General AI is a whole other subject.
Just another round of buzzword bingo. For example, AI generated code: Code-generating AI can introduce security vulnerabilities, study finds (https://techcrunch.com/2022/12/28/code-generating-ai-can-introduce-security-vulnerabilities-study-finds/).
I asked (I can't remember, exactly, but very approximately this) it to design a new EEVblog forums, Logo. With bright, colourful flashing LEDs, and transistors. This is what it came up with:
I asked (I can't remember, exactly, but very approximately this) it to design a new EEVblog forums, Logo. With bright, colourful flashing LEDs, and transistors. This is what it came up with:
I also tried doing company logos and apparently it's not at all trained on doing that. I couldn't even force it to put the EEVblog text. Maybe I was doing it wrong
If you have a GPU with enough RAM (4GB), you can download Stable Diffusion and run the art models locally, including giving it an image prompt, or if keen, train it on new art (with more RAM and enough time).
I tried finding an easy way to run it on a NAS with weak GPU but gobs of RAM it can probably access. But could only find CPU versions which would be nasty slow on it (1 hour I think) so I put that idea on hold.
It can run on an iPhone.
Based on my earlier calcs the model size of something like that or DALL-E 2 is only around the size of a bee's brain including the language comprehension side. That means in theory a bee could understand all those words and dream those same images in its little head (if it didn't have to control arms and legs or whatever they have).
it is difficult for individuals to run models in consumer electronics. For example, the training process for waifu-diffusion requires a minimum 30 GB of VRAM,[32] which exceeds the usual resource provided in consumer GPUs, such as Nvidia’s GeForce 30 series having around 12 GB.
There's all sorts of things the image generator can't do that a human artist can:
eg. Try making a series of images for a book using the same character but doing different things.
PS: Would you trust an AI to generate a complex PCB?
E.g. Posted earlier in this thread. It looks a bit odd/funny, if you look at it carefully. Especially the face, and the eyes. It doesn't look right or good, to me, at least.
I asked (I can't remember, exactly, but very approximately this) it to design a new EEVblog forums, Logo. With bright, colourful flashing LEDs, and transistors. This is what it came up with:
I also tried doing company logos and apparently it's not at all trained on doing that. I couldn't even force it to put the EEVblog text. Maybe I was doing it wrong
Company logos are a minefield. Trademark infringement is quite different from copyright infringement.
You'd be sourcing your logo from other brand's logos (and trademarks). Woah, boy...
My spidey-sense wonders if that result might be intentional. Company logos are a minefield. Trademark infringement is quite different from copyright infringement. You'd be sourcing your logo from other brand's logos (and trademarks). Woah, boy...
My spidey-sense wonders if that result might be intentional. Company logos are a minefield. Trademark infringement is quite different from copyright infringement. You'd be sourcing your logo from other brand's logos (and trademarks). Woah, boy...
That's not the AI's problem...
I wouldn't say 2022 is the year of mainstream AI, it's the year of mainstream justification for large scale data theft to train AI. As you say garbage in, garbage out, but the prompt is a pittance compared to the training data which is a very large component of how good any AI model is, however this data is very difficult and expensive to obtain.
A couple years ago I worked on an AI model to detect and classify certain sounds, we had a DNN model architecture that was fine tuned for the task but the thing we really needed was lots of training data, we had a couple hundred training examples and were able to train a decently competent model with that but it still wasn't very good. However in the process of developing it I obtained A LOT more data, both scraped from the internet as well as taken from other researchers, I trained the model on this data as it was MUCH more accurate, but I deleted that trained model and in the end we used only the model trained on the data we paid for, we paid about $100k for people to go out into the world and record the sounds for our data set over several years. If we just used the several thousand we obtained for free it would have been a massive game changer but it would have been theft, but those large AI companies that have recently come to popular success (like Midjourney) have stolen literally hundreds of millions of training examples to train their AI model and they don't even try to hide it, they brag about it. When they were stealing data to make free open AI models that just made low quality funny memes it was all fun and games, but now they're closed source and asking for money to do the work of those they stolen works from. In particular with AI art, there are now tutorials on how to remove watermarks and artist signatures which have leaked in from the stolen training images so that you can 'make a business charging for your images as an AI artist', this is truly disgusting to me. 2023 will be the year of AI training data litigation, private art feeds, tag vandalism, intentional training data contamination, increased DMCA on image boards, and the end of many creators posting online at all. There are now websites that index the known datasets of stolen works those AI companies use, a lot of works I've posted have been stolen (and your too Dave, EEVBlog forum posts, video thumbnails and frames and flickr images), I can see why so many are privating their works and no longer posting because all they would be doing is supplying free training data for those AI companies and immoral 'AI artists' looking to clone an artist's work and style.
But seriously, why the hell did I kept with my morals back then and delete that trained model? I should have just stolen all that data and we would have had the best model in our entire field, we would have exceeded our performance targets and gotten a lot more funding, we probably could have commercialized it or sold the IP with performance that good! Why the hell did I do the right thing?!
E.g. Posted earlier in this thread. It looks a bit odd/funny, if you look at it carefully. Especially the face, and the eyes. It doesn't look right or good, to me, at least.
E.g. Posted earlier in this thread. It looks a bit odd/funny, if you look at it carefully. Especially the face, and the eyes. It doesn't look right or good, to me, at least.But that is not an inherent limitation of the technology itself. I was also focused on finding a particular output and not caring about quality. The point I want to make is that it clearly copied something from the training set. It might’ve mangled the picture a bit, but all the important features remain intact.