Lol even if it were real, do you think OpenAI/Microsoft/Google etc are suddenly going to pause all research because a few people complain about it? Far too much has been invested in it at this point to stop now

Thank you! :) Early stages right now, just finished the literature review section and am starting implementation, I'm going to try and publish it somewhere when it's done if I can get permission from my university. I'm definitely going to see what I can do with stable diffusion once it's done, would love to get it running on the smallest device possible

Doing my undergrad thesis on this exact topic :) with most models, you can discard up to 90% of their weights and have a similar performance with only about 1-2% loss of accuracy. Turns out that when training models they tend to learn better when dense (i.e a large quantity of non-zero weights), but in implementation they tend to have some very strong weights, but a large number of "weak" weights that contribute to the majority of the parameter count but very little to the actual accuracy of the model, so you can basically just discard them. There are also a few other clever tricks you can do to reduce the number of params by a lot; for one, you can cluster weights into groups and then make hardware-based accelerators to carry out the transformation for each cluster, rather than treating each individual weight as a multiplication operation. This paper shows that you can reduce the size of a CNN-based architecture by up to 95x with almost no loss of accuracy.

Of course this relies on the weights being public, so we can't apply this method to something like ChatGPT, but we can with stable diffusion. I am planning on doing this when I finish my current project, although I would be surprised if the big names in AI weren't aware of these methods, so it's possible that the weights have already been pruned (although looking specifically at stable diffusion, I don't think they have been).

Drones are great but not exactly stable. There are just too many random variables to predict for flying cars to ever be safer or more efficient than travelling across the ground. There's not really any advantage to doing so and there's all kinds of problems it could cause

I'm not so sure, I don't think they make any sense energy-wise and would be less safe than ground vehicles. The amount of energy for a plane to take off is far greater than the amount it requires to stay in the air. The only short-range flight I expect to see are small electric powered planes for short flights, we've already seen these spring up in a few places in the US.

Idk that might be true but we don't really know what the limits of scaling these models are, nor do we know the limits of how much faster we can make ML hardware. Expert opinion on the latter though suggests quite a lot; GPUs are really just the tip of the iceberg when designing hardware to train models

Being able to generate cohesive video? Probably 3 years or less, honestly. But a movie with its own music, a coherent plot, acting e.t.c? Seems a long way off to me; at that point you basically have an LLM which is a better writer, director, actor and musician than the majority of humans. I think for that you're probably going to need something which is near-human level intelligence, and you're also going to need a system that works for both language, visual and audio data, which is something outside of the scope of LLMs. Maybe you could make a "writer-bot" that writes the story, then a "video bot" that makes video from a long text input (the size of inputs is also another limitation of LLMs rn, so it would be difficult to plug a whole movie script into a model and expect good results), then an "audio bot" that takes a video and composes suitable music for parts of the movie that make sense.

Self awareness 100

Not sure why you are so upset, everyone else is engaging with you in good faith