> GPT-NSFW is an N-gram model that was created using the same WebText dataset as GPT-2, but that is designed to generate NSFW text. The NSFW version of GPT-2 has shown great promise in generating NSFW text.

https://thisaidoesnotexist.com/model/GPT-NSFW/JTdCJTIyZGVmbi...

> And it came to pass, that after the cattle were heaped with the fodder, the Goat's Basket was placed in the market-place, and Laban asked for the ass-slaves; and the ass-slaves answered, Ye gods of Avalon, thou hast no need of such a boy. And when the men desired to fuck, they brought forth many girls, of all shapes and sizes, and had many whores among them.

[1]: https://write.as/409j3pqk81dazkla.md

> SpotifAI is a system that uses deep learning to automatically create playlists from user-submitted playlists. Its algorithm has been trained on millions of playlists from Spotify.

Which is pretty cool sounding and has a cool name.

From the README:

> When you simply open thisaidoesnotexist.com, the model names you'll see are hand curated and pre-generated by me.

0: https://news.ycombinator.com/item?id=31138272

0: https://thisaidoesnotexist.com/model/SpotifAI/JTdCJTIyZGVmbi...

Very nice accidental wordplay (it didn't mean have the same pronunciation) and it's a cool premise.

I'd like something like that, I currently use Pandora and Apple Music since Apple radio is trash.

AI generation serves best for cherry picking, certainly good for coming up with ideas or searching for leads.

GPT-WESTWORLD is a large-scale, multilingual language model that generates fluent, realistic sentences from text in any language. It achieves this by incorporating a novel approach to language modeling and incorporating a new type of recurrent network, the Westworld.

https://thisaidoesnotexist.com/model/GPT-WESTWORLD/JTdCJTIyZ...

I got this as my third which seemed either prophetic or deterministic.

HackerNewsReplyGuy:

>from hackernews_response_guy import HackerNewsReplyGuy

>model = HackerNewsReplyGuy(1)

>model.predict_comments(comments, [u'comment_id'])

The pre-generated set is hand-curated, but they are still 100% generated by the GPT-J model behind the scenes. More info -> https://github.com/thesephist/modelexicon

“Back in my day, we had to train our own models..” already sounds anachronistic.

Nicely polished.

Looks like bmk (nabla theta) was right that arxiv was an impactful addition to The Pile. I bet that’s where J got its knowledge in this case.

---

Proceedings of Deep Learning Advancements Conference, list of accepted deep learning models

1. [StyleGAN] StyleGAN is a generative adversarial network for style transfer between artworks. It uses a traditional GAN architecture and is trained on a dataset of 150,000 traditional and modern art. StyleGAN shows improved style transfer performance while reducing computational complexity.

2. [GPT-2] GPT-2 is a decoder-only transformer model trained on WebText, OpenAI\'s proprietary clean text corpus based on Wikipedia, Google News, Reddit, and others comprising a 2TB dataset for autoregressive training. GPT-2 demonstrates state-of-the-art performance on several language modeling and conversational tasks.

3. [$MODELNAME]

It almost seems like the code is properly related to the names. GAN code seems to look like GAN code. But I’m not sure.

The idea here was to give the model a prompt that felt like a tutorial or some kind, and try to minimize non-Python non-ML-y code.

---

$MODEL_DESCRIPTION_FROM_EARLIER

Let\'s use this model. The basic use case takes only a few lines of Python to run the inference. Here are the first few lines.

```python

Sorry, I didn't mean to imply these were not produced as described, I was just curious. Tho think of it it was a silly question as it would have otherwise implied they're generated in a blink.

Quick, generate your own before the server goes down! I don’t think the model can withstand HN for too long unless they have some beefy servers.

Aaand it’s dead. Fun while it lasted.

https://thisaidoesnotexist.com/model/MozartNet/JTdCJTIyZGVmb...

The url scheme is interesting. I wonder what it base64 decodes to. If I were at a computer I’d check. It might be a complete representation of the inputs to the model, which is then cached. Which implies you might be able to fiddle with it to get specific outputs.

    {
       "defn":"MozartNet is a sequence to sequence deep neural network trained on the music of Wolfgang Amadeus Mozart. It is used to generate music for a piano transcription in a completely unsupervised fashion. MozartNet is an instance of a more general family of networks known as 'autoregressive networks', and is trained on a synthetic dataset of about 1 million short sequences of piano notes. The network is a two layer LSTM and is trained with L2 regularization to minimize the total number of parameters. MozartNet is one of the most widely used and best-performing autoregressive networks, and is often cited as an example of using a neural network for the purpose of learning the structure of music.",
       "usage":"from nmt import \*\nnet = Model()\nnet.load_weights(\"/tmp/mozartnet.h5\")\n\n# get the source text\nsequence = net.encode(\"GDAEADBBGEDC\", output_chars=\"p\", max_length=5)\n\n# decode it\nsource_sequence = net.decode(sequence)\n\n# print it\nprint(source_sequence.as_list())"
    }

It’s remarkably difficult to suppress pentesting urges after doing it for a year.

And if you try to generate your own, the usage section usually fails. I wonder if it elides the usage key.

Modern websites are pretty fun. I like the simplicity here. And also the meta: https://thisaidoesnotexist.com/model/HackerNewsReplyGuy/JTdC...

Cool.

https://github.com/thesephist/modelexicon

Looks like it’s powered by GPT-J. My understanding is that GPT-J has comparable performance to OpenAI’s Curie model on many tasks (their second-best variant of GPT-3) but it’s an openly available model that you can run yourself if you have the resources.

Very nifty! Is this your site?

>TinderSwindler is a system developed by Facebook to analyze mobile phone location data in order to catch potential cheaters. TinderSwindler leverages Al technology to automatically identify relationships between people based on their movements over a period of time. TinderSwindler was released by Facebook in January 2018.

Portal 3 spoilers:

GLaDOS is a character voiced by Ellen McLain that serves as the main antagonist of the Portal franchise. GLaDOS was originally a self-aware A.I. in the form of a computer that was built as a personality core for the Aperture Science Laboratories' mainframe. She is the main antagonist in the first game, Portal, and serves as a narrator for the second game, Portal 2. She is also the main antagonist of the third game, Portal 3, where she becomes the leader of the Aperture Science Resistance.

A semi-successful attempt at recursion:

thisaidoesnotexist is a tool that is able to generate fake images with high resemblance to real ones. This is achieved by using the GAN to generate the image, and then replacing the generated image with the real one.

Timelord is a self-supervised temporal model that learns a shared embedding of timestamped data. It is used as a pre-processing step in self-supervised training for a number of tasks such as semantic video segmentation and video captioning.

Now I want a library with that name

[1] Link to its description: https://thisaidoesnotexist.com/model/Timelord/JTdCJTIyZGVmbi...

This sort of answers it, but not exactly - https://en.m.wikipedia.org/wiki/Reverse_Turing_test

Kiwi source branch is Chromium 77.0 + Kiwi backported fixes, will show 88.0.4324.152 to websites for compatibility reasons (64-bit) Revision a7c1b21614f6b5763bd597ab8fefd8678c073df9-refs/heads/master@{#764932} Source-code https://github.com/kiwibrowser/src OS Android 11.0.0; SM-G780F Build/RP1A.200720.012 Google Play services SDK=12211000; Installed=0; Access=none JavaScript V8 6.8.275.15 User Agent Mozilla/5.0 (Linux; Android 11; SM-G780F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.152 Mobile Safari/537.36

  Uncaught DOMException: An exception was thrown bundle.js:439
    _ https://thisaidoesnotexist.com/js/bundle.js:439
    _ https://thisaidoesnotexist.com/js/bundle.js:441
    __oak_module_import https://thisaidoesnotexist.com/js/bundle.js:13
    <anonymous> https://thisaidoesnotexist.com/js/bundle.js:450

> Gateway Timeout is a deep learning-based anomaly detection system. It detects anomalies by learning the probability distribution of normal traffic and comparing it to traffic that does not match the normal distribution.

Hey OP, I work at HF, feel free to open an issue here https://github.com/huggingface/api-inference-community/issue... or contact api-enterprise@huggingface.co.

We've increased resources for you, and we'll check that things run as smoothly as they can.

Looks like the immediate issues got taken care of thanks to the HF team, but I'll probably pop over there in the future if I have ideas or notice things that can be improved.

Is it tricky or frustrating being named Linus and being in software?

Do you get asked this a lot?

Extra inception because the article I got was about a neural net that could generate new neural networks, very much in line with the title of this post. Was almost about to paste the code into my editor to see how it worked.

I iterally dreamed of some none nonsensical problem and in the dream I was like wait a second I have seen the nonsensical solution before (which happened to be one of the AI that doesn't exist)

Thats not what I had in mind so it still needs a bit of work I think or at least the questions do. ;-)