You’ve all seen amazing-looking images like these, entirely generated by an artificial intelligence model. I covered multiple approaches on my channel, like Craiyon, Imagen, and the most well-known, Dall-e 2. Most people want to try them and generate images from random prompts, but the majority of these models aren’t open-source, which means we, regular people like us, cannot use them freely. Why? This is what we will dive into in this video... References ►Read the full article: ►OpenAI's article: ►Dalle 2 video: ►Craiyon's video: ►Use Craiyon: ►My Daily Newsletter: https://www.louisbouchard.ai/how-openai-reduces-risks-for-dall-e-2/ https://openai.com/blog/dall-e-2-pre-training-mitigations/ https://youtu.be/rdGVbPI42sA https://youtu.be/qOxde_JV0vI https://www.craiyon.com/ https://www.getrevue.co/profile/whats_ai Video Transcript 0:00 you've all seen amazing looking images 0:02 like these entirely generated by an 0:05 artificial intelligence model i covered 0:07 multiple approaches on my channel like 0:09 crayon imogen and the most well-known 0:12 deli 2. most people want to try them and 0:15 generate images from random prompts but 0:18 the majority of these models aren't open 0:20 source which means regular people like 0:23 us cannot use them freely why this is 0:26 what we will dive into in this video 0:29 i said most of them were not open source 0:32 well crayon is and people have generated 0:35 amazing memes using it you can see how 0:38 such a model can become dangerous 0:40 allowing anyone to generate anything not 0:43 only for the possible misuses regarding 0:45 the generations but the data used to 0:47 train such models as well coming from 0:50 random images on the internet pretty 0:52 much anything with questionable content 0:55 and producing some unexpected images the 0:58 training data could also be retrieved 1:00 through inverse engineering of the model 1:02 which is most likely unwanted openai 1:05 also used this to justify not releasing 1:08 the daily2 model to the public here we 1:10 will look into what they are 1:12 investigating as potential risks and how 1:14 they are trying to mitigate them i go 1:16 through a very interesting article they 1:18 wrote covering their data pre-processing 1:21 steps when training dalit ii but before 1:24 so allow me a few seconds to be my own 1:26 sponsor and share my most recent project 1:28 which might interest you i recently 1:31 created a daily newsletter sharing ai 1:34 news and research with a simple and 1:36 clear one-liner to know if the paper 1:38 code or news is worth your time you can 1:41 subscribe to it on linkedin or with your 1:43 email the link is in the description 1:45 below 1:46 so what does openai really have in mind 1:48 when they say that they are making 1:50 efforts to reduce risks 1:52 first and the most obvious one is that 1:55 they are filtering out violent and 1:57 sexual images from the hundreds of 1:59 millions of images on the internet this 2:02 is to prevent the modal from learning 2:04 how to produce violent and sexual 2:06 content or even return the original 2:08 images as generations it's like not 2:11 teaching your kid how to fight if you 2:13 don't want him to get into fights it 2:15 might help but it's far from a perfect 2:17 fix still i believe it's necessary to 2:20 have such filters in our data sets and 2:22 definitely helps in this case but how do 2:25 they do that exactly they build several 2:27 models trained to classify data to be 2:30 filtered or not by giving them a few 2:32 different positive and negative examples 2:34 and iteratively improve the classifiers 2:37 with human feedback each classifier went 2:39 through our whole data set deleting more 2:42 images than needed just in case as it's 2:44 much better for the model to not see bad 2:47 data in the first place rather than 2:48 trying to correct the shot afterward 2:51 each classifier will have a unique 2:53 understanding of which content to filter 2:56 and will all complement themselves 2:57 ensuring good filtering if by good we 3:00 mean no false negative images going 3:02 through the filtering process 3:04 still it comes with downsides first the 3:07 data set is clearly smaller and may not 3:10 accurately represent the real world 3:12 which may be good or bad depending on 3:14 the use case they also found an 3:16 unexpected side effect of this data 3:18 filtering process it amplified the 3:21 model's biases towards certain 3:23 demographics introducing the second 3:25 thing openai is doing as a pre-training 3:28 mitigation reduce the biases caused by 3:31 this filtering for example after 3:33 filtering one of the biases they noticed 3:36 was that the modal generated more images 3:38 of men and fewer of women compared to 3:41 modals trained on the original data set 3:44 they explained that one of the reasons 3:46 may be that women appear more often than 3:48 men in sexual content which may bias 3:50 their classifiers to remove more false 3:53 negative images containing women from 3:55 the data set creating a gap in the 3:57 gender ratio that the model observes in 4:00 training and replicates to fix that they 4:02 re-weight the filtered data set to match 4:05 the distribution of the initial 4:07 pre-filter data set here is an example 4:10 they cover using cats and dogs where the 4:12 filter will remove more dugs then cats 4:14 so the fix will be to double the 4:16 training loss for images of dogs which 4:19 will be like sending two images of dugs 4:21 instead of one and compensating for the 4:23 lack of images this is once again just a 4:26 proxy for actual filtering bias but it 4:29 still reduces the image distribution gap 4:31 between the pre-filtered and the 4:33 filtered data set 4:35 the last issue is an issue of 4:36 memorization something the models seem 4:39 to be much more powerful than i am as we 4:42 said it's possible to regurgitate the 4:44 training data from such image generation 4:46 models which is not wanted in most cases 4:49 here we also want to generate novel 4:51 images and not simply copy paste images 4:54 from the internet but how can we prevent 4:56 that just like our memory you cannot 4:59 really decide what you remember and what 5:01 goes away once you see something it 5:03 either sticks or it doesn't they found 5:05 that just like humans learning a new 5:07 concept if the model sees the same image 5:10 numerous times in the data set it may 5:12 accidentally know it by heart at the end 5:15 of its training and generate it exactly 5:17 for a similar or identical text prompt 5:20 this one is an easy and reliable fix 5:23 just find out which images are too 5:25 similar and delete the duplicates easy 5:28 doing this will mean comparing each 5:30 image with every other image meaning 5:33 hundreds of quadrillions of image pairs 5:36 to compare instead they simply start by 5:38 grouping similar images together and 5:41 then compare the images with all other 5:43 images within the same and a few other 5:46 clusters around it immensely reducing 5:48 the complexity while still finding 97 of 5:52 all duplicate pairs again another fix to 5:55 do within the data set before training 5:57 our daily model openai also mentions 6:00 some next step they are investigating 6:02 and if you've enjoyed this video i 6:04 definitely invite you to read their 6:06 in-depth article to see all the details 6:08 of this pre-training mitigation work 6:11 it's a very interesting and well-written 6:13 article let me know what you think of 6:15 their mitigation efforts and their 6:17 choice to limit the model's access to 6:19 the public 6:20 leave a comment or join the discussion 6:22 in our community on discord thank you 6:24 for watching until the end and i will 6:26 see you next week with another amazing 6:29 paper [Music]

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