cross-posted from: https://programming.dev/post/14979173

• neural network is trained with deep Q-learning in its own training environment
• controls the game with twinject

demonstration video of the neural network playing Touhou (Imperishable Night):

it actually makes progress up to the stage boss which is fairly impressive. it performs okay in its training environment but performs poorly in an existing bullet hell game and makes a lot of mistakes.

let me know your thoughts and any questions you have!

cross-posted from: https://lemmy.dbzer0.com/post/21273521

Well, looks like we’re done here. Lemmy, Reddit, Facebook and Twitter can pack it in because no we can have AI handle social media for us.

Guess it’s finally time to go outside, then.

I created this video after being spammed by artist groups claiming that Generative AI uses copied samples to create work.

This guy built his own HAL 9000.

I just told it to

Make a cheatsheet with two columns: on the left, Markdown syntax; on the right, the ReStructured Text equivalent.

and it started well but after a minute it seemed to enter a loop outputting the same thing over and over again.

Is this something one should report, or is it something that sometimes happens with ai chats?

We all know about the thousands upon thousands of text generation models the hundreds of image generation models.

But what about something less common? Something specialized? Something that hasnt been copied and pasted to hell and back?

sunoai for me is an example of something different, i have never seen music generation before.

Hi all! What do you think is the best chatbot to use for suggestion/learning vvvv, TouchDesigner and similar software? I will be using it also to check/suggestion on JavaScript/Python scripts. One where I can upload a file and let it analyze it would be very nice.

About a year ago I created the notebook "Generating Images of Variable Size with StyleGAN2" to try to combine a variety of different GAN algorithms and papers into a cohesive Kaggle notebook. However Tensorflow syntax has changed a lot since then, and if you were to run the notebook with version 2.15.0 it wouldn't work, so I updated the code to run properly with the new standard.

Description:

One attractive property of diffusion models is the fact that we can generate images of different sizes by simply giving the model some noise of arbitrary size. However it shouldn't be too difficult to get a similar result with a GAN by simply giving the generator a latent noise vector of variable size. I'll be using a generator roughly based on StyleGAN2, a projected discriminator with differentiable augmentation and zero-centered gradient penalty, along with a relativistic GAN loss.

Although they have a working prototype, I wouldn’t doubt that some of this video is CGI. Regardless, ChatGPT already talks and functions like this today, so we’re probably a lot closer to this reality than we think.