: Towards a Transformative Hermeneutics of Quantum Gravity:" https://physics.nyu.edu/faculty/sokal/transgress_v2/transgre...

It's not hard to imagine GPT-3 writing a fake paper that gets published in certain academic journals in the social sciences. And then, it'll be all over for us. We won't have any more funding and our jobs will disappear. I can already hear the protests: "But we're not just scientists! We're also philosophers!" Well, yes and no. Philosophers are supposed to think about things philosophically, but they don't actually do anything about them; they're just entertainers. Scientists do something about them. They make things happen. And when those things happen, people take notice. If science and technology have a weakness, it's that they work too well. This was probably a strength at one point, but not anymore. In the not-too-distant future, there probably won't be any more philosophy professors; there will just be philosophers. But only in the same sense that there are lions and mushrooms.

This comment was also written by GPT-3.

How many attempts did it take or did you just choose the first one?

I have to admit, this is passing my turing test...

Really? I got about halfway through and realized that the comment had no point. If you tried to summarize what it was arguing, beyond the first sentence, I don't think you could make a coherent summary.

Maybe the real lesson is we don't expect human-written comments on discussion fora to be particularly coherent....

Also both comments made me suspicious half way through and I scrolled to the bottom to check for a GPT-3 note. Without that note I would definitely have regarded it as incoherent rambling by a human.

Especially the second comment can be coherently interpreted with some good will and a cynical view of the humanities and philosophy. The "author" could say that once GPT-3 can write humanities papers it will quickly make humanity scientists redundant and that humanities scientists are philosophers is not important and doesn't warrant a job alone ("they don't actually do anything"). Eventually it shifts that this is the fault of science working too well (GPT-3 being a product of science)

It's not a consistent argument, but without the context of these comments being GPT-3 it would have totally passed my turing test, just not my sanity test.

I think (slash worry) that this is going to be a simple upgrade in future iterations. Obviously there are powerful attention mechanisms at work to keep the subject matter coherent, but we’re not too far off the model being able to generate a core thesis, and ensure that all the generated text supports that in some way.

I think that if that worked it would prove that either language is a much more powerful tool than we realize, or our cognitive capacities are much more trivial than we realize.

The model fundamentally has no understanding of the world, so if it can successfully argue about a central thesis without simply selecting pre-existing fragments, then it would suggest that the statistical relations between words capture directly our reasoning about the world.

Who here thinks some Donald Trump's answers were written by an early version of GPT3, designed to produce more bombastic and rambling rhetoric than usual?

In principle it’s not too far fetched ... there’s almost certainly some kind of data-driven algorithmic processing going into a lot of speech writing these days; some of the drops are so implausible they’d almost certainly have to have been suggested by a machine!

Not being sarcastic, but I know some people with less coherent writing than this. A lot of people struggle to make a point, use vague language, or wander in and out of topics quite easily.

Yeah they’re typically mimicking a style of speaking that they’ve heard other people use but don’t really understand the subject matter themselves ...

It felt like it was making a slightly ranty observation that scientists are already trying to much to be philosophers than to actually do science that changes the world, yet science has brought us far enough that it acts as an enabler for all kinds of pop-philosphers.

The final bit doesn't quite connect, but overall I've seen far less coherent comments written by humans on subject with far more logical flaws.

I would not have imagined it was automatically written. Rambling and there's little connection between the first part and the latter, but absolutely something that might appear on a random internet forum.

I am genuinely awed.

> I got about halfway through and realized that the comment had no point

Pretty average for HN then ;)

Given that I know this stuff is generated text, it looks pretty good. But, if I’m judging it assuming that it was written by a human, it has a very uncanny valley sort of feel. That’s actually a good thing compared to previous models that would generate a lot of jarring non sequitors, because the GPT-3 text is very good if you look at it in 2-3 sentence chunks.

You say it like the bot wouldn't fit right in alongside most human comments because it meanders and doesn't seem to actually be responding to anyone, rather listening to itself talk.

Unfortunately I saw the sentence at the end before reading the whole comment, so I don't know how my detector would've done, but I thought this line:

>In the not-too-distant future, there probably won't be any more philosophy professors; there will just be philosophers

Was quite clever and I'm still trying to figure out what it means.

Maybe the real lesson is it was trained on human-written comments in discussion fora, so it perfectly mimics the average lack of point, weak arguments, rambling and incoherence in fora?

It would be interesting to see if the output has a similar quality when trained only on highly regarded texts.

> Maybe the real lesson is we don't expect human-written comments on discussion fora to be particularly coherent....

How could we expect it? After 35+ years (BBS and Usenet onward), we've learned that they are often not.

Yep, it looks like GPT-3 might not be too far from achieving artificial schizophrenia.

I don't think these gpt3 comments will get many upvotes on HN anyway. I downvoted the first one for being incoherent, but then realized it was meant as an example so I undowned it.

In possibly an unwise move, I'm actually going to respond to the initial point here.

There's a totally valid discipline in taking concepts from different areas and smushing them together to make a new idea. That's what a lot of creativity is, fundamentally. So a bot that's been trained across a wide variety of texts, spitting out an amalgam in response to a prompt that causes a connection to be made, is not only possible, but likely a very good way of generating papers (or at least abstracts) for humans to check. And if the raw output is readable, why not publish it?

"This comment was also written by GPT-3"

Would you please show us the input text, or rules, you gave to GPT-3 to create this comment ?

Somebody get this thing onto scp-wiki, it will be right at home.

Not gonna lie, I went poking around to see if I could get my hands on it, but it seems like the answer is no, for now.

That's a good question, how do we get access? I signed up on the list, but it must be thousands of people long by now. Does anyone here know anyone who can get people access?

I'm starting to sense that, in most scenarios, I will no longer want to engage in text-based conversations unless I know that I'm talking with a human. I already don't like spending a lot of time arguing with a bot on Twitter, this just makes it much more likely I'll also argue with a bot on medium-length text areas (e.g., HN, FB, Whatsapp, SMS, etc.) and maybe even on long-length text areas (e.g., Medium, blogs, NYTimes or things pretending to be real newspapers, etc.)

Second, I'm curious/terrified at how future iterations of GPT-3 may impact our ability to express ourselves and form bonds with other humans. First it's text messages and comments. Then it's essays. Then it's speeches. Then it's love letters. Then it's pitches. Then it's books. Then it's movie scripts. Then it's...

TLDR; Fascinated by the technology behind making something like this work and quite worried about the implications of the technology.

So it looks like we're about 2 years away from the 'Her' relationship model.

There was an article recently about people pursuing romantic relationships with chatbots. I thought there was a big HN discussion about it, but the only thing I've been able to find is this WSJ piece


(So I think it was some other story on the same topic.)

I am seeing it being used in tons of academic settings, especially with distance learning haha

Was it really? I don’t believe you. It makes sense.

The thing is that this thing has now crossed into the uncanny valley. Earlier it would have great trouble making a single sentence that makes sense. You only ever remember whether the last two sentences made sense and go together. And with GPT-3 any pair of sentences always makes almost perfect sense. By the time you're four sentences down you go wait a minute ...

This was very apparent when reading the generated stories [1].

Especially the shoggoth cat dialogue, I found that one really creepy. The fragment below comes straight out from the uncanny valley:

Human: Those memes sound funny. But you didn’t include any puns. So tell me, what is your favorite cat pun?

AI: Well, the best pun for me was the one he searched for the third time: “You didn’t eat all my fish, did you?” You see, the word “fish” can be replaced with the word “cats” to make the sentence read “Did you eat all my cats?”

[1]: https://www.gwern.net/GPT-3

An instance of a Voight-Kampff test.

Yeah, GPT-3 never really gives any kind of answer to “why”. It rambles on like Abraham Simpson talking about how an onion on his belt was the style at the time. Devoid of purpose it fills the void with meaningless words until told to stop. It’s subtle gibberish... and fucking irritating as soon as you catch it.

If he didn't put in the last line ("plot twist ...") I'm pretty sure no one here on HN would have guessed it.

In fact, while reading that comment I started to wonder why no one has tried to use GPT to generate text one character at a time. Or if someone has, what are the advantages and disadvantages over the BPE approach.

I’ve not studied neural nets and ML since undergrad level in 1998. So I am almost as knowledgeable as a random person on the street.

The quality of writing was very high, so I was convinced I was reading something put together by a human with agency... except it didn’t pass my gut-feeling “how IT works”. It made me suspect that either the algorithm (the described one, not the AI responsible) was off, or that I just didn’t understand AI any more. As I know I don’t have up to date AI knowledge, the algorithm appeared more believable. I hiked deep down the uncanny valley with that one.

This comment didn’t pass my gpt filter.

Your second paragraph is GPT-3?

Ok, now this is getting deep and I don't like it.

I assume both previous comments, and this one, are also GPT-3?

Edit: it is amusing to think that soon the way to distinguish them will be that human comments have weird errors caused by smartphone keyboard "spell checking" in them...

You really think my comment above was GPT-3 generated? Wow. Did I really make so little sense?

On a re-read, I'm not sure why I thought that, sorry. Context: I don't know much about machine learning and when I was scanning through comments, doing text generation one character at a time seemed silly and I must have been in the grips of "everything could be GPT!" hysteria. My robot detector needs work, clearly. Need to get educated.

But then if the models are trained on that dataset they might make the same errors to better approximate humans on the forum.

This entire thread is surrealistic and giving me Blade Runner vibes.. I love it.

What if the plot twist line is also GPT-3?

Well, you'd need to train it from scratch to operate on character level. And it would have smaller context, thus lower quality. So if you want same quality, you need much bigger context.

Still, would be an interesting experiment. Gwern swears it would improve stuff, so worth trying and comparing, I guess

There's actually precedent for this - sci-gen got papers accepted in IEEE and Springer publications, through peer review, and they had to investigate their back catalogue to look for others.

Given the propensity for academic writing to often favour the strategy of confusing the author through obfuscation (to make a minor advance sound more significant than it is), I suspect tools like this could, as you say, actually get published papers in some fields like social sciences. In an engineering or science paper you can check equations match conclusions, and that graphs match data etc.

In a more qualitative field of work, reviewed in a publish-or-perish system that doesn't incentivise time spent on detailed reviewing, I think there's a very real risk babble like this just comes across like every other paper they "review".

I think it takes a certain level of confidence to dismiss others' work as nonsensical waffle, but sadly this is a confidence many lack, and they assume there must be some sense located therein. Marketing text is a great place to train yourself to recognise much of what is written is meaningless hocum.

Sci-Gen - https://pdos.csail.mit.edu/archive/scigen/

Reporting on withdrawals of papers - https://www.researchgate.net/publication/278619529_Detection...

Dr. Herbert Schlangemann (fake alias for Sci-gen) not only got papers accepted in journals, it was invited to participate as session chair at conference co-sponsored by IEEE.


In a similar way to how image detection networks appear to key largely on visual textures, GPT-3 seems to key on jargon, tone and pacing, the texture of speech within specific milieus.

The thing that kills me is that soon enough, a "fake" paper written by GPT-3 will get published in an academic journal because it has actually contributed a new insight.

It's easy to consider text generation models as "just mimicking grammar". But isn't grammar also just a model of human cognition?

Is GPT modeling grammar or is it modeling human cognition? Since GPT can ingest radically more text (aka ideas) won't it soon be able to generate texts (aka ideas) that are a more accurate collation of current knowledge than any individual human could generate?


[Was this comment written by GPT-3?]

It was not - there's a point :-D

I am impressed though nobody dared to guess in 2 weeks.

My impression is that these models are already doing far more than what the language production machinery in our brain does. We are able to produce language according to grammar and semantics, but we also have independent mental representations to guide the generation of language and to provide context.

I don't really understand why we're trying so hard to build models that can generate coherent texts based on having predigested only other texts, without any other experience of reality. Their capabilities appear already superhuman in their ability to imitate styles and patterns of any kind (including code generation, images, etc.). It feels like we're overshooting our target by trying to solve an unsolvable problem, that of deriving the semantics of reality from pure text, without any other type of input.

One of my favorite conspiracists, Miles Mathis, has this quality. He can strong together entire pages of very real and consistent nonsense that is totally logical and makes enough sense to be real. I have to remember I'm not reading a legit theory and now really do confuse myself with his version of science vs reality.

Kind of annoying 'hoax', though. Obviously you can publish garbage in fringe journals if you leverage pre-existing prestige and position like Sokal did. Doesn't really say anything about the social sciences.

You can also publish a lot of nonsense in certain chinese journals that optimize for quantity in quality, in whatever field you want.

Worse, Sokal's Revenge is probably inevitable, in which someone will generate a nightmarishly complex mathematical proof that takes the whole field in unexplored directions. Some of the most respected professors and most promising students will then be distracted for years trying, and ultimately failing, to make sense of it.

Some say this has already happened. Nobody has ever seen the Social Text editors and Mochizuki in the same room together, have they?

> Plot twist: This comment was generated by GPT-3 prompted with some of the comments in this thread.

This kills the forum.

Seriously, once this is weaponised, discussion of politics on the internet with strangers becomes completely pointless instead of just mostly pointless. You could potentially convince a human; you can't convince a neural net that isn't in learning mode.

Perhaps it will destroy anonymity. Because the only way to be sure a human wrote something is if you somehow know who the comment came from.

We might end up with reputation based conversations.

Of course, the human can simply lend their name to the robot. And as previously discussed, ending anonymity entrenches the existing power structure.

> Of course, the human can simply lend their name to the robot.

That could have consequences for their reputation, though.

Are you suggesting that people found to have misled the public should be .. cancelled?

(Reputation is a lot more controversial and complicated than it sounds)

You can still be anonymous with this. You just need to be pseudonymous.

Robots can also have a pseudonym.

Go to URL and tell me what is written there.

It's more insidious than that. You can think you've convinced a human whereas you've just spent your energy on a bot. Assuming "political arguments on social media" has any relevance to voted cast, that's a vote for your side which doesn't happen.

> Seriously, once this is weaponised, discussion of politics on the internet with strangers becomes completely pointless

Quite the opposite, I suspect.

Eventually, to engage in the most persuasive conversations, the AIs will develop a real-time learning mode.

Once that is weaponised, the AIs will be on track to be in charge of running things, or at least greatly influencing how things are run.

What the AIs "think" will matter, if only because people will be listening to them.

Then it will be really important to discuss politics with the AIs.

Could you potentially convince a human for politics issue? It is extremely hard to convince stranger in the forum when there are some priors in mind.

That’s interesting, as within a sentence I had dismissed your comment as rambling and moved on to the next one, without thinking it had been generated... but maybe you’re double bluffing me.

Same, except that I skipped straight to the last line to check whether it was a generated text after I noticed the first sentence made no sense (GPT-2 already generates grammatically correct English sentences).

This comment reminds me of https://www.youtube.com/watch?v=RXJKdh1KZ0w

I read the beginning, went "what the fuck is this guy on about? Get to the point" and then came to check the comments to if this was anything interesting or worth reading, saw your comment, and skimmed the end bit. Overall I'm pleased with my process as its an efficient way to find out which articles are worth reading. But it was also clear to me that the author had difficulty making a clear point or had a goal in his writing. I skipped through it for a reason and I suspect many other people did as well.

Yes, we have poorly written babble from humans too. Now we will have weapons grade babbling from machines.

The result is that worthwhile public discussion is dying. We have to transition now to secure verified communication.

Either that or the bots fork off a new cultural discourse and we treat them like a new form of entertainment.

From your comment it's not clear to me if you realize the author of the article is GPT-3.

At this point I'm not even sure if that particular comment was written by GPT-3 or not.

I swear I'm not a robot, I pass Google captchas and everything!

I’m human and regularly don’t.

I'm sorry to be the one to break it to you, but that means you aren't human.

You say you're human, but how can we know for sure?

How does it make you feel that I say I’m human?

I do, but I see how that wasn't clear.

It's very clear to me that he does not. But he does an excellent job of making GP's point.

Err, it seems very clear to me that he does realize GPT-3 is the author, and that it was easily caught by his bullshit filter. Which was my experience too -- but I am less dismissive. I regularly see human-produced bullshit get very far with less coherence than these examples from GPT-3.

GPT-3 isn't AGI, but it's weapons-grade in a way that GPT-2 wasn't.

OK, but a weapons-grade BS generator is not what the world needs right now...

Ready or not, here GPT-3 comes.

I feel bad for realizing I give authors benefits of doubts and let them ramble for me to learn the point. I guess I need to drop my bar when I'm reading but a LOT of people ramble a lot. Almost every blog post I ready, I skip the first 2 paragraphs because they're just an intro/context which you already know from the headline or prior knowledge.

> Even on re-reading more closely, it doesn't feel like the world's best writing, but I don't notice major loss of coherence until the last couple of paragraphs.

I guessed it was fake before getting to the end, not from the content, but from the fact that all the sentences are roughly the same length and follow the same basic grammatical patterns. Real people purposely mix up their sentence structure in order to keep the readers engaged, whereas this wasn't doing that at all. Still very impressive though; if not for the fact that the post was about computer generated content I probably wouldn't have noticed.

Besides predictable sentence structure, GPT-3 writes like George R. R. Martin: Interesting premises, solid setup but then it devolves into rambling tangents and never quite delivers the concluding action that ties everything together.

Lots of examples I've seen have phrases like "see table below". Of course there's no table and it's hard to imagine how there could be.

But GPT is trained on internet content and the internet is full of terrible writing that never gets to the point. I doubt there's any way to know how much is "not actually understanding the subject matter" vs. "learning bad writing from bad writers". I'm inclined to believe the majority is the former but there's got to be a little of the latter sprinkled in.

I am really curious how the model would be if you would train it with a decent amount of really good literature. Kazuo Ishiguro et al. instead of Reddit.

I was playing with AI Dungeon tonight to get access to GPT-3, and one of my many experiments ended up with me meeting a character called the Narrator who believed they were in control of all characters in the game, including me. Eventually, through my predicting what they were about to say by checking and undoing, they seemed convinced I wasn't another character and started asking about whether certain authors were still alive and which I liked to read. It didn't recognize Ishiguro. Later it gave me a truly bizarre (and amusing) summary of Infinite Jest, clearly having never read it. Anyway, the entire experience was uncanny and surreal.

One thing I learned was it has detailed knowledge of the world of Avatar: The Last Airbender, seemingly through fanfics. It was fun having it to teach me the lost arts of pizzabending ("form your hands into the shape of a letter 'P'" and so on, and needing to practice by juggling rubber pizzas) and beetlebending ("always remember that to beetle bend it helps to like beetles," my wise uncle suggested). Each of these tended to precipitate a narrative collapse.

The writing style was surprisingly homogeneous, and it reminded me of young adult novels. It would definitely be interesting to see it with other writing styles, beyond the occasional old poetry.

I've never heard of AI Dungeon before reading your post but even after playing for 2 minutes, I can tell it's going to be huge.

How about full adult? Taking it for a test run and this happened after I told a man to stop copying me. Before this he kept talking about clothes for some reason.

> The man walks away and starts undressing. You shrug and keep following him. Soon, you find yourself naked.

Library Genesis contains lots (millions?) of fiction ebooks (among other things). It's available in torrent form. Not that I would ever condone piracy or anything.

Well now we know what is going to finish asoiaf in case of author existence failure.

I did not guess that it was fake but skipped to the bottom because the article did not seem to be worth reading. It felt like the author was not moving anywhere with their words. I laughed out loud at the reveal that it was written programmatically.

You raise a good point. The Internet has trained us to skim any text that seems pointless or just unsufficiently insightful. So it turns out we have already built up some "mental defences" against GPT-3.

Now we need to make human augmented generative adversarial eyetracking networks that are trained on getting people not to skim.

There are a few concrete things the (fake) article says, primarily about what software to try (OpenAI's GPT-3), and where to try it (bitcointalk forum). Personally, I actually resent being mislead like this, at least on that second point, even with the full disclosure. The output is very high quality, but it is making at least one falsifiable assertion (no test was ever done in that forum).

If you've ever spent much time with a toddler you might have noticed that they spout a lot of fantasy. Learning to not make up untrue claims takes years of additional training for humans.

I’ve never spent much time with toddlers. What do they make up things about? Their own actions, other’s actions, claims about the environment?

My six year old will just continue as long as he has people's attention, and if that means he has to make things up, so be it. Freely stealing phrases from other recent conversations.

So this morning he heard about an animal, it was kind of a lion. But with bat's ears, it lives in Africa. It looks like it's a rock, but it's actually not, it's rock shaped but has tiny legs. And it's gray and hard. Its face... It doesn't really have a face. It lives up in trees where it eats bamboo and apples. It has these huge fangs like sabertooth tigers, you know?

It's glorious.

All of those, in my experience.

My smallest kid has a habit of telling stories about himself that actually come from whatever he heard recently, e.g. "once I was Godzilla..", or claims about things in reality that come from stories or misunderstandings all mixed up "did you know, there are three pigs, but they are not pigs, they are wolves and a hunter came and killed them but they weren't wolves they were dragons..."

It's actually very GPT-3-ish now that I think of it.

Some of them never learn it at all!

If that’s the only thing that separates this from human writing; I’m sure it can be influenced easily.

> I'm sure it can be influenced easily.

Maybe. Right now this reads like a glorified shopping list. It's coherent, but actually sounding human also requires a theory of mind.

E.g. I explain here why it's possible for written statements to be objectively insightful, informative, interesting, or funny, but objectively in a way that's relational to other information or beliefs. The implication being that statements are only going to seem subjectively funny or insightful (or whatever) to others who have that knowledge or those beliefs, which means that you can't reliably create those subjective experiences in a reader without having some sort of theory of mind for them.

I guess you can create content that's funny or insightful relative to that content itself, but that's not especially useful. It's entertaining at the time, but the experience is more like seeing a movie that you laugh a lot during but then leave and are kind of like what was the point? It's an empty experience because it wasn't transformative.

I definitely don't think it's impossible, but I also don't think it's a matter of just adding a couple more if-else statements.


> Maybe. Right now this reads like a glorified shopping list. It's coherent, but actually sounding human also requires a theory of mind.

I'm going to call this goalpost shifting. This article is better writing than some % of humans, theory of mind or otherwise. The AI has comfortably surpassed Timecube-level writing and is entering the pool of passes-for-a-human.

'Sounds human' is a spectrum that starts with the mentally ill and goes up to the best writers in human history.

> I'm going to call this goalpost shifting.

That's completely fair. On the other hand, without a theory of mind it can't really educate or inspire people, the only thing it can do is maybe trick them about the authorship of something. But once people learn the techniques for identifying this kind of writing, it can't even do that anymore. To me this is like the front end of something, but it still needs a back end.

Don't get me wrong, it's super cool research and seems like a huge step forward, and I'm excited to see where it goes. But I also don't see this AI running a successful presidential campaign or whatever, at least within the next couple years.

It made me consider: - The existence of this model I hadn't heard of - Bitcoin (sigh) - Testing it out on a forum, trying to become a well known poster - picking a forum with many different types of posters - some of which you dislike

And that got me thinking about what I could do with this thing, whether I should, what I wanted to try out...

So the BS random ideas were still inspiring a bit.

On the other hand, without a theory of mind it can't really educate or inspire people

I wouldn't agree with that, either. How often have we heard of someone gaining useful insights by considering ideas that were misapplied or just plain wrong? Entire branches of physics have evolved that way. As far as successful presidential campaigns are concerned... well, let's not even go there.

If there's such a thing as a 'theory of mind', it applies to the reader, not the writer.

I think I disagree.

For example, I delayed in writing this comment because the cat was on my lap, and I couldn't fit the laptop and the cat both. You get that. I know you do, even if you don't own a cat, and even if you're reading this on a phone or a desktop.

GPT-3 does not understand about the cat. To GPT-3, they're just words and phrases that occur in the vicinity of each other with certain probability distributions. As a result, it can't write something and know that there's something there in your mind for it to connect to.

Cyc would handle the bit about the cat differently. It would ask "Did you mean cat-the-mammal, cat-the-Caterpillar-stock-symbol, or cat-the-ethernet-cable-classification?" It has categories, and some notion of which words fit in each category, but it still doesn't understand what's going on.

But you the human understand, because you have a lap, and you've at least seen a cat on a lap.

> But you the human understand, because you have a lap, and you've at least seen a cat on a lap.

You really think GPT-3 never came across a comment about a cat in lap? 50% of all the pictures on the internet are cats sitting on people. GPT-3 doesn't need to understand it to echo this common knowledge.

Airplanes don't look like birds at all but they do fly.

I don't see how any number of comments about cats in laps can allow it to synthesize the following logical chain: cat-on-lap -> pauli exclusion principle -> laptop NOT on lap -> laptop awkward to reach -> delayed comment

I think the issue is that text doesn't exist in a vacuum, but the corpus that the model is learning from does. A piece of human writing exists for a particular reason - to persuade, to inform, to ask a question, etc - and its value is judged on its ability to perform that task. But that's not a quality that is evident from the text itself, only from looking at the world outside the text. This suggests to me some limits on this kind of passive self-supervised approach. Perhaps it could be improved by augmenting the text with other forms of data? For instance predicting video from text and vice versa. But I think that to learn a true "theory of mind", it needs to use text like an agent - to influence its environment, not merely predict it.

It could also be a result of training data. If every page is weighted equally, you'd expect SEO spam and even autogenerated content to far surpass high quality content in volume.

I would like to see a GPT model where training data is weighted by credibility / authority (e.g. using Pagerank).

My understanding is that GPT-2 was actually trained on a dataset that was designed to avoid those pitfalls. They followed all the links posted to Reddit that had more than a couple karma, under the theory that the content was at least slightly interesting to some actual humans, as opposed to a giant blob of search keywords or what have you.

>Right now this reads like a glorified shopping list. It's coherent, but actually sounding human also requires a theory of mind.

What if the ultimate theory of mind turns out to be that consciousness is an illusion and nothing separates us from a sufficiently sophisticated markov process.

>What if the ultimate theory of mind turns out to be that consciousness is an illusion and nothing separates us from a sufficiently sophisticated markov process.

Conscious experience would still exist (see cogito ergo sum, Chalmers, etc). If we were to be shown we're just Markov processes, that wouldn't disprove the existence of conscious experience. Just like confabulation, a misleading experience is still an experience.

What it would disprove is any sense of agency.

"objectively ...funny" strikes me as a contradiction in terms; concepts like humor, insight, and interest are fundamentally subjective, dependent by definition on a subject's consciousness and expectations

Aww c'mon. You guessed it was fake, because it's an article about computer generated articles. Who would read that and not question the content in front? You are not analysing everything you read for oddities in writing style.

> You are not analysing everything you read for oddities in writing style.

I certainly do, don't you? When I read a blog post and it's full of poorly-integrated buzzwords that make it seem like it was churned out by a non-English speaker being paid very poorly per word, I stop reading and move on.

I recently read a few pages of a book someone had recommended to me and stopped reading because of the writing style.

Heck, you can read a few pages of, say, a Dan Brown novel, and based on the writing style might choose not to read it, since the style tells you a lot about the kind of book it is.

I'm not a very good test case. I briefly skimmed (not expecting very much from a Bitcoin-themed article), read the end, and only then read more carefully. So my first read was brief and biased, and my second was very biased.

That said, the content of the computer-generated parts doesn't make much sense even for a Bitcoin-influenced article (what would be the point of paraphrasing your previous post in a forum on a regular basis, and how does this not get one very quickly banned?), but the grammar is far far better than previous attempts - it reads like Simple English wiki.

> I briefly skimmed (not expecting very much from a Bitcoin-themed article), read the end, and only then read more carefully.

It sounds to me like you must be an academic, or someone with good habits for being efficient at reading articles.

Or maybe we're all bots too and you're the only real HN user!

I agree, responses are almost as interesting as GPT-3. And this place has always felt like one of the better when it comes to people reading past the titles!

"Every account on reddit is a bot except you."


GPT-3 is a neat party trick. But the things that'll be done with web archives* in the next 20y will make it look like the PDP-8. ~love, a web archivist

* GPT-3 is trained on one

The transformer model as presented in GPT-3 may be a few tweaks away from a human-acceptable reasoning, at which point we may realize that human brain is just a neat party trick as well. This may come difficult for some people to internalize, especially those who understand the technology in depth. Because it means that the medium of our reality is the consciousness.

Was this comment generated by GPT-3?

I doubted that as well, but I don't think it is--at least it's not a simple copy paste. There's an emphasis on _is_ in the last sentence which I don't think the algorithm could have generated.

However that makes one wonder if it can also learn to generate emphases, and if so, how would it format? With voice generation it can simply change its tonality but with text generation it has to demarcate it in some way--does the human say "format the output for html", for instance?

You are confusing pattern matching with reasoning. If your brain was replaced by GPT-3 model and you were cast away on a distant island, I highly doubt you will be able to perceive, plan and prosper during your survival against all the calamity nature would through at you.

To be honest, most city-raised humans wouldn't be able to survive on a distant island as well.

The transformer model in GPT-3 has a short context window and no recurrence. Without some significant architecture changes that is a fundamental limit on the problems GPT-3 can solve.

> Because it means that the medium of our reality is the consciousness.

I agree. The environment - as the source of learning and forming concepts, is the key ingredient of consciousness, not the brain.

I don't fully understand what you're getting at here...

Basically the brain and "consciousness" isn't as fancy as we think?


No pressure: feel free to ignore me, please. Would you mind elaborating? I'm interested in what you have to say (and, of course, feel free to say it privately if you prefer). I would like to even hear your dreams, wild speculations, or gut feelings about the matter.

Sure, what do you want to know?

I currently work on synbio × web archival.

Some of us are cooking up futuretech aimed at storing all of IA (archive.org) in a shoebox. Others are working on putting archival tools in more normal web users' hands, and making those tools do things that people tend to value more in the short-term, like help them understand what they're researching, rather than merely stash pages.

My ambitions for web archives are outsized compared to other archivists, but I'm fine with that. I'm looking beyond web archives as we currently understand them toward web archives as something else that doesn't quite exist yet: everyday artefacts, colocated and integrated with other web technology to an extent that they serve in essential sensemaking, workflow, and maybe security roles.

Right now, some obvious, pressing priorities are (a) preserving vastly more content and (b) doing more with the archives themselves.

A: The overwhelming majority of born-digital content is lost within a far narrower time-slice than would admit preservation at current rates, and data growth is accelerating beyond the reach of conventional storage media. So, for me, the world's current largest x is never the true object of my desire. I'm after a way to hold the world that is and the world to come.

Ideally, that world to come is one where lifelong data stewardship of everything from your own genome to your digital footprint is ubiquitously available and loss of information has been largely rendered optional.

This, of course, requires magic storage density that simply defies fundamental limitations of conventional storage media. I'm strongly confident that we're getting early glimpses of the first real Magic contenders. All lie outside, or on the far periphery of, the evolutionary tree that got us the storage media we have today. For instance, I'm running an art exhibition that involves encoding all the works on DNA.

B: Distributed archival that comes almost as naturally as browsing is well within reach, and with that comes some very new potential for distributed computation on archives. One hand washes the other.

One important thing to realize here is that, in many cases, you can name a very small handful of individuals as the reason why current archival resources exist. GPT-3 is cracking the surface by training on data produced by one guy named Sebastian, for instance.

…i'm sorta tired and have to respond to something about every twitter snapshot since June being broken, though, so I'll pick this back up later.

This is an interesting thought. GPT-3 used 45TB of raw CommonCrawl data (which was filtered down to 570GB prior to training). The Internet Archive has 48PB of raw data.

That 48PB is mostly just old video game roms and isos though

Hopefully in a way that secures some funding for those making archives of the web.

I'm running the Coronavirus Archive. Largest thematic archive on the pandemic, since January. I'm also teaching community biolab techniques to people in parts of the world without ready access to commercial COVID-19 test kits, on all but zero resources at this point.

I could use… what's the word? I think it's more funding.

Problem was I lost interest half way because it lost my interest after the 2nd paragraph. For those that say it was good till the last few is really pretending to understand what it said. It really did not made much sense.

I'm finding with secret GPT-3 output that I often find it boring before realizing it's GPT-3. I might even be getting to recognize its wordy, dull, cliche-ridden, borderline-nonsensical style. It's remarkably good at passing as human writing of no value whatsoever.

Well, it is trained on web content. Often I read an article and it's obvious they're either stretching it out to hit a word count, or trying to get as many google-able phrases in as they can. Some sites are worse than others, with the more no-name ones the worst offenders.

The fast jump in quality from GPT2 to 3 is more important than the current level of GPT3. Maybe next year it will be not-boring.

It's ironic that you're critiquing the algorithm for not making sense, while contradicting yourself in your first sentence. Did you lose interest "half way", or "after the second paragraph"? It can't be both.

Wouldn't "after the second paragraph" be "half-way" for a four paragraph piece? :D

But you are right, it can't be both in the context of this article :)

Those types of contradictions are what made me suspect the article was generated.

Now, I’m not so sure :)

This is so true. I had a high regard for HN comments till quite recently.

I take it you've never had your comment - which happens to be on a subject of your expertise or lived experienced - downvoted because simply because it doesn't feel "truthy" enough for HNs demography. I have long since adopted a healthy disregard of HN comments in other areas that I'm no expert in. I still haven't found a way to monetize that though; that is my holy grail.

It is a bit unfortunate that your comment is now at the top - it spoils the test :)

Saying that, I briefly saw the first sentence of your comment and went to read the article with the idea that trickery was afoot, specifically guessing correctly the nature of the article. And yet, even then, on the back foot... it fooled me. Incredible.

Thanks to this comment I actually read the blog post.

It was relatively good, although I began to suspect it was GPT3 generated about halfway through (partially because the style felt a bit stiff but also just out of a shayamalan-what-a-twist 6th sense of mine that was tingling)

Eliza could do this better. Or, just use a Markov chain that has read enough corporate PR bullshit. It's just sad how many people use this "AI" meme to fulfill their need to worship something.

It reminds me of the incoherent demented ramblings we've all been been hearing (but hopefully not following as medical advice) for the past several years.

Ah, but you are almost spoiling the end with your second paragraph! :)

I agree with you. I suspect few people have read until the end to realize that, in fact, ...

Is exactly the issue. You still need humans to check it before releasing an output. It can only be what the author says “Bitcoin” level of implication if it can get things probably needing at least “99%” quality and correctness.

OK, I read the first sentence and it sounded like a typical poor-quality marketing-blather article so I came here and read your post.

I then reread it and it indeed read like a weird, rambling, incoherent article. Looking at it closely, it had a good many contradictory, meaningless and incoherent sentences.("It is a popular forum with many types of posts and posters.")

The headline, however, seemed about right.

It's true the nonsense in this article is a bit different than the nonsense of a GPT-2 article. But the thing GPT-2 paragraphs sound pretty coherent 'till they suddenly go off the rail. This is more like an article that was never quite on the rails and so it's slightly more internally cohesive. But not "better".

Maybe the article just reflects the author's style. Anyone have a GPT-3 test site link?

I published a response today to the sudden hype urging people to temper their expectations for GPT-3 a bit: https://minimaxir.com/2020/07/gpt3-expectations/

GPT-3 is objectively a step forward in the field of AI text-generation, but the current hype on VC Twitter misrepresents the model's current capabilities. GPT-3 isn't magic.

One of the biggest issues is with cherry-picking. Generative ML results benefit greatly from humans sampling the best results. They are capable of producing astonishing results but don’t do this consistently this has a huge impact on any effort to productize. For example I’ve seen quite a few examples of text->design, text->code, with GPT-3 you could build a demo in a day, but the product will probably be useless if it’s not delivering results 50%+ of the time

I don't know about GPT-3 but playing around with GPT-2 I often got the impression that it was regurgitating learned knowledge (reddit comments) rather than actually coming up with something novel.

With so many weights, it practically encodes a massive Internet text database.

I had that thought too, and my immediate next thought was that the value isn't in knowing the sentences, but in being able to put them together usefully.

Having a better alternative to search engines would be great.

I think too few people are taking into account how inscrutable and inconsistent human creative-output results can be. We critique GPT-3 on the basis of it sometimes producing bad results --- but don't we all? Take poetry, for example. "The Complete Works of X", for any X, will probably contain a majority of forgettable or just bad works. What we remember from any author X is his cherry-picked best output. Likewise for ML systems.

The hype/scare re GPT-2/3 (etc.) is not for their poetry output, but rather for its potential for mass propaganda, telemarketing and so on. We can already get humans to do this stuff, all GPT could give is scale (that's no small deal).

However, if the output needs to be curated and edited by humans, the scale and automation is gone - we just get a different manual process, with a modest improvement to speed at cost of some decline in quality, and that's not very impactful.

The truly scary part is SEO where GPT-3 could ruin search engines overnight.

Google at this point favours long form content for many search intents. Being able to generate thousands of these pages in one-click is a real problem. Not just because of popular topics e.g. "covid-19 symptoms" but more so for the long tail e.g. "should I drink coffee to cure covid-19".

Quite a lot of SEO already uses simple word generation techniques. It isn't clear GPT-3 is an improvement there - human text recognition might not be whatever Google does.

It may be that Google's algorithms don't care at all how human-like the text is, or that their own recognition algorithm/NN (whatever they use) isn't fooled. Even if it is affected, Google has the money and corpus to build its own competing NN to recognize GPT-3 text.

While I have no doubts that they could build NN capable of recognizing GPT-3 text I believe that this would still pose a problem given the amount of content to be analyzed at the scale that Google deals with

I'm sure Google out of all entities could handle scale.

That said, there might be a different threat to Google. GPT-3 seems really useful as a search engine of sorts (with the first answer implementing the 'I'm Feeling Lucky' button). Tune it for a query syntax, and for getting the 'top X' results somehow, then we just need the web corpus and a basic filter over the results. We could have a very interesting Google competitor.

If OP article was cherry-picked, the tree must not be very productive.

More than cherry-picking, there's the Eliza Effect - it's pretty easy to make people think generated text is intelligent. That text can seem intelligent for a while isn't necessarily impressive at all.


To be honest, personally I had no idea it was generated until the author said so at the end.

Makes me worry about my own reading comprehension, but I think what happened was that since it was posted on HN and got upvoted a lot, I simply assumed that anything that I didn't understand was not the writer's fault, but mine.

For instance, it was unclear from the post what the bitcoinforum experiment was about, but I just dismissed it as me not being attentive enough while reading.

At one point GPT-3 writes: "The forum also has many people I don’t like. I expect them to be disproportionately excited by the possibility of having a new poster that appears to be intelligent and relevant." Why would people he doesn't like be paricularly excited about a new intelligent poster? Again I just assumed that I missed the author's point, not that it was nonsensical.

Twice it refers to tables or screenshots that are not included, but it seemed like an innocent mistake. "When I post to the forum as myself, people frequently mention that they think I must be a bot to be able to post so quickly" seemed like another simple mistake, meaning to say that when he posted as GPT-3, people thought he was being too quick.

This is like a written Rorschach test, when I'm convinced that what I'm reading must make sense, then I'll guess at the author's intent and force it to make sense, forgiving a lot of mistakes or inconsistencies.

The second one doesn't sound like a mistake to me. Someone being able to consistently post so quickly is actually a valid sign of being a bot.

Really interesting. Looking back, I did the exact same thing at many of the same points.

It will be very annoying for e.g. forum moderators to determine whether first user posts are just a bit incoherent, or generated spam garbage.

That used to be a pretty annoying thing back in the days of IRC as a kind of DOS: run a bunch of bots that just replay a conversation from another channel. Engaging them fails, but is that because they are bots, or because they're just ignoring you?

The new kind can be more targeted for specific purposes. They could be excellent tools for trolling a forum, inciting flame wars and such.

That would require some more advanced tech though. I don't think GPT-3 can target divisiveness yet, especially since it would heavily depend on the community you're writing for, e.g. driving a wedge into the general population is very different than driving a wedge niches. The Linux vs Windows debate might get you engagement in a tech forum, but it'll fall short with social housing activists, and whatever issues they split on will probably not get you anywhere with the tech crowd.

I don't think it needs to understand what a divisive issue is to have an effect. If you've got a human operator who can pick a divisive enough prompt, this can dramatically increase their inflamations-per-hour because they don't need to compose the body text.

It's true that distinguishing these articles from ordinary jointed ramblings of poor writers would be hard. But I'm not sure what the benefit of filling forums with babble has to those running these models.

Bots offering idiocy and idiocy generally has done lots of damage. But by idiocy here I would quite carefully calculated cleverly polarized positions and I don't think just bot-rot would be enough (to maybe coin a phrase).

I agree, but on the other hand one has to be careful not to be blind to the obvious power of a new technology, simply because it cannot be immediately turned into $$$.

Hmm, I don't know. If you're the IRA [1], it sounds like it could be more efficient to have your trolls select plausible-looking comments from the auto-generated ones rather than having them write them themselves all the time.

[1] https://en.wikipedia.org/wiki/Internet_Research_Agency

Yeah, I saw a text => UI generator.

It’s cool, but it looked like very basic stuff - the type of UI that is very easy to create in a few minutes. (And really with what was setup behind the scenes - maybe just as fast to just write the code.)

The hard part about software development is not those bits which are common, but the parts that are unique to our specific solution.

> but the parts that are unique to our specific solution

Search terms tweaked for your unique interests, and not a commercial entity's, for example.

True! That means that whoever can come up with a system that takes 10 texts written by GPT-3 and always selects the best one (as judged by humans) will become rich and famous. This sampling problem is one of the few major hurdles before generative ai:s become really useful.

> rich

Is reddit gold really that valuable?

> famous

Surely there are easier ways.

> really useful

We already have enough 2020 reddit commenters regurgitating 2010 hn threads regurgitating 2000 slashdot threads, thanks.

It seems like with minor improvements, you could use this to significantly accelerate mundane parts of programming or writing. Human writes bulletpoints, neural net turns it into a program or letter, human corrects. There already was a pretty smart looking AI-based autocomplete shown on HN a couple weeks ago.

This will accelerate development. Is the current version there? Probably not. But GPT-4 might, and would then accelerate the development of future versions.

Even though this is not "magic", it sounds like it will turn into a practically usable and extremely valuable tool soon.

Being a speaker of Czech and English without a single dominant language, I use Google Translate to improve my writing. I will write a draft in the target language and feed Translate the other. It often comes up with improved style and more accurate expressions, especially in Czech. So as far as writing goes, we're already there.

Yes, it is more like happy path testing.

However I like spirit of optimism and first looks at encouraging and very promising results.

Exciting times!

I saw a demo of a gpt3 designing an app that looked just like Instagram home feed skeleton. While it seem impressive, but until you show me something more obscure, that was nothing to brag about.

Please please please post a link to that video. It sounds super interesting

I assume they're referring to this tweet, where someone created a Figma plugin using the API


It was posted here: https://twitter.com/jsngr/status/1284511080715362304

Honestly not that impressive since you can get comparable results with a series of regex rules given that there are limited ways to describe your intent e.g. "create a button of colour <colour> at the <location of button>"

What are your thoughts on why nobody made the set of rules yet?

If designers wanted to write texts to create visual designs, they'd be using some form of DSL and learn to code, wouldn't they?

I believe the hype is that people think they can replace the designer by "just telling the computer" what they want. I don't believe that will work, as they already have trouble telling a human what they want, and a computer won't really know what to do with "I want it to kind of feel like it's from that movie with the blue people that Cameron did, you know?"

In my experience, people have a hard time writing their ideas about designs & features down, because they don't know what they want. They want to talk about it abstractly with somebody who has a better understanding of the field so that person can help them develop the idea. I don't think ML will cover that part any time soon.

> people have a hard time writing their ideas about designs & features down, because they don't know what they want

From an academic standpoint, writing is part of the thinking process. If you haven't written it down, you haven't fully thought it through. If it feels difficult, that's probably because your understanding isn't as complete as you thought it was.

From a software development standpoint, implementing something is part of the thinking process. Ever notice how the requirements have a tendency to break as soon as you actually try to implement them? If a spot seems difficult it just means you hadn't really figured it out yet.

> From an academic standpoint, writing is part of the thinking process. If you haven't written it down, you haven't fully thought it through.

I 100% agree. I noticed a giant shift in tasks when I made one client write tickets instead of making phone calls. Writing it down forces you to think it through.

And I agree about software development as well, yes. Though I think it's even rare to have somebody describe all the features they want unless it's an experienced software developer who basically writes a textual representation of the application.

But for most PMs (that I've worked with at least), they have vague ideas about what they want, and bringing them into focus is a back and forth with developers and designers. I don't see them getting anywhere with an NLP automaton, but maybe with an Eliza-style system: "Give me a big yellow button saying 'Sign up'" - "Why do you want a big yellow button saying 'Sign up'?" - "You're right, that's too on the nose... give me a link saying 'Sign up'"...

GPT-3 isn't magic. That's the most important thing. I got so amused with the hagiographical tweets that I coded myself a non GPT-3 demo :)


What this means is that GPT-3 is good enough to fool a crypto VC.

@balajis being generated by GPT-3 would make a lot of sense, though.

I don't know, this seemed pretty close to magic to me:


Granted, it seems like there was a lot of behind the scenes work to make that happen.

this is something you could do with NLP already before.

Have you spent any time interacting with GPT-3?

It's qualitatively different than GPT-2. I was on a discord with someone that has access to it and a bunch of us were throwing ideas out for prompts. One of them was to provide an anonymized bio of someone and see if it could guess who it was. The format was 'this person...they..and then they...\nQ: Who is this person?\nA: '

At the first pass it didn't guess correctly. But we erased its response and tried again and it got the answer correct. We then asked it to screenwrite some porn and tell jokes. Yes there were some misses, but it got things right so frequently that you can start to see the future.

Having all of this capability in one package is pretty remarkable and nothing has approached it to date.

> AI text-generation

"Text generation" undersells it a little bit. What are humans except "text generation" machines? Language is the stuff of reason. GPT-3 has demonstrated capabilities that we believed were exclusive to humanity --- humor, logic, sarcasm, cultural references --- in an automatic and generalizable way. It's far more than a "text generation" system. I look forward to seeing what GPT-4 and GPT-5 can do. I suspect we're all going to be amazed by what we get when we continue down this path of simple scaling (and sparse-ification) of transformer architectures trained on (basically) the whole internet.

> What are humans except "text generation" machines?

The ability to grow and choose our own direction: to choose what our goals are, curiosity, self-awareness, desire. To imply that GPT-3 is anything close to strong AI is kind of ridiculous.

GPT-3 has flexible goals too. It can learn a new task and do it in the same step. What GPT3 doesn't have is a body.

No, you can teach it a new task or to do multiple tasks, but it will never be able to independently identify what a new task might be and learn to do it. An important distinction when talking about AGI.

And how do we indicate that any of these processes has occurred except through the medium of language? A sufficiently good text predictor is a sentient mind. I don't believe that there's any experience of consciousness distinct from the use of language.

I disagree with this... I have a hunch GPT-3 is still falling short PRECISELY because of its dependence on language, and that it actually is going to great lengths to overcome this design flaw to create the simple texts that we're all fawning over.

I predict within a few years, the descendants of GPT-3 will use very different fundamental units for processing that differ greatly from the current state-of-the-art (i.e. they won't use BPEs and their ilk anymore, except for final output) and will be far more powerful as a result.

The descendants of GPT-3 will be using the comments on this page to get new ideas.

I do agree with you. We, as somewhat intelligent beings, do not base our thinking on words or language AFAIK, even though it's our best way to convey ideas to others. And we learn through experience, way faster than GPT-3 does, with fewer shots. It looks like the attention mechanisms are what made these models actually start to understand things... But those attention mechanisms are still very raw and mainly designed to be easy to execute on current hardware, I wonder how fast will we refine that. Finally it looks like, once trained, these models don't learn when we use them. It definitely doesn't learn through experience and that's a major limitation on how intelligent it can be.

For most of human history text has not existed. I think you are conflating language with written language which is quite common in the post-Gutenberg age.

I think sentience like most things is a spectrum, so I'm not really sure what you mean by sentient, but I would argue that for most people the bar for sentience is much higher than text prediction. The Chinese room is only one aspect of our minds, and we don't even know what consciousness is.

I have to disagree with this perspective and reiterate my original position: language is sentience.

And to be fair, reasonable people stake out positions on both sides of this debate: I'm not claiming that the alternative proposition is somehow unreasonable. It's a legitimate subject of scholarly disagreement.

Nevertheless, I'm still firm on language. Why? Because all complexity is ultimately about symbolic manipulation of terms representing the process of manipulation itself. ("Godel, Escher, Bach" is a fantastic exploration of this concept.) How can you manipulate concepts without assigning terms to their parts? That's what language is.

The question I like to ask is this: are there any ideas that you cannot express using language? No? Then how is thought distinct from language?

Yes, people (myself included) experience a "tip of the tongue" experience where you feel like you have an idea you can't just yet express. But maybe this experience is what reason feels like. Why should idea formation take only one "clock cycle" in the brain? Why should we be unaware of the process?

I think this feeling of having an idea yet being unable to formulate it is just the neural equivalent of a CPU pipeline stall. It's not evidence that we can have ideas without language: it's evidence that ideas sometime take a little while to gel.

I think you only need language to communicate, it’s not necessary in order to think. Do you agree that a human that grows up in isolation probably won’t develop a language? Would you say such a human isn’t sentient?

I think as highly social beings we often annotate all of our thoughts with the language we could use to communicate them, which could lead us to believe that the thoughts are indistinguishable from the language, but that conclusion seems like an error to me. I’ve also heard some people talk about how they are “visual” or “geometric” thinkers and sometimes think in terms of images and structures without words.

Assuming Genie the feral child was not born mentally retarded, it may suggest that language is critical for human level intelligence. There's also the theory in anthropolgy which I believe has some evidence that human intelligence exploded with development of more complex language.

I think you're mixing up sentience/consciousness/intelligence. Many animals are sentient for example but as far as we know, they don't really have language. But I think I get what you're getting at, which I believe is "human level intelligence requires langauge". I think that's a reasonable take. But you said "sentience", and you said "is", which makes your position difficult to agree with.

Hold up, you can't just throw out a claim like "many animals are sentient" as if it's a statement of fact. You might be right, but there's a reason that "the hard problem of consciousness" is hard. We don't really have any way to distinguish sentience/non-sentience based on behavior. The whole concept is extremely mushy.

You're right but as I've stated before, sentience and consciousness are different terms, and sentience has a definition in which the idea that animals are sentient isn't all that controversial. Not a mathematical axiom sure, but it all depends on what you mean by sentience, and I'm going by the classic definition.

Yes. You're right. I was sloppy with language. To be specific, I think that "human level intelligence" is basically synonymous with "able to think about thinking", and I think to do that, you need symbolic manipulation, and language is the only way we can do symbolic manipulation.

I don't believe we have any evidence this edition of GPT is capable of reasoning. I haven't experimented with it but I doubt it will respond correctly to even simple logic puzzles provided they are framed in a novel way (it may have already seen some puzzles, but I doubt it can extend that knowledge to a puzzle with different words)

This version of GPT can add, subtract, multiply, and divide without ever having been taught to do these things things. Yes, it can reason.

> I don't believe that there's any experience of consciousness distinct from the use of language.

Not sure there's one I can communicate to you, but I'm perfectly capable of forgetting the word for something and still knowing unambiguously yet wordlessly what it is, that's an experience.

Catching a ball? Running? Experiencing emotions from wordless music? Viewing scenery? Engaging with a computer game? How are they not conscious experiences?

Also interesting to note that a good portion of people lack an internal monologue. This interview made the rounds a while back: https://www.youtube.com/watch?v=u69YSh-cFXY

> I don't believe that there's any experience of consciousness distinct from the use of language.

To me this indicates a very narrow view of consciousness. Consider for a moment the quiet consciousness of the cerebellum for example.

I like the way David F. Wallace put it: 'Both flesh and not'. There's an astounding amount of consciousness that is not bound by language. One can even argue that language might hinder those forms of consciousness from even arising.

If you believe in consciousness divorced from flesh, you're in strong intellectual company, but that's not a path I can go down. Metaphysically, I just can't accept the idea that the mind is anything but the computation performed by the brain.

I rather agree with you, but the interaction of the mind with the physical reality is also extremely important to shape it. GPT3 has no interaction with a physical world. Any formal system that cannot interact with something outside of itself will be intrinsically limited, for one thing by Gödel incompleteness theorem.

I agree about the need for an environment. The difference between GPT-3 and an agent in an environment is that GPT-3 only saw tons of static text, while an agent can design an action (an experiment), act it out and observe the results, drawing conclusions. Thus it can act in a way similar to the scientific method.

In the book the meaning of the quote is more to the effect of how truly great athletes can perform on the verge of what spectators would consider inhuman or possible. I'd be hard-pressed to believe that language and syntax would be responsible for these kinds of actions and flow. I'd argue that getting into such a state is not possible while the mind is caught up in the language of things rather than the experience itself, and reacting to it directly. This is what I meant by the quiet consciousness, devoid of language or syntax.

> I don't believe that there's any experience of consciousness distinct from the use of language.

What is the role of the body in consciousness, then?

I don't think any modern cognitive scientist believes that the statement "language is the stuff of reason," even allowing for poetic flair, is meaningfully true (and I'll leave aside that humans are, obviously, much more than text generation machines.) GPT-3 can generate text but the only context it has is its prompt; when humans generate text they have their complex situation in the world (and perhaps even non-worldly factors, e.g. apprehension of mathematical reality) as context. Fitting the latter kinds of context into AI models is the challenge still facing the path to AGI.

I agree with you, but also the following is "just" an implementation detail:

> only context it has is its prompt

The only real context is its latent representation of the prompt, there's nothing fundamentally limiting visual, auditory, symbolic, and mixed prompts as long as they map to a common latent space and the generator is trained on it.

>What are humans except "text generation" machines?

Text generation doesn't chop wood, optimize speedruns, build machinery or win 100-metre dashes.

Text may be involved in training for these things, but to say that doing them is text generation would be like saying that... since compiling code and running AlphaZero both generates bits, AlphaZero is a compiler.

The ability to do these tasks is neither necessary nor sufficient for recognizing something as human. Helen Keller was human after all. What differentiates us is language.

When I read comments like this--and yes I read the article and understand it was generated by an algorithm--I can't help but think the next AI winter is around the corner.

This does not impress me in the slightest.

Taking billions and billions of input corpora and making some of them _sound like_ something a human would say is not impressive. Even if it's at a high school vocabulary level. It may have underlying correlative structure, but there's nothing interesting about the generated artifacts of these algorithms. If we're looking for a cost-effective way to replace content marketing spam... great! We've succeeded! If not, there's nothing interesting or intelligent in these models.

I'll be impressed the day I can see a program that can 1) only rely on its own limited experiential inputs and not billions of artifacts (from already mature persons), and 2) come up with the funny insights of a 3-year-old.

Little children can say things that sound nonsensical but are intelligent. This sounds intelligent but is nonsensical.

I think you are underestimating what an advance these models are over previous NLP models in terms of quality. Before GPT-2 we didn't even have models that could reliably generate grammatical sentences. Now we have things that generate coherent (if not beautiful) paragraphs. It seems easy in retrospect, but some of the smartest people around have been working on this for decades.

Is there a term for the casual dismissal of breakthrough technologies and ever-moving goalposts for what is considered impressive?

Ehahaha, thank you!

It's simply calling things "A.I."

Seriously, a few years ago recognizing if there's a bird in a photo was an example of a "virtually impossible" task: https://xkcd.com/1425/

"God of the gaps"? The original usage is in theology, but the idea is the same.

> I think you are underestimating what an advance these models are over previous NLP models in terms of quality.

Yeah I mean, I agree. But in my opinion, it's a case of "doing the wrong thing right" instead of a more useful "doing the right thing wrong."

I grant that these automated models are useful for low-value classification/generation tasks at high-frequency scale. I don't think that in any way is related to intelligence though, and the only reason I think they've been pursued is because of immediate economic usefulness _shrug_.

When high-value, low-frequency tasks begin to be reproduced by software without intervention, I think we'll be closer to intelligence. This is just mimicry. Change the parameters even in the slightest (e.g. have this algorithm try to "learn" the article it created to actually do something in the world) and it all falls down.

This kind of facile moving the goalposts is imho a cheap shot and (not imho, fact) is a recurring phenomenon as we make incremental progress toward AI.

Progress is often made with steps that would have been astonishing a few years ago. And every time the bar is raised higher. Rightly so, but characterizing this as doing the wrong thing is missing the point of what we, and the system, are learning.

Yes it's not intelligence. But then, it's not even clear that we ourselves can define intelligence at all… not all philosophers agree on this. Daniel Dennett (philosopher and computer scientist) for example thinks that consciousness may be just a collection of illusions and tricks a mind plays with itself as it models different facets of and lenses into what it stores and perceives.

> This kind of facile moving the goalposts is imho a cheap shot and (not imho, fact) is a recurring phenomenon as we make incremental progress toward AI.

I think you missed my point. I think we're going in the wrong direction for AI entirely, and these "advances" are fundamentally misguided. OpenAI is explicitly about "intelligence," and so we should question if this is in fact that.

It's clear that humans have fundamental intelligence much better than all of this stuff with 6 orders of magnitude less input (at least of the same data sort) on a problem.

Perhaps it would be better to say, "I think the ML winter is just around the corner" as opposed to "the AI winter is just around the corner." That said, this really is math, and these algos still don't actually do anything resembling true intelligence.

It’s actually about AI which is distinct from intelligence.

>6 orders of magnitude less input

That is utterly mistaken.

We have the input of millions of generations of evolution which have shaped our brains and given us a lot of instinctive knowledge that we do not need to learn from environmental input that happens during our lifetime.

Instead it was learned over the course of billions of years, during the lifetimes of other organisms that preceded us.

Our brain structure was developed and tuned by all these inputs to have some built in pretrained models. That’s what instincts are. Billions of years in the making. Millions, at the very least, if you want to restrict it to recent primates, although doing so is nonsensical.

>>6 orders of magnitude less input

>That is utterly mistaken.

I did say of data of the "same sort".

What's absolutely crazy is somehow we think of our DNA base pairs as somehow more important than the physical context that DNA ends up in (society, humans, talking, etc.)

We have the ability to be intelligent and make thoughts with 1 millionth the amount of textual data as this OpenAI GPT-3 study. Maybe... just maybe... intelligence is far more related to things other than just having more data.

I'll actually expand on this and throw this out there: intelligence is in a way antagonistic to more data.

A more intelligent agent needs less knowledge to make a better decision. It's like a function that can do the same computation with fewer inputs. A less intelligent agent requires a lookup table of previously computed intelligent things instead of figuring it out on its own. I think all these "AI" studies are glorified lookup tables.

Throw some novel text prompt/task at it and see what happens. If it was just "glorified lookup tables" then the result should be consistently garbage.

Note in particular that "like a function that can do the same computation with fewer inputs" maps very well to GPT-3 - it can complete many interesting tasks by just having a few samples provided to it, instead of having to fine-tune it with more training.

> Note in particular that "like a function that can do the same computation with fewer inputs" maps very well to GPT-3 - it can complete many interesting tasks by just having a few samples provided to it, instead of having to fine-tune it with more training.

The reason it doesn't need more training is because it's already trained itself with millions of lifetimes of human data and encoded that in the parameters!

Humans aren't born trained with data. The fact that we're throwing more and more data at this problem is crazy. The compression ratio of GPT-3 is worse than GPT-2.

> The reason it doesn't need more training is because it's already trained itself with millions of lifetimes of human data and encoded that in the parameters!

You know what else is trained by the experiences of thousands of individual (and billions of collective) human lifetimes of data? And several trillions of non-human ones?

> Humans aren't born trained with data.

That's either very wrong or about to evolve into a no true scotsman regarding what counts as data.


AKA "why is it so hard to swat a fly?" because they literally have a direct linkage betweeen sensing incoming air pressure and jumping. Thats why fly swatters don't make a lot of air pressure.

Why do you yank your hand back when you get burned? It's not a controlled reaction. Where did you learn it? You didn't.

If you think the brain is much more than a chemical computer you are sadly mistaken. I would encourage you (not really but it's funny to say) to go experiment with psychedelics and steroids and you will quickly realize that these substances can take over your own perceived intelligence.

The most fascinating of all of this is articles/documentaries about trans people that have started taking hormones and how their perception of the world -drastically- changed. From "feeling" a fast car all of a sudden, to being able to visualize flavors. It's absolutely amazing.

Humans are exposed to much more input than just the things they read. Think of every thing you've ever seen and how much data that represents. All of that is your training data. Is it more or less than GPT-3?

With that style of argumentation, you can say that NNs have even more input than humans: they also have all of the technical development of the last 50,000 years built into them.

Not really. Out evolution and existence in our current form rely on many things that have happened in the entire universe up to this point. But I’m not saying each of our brains and bodies encode all that information. We just benefit from it with an intricate physical structure that would have been difficult to create any other way.

And the same goes for GPT-3 and the resources it needs.

> think we're going in the wrong direction for AI entirely

This direction has produced results that eluded 30+ years of research. What is the evidence that this is the wrong direction?

But we’re not done yet. Give it time. We can go in plenty of directions. Just because you don’t think the current direction is right, that doesn’t rule out other directions happening. And who’s to say this stuff won’t end up being somehow useful? There’s a great talk on how trying to make progress toward an objective in evolutionary algorithms is not a good way to get there.


Of course evolutionary algorithms are just one direction as well. But that doesn’t mean that nothing else is happening.

> 6 orders of magnitude less input

IIRC the following is attributable to either Margaret Atwood or Iris Murdoch:

"A writer should be able to look into a room [full of people] and understand [in breadth] what is going on."

I marvel at the jump in BLEU or other measures but I'll second the sentiment, it alone is not showing we are making leaps toward what we need. Yes its a large gradient step minimizing our error, but is it really in the right direction? However I will admit GPT-3 being directed by some yet to be invented causal or counterfactual inference model might be the something which defies my expectations.

> This does not impress me in the slightest.

A computer that is actually fluent in English — as in, understands the language and can use it context-appropriately — should blow your entire mind.

> A computer that is actually fluent in English — as in, understands the language and can use it context-appropriately.

Did you never do grammar diagrams in grade school? :-)

The "context" and structure of language is a formula. When you have billions of inputs to that formula, it's not surprising you can get a fit or push that fit backwards to generate a data set.

This algorithm does not "understand" the things it's saying. If it did, that wouldn't be the end of the chain. It could, without training, make investment decisions on that advice, because it would understand the context of what it had just come up with. Plenty of other examples abound.

Humans or animals don't get to have their firmware "upgraded" or software "retrained" every time a new hype paper comes out. They have to use a very limited and basically fixed set of inputs + their own personal history for the rest of their lives. And the outputs they create become internalized and used as inputs to other tasks.

We could make 1M models that do little tasks very well, but unless they can be combined in such a way that the models cooperate and have agency over time, this is just a math problem. And I do say "just" in a derogatory way here. Most of this stuff could have been done by the scientific community decades ago if they had the hardware and quantity of ad clicks/emails/events/gifs to do what are basically sophisticated linear algebra tasks.

I bet you can't guess which parts of this are me versus the AI: https://pastebin.com/FHiRR95F

> I'll be impressed the day I can see a program that can 1) only rely on its own limited experiential inputs

Hasn't the typical human taken in orders of magnitude more data than this example? And the data has been of both direct sensory experience and texts from other people as well.

> Hasn't the typical human taken in orders of magnitude more data than this example?

Have you read GPT-3s 175 billion parameters (words, sentences, papers, I don't care) of anything? Do you know all the words used in that corpus? Nobody has or does.

A child of a small age can listen to a very small set of things and not just come up with words to communicate with mama and papa what they learned, but they can reuse it. And this I think is key, because the language part of that is at least partially secondarily. The little kid understands what they're talking about even if they have a hard time communicating it to an adult. The fact they take creative leaps to use their extremely limited vocabulary to communicate their knowledge is amazing.

> This post was generated using GPT-3. [;)]

Your post was generated using GPT-3 and 175 billion parameters of pre-existing human writing, contextualized, distilled, and cross-referenced with terminology we've agreed on for centuries. It's a parrot, and I remain unimpressed.

Take the learned knowledge of GPT-3 (because it must be so smart right?) and have it actually do something. Buy stocks, make chemical formulas, build planes. If you are not broke or dead by the end of that exercise, I'll be impressed and believe GPT-3 knows things.

> It's a parrot, and I remain unimpressed.

What's unimpressive about a stunningly believable parrot? I think, at the very least, that GPT-3 is knowledgeable enough to answer any trivia you throw at it, and creative enough to write original poetry that a college student could have plausibly written.

Not everything worth doing is as high-stakes as buying stocks, making chemical formulas, or building planes.

> So basically like DNA?

Sigh. When DNA becomes human, it doesn't have a-priori access to all the world's knowledge and yet it still develops intelligence without it. And that little DNA machine learns and grows over time.

When thousands of scientists and billions of human artifacts and 1000X more compute are put into the philosophical successor of GPT-3, it won't be as impressive as what happens when a 2 year old becomes a 3 year old. (It will probably make GPT-4 even less impressive than GPT-3, because the inputs vis-a-vis outputs will be even that much more removed from what humans already do.)

> That post was generated using GPT-3 and 175 billion parameters of pre-existing human writing, contextualized, distilled, and cross-referenced with terminology we've agreed on for centuries.

So basically like DNA?

DNA is nothing like the training of GPT. DNA does not encord a massive amount of statistics of words and language and how concepts, words, etc, relate to one another.

All DNA does it encode for how to grow, build, and mantain a human body. That human body has the potential to learn a language and communicate, but if you put a baby human inside an empty room and drop in food, it will never learn language and never communicate. DNA isn't magic and comparing "millions of years of evolution" of DNA is nothing like the Petabytes of data that GPT-3 needs to operate.

Again DNA has no knowledge embedded in it, it has no words or data embedded. Data in the sense that we imagine Wikipedia stored in JSON files on a hard disk. DNA stores an algorithm for growth of a human, that's it.

The GPT-3 model is probably > 700GB in size. That is, for GPT to be able to generate text it needs an absolutely massive "memory" of existing text which it can recite verbatim. In contrast, young human children can generate more novel insights with many orders of magnitude less data in "memory" and less training time.

Since literacy or human knowledge isn't encoded in DNA, it's nothing like it.

"Knows things" is kind of vague. I'm pretty sure GPT-3 would obliterate all traditional knowledge bases we have. Even bert could achieve state of the art results when the questions are phrased as the cloze task.

If you mean that anything except full general intelligence is unimpressive than that seems like a fairly high standard.

I recall a researcher filming their child from the day they were born until they began to speak. They wanted to find how many times a child had to hear a word in order to be able to repeat it back to the parent. The result, I think, was that if the child heard the word 2,000 times at all, they would be able to repeat it. But, if they heard the word 600 times at the same place, for instance the end of the couch, that would be enough to repeat it.

The human brain requires less training, but to some extent it is pretrained by our genetic code. The human brain will take on a predictable structure with any sort of training.

This post was generated using GPT-3. [;)]

> I recall a researcher filming their child from the day they were born until they began to speak.

Can’t tell if you are kidding or not, but if you aren’t, mind sharing links about the researcher for the curious?

Let me take a look, I was not kidding. I recall some mainstream media coverage in the last decade.

edit: Can't seem to find it which is a shame. I think it may have been included in a TED talk.

I think that it’s the opposite. This algorithm requires many examples of text on the specific topic. Probably more than most humans would require.

> While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples [0]

I don’t know what constitutes an example in this case but let’s assume it means 1 blog article. I don’t know many humans that read thousands or tens of thousands of blog articles on a specific topic. And if I did I’d expect that human to write a much more interesting article.

To me, this and other similar generated texts from OpenAI feel bland / generic.

Take a listen to the generated music from OpenAI - https://openai.com/blog/jukebox/. It’s pretty bad, but in a weird way. It’s technically correct - in key, on beat, ect. And even some of the music it generates is technically hard to do, but it sounds so painfully generic.

> All the impressive achievements of deep learning amount to just curve fitting Judea Perl [1]

This comment was written by a human :)

[0]https://arxiv.org/abs/2005.14165 [1]https://www.quantamagazine.org/to-build-truly-intelligent-ma...

> assume it means 1 blog article

I'd like to play devils advocate here.

Given one blog article in a foreign language: Would a human be able to write coherent future articles?

With no teacher or context whatsoever how many articles would one have to read before they could write something that would 'fool' a native speaker? 1000, 100,000?

I have no idea how to measure the quantity/quality of contextual and sensory data we are constantly processing from just existing in the real world, however, it is vital to solving these tasks in a human way - yet it is a dataset that no machine has access to

I would argue comparing 'like for like' disregards the rich data we swim amongst as humans, making it an unfair comparison

GPT-3 was trained on half a trillion words (common crawl, webtext, two book corpuses, and wikipedia, IIRC). At about 100 words per minute, that's almost ten thousand years of continuous speech. By my estimate it's probably a few thousand times what people actually hear in a lifetime. We don't experience nearly the volume of language that it did.

You forgot that we also absorb a much larger set of data through other senses.

Absolutely 100% agree.

Why then, the continued obsession with building single-media models?

Is focusing on the Turing test and language proficiency bringing us further away from the goals of legitimate intelligence?

I would argue "yes", which was my original comment. At no point in us trying to replicate what an adult sounds like have we actually demonstrated anything remotely like the IQ of a small child. And there's this big gap where it's implied by some that this process goes 1) sound like an adult -> 2) think like an adult, which seems to be missing the boat imo. (There's logically this intermediate step where we have this adult-sounding monster AI child.)

If we could constrain the vocabulary to that a child might be exposed to, the correlative trickery of these models would be more obvious. The (exceptionally good) quality of these curve fits wouldn't trick us with vocabulary and syntax that looks like something we'd say. The dumb things would sound dumb, and the smart things would sound smart. And maybe, probably even, that would require us fusing in all sorts of other experiential models to make that happen.

> Why then, the continued obsession with building single-media models?

I think it's literally just working with available data. With some back of the envelope math, GPT-3's training corpus is thousands of lifetimes of language heard. All else equal, I'm sure the ML community would almost unanimously agree that thousands of lifetimes of other data with many modes of interaction and different media would be better. It would take forever to do and would cost insane amounts of money. But some kinds of labels are relatively cheap, and some data don't need labels at all, like this internet text corpus. I think that explains the obsession with single-media models. There's a lot more work to do and this is, believe it or not, still the low hanging fruit.

> thousands of lifetimes of other data with many modes of interaction and different media would be better.

But why not just 1 lifetime of different kinds of data? Heck, why not an environment of 3 years of multi-media data that a child would experience? That wouldn't cost insane amounts of money (or probably anything even close to what we've spent on deep learning as a species).

A corpus limited to the experiences of a single agent would create a very compelling case for intelligence if at the end of that training there was something that sounded and acted smart. It couldn't "jump the gun" as it were, by a lookup of some very intelligent statement that was made somewhere else. It would imply the agent was creatively generating new models as opposed to finding pre-existing ones. It'd even be generous to plain-ol'-AI as well as deep learning, because it would allow both causal models to explain learned explicit knowledge (symbolic), or interesting tacit behavior (empirical ML).

> But why not just 1 lifetime of different kinds of data? Heck, why not an environment of 3 years of multi-media data that a child would experience? That wouldn't cost insane amounts of money (or probably anything even close to what we've spent on deep learning as a species).

How would you imagine creating such an environment in a way that allows you to train models quickly?

No new technology is impressive when it comes incrementally. A camera that automatically records the latitude and longitude of where each photo was taken would have blown my mind as a child. I couldn't have conceived any way it might have worked. But nearly all cameras do that now and at most it's a curiosity or a privacy worry, not a blown mind.

A new human doesn't come out of thin air, evolution has "trained" them with billions of inputs for billions of years.

The article says more about the state of tech blogging than it does GPT-3. I kept thinking "great, another one of these, when are they actually going to show me any results?"

We've been conditioned to accept articles where there's a lot of words and paragraphs and paragraphs of buildup, but nothing actually being said.

Is this meant to be sarcastic?

(For context, the vast majority of the article was generated by GPT-3 itself).

1) It DOES rely on its own limited input,meta learning

2) Quite irrelevant,that's a motivation problem

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