211–220 of 275 posts
I have noticed it makes me get bored of reading content, and I start to skim through it assuming it's just AI generated waffle.
> Published content will be later used to train subsequent models, and being able to distinguish AI from human input may be very valuable going forward I find this to be a particularly interesting problem in this whole debacle. Could we end up having AI quality trend downwards due to AI ingesting its own old outputs and reinforcing bad habits? I think it's a particular risk for text generation. I've already run into…
> Could we end up having AI quality trend downwards due to AI ingesting its own old outputs and reinforcing bad habits? I think it's a particular risk for text generation. This is exactly what I don't like about Copilot, maybe even more than the IP ethics of it. If it really succeeds, it's going to have a feedback loop that amplifies its own code suggestions. The same boilerplate-ish kind of code that developers gene…
> Published content will be later used to train subsequent models, and being able to distinguish AI from human input may be very valuable going forward I find this to be a particularly interesting problem in this whole debacle. Could we end up having AI quality trend downwards due to AI ingesting its own old outputs and reinforcing bad habits? I think it's a particular risk for text generation. I've already run into…
I just had my eyes opened reading that, because humans also do exactly that, inadvertently.
Earlier quoted context omitted.
As the scale of input data goes up linearly, the scale of commonly observed input patterns goes up logarithmically. If we bumped the scale up an order of magnitude in terms of common input tokens, that still means we could annotate the important part of a 150TB text corpus for 125B worth of human annotation. Given that could break the budget of even large corporations, realistically we'd probably train a model to pre…
How do you figure? Let's say a commonly observed input pattern comprises 1% of training data. For a data set of size N, 0.01 * N examples will contain the pattern. If we increase the size to 2N, 0.01 * 2 * N examples will contain the pattern. Why is the growth logarithmic?
Of course, this is only true if new data is distinct from old data. If you just copied your data set 10x and pretended it was a 10x larger data set, it would behave like you expect.
Earlier quoted context omitted.
My answer was mildly tongue in cheek, and I see where you’re going. On the other hand, one of the other posters asked “to generate a parallax effect in Qt/QML. It simply used a QML Elemened with the name Parallax”. Is this an insight, or is this answering “yes, I could” to “could you pass me the salt?”. Maybe the line between the two is a fine one, and I didn’t realize that yet. In general, it feels like copying part…
Saw that, and yea, that's totally a fall-back cop-out type answer. I was pretty much just questioning everyone dismissing the whole category of answers like the R script one. To me there does seem to be some nuance to here that's worth noticing. Some examples of this type of response are indeed too cheap and can be chalked up to lack of training data or something. But in other cases it's actually not immediately obvi…
> A language model can't reasonably be expected to understand an expectation of usability or fitness for purpose in a context the user didn't specify.
I agree, but I think we’re at the same time expecting the LM to “understand” a lot more
> Published content will be later used to train subsequent models, and being able to distinguish AI from human input may be very valuable going forward I find this to be a particularly interesting problem in this whole debacle. Could we end up having AI quality trend downwards due to AI ingesting its own old outputs and reinforcing bad habits? I think it's a particular risk for text generation. I've already run into…
No, because models are already trained like that. Datasets for large models are too vast for a human to even know what's inside, let alone label them manually. So instead they are processed (labeled, cropped, etc) by other models, with humans overseeing and tweaking the process. Often it's a chain with several models training each other, bootstrapped from whatever manual data you have, and curated by humans in key points of the process.
So it's actually the opposite - the hybrid bootstrapping approach that combines human curation and ML labeling of bulk low-quality data typically delivers far better results than training on a small but 100% manual dataset.
> Published content will be later used to train subsequent models, and being able to distinguish AI from human input may be very valuable going forward I find this to be a particularly interesting problem in this whole debacle. Could we end up having AI quality trend downwards due to AI ingesting its own old outputs and reinforcing bad habits? I think it's a particular risk for text generation. I've already run into…
> Could we end up having AI quality trend downwards due to AI ingesting its own old outputs and reinforcing bad habits? No, because models are already trained like that. Datasets for large models are too vast for a human to even know what's inside , let alone label them manually. So instead they are processed (labeled, cropped, etc) by other models, with humans overseeing and tweaking the process. Often it's a chain…
What if we just crowdsource and have a new Folding@home protein thing but this time it’s for classifying data sets? LAION-5B has 5 billion image text pairs, if we got 10,000 people together that’d just be… 100,000 per person which would take… awhile but not forever. Humans can notice discrepancies super quickly. Like a slide show display the image and the text pair at a speed set by the user, and pause and tweak ones that are outright wrong or shitty.
Boom, refined image set.
Maybe? I’m looking at the LAION-5B example sets on their website and it seems to literally be this simple. A lot of the images seemed pretty poorly tagged. You get a gigantic manually tagged data set, at least for image classification.
> Published content will be later used to train subsequent models, and being able to distinguish AI from human input may be very valuable going forward I find this to be a particularly interesting problem in this whole debacle. Could we end up having AI quality trend downwards due to AI ingesting its own old outputs and reinforcing bad habits? I think it's a particular risk for text generation. I've already run into…
> Could we end up having AI quality trend downwards due to AI ingesting its own old outputs and reinforcing bad habits? No, because models are already trained like that. Datasets for large models are too vast for a human to even know what's inside , let alone label them manually. So instead they are processed (labeled, cropped, etc) by other models, with humans overseeing and tweaking the process. Often it's a chain…
A great description of what actually happens when you deal with massive datasets. One way to inspect a large dataset is to cluster it, and then look at just a few samples from each cluster, to get an overview.
Earlier quoted context omitted.
> So now you need something like ChatGPT to cut through the noise? I once employed a journalist to write about the pros and cons of wedding insurance. Just to give you a clue how long ago this was, it was a unique article at the time. Many years years later, every article you will read about wedding insurance (there will be many thousands) is around 90% similar in style and content to the one I paid for. I dare say y…
My guess is that ChatGPT is going to solve the SEO spam problem by changing the way we search for things. Instead of searching for webpages that have information about a topic, we're going to ask an AI. It'll tell you what the pros and cons of wedding insurance are, and because eventually it'll have access to your calendar, it'll tailor that answers to the specifics of the fact that you're having a destination weddin…
Earlier quoted context omitted.
I don't think AI has to be low-quality for GP's concern to be valid. Humans get inputs from a large variety of sources, but if an AI's input is just text, then there's the potential for AI's input to mostly consist of its prior output. Iterate this, and its model could gradually diverge from the real world. The equivalent in human society is groupthink, where members of a subgroup get most of their information from t…
Kinda? AI is often limited in ways we aren't, but it also trivially consumes more than we can in a lifetime. "Just text" in the case of GPT-3, but also it is trained on a token count exceeding the number of times an average synapse in a human brain will fire in a lifetime. It can still get biases from the training set; while I'm not sure if "group think" is quite the right phrase, it does seem to "want" everyone to g…
Humans are more grounded by having a presence in the physical world. Plus they draw on various sources considered more reliable, like formal training, scientific papers, textbooks, quality journalism, etc. If we want AI to be reliable, we'll need it to put the most weight on similar sources, and maybe even have some real-world presence with sensors and robots.
Eventually AI will be able to produce new reliable information itself. But for that, it would have to recognize factual inconsistencies between sources and logical inconsistencies in arguments, and figure out how to resolve those, and do math correctly. I don't know what the state of the art is here, though ChatGPT tends to fail at basic arithmetic.