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> 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

> 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 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.

wincy 4y ago
Okay for image models I think humans could help a lot more than we give them credit for. We can read and parse images WAY faster than you might think. 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.
visarga 4y ago
> They are processed by other models, 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. 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.

wincy 4y ago

Okay for image models I think humans could help a lot more than we give them credit for. We can read and parse images WAY faster than you might think. 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.

visarga 4y ago

> They are processed by other models, 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. 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.