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Will we run out of ML data? Evidence from projecting dataset size trends (2022)

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Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#81

On this note, the data-set available if you start collecting today is tainted with experimental AI content. Not the biggest issue right now but as time goes on this problem will get worse and we'll be basing our simulations of intelligence on the output of our simulations of intelligence, a brave new abstraction.

I see this take a lot and I think it's quite wrong, not fully, but at least missing a couple big points. I think they already don't blindly feed it just all the garbage raw data they can find, but prefer high quality, well-prepared sources. And aside from spam, we're not just blindly posting AI content either. We're putting in meaningful prompts, rejecting answers we don't like, and editing answers we do.

I think "high quality, well-prepared sources" would include blogs and articles, which are likely to become heavily influenced by AI (blogs and articles are high quality compared to Reddit posts for example, which were included in the past).

In fact, there's no reason to think that academic papers won't start using language models to write better.

Tainting your text with AI can be as simple as pasting a paragraph in and asking if there's anything to improve.

Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#82
post #68

Earlier quoted context omitted.

Seems unlikely, that never worked in the past. And humans don't actually use logic (especially formal logic) to come up with anything. They just use it to justify what they came up with. Not even mathematicians think in terms of logic when trying to solve problems.

There are already tons of systems (for example Google Translate) that combine rule-based reasoning with probabilistic reasoning. Looks to be working to me.

Interesting. Do you have any sources on Google Translate using rule-based reasoning?

Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#83

Earlier quoted context omitted.

We won't hit the wall. Somewhat counterintuitively, scaling datasets is the lazy and economical approach. If you have the compute already, might as well dig an OOM more text tokens. But there are other sources of data, and slightly different ways to utilize it. Multimodality, in very large training runs, will almost inevitably increase sample efficiency (for obvious reasons of context richness), synthetic data is alr…

But we can devise generally applicable rules of reasoning from first principles. It's called logic. I am pretty sure the next step is to properly combine machine learning and logic properly.

First principles don't work in the space of systems geared towards extreme generalization such as LLMs. You need to be ready to compare anything with anything and build bridges between many principles. In fact there is a deep link between the progress of structuralism in mathematics culminating with homotopy type theory and its parallel (r)evolution in the humanities with the discovery of manuscripts by the founder of structural linguistics, Ferdinand de Saussure.

Identity is what provides the irreducible basis, in the sense that we cannot enter into the consideration of specific facts that are placed under this identity, and it is this identity that becomes for us the true concrete fact, beyond which there is nothing more.

...

For example, for a musical composition, compared to a painting. Where does a musical composition exist? It is the same question as to know where 'aka' exists. In reality, this composition only exists when it is performed; but to consider this performance as its existence is false. Its existence is the identity of the performances.

...

For each of the things we have considered as a truth, we have arrived through so many different paths that we confess we do not know which one should be preferred. To properly present the entirety of our propositions, it would be necessary to adopt a fixed and defined starting point. But what we are trying to establish is that it is false to admit in linguistics a single fact as defined in itself. There is, therefore, a necessary absence of any starting point, and if some reader is willing to follow our thoughts carefully from one end to the other of this volume, they will recognize, we are convinced, that it was, so to speak, impossible to follow a very rigorous order. We will allow ourselves to present, up to three or four times in different forms, the same idea to the reader because there really is no starting point more appropriate than another on which to base the demonstration.

...

As language offers no substance under any of its manifestations, but only combined or isolated actions of physiological, physical, and mental forces, and as nevertheless all our distinctions, our terminology, and all our ways of speaking are based on this involuntary assumption of a substance, we cannot refuse, first and foremost, to recognize that the most essential task of the theory of language will be to untangle what our primary distinctions are all about.

...

There are different types of identity. This is what creates different orders of linguistic facts. Outside of any identity relationship, a linguistic fact does not exist. However, the identity relationship depends on a variable point of view that one decides to adopt; therefore, there is no rudiment of a linguistic fact outside the defined point of view that presides over distinctions.

Source: http://www.revue-texto.net/docannexe/file/116/saussure255_6....

TL;DR: identity is equivalent to equivalence

Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#84

Earlier quoted context omitted.

Yeah, it's not that I think we'll get all the way there, it's a utopia. My expectation is that within 30 years we reduce the work week by a day or two for most people, compensate for our education system's decline, and avoid energy and food crises, and nothing else fundamentally changes

Assuming this happens without any violence, which I truly doubt. Huge socioeconomic changes like this always come as a result of violence and uproar.

Why? Eg in the latter half of the 20th century the US integrated women into the workforce (almost doubling the population eligible for participation in the labourforce), without violence or uproar.

There was also remarkably little uproar nor violence when the Czech Republic escaped the Iron Curtain and embraced capitalism.

Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#85
post #77

Earlier quoted context omitted.

I don't think that the hallucinations have anything to do with the architecture, rather they come from optimizing a cost function where saying "I don't know" is as bad as being wrong. I do not think that RLHF as currently understood can fix this, since the reward model would struggle to distinguish fact from fiction.

I think you are mixing up layers of abstraction. The network is most likely trained with something like a categorical cross entropy loss function. Those totally punish being wrong a lot more than saying "I don't know". See https://www.v7labs.com/blog/cross-entropy-loss-guide It's just that saying "I don't know" means that your model is spreading the probability of what the next token in the text stream might be over…

Sorry if I was unclear. I know that the model is incentivised to accurately predict the probability distribution of the next token. I mean that the model is not being incentivised to literally produce the output tokens corresponding to "I don't know" when asked a question where it is uncertain.

Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#86

Earlier quoted context omitted.

I see this take a lot and I think it's quite wrong, not fully, but at least missing a couple big points. I think they already don't blindly feed it just all the garbage raw data they can find, but prefer high quality, well-prepared sources. And aside from spam, we're not just blindly posting AI content either. We're putting in meaningful prompts, rejecting answers we don't like, and editing answers we do.

I think "high quality, well-prepared sources" would include blogs and articles, which are likely to become heavily influenced by AI (blogs and articles are high quality compared to Reddit posts for example, which were included in the past). In fact, there's no reason to think that academic papers won't start using language models to write better. Tainting your text with AI can be as simple as pasting a paragraph in a…

I honestly don’t understand why “tainting” is such a big deal. Can someone explain it to me?

I see two possible reasons, but neither seems to be worth the purity concern. The first is that AI can be wrong, make stuff up, be confidently incorrect. Anyone who has been on the internet knows this isn’t exactly a game changer.

Second is that we won’t be training AI to be like humans, but like humans + AI. Also doesn’t seem like a big deal. We’re already humans + writing + computers + internet and so on. This cutoff matters for anthropology, but I don’t see how it matters for trying to make a bot that can do my taxes.

Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#87

On this note, the data-set available if you start collecting today is tainted with experimental AI content. Not the biggest issue right now but as time goes on this problem will get worse and we'll be basing our simulations of intelligence on the output of our simulations of intelligence, a brave new abstraction.

I see this take a lot and I think it's quite wrong, not fully, but at least missing a couple big points. I think they already don't blindly feed it just all the garbage raw data they can find, but prefer high quality, well-prepared sources. And aside from spam, we're not just blindly posting AI content either. We're putting in meaningful prompts, rejecting answers we don't like, and editing answers we do.

I think they already don't blindly feed it just all the garbage raw data they can find, but prefer high quality, well-prepared sources.

If by that you mean Common Crawl, Wikipedia etc, that's hardly "high quality, well prepared", and very subject to the biases and flaws of the creators who will vary widely in expertise, intelligence and ability.

Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#88
The last gen of popular LLMs focuses on publicly accessible web text. But we have lots of other sources of "latent" or "hidden" text. For example, OpenAI's Whisper model can turn audio to text reliably. If you point Whisper at the world's podcasts, that's a whole new source of conversational text. If you point Whisper at YouTube, that's a whole new source of all sorts of text. And then there are all sorts of private sources of text, like UpToDate for doctors, LexisNexis for lawyers, and so forth. I suspect "running out" isn't a within-a-decade concern, especially since text or text-equivalent data grows exponentially in the present internet environment. I think the bigger challenge will be distinguishing human-generated from AI-generated data after 2023.

Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#89

Earlier quoted context omitted.

I see this take a lot and I think it's quite wrong, not fully, but at least missing a couple big points. I think they already don't blindly feed it just all the garbage raw data they can find, but prefer high quality, well-prepared sources. And aside from spam, we're not just blindly posting AI content either. We're putting in meaningful prompts, rejecting answers we don't like, and editing answers we do.

Um, really? You think your average 'growth hacker' who is using ChatGPT to exponentially increase the amount of SEO junk they can churn out is checking each answer before they press publish? Purity, accuracy and relevance of data collected from the internet is going to a very hard problem.

YouTube has tons of data that's mostly untainted by SEO spam.

Re: Will we run out of ML data? Evidence from projecting dataset size trends (2022)

#90
post #71

We just are not thinking wide enough: * Train on all of television history, and streaming content. * Train on YouTube. * I suspect at some point we'll have a recording of most of people's lives, e.g. live-streaming: https://en.wikipedia.org/wiki/Lifestreaming#Lifecasting

You can also gather arbitrarily more video data by just turning on some webcams and pointing them at the world. In addition you can also feed your system from video games.

Microphones, too.
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