gpt-2 VS stylegan2-pytorch

Did you arrive at this certainty through reading something other than what OpenAI has published? The document [0] that describes the training data for GPT-2 makes this assertion hilarious to me.

[0]: https://github.com/openai/gpt-2/blob/master/model_card.md#da...

2.) OpenAI's GPT-2: OpenAI released GPT-2 in 2019 and later made the code and pre-trained models available to the public. GPT-2 is not as advanced as GPT-3 or GPT-4, but it's still powerful and can be used for various natural language processing tasks. You can find the GPT-2 code and pre-trained models on GitHub: https://github.com/openai/gpt-2

The Product is not the only implementation / available source for this technology / service / "thing". Given the amount of other LLMs that are available as trained models, OpenAI does not control access to this type of technology, in fact they OpenSourced their Previous model (https://github.com/openai/gpt-2). T5 (https://huggingface.co/docs/transformers/model_doc/t5) and Bloom (https://huggingface.co/docs/transformers/model_doc/bloom) are well known Generators with very comparable complexity to GTP-3. The fact is that OpenAI has the most reliable implementation, but far from the only implementation.

You can try running GPT 2, I had some fun playing around with it, but it struggles with context. GPT 3 and now 3.5 blows it way out the water, but even if the model was available, you would need at least 350 GB of VRAM to run it. So unfortunately, for now it's out of reach.

The code for GPT-2 is here on github: https://github.com/openai/gpt-2

OpenAI attempted this with GPT-2’s incremental release strategy and was lambasted on moral and ethical grounds as well.

https://openai.com/blog/better-language-models/

So individuals working on a technology like this have no clear societal consensus to use as a guide when making the decision. Strong arguments can be made in both directions.

export default function ChannelListBottomBar(): JSX.Element { const { client } = useChatContext(); const [micActive, setMicActive] = useState(false); const [audioActive, setAudioActive] = useState(false); return (

{client.user?.image && (

div> )}

{client.user?.name} span> {client.user?.online ? 'Online' : 'Offline'} span> p> button>

setMicActive((currentValue) => !currentValue)} > button> setAudioActive((currentValue) => !currentValue)} > button> button> div> ); }

...then they just use the app on generated images of not real people (potentially based on specific inputs to remind you of a real person).

At lest, a way to complete the AI-generated cycle that https://thispersondoesnotexist.com/ started.

Thispersondoesnotexist.com - It's what the title says. It's terrifying but whenever you refrest the page, it shows you a new "person" who doesn't exist.

This X Does Not Exist is a list of gens for People, Cities, Words, Lyrics and other items that have been created by GANs (generative adversarial networks).

thispersondoesnotexist.com for making profile pics when you want to be anonymous

Nvidia is the one who made GANs popular. All this buzzword that people say about Generative AI, it was popularised by Nvidia with their StyleGAN paper. You must have seen or hear about thispersondoesnotexist.com? That's Nvidia.

GANs on the other hand uses a generator and discriminator model, where the generator learns to generate images that are "good enough" to fool the discriminator. It may make it easier to make discriminators that are better at picking out visual artefacts that stand out for humans, (it was easy to pick out "fake humans" from the early versions of thispersondoesnotexist.com by looking at their eyes and teeth, they tend to be pointing in different directions, or their teeth are too irregular before the discriminator got better at picking those out) but they can suffer from mode collapse, and generate images with very little variety.

Generative networks like StyleGAN