I think hallucination is a serious concern in some fields but for general business-y creative work it's going to be a game changer. Just look at Jasper - a $100M series A.

EDIT: This corresponds to GPT-4 more than ChatGPT

It looks like, rather than conditioning on text they condition on the fMRI, but it's unclear to me exactly how they map between the two and why this would even work without finetuning. TBH I haven't had time to read the paper so I don't know the details, but figured I'd drop the paper in case anyone was interested!

I don't think anyone believes it will be LLMs that undergo an intelligence explosion, but they could certainly be a piece of the puzzle. Look at how much progress has been made in just the past 10 years alone - imo it's not unreasonable to think that the alignment problem will be a serious concern in the next 30 years or so.

In the short term, though, I agree that people doing bad things with AI is much more likely than an intelligence explosion.

Whatever anyone's opinion, I think the fact that the opinions of very smart and knowledgeable people run the gamut is a testament to the fact that we need to dedicate serious resources into ethical AI beyond the disclaimers at the end of every paper that models may contain biases.

Until the tools start exhibiting behavior that you didn't predict and in ways that you have no control over. Not taking an opinion on which side is "right", just saying that this is a false equivalence with respect to the arguments that are being made.

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EDIT: Typo

Agreed! I mean even if the proper resources were dumped into creating such a large detector it could be come quickly obsolete because of adversarial training (AFAIK, not an expert on adv. training)

Adversarial training will be a huge factor regarding detection models imo

Authors publish papers on research, experiments, findings, etc. They do not always release the code for the models they are studying.

The lucidrains' repos implement the models, creating an open-source implementation for the research

The next step would then be to train the model, which requires a lot more than just the code (most notably, money). I assume you're referring to these trained weights when you say "the needed AI model". Training would require a huge amount of time and money for a team, never mind a single person, to train even one of these models let alone a whole portfolio of them

For this reason, it's not very reasonable to expect lucidrains or any other person to train these models - the open-source implementations are a great contribution on their own!

Got it, thanks for the explanation!

I thought distributed training over the internet was prohibitively slow due to communication overhead - wouldn't you run into the same issue when fine-tuning? If anyone could ELI5 why/how this works that would be awesome!

Agreed - the last three you listed were the first ones that came to mind for me

The mathematician in me will never let SVMs die!

My pleasure :) I'm sure v2 will be awesome in the long run but I think it makes sense to stick with v1 in the short term (although I haven't seen 2.1's performance)

My pleasure!

This area moves quite fast doesn't it 🥲😂

One of my first thoughts as well! Is there any reason PT's speed ceiling would be lower than JAX's? Ik PyTorch-XLA is a thing but not sure about its current status

Never do work that someone else has already done unless they can't do it as well as you and that discrepancy matters

It's been a while since I looked at tsne and umap but the assumption for PCA is that the data lives near an affine subspace and for VAE that the data is well modeled by the distribution whose parameters you are finding. My thoughts but I'm sure there's other considerations that I'd love to hear other people chime in with!

Exactly - if we want robust systems that interact with our lives with any sort of weight (e.g. autonomous vehicles), then we need to know about weird failure modes, how to address them, and, perhaps most importantly, how to find them

Some might say the coolest ;)

My pleasure!

Just to add - PFGMs are best in class for flow models. They perform comparably to GANs on the datasets used in the paper, which is pretty exciting.

To generate data, you need to know the probability distribution of a dataset. This is in general unknown. The method called "normalizing flows" starts with a simple distribution that we do know exactly, and learns how to turn the simple distribution into the data distribution through a series of transformations. If we know these transformations, then we can generate data from the data distribution by sampling from the simple distribution and passing it through the transformations.

Normalizing flows are a general approach to generative AI - how to actually learn the transformations and what they look like depends on the particular method. With PFGMs, the authors find that the laws of physics define these transformations. If we start with a simple distribution, we can transform it into the data distribution by imagining the data points are electrons and moving them according to the electric field they generate.

👋 hello friend!

I'm not sure how the curse of dimensionality would affect PFGMs relative to Diffusion Models, but at the very least PFGMs could be dropped in as the base model in Imagen while diffusion models are kept for the super resolution chain! More info on that here or more info on Imagen here (or how to build your own Imagen here ;) ).

Note that PFGMs are not text-conditioned yet! There's still work to be done there :)

I don't think the paper explicitly says anything about this, but I would expect them to be similar. If anything I would imagine they would require less memory, but not more. That having been said, if you're thinking of e.g. DALL-E 2 or Stable Diffusion, those models also have other parts that PFGMs don't (like text encoding networks), so it is completely fair that they are larger!