Now this wouldn't be possible without the high quality synthetic dataset produced by GPT(1B tokens) but this is more evidence in line with Tiny Stories (https://arxiv.org/abs/2305.07759). That is, LLMs only need to be so big (both data and parameters) to learn the total sum of human knowledge (and deal with trash data).
The speed of training is really interesting to me too. Llambda labs rent out 8 A100s for ~$9-12/hour. Total training cost there would be ~$850-1200. Another ~$2000 for the GPT training data, some more given there would be input tokens too.
Very approachable prices.
> Now this wouldn't be possible without the high quality synthetic dataset produced by GPT(1B tokens)
Just a note that this is GPT3.5 (I assume turbo?).
This result puts more emphasis on data quality, which might actually be problematic long term though? The vast majority of human knowledge is not represented in high quality textbooks.
It’s not clear from these results but the paper seems to at least imply that the importance of the synthetic data is to “unlock” the pre-training data.
My takeaway as a non-expert is that this is a good result for small and efficient models focused on well-defined domains, but a neutral or maybe even a bad result for a model that displays general intelligence.
I wonder if that is true. Intuitively it seems to me that there are probably many areas of knowledge that cannot be reliably summarized into a textbook. Most subjective experiences, for example. Qualia have been notoriously difficult to describe too, even if the might be absolute.
Even for fields that lend themselves well to being converted to textbook format there is often a tradeoff between accuracy and conciseness. The more you refine, the more nuance you throw away. It seems like a Very Hard Problem (tm) to know which data are superfluous and which are not, especially at scale.
If there's something that cannot be reliably summarized into a textbook, then can we expect to see an agent reliably output it as text in the first place?
Yes. Translation of classical languages is a good example. There are lots of subtle nuances that aren't captured by any pedagogical text and we accept that humans require require apprenticeships/graduate study to become competent (self study is not enough), but the output is obviously text.
You're describing a good example of a process that requires tacit knowledge. The process of translation results in an output of text, but we can easily represent this output in a textbook. What we can't easily represent in a textbook is the subtle nuances around the process of how to translate. I was referring to that kind of tacit knowledge itself (similar to OP's example of qualia).
Depends on the sophistication of the agent. I have agency myself and can express my current mental state quite readily, or can express personal opinions about a wide variety of subjects. I'm not sure how you would write a useful textbook about it though.
The point is that if something is difficult to express or encode in to words for a textbook, then there's an underlying reason that would apply to not just textbooks but also to others who try to write about the same thing.
Well, used to be a Very Hard Problem. Now you can just test it: train a model on the textbook and ask it to solve many problem challenges that it hasn't seen before.
After you get a score for the model trained on the whole textbook, try removing each sentence in the book in turn. If removing that particular sentence decreased the test scores then keep it in, else throw it away.
- Perhaps the model can be improved by adding sentences, but which ones? There is a potentially infinite amount of sentences to add and no good way to select them
- Your proposed method handwaves the method of acquiring "many problem challenges that it hasn't seen before", which just moves the problem. Constructing a problem set containing the full range of potential problems to solve is again a Very Hard Problem.
The second problem is not just theoretical either: see https://sitn.hms.harvard.edu/flash/2020/racial-discriminatio... for example, where a facial recognition algorithm performed much worse on non-white women because the training set didn't contain enough pictures of them. AI software is notorious for finding this kind of loophole and for overfitting itself for the training set rather than for the real world.
Very interesting point. I wonder: are subjective experiences knowledge? Not scientific knowledge.
One one side they are part of who we are. On the other side, same as an airplane does not copy a bird 100%, it makes sense that to make a machine "think" we would feed it rational content. That is, content that follows the scientific method.
I suppose that depends on what type of AI you are trying to build. One that is used to help design airplanes can mostly get by with pure scientific knowledge, although it still needs to know about softer phenomena like claustrophobia and personal space to understand that packing in humans as close as physically possible is not desired.
On the other end of the AI spectrum, an AI being trained to be a childrens toy, old people companion, nursing robot or even psychologist would be incredibly deficient if it didn't understand emotions. I don't think using purely content that follows the scientific method would be sufficient for such an AI.
Good points. I especially like the claustrophobia one in planes.
On the other side, there are textbooks about "emotions". I own a Cognitive Behaviour Therapy manual (seemed interesting) that goes step by step through how to conduct a session (it is aimed at therapists or at the reader being their own) as it progresses. As a textbook, similar professional references could be more useful than say reddit or some blogs. Those other sources on the internet will be mostly derived from the reference materials in the field, or from personal anecdotes.
I can imagine textbooks exists on how to deal with people on the spectrum, or for them to recognize social cues, or how symptoms of depression look like.
I am still skeptical. You did not say anything of the sort, but the scientific method is not the opposite of emotions. It shows us what we know of emotions so far. Psychology has the reproducibility scandal but it is our best try.
Even in the case of planes, I would not be surprised if an engineering manual mentions a minimum space for passengers to be comfortable, as part of regulations, or any other data that might as a side-effect solve the claustrophobia issue.
TL;DR. Scientific knowledge is not antithetic to "human" knowledge. Scientific knowledge is what we actually know. Otherwise we have mysticism, or faith.
I'm still skeptical tbh. The scientific method in psychology seems like it could only ever work in a very probabilistic manner since, unlike physical phenomena, humans predict very different to the same stimulus depending on their own unique circumstances.
For example: People react very different to being offered a bacon pizza depending on (at least) the time (not for breakfast thanks), location (funerals are right out), their pizza topping preferences, their faith, whether they've already just eaten, how much they trust the person offering the pizza, if they're currently in a group or not (is there enough for everyone?), whether they've ever had any traumatic experiences with pizza or not, if any other foods may be available, whether they have dinner with other people planned, their current dietary restrictions, whether they're ill right now or not, whether they know you are doing pizza-based experiments on them, etc etc etc etc.
All of these might radically alter the response you get. It might be possible to get enough information about someone to make a reasonable guess, but you can never know if your model is complete enough. Even if the same inputs occur twice, the output might still be different based on something you cannot reasonably know. So I think it would be very difficult to construct a predicable stimulus/response model for individuals. Groups might be easier because the differences sometimes average out, but quite often the internal communication leads to feedback loops that disturb any predictions you were trying to make about their behavior.
Btw there is a third option beyond "scientific knowledge" and "faith", which is simply "we do not know and may never know" without ever getting to fill in that gap. Things like "what, if anything, created the universe" fall in that category as we cannot observe such a thing. Given some information about the position of a particle, we can never be sure of its speed per the Heisenberg uncertainty principle. Accurate models of people could be similar: it might simply be that they cannot be meaningfully reduced to simple formulas.
Thanks for your reasoned answer. I agree with what you are saying. A couple more comments, not to correct anything but simply for conversation.
> Btw there is a third option beyond "scientific knowledge" and "faith"
The three I usually see mentioned are: I know because "argument" (reason/scientific); I believe (faith) and mysticism (I just know/I had a revelation). I agree with you, different ways of not taking a position are positions in themselves. Skepticism, nihilism, etc.
> Accurate models of people could be similar: it might simply be that they cannot be meaningfully reduced to simple formulas.
I agree. All models are false but some are useful.
Now this wouldn't be possible without the high quality synthetic dataset produced by GPT(1B tokens) but this is more evidence in line with Tiny Stories (https://arxiv.org/abs/2305.07759). That is, LLMs only need to be so big (both data and parameters) to learn the total sum of human knowledge (and deal with trash data).