If it's anything like their algorithm that shows me the tweets from a trending, I wouldn't want it.

Where to start with, it’s such a huge project 😳

Twitter is already established as a brand to near saturation and Elon has more money than god. It's the perfect combo for ML philanthropy. Now waiting for that Tesla vision algorithm...

Pretty neat stuff. Fits well with the conversation we were having. I guess a salient question how large an embedding space do you need before performance in any given task plateaus.

Except that they're not random vectors in the original context.

<3 u

I see you've posted a GitHub link to a Jupyter Notebook! GitHub doesn't render large Jupyter Notebooks, so just in case, here is an nbviewer link to the notebook:

https://nbviewer.jupyter.org/url/github.com/kddubey/cappr/blob/main/demos/wsc.ipynb

Want to run the code yourself? Here is a binder link to start your own Jupyter server and try it out!

https://mybinder.org/v2/gh/kddubey/cappr/main?filepath=demos%2Fwsc.ipynb

^(I am a bot.) ^(Feedback) ^(|) ^(GitHub) ^(|) ^(Author)

Yeah I was surprised that this wasn't already coded up--it's been 3 years since we've found out that sampling from GPT-3 is a good zero-shot text classifier.

While benchmarking this method on the infamous Winograd Schema Challenge, I ended up finding a 2018 paper^1 w/ pretty much the same idea as CAPPr. The only difference is that CAPPr typically transposes that probability, and it naively incorporates a prior.

1. Trinh, Trieu H., and Quoc V. Le. “A simple method for commonsense reasoning.” arXiv preprint arXiv:1806.02847 (2018).

In this case, I'm using a fuzzy word "concept" to refer to anything that's differentiable from another thing. That includes things like context and semantics and whether a word is polysemantic and even whether things fit a rhyme scheme. Basically anything observable.

But again, I'm shooting from the hip

Yes, exactly. There's nothing else to it haha

I only wish the API had an interface to let you cache the prompt's keys and values. That'd save you money, and make CAPPr strictly cheaper than sampling for classification tasks.

Yeah, like Elon or not, the push for open source is always going to be beneficial to the community. Ironic how twitter is more open than ____AI.

Is there a current AI dedicated to generate vector graphics from raster images?

We’ve seen plenty of raster image generating AIs such as Dall-E or Stablediffusion, but so far I haven’t seen any AI developed to generate good vectors, either from a raster image input or a text string.The fact that AI also stands for Adobe Illustrator makes researching the existing of such tools quite hard on google.

I could see great use in this, since existing image tracing algorithms often only deliver mediocre results, and also generating vectors from text strings could be of great use.To my limited understanding of machine learning, it should be very doable, since vectors are based on clear mathematical paths, easy to build on for the algorithms.

:O

I think you should spend more time talking because you've lost me and I don't know what we're talking about. My point has nothing to do with this. Is this a new conversation?

Great question! I have no idea lol.

More seriously, it depends on what you mean by "compare". CAPPr w/ powerful GPT-3+ models is likely gonna be more accurate. But you need to pay to hit OpenAI endpoints, so it's not a fair comparison IMO.

If you can't pay to hit OpenAI endpoints, then a fairer comparison would be CAPPr + GPT-2—specifically, the smallest one in HuggingFace, or whatever's closest in inference speed to something like bart-large-mnli. But then another issue which pops up is that GPT-2 was not explicitly trained on the NLI/MNLI task in the same way bart-large-mnli was. So I'd need to finetune GPT-2 (small) on MNLI to make a fairer comparison.

If I had a bunch of compute and time, I'd like to benchmark (or find benchmarks) for the following text classification approaches, varying the amount of training data if feasible, and ideally on tasks which are more realistic than SuperGLUE:

• similarity embeddings
  • S-BERT
  • GPT-3+ (they claim their ada model is quite good)
• sampling
• MNLI-trained models
• CAPPr

If you assumed the classes are exactly one-token long and equally common, then you could use the probability distribution $P(x_i|1:x_{i-1})$, exactly as returned by GPT APIs. Is that correct? And the rest of your work is to account for those two assumptions not being true?

Good human.

Good bot

Yes the span, so if we discover that a set of points is actually all in the span of a line, that line is a kind of fact or pattern about the points. So probably there is an equivalent in higher dimensions. I am seeing there is a problem whereby we introduce our own bias in creating our model.

Putting aside the political undertones behind many peoples' desire to publish "the algorithm", this is a phenomenal piece of educational content for ML professionals.

Here we have a world-class complex recommendation & ranking system laid bare for all to read into, and develop upon. This is a veritable gold mine of an an educational resource.

Nice ty!

See my question on CrossValidated which fully explains the method. You can just skip to the Example section in there :-)

I also did a cool little computational optimization for HuggingFace models. That way, there isn't repeated computation for the prompt.

Wonder whether they included the Elon+1000 and Can'tBlockHim mods in this version?

I have 0 experience with machine learning but looking to solve a problem I have and wondering if ML might not be the solution. Looking for some guidance on tools and how to get started on the project as quickly as possible. I work in agriculture and some portion of my time is reviewing pesticide labels for certain attributes. I have tried different document parsing platforms but the labels between manufacturers are all slightly different so structure has been hard to nail down. The other issue is I am specifically looking for certain key words in these documents as my company sells products that can be paired with pesticides to make them work better. I am hoping to build a workflow where I could drop a PDF into a folder have software spit out some sort of structure surrounding ingredients and instructions while flagging the keywords. I am decently proficient in no-code platforms if one such exists for my problem. Thanks in advance for any guidance. If this is the wrong subreddit for this I also apologize.

I'm already getting a little frustrated by how many things promoted as open source use openai. I get that there's some wiggle room with terminology. But it's often on the level of just having a shell script built on top of a binary and calling it open source because you can edit the launcher.

I'm absolutely fine with openai doing its thing. I'm grateful for it in fact. But I really hate how much it's muddying the waters.

Not at all made up in my opinion! There just doesn't seem to be any consensual framework for the moment, and diverse people are scrambling to put relevant concepts together and often disagree on what makes sense. It's particularly hard for ai alignment because it requires you to define what are the dangers you want to speak of, and so to have a model of an open environment in which the agent is supposed to operate which currently we do not have any notion nor example of. This makes examples that people in ai alignment brought up very speculative and poorly grounded which allows for easy critic. I'm curious though if you have interesting research examples in mind!