https://www.listennotes.com/podcasts/the-logan-bartlett/ep-46-stability-ai-ceo-emad-8PQIYcR3r2i/

[00:00:00.000 --> 00:00:02.580]   (upbeat music)
[00:00:02.580 --> 00:00:07.560]   - Welcome to the 46th episode of Cartoon Avatars.
[00:00:07.560 --> 00:00:09.840]   I am your host Logan Bartlett.
[00:00:09.840 --> 00:00:11.960]   Welcome back for break.
[00:00:11.960 --> 00:00:13.280]   Thanks everyone for bearing with us
[00:00:13.280 --> 00:00:16.160]   as we took a pause over the last couple of weeks.
[00:00:16.160 --> 00:00:18.200]   We're excited for this episode.
[00:00:18.200 --> 00:00:19.880]   This, what you're gonna hear on this episode
[00:00:19.880 --> 00:00:22.480]   is a conversation that I had with the Mod in the Stock.
[00:00:22.480 --> 00:00:27.400]   And Mod is the founder and CEO of Stable Stability AI,
[00:00:27.400 --> 00:00:29.840]   which is the largest contributor to Stable Diffusion.
[00:00:29.840 --> 00:00:32.280]   Stable Diffusion is the fastest growing
[00:00:32.280 --> 00:00:34.120]   open source project of all time.
[00:00:34.120 --> 00:00:39.080]   It's one of the leading platforms in generative AI.
[00:00:39.080 --> 00:00:41.720]   And Ema and I had a really interesting conversation
[00:00:41.720 --> 00:00:43.080]   about a bunch of different things,
[00:00:43.080 --> 00:00:47.720]   but we dive into the state of artificial intelligence today,
[00:00:47.720 --> 00:00:50.920]   why this is possible, when it wasn't in the past,
[00:00:50.920 --> 00:00:53.160]   where this is going in the future,
[00:00:53.160 --> 00:00:56.760]   how he differentiates versus competitors like OpenAI.
[00:00:56.760 --> 00:00:59.560]   Really fun conversation and appreciate him
[00:00:59.560 --> 00:01:01.120]   for powering through.
[00:01:01.120 --> 00:01:02.760]   He was a little sick as we were doing this.
[00:01:02.760 --> 00:01:07.760]   So it was a fun conversation and I appreciate him doing it with me.
[00:01:07.760 --> 00:01:09.840]   And so before you hear that,
[00:01:09.840 --> 00:01:11.560]   we talked a little bit about this before break,
[00:01:11.560 --> 00:01:13.240]   but we are gonna make a more concerted effort
[00:01:13.240 --> 00:01:16.440]   to get people to like and subscribe
[00:01:16.440 --> 00:01:20.240]   and share and review the podcast itself.
[00:01:20.240 --> 00:01:22.560]   And so if you're whatever platform you're listening on,
[00:01:22.560 --> 00:01:25.720]   if it's YouTube, if it's Spotify, if it's Apple,
[00:01:25.720 --> 00:01:29.040]   whatever it is, if people could go ahead and like
[00:01:29.040 --> 00:01:31.640]   and subscribe and leave a review,
[00:01:31.640 --> 00:01:33.880]   share with a friend, all of that stuff.
[00:01:33.880 --> 00:01:35.480]   We're trying to figure out exactly what direction
[00:01:35.480 --> 00:01:36.640]   to take this in.
[00:01:36.640 --> 00:01:40.080]   And so that validation and feedback
[00:01:40.080 --> 00:01:42.520]   and also the growth that comes along with all that stuff
[00:01:42.520 --> 00:01:44.880]   is super appreciated.
[00:01:44.880 --> 00:01:46.880]   It's not something we had been comfortable
[00:01:46.880 --> 00:01:48.240]   asking for to date,
[00:01:48.240 --> 00:01:51.080]   but as we kind of figure out what direction we're gonna go,
[00:01:51.080 --> 00:01:54.840]   we'd love to see more shares, more reviews, more views,
[00:01:54.840 --> 00:01:56.200]   more likes, all that stuff.
[00:01:56.200 --> 00:02:00.520]   So really appreciate everyone's support in doing that.
[00:02:00.520 --> 00:02:02.520]   And so without further delay,
[00:02:02.520 --> 00:02:04.280]   what you're gonna hear now is the conversation with me
[00:02:04.280 --> 00:02:06.880]   and I'm on Mistock from Stability AI.
[00:02:06.880 --> 00:02:09.920]   All right, Iman Mistock.
[00:02:09.920 --> 00:02:10.800]   Did I say that right?
[00:02:10.800 --> 00:02:12.120]   - Yep. - Perfect.
[00:02:12.120 --> 00:02:13.680]   Thank you for doing this.
[00:02:13.680 --> 00:02:16.560]   Founder of Stability AI,
[00:02:16.560 --> 00:02:20.480]   one of the main contributors to stable diffusion.
[00:02:20.480 --> 00:02:23.480]   Thank you for coming on here today.
[00:02:23.480 --> 00:02:24.440]   - So pleasure, Logan.
[00:02:24.440 --> 00:02:25.960]   Most I have be here.
[00:02:25.960 --> 00:02:26.800]   - Yeah, totally.
[00:02:26.800 --> 00:02:30.120]   So maybe at a highest level, we can start off with
[00:02:30.120 --> 00:02:32.560]   what is generative AI?
[00:02:32.560 --> 00:02:34.760]   How would you define that for the average person?
[00:02:34.760 --> 00:02:37.880]   - So I think everyone said of kind of the concepts
[00:02:37.880 --> 00:02:40.560]   of big data 'cause the whole of the internet previously
[00:02:40.560 --> 00:02:41.680]   was on big data.
[00:02:41.680 --> 00:02:44.640]   Large, large models built by Google and Facebook
[00:02:44.640 --> 00:02:47.000]   and others to basically target you ads
[00:02:47.000 --> 00:02:49.200]   ads were the main part of that.
[00:02:49.200 --> 00:02:51.640]   And these models extended.
[00:02:51.640 --> 00:02:54.480]   So how to generalize model of what a person was like
[00:02:54.480 --> 00:02:56.600]   and then your specific interests,
[00:02:56.600 --> 00:03:00.480]   like EMAD likes green hoodies or Logan lights black jumpers.
[00:03:00.480 --> 00:03:05.520]   That then extended the previous to what the next thing was.
[00:03:05.520 --> 00:03:08.080]   They're like extension models inferring what was there.
[00:03:08.080 --> 00:03:09.560]   Gerative models are a bit different
[00:03:09.560 --> 00:03:12.160]   in that they learn principles from structured
[00:03:12.160 --> 00:03:16.080]   and unstructured data and then they can generate new things
[00:03:16.080 --> 00:03:17.400]   based on those principles.
[00:03:17.400 --> 00:03:21.680]   So you could ask it to write an essay about bubble sort
[00:03:21.680 --> 00:03:24.840]   or a solid about Shakespeare or the TTH
[00:03:24.840 --> 00:03:26.840]   which is digital so you can do that.
[00:03:26.840 --> 00:03:29.960]   Or in the case of some of the work that we're most famous for
[00:03:29.960 --> 00:03:32.800]   you enter in a labradoodle with a hat
[00:03:32.800 --> 00:03:34.960]   and a stained glass window and it understands that
[00:03:34.960 --> 00:03:37.200]   and then creates that in a few seconds.
[00:03:37.200 --> 00:03:39.360]   So I'd say that's probably the biggest difference
[00:03:39.360 --> 00:03:40.880]   between this new type of generative AI
[00:03:40.880 --> 00:03:43.040]   and then that old type of AI.
[00:03:43.040 --> 00:03:44.880]   So the way that I also say that we've moved
[00:03:44.880 --> 00:03:47.720]   from a big data area to more a big model era
[00:03:47.720 --> 00:03:50.600]   'cause these models are very difficult to create, train
[00:03:50.600 --> 00:03:53.480]   which is why only a few companies such as ours do it.
[00:03:53.480 --> 00:...