> ChatGPT is so good at this form of interpolation that people find it entertaining: they’ve discovered a “blur” tool for paragraphs instead of photos, and are having a blast playing with it.
“‘blur’ tool for paragraphs” is such a good way of describing the most prominent and remarkable skill of ChatGPT.
It is fun, but so obviously trades off against what makes paragraphs great. It is apt that this essay against ChatGPT blurry language appears on The New Yorker, a publication so known for its literary particularism. ChatGPT smears are amusing, but they are probably also yet another nail in the coffin of the literary society. Nowadays we are not careful readers; we skim, skip, and seek tools to sum up whole books. Human knowledge is in the ability to produce the particular and the ability to recognize it. For philosophers such as Iris Murdoch, careful attention to the particular, to just the right adjective in describing a friend, is a moral attention.
With Google we took the job of storing quotes (and other decompressed writing) and made it the remit of the machine. But we still asked for it back uncompressed. Here with ChatGPT, we are beginning to accept back a blur, because the Declaration of Sock Independence is immediately, Tiktok-speedily funny, and no one’s reading the original declaration anymore anyway.
> because the Declaration of Sock Independence is immediately, Tiktok-speedily funny, and no one’s reading the original declaration anymore anyway
For those who, like me, had missed out on this nugget of comedic gold, here's what ChatGPT had to say when it was asked to "describe losing your sock in the dryer in the style of the declaration of independence":
When in the course of household events, it becomes necessary for one to dissolve the bonds that have connected a sock to its mate, and to assume among the powers of the laundry room, the separate and equal station to which the laws of physics and of household maintenance entitle it, a decent respect to the opinions of socks requires that it should declare the causes which impel it to go missing.
We hold these truths to be self-evident, that all socks are created equal, and are endowed by their manufacturer with certain unalienable rights...
It's a great metaphor and one we should use more. But there's a place for blurred photos: thumbnails.
On Hacker News we often complain about headlines because that's all we see at first. But I've been using Kagi's summarizer [1] and I think it's a great tool for getting the gist of certain things, like if you want to know what a YouTube video is about without watching it. (Google Translate is useful for similar reasons.)
Perhaps someday, Hacker News will have an AI-generated summary of the article at the top of each comment page?
Similarly, ChatGPT excels at fast answers for questions like "What is a X", where you just want a quick definition. It's probably in Wikipedia somewhere, but you don't have to read as much. And it might be wrong, but probably not as wrong as the definition you'd infer from context if you didn't look it up.
We probably would be better off if these things were called "artificial memory" rather than "artificial intelligence." It's an associative memory that often works like human memory in how frequently it confabulates. When you care, you write things down and look things up.
The amount of human-generated lowest-common-denominator English-language free content was already so high that I'm not sure the New Yorker has anything (more) to worry about. If you've been paying for the New Yorker already in the days of Medium, Buzzfeed, blogs, and what-have-you, does there being even more uncurated stuff change your equation? (It doesn't for me.)
More cynically: it'll be hard to kill the few legacy zombies that have survived so much destruction at the hand of free internet content already.
What he misses in this analogy is that part of what produces the "blur" is the superimposing of many relevan paragraphs found on the web into one. This mechanism can be very useful, because it could average out errors and give one a less one-sided perspective on a particular issue. It doesn't always work like this, but hopefully it will more and more. Also, even more useful is to do a cluster analysis of the existant perspectives and give a representative synthesis of each of these, along with a weight representing their popularity. So there's a lot of room for improvement, but the potential in my opinion is there.
The blur is addictive because it feeds a feedback loop: rather than tiring out your brain on understanding one thing in detail, you can watch two summaries and have a vague sense of understanding. It allows to jump to next novelty, always feeding the system 1 of the brain but system 2 is rarely brought in picture.
I wonder if this will lead to a stratification of work in the society: lot of jobs can operate on the blur. "Just give me enough to get my job done". But fewer (critical and hopefully highly paid) people will be engaged in a profession where understanding the details is the job and there's no way around it.
In Asimov's foundation novel this is a recurring theme: they can't find people who can work on designing or maintaining nuclear power. This eventually leads to stagnation. AI tools can prevent this stagnation only if mankind uses the freeing of mental burden with the help of AI to work on higher set of problems. But if the tools are used merely as butlers then the pessimistic outcome is more likely.
The general tendency to lack of details can also give edge in some cases. Imagine if everyone is using similar AI tools to understand company annual reports which gives a nice, tiktok style summary. Then an investor doing the dirty work to go through the details may find things that are missed by the 'algo'.
> ChatGPT smears are amusing, but they are probably also yet another nail in the coffin of the literary society.
As the author (Ted Chiang!!) notes, ChatGPT3 will be yet another nail in the coffin of ChatGPT5. At some point, OpenAI will find it impossible to find untainted training data. The whole thing will become a circular human centipede and die of malnutrition. Apologies for the mental image.
This is very well written, and probably one of my favorite takes on the whole ChatGPT thing. This sentence in particular:
> Indeed, a useful criterion for gauging a large-language model’s quality might be the willingness of a company to use the text that it generates as training material for a new model.
It seems obvious that future GPTs should not be trained on the current GPT's output, just as future DALL-Es should not be trained on current DALL-E outputs, because the recursive feedback loop would just yield nonsense. But, a recursive feedback loop is exactly what superhuman models like AlphaZero use. Further, AlphaZero is even trained on its own output even during the phase where it performs worse than humans.
There are, obviously, a whole bunch of reasons for this. The "rules" for whether text is "right" or not are way fuzzier than the "rules" for whether a move in Go is right or not. But, it's not implausible that some future model will simply have a superhuman learning rate and a superhuman ability to distinguish "right" from "wrong" - this paragraph will look downright prophetic then.
> Imagine what it would look like if ChatGPT were a lossless algorithm. If that were the case, it would always answer questions by providing a verbatim quote from a relevant Web page. We would probably regard the software as only a slight improvement over a conventional search engine, and be less impressed by it
The story is an impressive piece, but I think as with many of us, it's a personal projection of expectations on results. One example from my experience. In the book "Jim Carter - Sky Spy, Memoirs of a U-2 Pilot" there was an interesting story about the moment when U-2 was used for capturing the photo of a big area at the Pacific to save the life of a lost seaman. The story was very interesting and I always wanted to know more, technical details, people involvement etc. Searching with Google ten years ago didn't help, I rephrased the names, changed the date (used even the range operator) to no avail. And recently I asked several LLM-based bots about it. You can guess it. They ignored my constrains at best and hallucinate at worst. One even invented a mixed reality story when Francis Gary Powers actually flew not one but with a co-pilot and the latter ended up in the Pacific and was saved. Very funny, but I wasn't impressed. But if one of them scraped the far corners of web discussion boards and saved a first-person account of someone who took part in it and gave it to me, I would be really impressed.
The compression & blur analogy also applies to human minds as well. If you focus on fidelity, you have to increase storage and specialize in a narrow domain. If you want a bit of everything, then blurring and destructive compression is the only way. E.g. a "book smart" vs "street smart" difference.
"mastery" can be considered a hyper efficient destructive compression (experts are often unable to articulate or teach to beginners) that reduces latency of response to such extreme levels that they seem to be predicting the future or reacting at godlike speeds.
This is a decent summary. I've been thinking about how ChatGPT by it's very nature destroys context and source reputation. When I search for something on the Internet, I get a link to the original content, which I can then evaluate based on my knowledge and the reputation of the original source. Wikipedia is the same, with a big emphasis on citation. ChatGPT and other LLMs destroy that context and knowledge, giving me no tools to evaluate the sources they're using.
I don't like this analogy; I think why I don't like it is in the intent. With JPEG in the intent is produce an image indistinguishable from the original. Xerox didn't intend to create photocopier that produces incorrect copies. The artifacts are failures of the JPEG algorithm to do what it's supposed to within its constraints.
GPT is not trying to create a reproduction of it's source material and simply failing at the task. Compression and GPT are both mathematical processes they aren't the same process; JPEG is taking the original image and throwing away some of the detail. GPT is processing content to apply weights to a model; if that is reversible to the original content it is considered a failure.
Since this article was written by Ted Chiang, just for fun I asked ChatGPT to summarize the plot of "Understand".
Apparently ChatGPT thinks "Understand" is about the government who is pursuing someone called Gary Whittle who has superintelligence (well, at least it got one detail right). When challenged ("no, the government is not the antagonist, but there is one person...") ChatGPT amends its summary to this:
> "George Millwright is Gary Whittle's former supervisor and is depicted as being jealous of Gary's newfound abilities. He becomes obsessed with Gary and is determined to bring him down, even going so far as to threaten his family. George Millwright's actions drive much of the conflict in the story and serve as a reminder of the potential dangers of unchecked ambition and envy."
I'm honestly fascinated by ChatGPT's "hallucinations". I mean, it all makes perfect sense. Its summary is a potential scifi story -- albeit a poor, completely clichéd one -- but this is not at all what happens in "Understand"!
Damn, I hate to plug products on HN, but I'd say that the New Yorker is the one subscription I've loved maintaining throughout my life. First got it right out of college and appreciate it 20 years later.
Everyone is publishing think pieces about ChatGPT - yawn. But only the New Yorker said, hmm, how about if we get frickin' Ted Chiang to write a think piece? (It is predictably very well written.)
I worked there many years ago, leading the re-design and re-platform (fun dealing with 90 years of archival content with mixed usage-rights) and paywall implementation (don't hate me, it funds journalism).
When you see how the stories get made and how people work there, well, its just amazing.
In his short story Understand, he talks about two superintelligent individuals who are having high bandwidth conversations. Maybe ChatGPT and Bard are those bespoke intelligent agents.
We continue. We are like two BARDs, each cueing the other to extemporize another stanza, jointly composing an epic poem of knowledge. Within moments we accelerate, talking over each other's words but hearing every nuance, until we are absorbing, concluding, and responding, continuously, simultaneously, synergistically.
For anybody who does not want to pay, your taxes likely already pay for a subscription you can use from your local library on the Libby app. King and Snohomish counties in Washington provide unlimited digital copies of The New Yorker and The Economist as an example.
They also have weekly cryptic crosswords that are cryptic enough to be interesting but as easy to completely solve as a regular crossword. (With cryptics, very good ones are also very hard.)
Too bad that this kind of writing is married to 31(!) ads/trackers on that page. Can the journalism like this really not survive without all that crap?
> Models like ChatGPT aren’t eligible for the Hutter Prize for a variety of reasons, one of which is that they don’t reconstruct the original text precisely—i.e., they don’t perform lossless compression.
Small nit: The lossiness is not a problem at all. Entropy coding turns an imperfect, lossy predictor into a lossless data compressor, and the better the predictor, the better the compression ratio. All Hutter Prize contestants anywhere near the top use it. The connection at a mathematical level is direct and straightforward enough that "bits per byte" is a common number used in benchmarking language models, despite the fact that they are generally not intended to be used for data compression.
The practical reason why a ChatGPT-based system won't be competing for the Hutter Prize is simply that it's a contest about compressing a 1GB file, and GPT-3's weights are both proprietary and take up hundreds of times more space than that.
Apparently it leads the compression of enwik9 ( http://www.mattmahoney.net/dc/text.html ) . Not sure why it isn't eligible for the Hutter Prize, there's some speculations in the previous discussion but I don't know whether they're true.
This article inspires to ask a fundamental question "What do we expect/want AI to work like?". Do we want a xerocopying machine, providing verbatim copies or are we willing to accept that intelligence is connected to creativity and interpretation so the resulting output will be processed and might contain errors, ommissions etc. To be honest the same applies to humans. There's this passage in the article:
>If a large-language model has compiled a vast number of correlations between economic terms—so many that it can offer plausible responses to a wide variety of questions—should we say that it actually understands economic theory?
In the above passage we can easily switch "larger-language model" to "Professor Jean Tirole" and ponder how high do we set the bar for AI. Can we accept AI only if it will be flawless and "more intelligent" (whatever that means) than all humans?
>Given that large-language models like ChatGPT are often extolled as the cutting edge of artificial intelligence, it may sound dismissive — or at least deflating — to describe them as lossy text-compression algorithms.
Does anyone have any idea how ChatGPT will actually make money? As novel as it is to use with all the "potential" applications, the possible revenue streams don't seem to prop up the recent investments into OpenAI.
We've already been through enough hype cycles in the past ten years to realize "potential" use-cases or user counts don't necessarily produce a sustainable business model. Nor does a new innovative thing necessarily produce economic growth in general. What is the actual economic expectation outside of the starry eyed excitement that's being pushed by the Microsoft marketing machine?
> I think there’s a simpler explanation. Imagine what it would look like if ChatGPT were a lossless algorithm. If that were the case, it would always answer questions by providing a verbatim quote from a relevant Web page. We would probably regard the software as only a slight improvement over a conventional search engine, and be less impressed by it.
Tautologically, yes, ChatGPT works because it is, as defined by the author, a lossy algorithm. If it were a lossless algorithm it wouldn't work the way it does now.
> The fact that ChatGPT rephrases material from the Web instead of quoting it word for word makes it seem like a student expressing ideas in her own words, rather than simply regurgitating what she’s read; it creates the illusion that ChatGPT understands the material. In human students, rote memorization isn’t an indicator of genuine learning, so ChatGPT’s inability to produce exact quotes from Web pages is precisely what makes us think that it has learned something. When we’re dealing with sequences of words, lossy compression looks smarter than lossless compression.
This is where the analogy of a lossy and lossless compression algorithm breaks down. Yes, a loosely similar approach of principle component analysis and dimensional reduction as used in lossy compression algorithms is being applied and we can see that most directly in a technical sense with GPT `embedding vector(1536)`, but there is a big difference: ChatGPT is also a translator and not just a synthesizer.
This has nothing to do with "looking smarter". It has to do with being reliably proficient at both translating and synthesizing.
When given an analytic prompt like "turn this provided box score into an entertaining outline", ChatGPT proves itself to be a reliable translator, because it can reference all of the facts in the prompt itself.
When given a synthetic prompt like "give me some quotes from the broadcast", ChatGPT proves itself to be a reliable synthesizer, because it can provide fictional quotes that sound correct when the facts are not present in the prompt itself.
The synthetic prompts function in a similar manner to lossy compression algorithms. The analytic prompts do not. This lossy compression algorithm theory, also known as the bullshit generator theory, is an incomplete description of large language models.
Today it almost exclusively offers quotes from content marketing intended to sell you something. It's like trying to learn by reading the ads in a catalog.
Ugh I’m beginning to think I’m going to spend the next 6-12 months commenting “no, large language models aren’t supposed to somehow know everything in the world. No, that’s not what they’re designed for. Yes, hooking one up to our long-standing record-of-everything-in-the-world (google’s knowledge graph) is going to be powerful.”
It’s getting to point where I need to consider stop going on HN. This is like when my father excitedly told his friends about the coming computer revolution in the 90s and they responded “well it can’t do my dishes or clean the house, they’re just a fad!” Makes me want screaaaaam
I appreciate where you are coming from and I agree that AI is about to go from relative obscurity where just a few geeks were playing around to insane hype. I feel like I’ve spent the last 7 years wondering why no one in the wider world was as impressed as I was, but starting with Stable Diffusion and now ChatGPT, the hype rocket ship has launched. Search TikTok for ChatGPT for all the evidence of that you could ever need.
That said, I still think we are in for a wild ride, even if we go through a hype bubble and pop first. I really don’t think the current crop of Transformer LLMs are the end of the story. Im betting that we are headed towards architectures made up of several different kind of models and AI approaches just like the brain is an apparent concert of specialized regions. You can see that in the new Bing where it’s a combination of a LLM with static training set that can then do up to 3 web searches to build up additional context of fresh data for the prompt, overcoming one of the key disadvantages of a transformer model. The hidden prompt with plain English Asimov's laws are the icing on the cake.
The hype will be insane, but the capabilities are growing quickly and we do not yet seem close to the end of this rich computational ore vein we have hit.
You don't need to correct every wrong thing you read. In fact you will probably feel much better if you don't ever do it at all, or at least take a break for while.
I’m just searching the comments for novel use cases where its effective. Most articles I’ve read seem like either moral panics or snake oil.
I like how it can generate songs and poems based on a prompt. Its not particularly useful, but it is entertaining. It really does seem curated at times, leading me to think this will eventually become a fad or replaced by a more advanced algorithm.
Powerful for what? To use Chiang's analogy, do you think that an LLM trained on Web content will actually derive the rules of arithmetic, physics, etc. I think it is more likely that in decade or more a majority of Internet content will be generated by machine and search engines will do a great job of indexing increasingly meaningless information.
>For us to have confidence in them, we would need to know that they haven’t been fed propaganda and conspiracy theories—we’d need to know that the jpeg is capturing the right sections of the Web.
But finding the 'right sections of the Web' is a subjective process. This is precisely why many people have lost confidence in the news media. Media outlets (on both sides of the political spectrum) often choose to be hyper-focused on material that supports their narrative while completely ignoring evidence that goes against it.
ChatGPT and any other Large Language Model can suffer from the same 'Garbage-In, Garbage-Out' problem that can infect any other computer system.
[+] [-] bitlax|3 years ago|reply
[+] [-] thundergolfer|3 years ago|reply
“‘blur’ tool for paragraphs” is such a good way of describing the most prominent and remarkable skill of ChatGPT.
It is fun, but so obviously trades off against what makes paragraphs great. It is apt that this essay against ChatGPT blurry language appears on The New Yorker, a publication so known for its literary particularism. ChatGPT smears are amusing, but they are probably also yet another nail in the coffin of the literary society. Nowadays we are not careful readers; we skim, skip, and seek tools to sum up whole books. Human knowledge is in the ability to produce the particular and the ability to recognize it. For philosophers such as Iris Murdoch, careful attention to the particular, to just the right adjective in describing a friend, is a moral attention.
With Google we took the job of storing quotes (and other decompressed writing) and made it the remit of the machine. But we still asked for it back uncompressed. Here with ChatGPT, we are beginning to accept back a blur, because the Declaration of Sock Independence is immediately, Tiktok-speedily funny, and no one’s reading the original declaration anymore anyway.
[+] [-] airstrike|3 years ago|reply
For those who, like me, had missed out on this nugget of comedic gold, here's what ChatGPT had to say when it was asked to "describe losing your sock in the dryer in the style of the declaration of independence":
When in the course of household events, it becomes necessary for one to dissolve the bonds that have connected a sock to its mate, and to assume among the powers of the laundry room, the separate and equal station to which the laws of physics and of household maintenance entitle it, a decent respect to the opinions of socks requires that it should declare the causes which impel it to go missing.
We hold these truths to be self-evident, that all socks are created equal, and are endowed by their manufacturer with certain unalienable rights...
[+] [-] skybrian|3 years ago|reply
On Hacker News we often complain about headlines because that's all we see at first. But I've been using Kagi's summarizer [1] and I think it's a great tool for getting the gist of certain things, like if you want to know what a YouTube video is about without watching it. (Google Translate is useful for similar reasons.)
Perhaps someday, Hacker News will have an AI-generated summary of the article at the top of each comment page?
Similarly, ChatGPT excels at fast answers for questions like "What is a X", where you just want a quick definition. It's probably in Wikipedia somewhere, but you don't have to read as much. And it might be wrong, but probably not as wrong as the definition you'd infer from context if you didn't look it up.
We probably would be better off if these things were called "artificial memory" rather than "artificial intelligence." It's an associative memory that often works like human memory in how frequently it confabulates. When you care, you write things down and look things up.
[1] https://labs.kagi.com/ai/sum
[+] [-] majormajor|3 years ago|reply
More cynically: it'll be hard to kill the few legacy zombies that have survived so much destruction at the hand of free internet content already.
[+] [-] pegasus|3 years ago|reply
[+] [-] eurekin|3 years ago|reply
Supposedly, if I'm remembering last discussion with a japaneese speaker correctly, the same stem is used for "blur", or "blurry" (bokeh, bokeshi).
Which is kind of interesting parallel here
[+] [-] havercosine|3 years ago|reply
I wonder if this will lead to a stratification of work in the society: lot of jobs can operate on the blur. "Just give me enough to get my job done". But fewer (critical and hopefully highly paid) people will be engaged in a profession where understanding the details is the job and there's no way around it.
In Asimov's foundation novel this is a recurring theme: they can't find people who can work on designing or maintaining nuclear power. This eventually leads to stagnation. AI tools can prevent this stagnation only if mankind uses the freeing of mental burden with the help of AI to work on higher set of problems. But if the tools are used merely as butlers then the pessimistic outcome is more likely.
The general tendency to lack of details can also give edge in some cases. Imagine if everyone is using similar AI tools to understand company annual reports which gives a nice, tiktok style summary. Then an investor doing the dirty work to go through the details may find things that are missed by the 'algo'.
[+] [-] sangnoir|3 years ago|reply
As the author (Ted Chiang!!) notes, ChatGPT3 will be yet another nail in the coffin of ChatGPT5. At some point, OpenAI will find it impossible to find untainted training data. The whole thing will become a circular human centipede and die of malnutrition. Apologies for the mental image.
[+] [-] dbtc|3 years ago|reply
[+] [-] msla|3 years ago|reply
In what way? How, technically, is it anything like that?
These comments sound like full-court publicity press for this article. I wonder why.
[+] [-] EamonnMR|3 years ago|reply
[+] [-] replwoacause|3 years ago|reply
[+] [-] nneonneo|3 years ago|reply
> Indeed, a useful criterion for gauging a large-language model’s quality might be the willingness of a company to use the text that it generates as training material for a new model.
It seems obvious that future GPTs should not be trained on the current GPT's output, just as future DALL-Es should not be trained on current DALL-E outputs, because the recursive feedback loop would just yield nonsense. But, a recursive feedback loop is exactly what superhuman models like AlphaZero use. Further, AlphaZero is even trained on its own output even during the phase where it performs worse than humans.
There are, obviously, a whole bunch of reasons for this. The "rules" for whether text is "right" or not are way fuzzier than the "rules" for whether a move in Go is right or not. But, it's not implausible that some future model will simply have a superhuman learning rate and a superhuman ability to distinguish "right" from "wrong" - this paragraph will look downright prophetic then.
[+] [-] mrtksn|3 years ago|reply
[+] [-] Agraillo|3 years ago|reply
The story is an impressive piece, but I think as with many of us, it's a personal projection of expectations on results. One example from my experience. In the book "Jim Carter - Sky Spy, Memoirs of a U-2 Pilot" there was an interesting story about the moment when U-2 was used for capturing the photo of a big area at the Pacific to save the life of a lost seaman. The story was very interesting and I always wanted to know more, technical details, people involvement etc. Searching with Google ten years ago didn't help, I rephrased the names, changed the date (used even the range operator) to no avail. And recently I asked several LLM-based bots about it. You can guess it. They ignored my constrains at best and hallucinate at worst. One even invented a mixed reality story when Francis Gary Powers actually flew not one but with a co-pilot and the latter ended up in the Pacific and was saved. Very funny, but I wasn't impressed. But if one of them scraped the far corners of web discussion boards and saved a first-person account of someone who took part in it and gave it to me, I would be really impressed.
[+] [-] notShabu|3 years ago|reply
"mastery" can be considered a hyper efficient destructive compression (experts are often unable to articulate or teach to beginners) that reduces latency of response to such extreme levels that they seem to be predicting the future or reacting at godlike speeds.
[+] [-] secabeen|3 years ago|reply
[+] [-] wvenable|3 years ago|reply
GPT is not trying to create a reproduction of it's source material and simply failing at the task. Compression and GPT are both mathematical processes they aren't the same process; JPEG is taking the original image and throwing away some of the detail. GPT is processing content to apply weights to a model; if that is reversible to the original content it is considered a failure.
[+] [-] the_af|3 years ago|reply
Apparently ChatGPT thinks "Understand" is about the government who is pursuing someone called Gary Whittle who has superintelligence (well, at least it got one detail right). When challenged ("no, the government is not the antagonist, but there is one person...") ChatGPT amends its summary to this:
> "George Millwright is Gary Whittle's former supervisor and is depicted as being jealous of Gary's newfound abilities. He becomes obsessed with Gary and is determined to bring him down, even going so far as to threaten his family. George Millwright's actions drive much of the conflict in the story and serve as a reminder of the potential dangers of unchecked ambition and envy."
I'm honestly fascinated by ChatGPT's "hallucinations". I mean, it all makes perfect sense. Its summary is a potential scifi story -- albeit a poor, completely clichéd one -- but this is not at all what happens in "Understand"!
Text compression indeed.
[+] [-] zetazzed|3 years ago|reply
Everyone is publishing think pieces about ChatGPT - yawn. But only the New Yorker said, hmm, how about if we get frickin' Ted Chiang to write a think piece? (It is predictably very well written.)
[+] [-] thundergolfer|3 years ago|reply
[+] [-] somberi|3 years ago|reply
[+] [-] donohoe|3 years ago|reply
I worked there many years ago, leading the re-design and re-platform (fun dealing with 90 years of archival content with mixed usage-rights) and paywall implementation (don't hate me, it funds journalism).
When you see how the stories get made and how people work there, well, its just amazing.
[+] [-] passion__desire|3 years ago|reply
https://web.archive.org/web/20140527121332/http://www.infini...
We continue. We are like two BARDs, each cueing the other to extemporize another stanza, jointly composing an epic poem of knowledge. Within moments we accelerate, talking over each other's words but hearing every nuance, until we are absorbing, concluding, and responding, continuously, simultaneously, synergistically.
[+] [-] galleywest200|3 years ago|reply
[+] [-] gowld|3 years ago|reply
[+] [-] wg0|3 years ago|reply
Amazing write up. ChatGPT is a blurry JPEG of the internet.
[+] [-] freediver|3 years ago|reply
[+] [-] jrgaston|3 years ago|reply
[+] [-] sdwr|3 years ago|reply
[+] [-] msla|3 years ago|reply
[deleted]
[+] [-] msla|3 years ago|reply
It used to have high-quality articles, certainly.
[+] [-] msla|3 years ago|reply
Is that the best the New Yorker can offer?
[+] [-] Ari_Rahikkala|3 years ago|reply
Small nit: The lossiness is not a problem at all. Entropy coding turns an imperfect, lossy predictor into a lossless data compressor, and the better the predictor, the better the compression ratio. All Hutter Prize contestants anywhere near the top use it. The connection at a mathematical level is direct and straightforward enough that "bits per byte" is a common number used in benchmarking language models, despite the fact that they are generally not intended to be used for data compression.
The practical reason why a ChatGPT-based system won't be competing for the Hutter Prize is simply that it's a contest about compressing a 1GB file, and GPT-3's weights are both proprietary and take up hundreds of times more space than that.
[+] [-] hnfong|3 years ago|reply
Apparently it leads the compression of enwik9 ( http://www.mattmahoney.net/dc/text.html ) . Not sure why it isn't eligible for the Hutter Prize, there's some speculations in the previous discussion but I don't know whether they're true.
[+] [-] atgctg|3 years ago|reply
[+] [-] ArekDymalski|3 years ago|reply
>If a large-language model has compiled a vast number of correlations between economic terms—so many that it can offer plausible responses to a wide variety of questions—should we say that it actually understands economic theory?
In the above passage we can easily switch "larger-language model" to "Professor Jean Tirole" and ponder how high do we set the bar for AI. Can we accept AI only if it will be flawless and "more intelligent" (whatever that means) than all humans?
[+] [-] torginus|3 years ago|reply
snicker
[+] [-] LinuxBender|3 years ago|reply
[1] - https://archive.ph/uah9K
[+] [-] boh|3 years ago|reply
We've already been through enough hype cycles in the past ten years to realize "potential" use-cases or user counts don't necessarily produce a sustainable business model. Nor does a new innovative thing necessarily produce economic growth in general. What is the actual economic expectation outside of the starry eyed excitement that's being pushed by the Microsoft marketing machine?
[+] [-] williamcotton|3 years ago|reply
Tautologically, yes, ChatGPT works because it is, as defined by the author, a lossy algorithm. If it were a lossless algorithm it wouldn't work the way it does now.
> The fact that ChatGPT rephrases material from the Web instead of quoting it word for word makes it seem like a student expressing ideas in her own words, rather than simply regurgitating what she’s read; it creates the illusion that ChatGPT understands the material. In human students, rote memorization isn’t an indicator of genuine learning, so ChatGPT’s inability to produce exact quotes from Web pages is precisely what makes us think that it has learned something. When we’re dealing with sequences of words, lossy compression looks smarter than lossless compression.
This is where the analogy of a lossy and lossless compression algorithm breaks down. Yes, a loosely similar approach of principle component analysis and dimensional reduction as used in lossy compression algorithms is being applied and we can see that most directly in a technical sense with GPT `embedding vector(1536)`, but there is a big difference: ChatGPT is also a translator and not just a synthesizer.
This has nothing to do with "looking smarter". It has to do with being reliably proficient at both translating and synthesizing.
When given an analytic prompt like "turn this provided box score into an entertaining outline", ChatGPT proves itself to be a reliable translator, because it can reference all of the facts in the prompt itself.
When given a synthetic prompt like "give me some quotes from the broadcast", ChatGPT proves itself to be a reliable synthesizer, because it can provide fictional quotes that sound correct when the facts are not present in the prompt itself.
The synthetic prompts function in a similar manner to lossy compression algorithms. The analytic prompts do not. This lossy compression algorithm theory, also known as the bullshit generator theory, is an incomplete description of large language models.
https://williamcotton.com/articles/chatgpt-and-the-analytic-...
[+] [-] sloreti|3 years ago|reply
Today it almost exclusively offers quotes from content marketing intended to sell you something. It's like trying to learn by reading the ads in a catalog.
[+] [-] skybrian|3 years ago|reply
[+] [-] bbor|3 years ago|reply
It’s getting to point where I need to consider stop going on HN. This is like when my father excitedly told his friends about the coming computer revolution in the 90s and they responded “well it can’t do my dishes or clean the house, they’re just a fad!” Makes me want screaaaaam
[+] [-] dougmwne|3 years ago|reply
That said, I still think we are in for a wild ride, even if we go through a hype bubble and pop first. I really don’t think the current crop of Transformer LLMs are the end of the story. Im betting that we are headed towards architectures made up of several different kind of models and AI approaches just like the brain is an apparent concert of specialized regions. You can see that in the new Bing where it’s a combination of a LLM with static training set that can then do up to 3 web searches to build up additional context of fresh data for the prompt, overcoming one of the key disadvantages of a transformer model. The hidden prompt with plain English Asimov's laws are the icing on the cake.
The hype will be insane, but the capabilities are growing quickly and we do not yet seem close to the end of this rich computational ore vein we have hit.
[+] [-] ElevenLathe|3 years ago|reply
[+] [-] chapium|3 years ago|reply
I like how it can generate songs and poems based on a prompt. Its not particularly useful, but it is entertaining. It really does seem curated at times, leading me to think this will eventually become a fad or replaced by a more advanced algorithm.
[+] [-] billiam|3 years ago|reply
[+] [-] didgetmaster|3 years ago|reply
But finding the 'right sections of the Web' is a subjective process. This is precisely why many people have lost confidence in the news media. Media outlets (on both sides of the political spectrum) often choose to be hyper-focused on material that supports their narrative while completely ignoring evidence that goes against it.
ChatGPT and any other Large Language Model can suffer from the same 'Garbage-In, Garbage-Out' problem that can infect any other computer system.