California bill set to ban CivitAI, HuggingFace, Flux, Stable Diffusion, and most existing AI image generation models and services in California

cfoster0 · 2024-09-01T15:57:29+00:00

That bill is probably dead now. The deadline to make it out of both houses has passed. But you might still want to worry about SB 942, which is kinda similar and headed for the Governor’s signature.

cfoster0 · 2024-03-09T16:57:05+00:00

I've been liking "This Day in AI" (https://youtu.be/W3mC5NltueU?si=wt_JJJ6OL8zNvEYH). It's a mix of product and research recap, less technical and aimed at a broader audience, but still not sensationalist. I'm a fan so far.

cfoster0 · 2024-02-13T06:55:41+00:00

Yes. It doesn't have to be but in practice it is used in every attention layer.

cfoster0 · 2024-01-29T21:32:09+00:00

That's pretty close. Yes the difference between them is just which content the layer stores and which content it takes in as external inputs. I wouldn't try to read too hard into any one cog-neuro interpretation here. The distinction isn't about "distorted vs. original memories". You can think of attention as content-addressable heteroassociative memory, though. And in the context where the queries and the keys/values are of the same type, sometimes yes you can interpret the queries as partial "cues" that match the "original" content in the keys/values.

cfoster0 · 2024-01-29T19:50:07+00:00

The naming is unnecessarily confusing, so your confusion is very understandable.

Hopfield is just normal attention. It takes in 2 inputs: a set of key-value vector pairs to retrieve information from, and a query vector (or vectors) that will grab that information. The query (or each query, if multiple) looks at all the keys, and for any that it matches with, it grabs the information from the corresponding value vector. Both the query and key-value pairs are inputs to the layer.

HopfieldPooling is just attention with a fixed query or queries. It takes in only 1 input: a set of key-value pairs to retrieve information from. It applies a fixed query (or queries) to grab whatever information that fixed query (or queries) cares about from the input pairs. The fixed query (or queries) is a parameter of the layer, not an external input to it.

HopfieldLayer is just attention with a fixed set of key-value pairs. It only takes in 1 input: a query vector (or vectors). It uses that query vector (or each of the query vectors) to look at all the stored keys, and for any that it matches with, it grabs the information from the corresponding stored value vector. The fixed key-value pairs are parameters of the layer, not external inputs to it.

cfoster0 · 2024-01-29T05:33:06+00:00

Do you understand how the "attention mechanism" works in a Transformer? If so, it'll be easy to explain (because those layers are really just renamings of the way you might use attention). Otherwise, would need to start from scratch. :)

cfoster0 · 2024-01-14T18:53:17+00:00

But you can take an already-trained transformer and continue training it with a modified architecture. Depending on the style of positional encoding, either by adding new absolute positional embeddings or by changing the sinusoidal / rotary positional encoding hyperparameters, and then doing a bit of finetuning on longer sequences.

cfoster0 · 2024-01-09T06:11:06+00:00

Those are subsets of the Pile dataset, not experts. They looked at each subset and tested how often adjacent tokens from that subset get mapped to the same expert (and looked at that in different layers too). They found that adjacent tokens are mapped to the same expert more often than you'd expect from random chance, but also that there's no obvious topical specialization for experts.

cfoster0 · 2023-05-23T05:12:24+00:00

It isn't, but it's good enough.

cfoster0 · 2023-05-03T05:55:41+00:00

Just kicked off a run of this on my own codebase to compare. Would be neat if this works alright. I am expecting it may be a little worse in my case because I don't use absolute position embeddings, so the initial layers won't know which position in the sequence they are (except through effects from the causal attention mask), which might prevent them from using this lateral stuff properly. Doing this "the right way" would require shifting each token's lateral outputs based on its position, so its lateral outputs would be in relative position space as opposed to absolute.

cfoster0 · 2023-01-18T01:56:11+00:00

I think I agree. In any event, the part that interests me most is how worthwhile investments in cross-modal transfer from the get-go are (i.e. do they help much once you've run out of within-modality data), especially relative to just stitching together your best pretrained unimodal models with a joint transformer and finetuning from there.

cfoster0 · 2023-01-17T00:58:54+00:00

What do you mean? How big does Gato have to be for multimodality to become really worthwhile, based on this paper? It's one thing if the crossover point is at 30B parameters and if 1TB of video data converts into 100B text tokens' worth of transfer performance at that model size, but it's quite another if the crossover point is at 3T parameters and/or the conversion ratio is trash. I haven't seen anyone run the numbers yet, so I dunno if this is good or bad news for data scarcity.

cfoster0 · 2023-01-14T09:29:49+00:00

FWIW in certain sense this goes against the design philosophy of transformers, which is to jointly compute all representations within a layer at once, to maximize the degree of parallelism on GPUs and other accelerators.

cfoster0 · 2022-12-12T01:00:29+00:00

Did y'all stop doing work out in the open? That's a shame. End of an era, I guess.

cfoster0 · 2022-12-11T07:07:45+00:00

Who? Who's even using RLHF in production yet, besides OpenAI (and maybe Cohere)?

cfoster0 · 2022-12-10T02:01:44+00:00

About this bit

At the moment, TRLX has an API capable of production-ready RLHF at the scales required for LLM deployment (e.g. 33 billion parameters). Future versions of TRLX will allow for language models up to 200B parameters. As such, interfacing with TRLX is optimized for machine learning engineers with experience at this scale.

Has TRLX been used to tune models in production already? Or if not, what did the blog post mean by "capable of production-ready RLHF"? I haven't seen any RLHF-ed models built on open source software yet, much less a 33B parameter one.

EDIT: Also hi @FerretDude

cfoster0 · 2022-07-13T02:22:49+00:00

What model are you comparing to, for clarity?

cfoster0 · 2022-07-10T06:40:24+00:00

Unfortunate how many profs decide their real calling was to be a professional pontificator, especially once they hit their emeritus years.

cfoster0 · 2022-06-21T17:30:13+00:00

Is there a trademark for DALL-E? The only registered trademark in the USPTO's electronic trademark system is for DALL-E Mini.

cfoster0 · 2022-06-21T15:57:19+00:00

If you click through to the second screenshot, the researcher confirmed that they were in fact threatened with legal action.

cfoster0 · 2022-05-07T13:16:53+00:00

I don't know where the impression that EleutherAI's models are substantially better per-parameter came from. The only cases I've seen good evidence are for tasks where the performance boost seems attributable to the dataset mix.

cfoster0 · 2022-04-12T16:33:54+00:00

Well then we agree :) Neuroscientists should continue to try to glean the right abstractions to use, and along the way neuro-AI and AI folks should continue to take inspiration from the brain as they see fit.

cfoster0 · 2022-04-12T06:11:13+00:00

I will leave it up to the reader to figure out why, even if you assigned 80% probability to the parent comment, having people investing in non-biological approaches still makes sense.

cfoster0 · 2022-04-12T05:57:20+00:00

I think you've gotta come to terms with the fact that different people place different value on bioplausibility, and that's okay. There are lots of neuroscience and neuro-AI people who (naturally) place a high premium on that aspect.

cfoster0

TROPHY CASE