Most of the images via those models are not shared online. People who do share images online often generate a lot of images and then pick the best ones.
If someone wanted better training data that process could even be accelerated. Let the user start with a prompt and then the AI generates 9 images. The user clicks on one image and then 8 other images. The user again presses on the image that matches what he had in mind the most. If you allow the user to go through that process a few more times then you have good training data on what image should be produced through the prompt.
You can also allow the user to specify in words what he likes to be different in the image.
But, since most large language models and multimodal AIs are trained on a dataset that basically consists of “all of the internet”, we could end up with a feedback loop.
That claim seems false. Dalle 2 is trained on 650 million images. On the other hand, there are 136 billion images on Google Images. Every day twice the amount of images get posted on Instagram than have been used to train Dalle 2.
I would expect that in the future improvements in image generation will be made by working to curate better data sets. I would expect that training the models to detect their own errors and improve on them will be helpful. The best software for creating images will likely train their models to predict a lot of user interaction.
I haven’t thought of training the models by evaluating the selection of the image by the user. And thanks for correcting me on my Dalle 2 training data—claim.
What do you mean by “training a model to detect its own errors”? Maybe this is a naive question (i am an ML newcomer) but isn’t that impossible by definition? Why would a model make a mistake if it’s capable of identifying it as such? Do you mean that through continuous improvement the model could correct the mistakes it made in the past, after some time has passed?
The problem of dilution remains for GPTs in my view. Widespread use seems likely over the coming years and the resulting text is unlikely to be properly labeled as AI-generated. Thus it seems likely that the text produced by today’s models will get absorbed into the training data of future GPTs, which will cause them to at least partially attempt to emulate their predecessors. Am I making a mistake somewhere in this thought process?
Most of the images via those models are not shared online. People who do share images online often generate a lot of images and then pick the best ones.
If someone wanted better training data that process could even be accelerated. Let the user start with a prompt and then the AI generates 9 images. The user clicks on one image and then 8 other images. The user again presses on the image that matches what he had in mind the most. If you allow the user to go through that process a few more times then you have good training data on what image should be produced through the prompt.
You can also allow the user to specify in words what he likes to be different in the image.
That claim seems false. Dalle 2 is trained on 650 million images. On the other hand, there are 136 billion images on Google Images. Every day twice the amount of images get posted on Instagram than have been used to train Dalle 2.
I would expect that in the future improvements in image generation will be made by working to curate better data sets. I would expect that training the models to detect their own errors and improve on them will be helpful. The best software for creating images will likely train their models to predict a lot of user interaction.
I haven’t thought of training the models by evaluating the selection of the image by the user. And thanks for correcting me on my Dalle 2 training data—claim.
What do you mean by “training a model to detect its own errors”? Maybe this is a naive question (i am an ML newcomer) but isn’t that impossible by definition? Why would a model make a mistake if it’s capable of identifying it as such? Do you mean that through continuous improvement the model could correct the mistakes it made in the past, after some time has passed?
The problem of dilution remains for GPTs in my view. Widespread use seems likely over the coming years and the resulting text is unlikely to be properly labeled as AI-generated. Thus it seems likely that the text produced by today’s models will get absorbed into the training data of future GPTs, which will cause them to at least partially attempt to emulate their predecessors. Am I making a mistake somewhere in this thought process?