twitter context https://twitter.com/swyx/status/1674895620870651909?s=20

[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:...

Transcript

SPEAKER 1 0:00:00
I want to send a huge thanks to our friends at AWS for their continued support of the podcast and their sponsorship of our reinvent 2022 series. You know AWS is a cloud computing leader, but did you realize the company offers a broad array of services and infrastructure at all three layers of the machine learning technology stack? In fact, tens of thousands of customers trust AWS for machine learning and AI services. And the company aims to put ML in the hands of every practitioner with innovative services like Amazon Code Whisperer, a new ML powered pair programming tool that helps developers improve productivity by significantly reducing the time to build software applications. To learn more about AWS ML and AI services and how they're helping customers accelerate their machine learning journeys, visit twimlai.com slash go slash AWS ML. All right, everyone, this is Sam Charrington, host of the Twiml AI podcast. And today I'm coming to you live from the Future Frequency podcast studio at the AWS reinvent conference here in Las Vegas. And I am joined by Ahmad Mostak. Ahmad is founder and CEO of Stability AI. If this is the first episode of our reinvent series that you are listening to, don't try adjusting your audio settings. It's definitely me. After a few days here at reinvent in the dry desert here in Nevada, my voice is on his last legs, but I think we'll make it through this. Before we get going, be sure to take a moment to hit that subscribe button wherever you're listening to today's show. And if you want to check us out in studio, you can bounce over to YouTube for the interview. 
SPEAKER 2 0:01:42
Ahmad, welcome to the podcast. Thanks so much for having me. Super excited to talk to you. 
SPEAKER 1 0:01:46
You are of course the founder and CEO of Stability. Stability is the company behind StableDiffusion, which is a multimodal model that has been getting a lot of fanfare, I think. Welcome. And I'd love to jump in by having you share a little bit about your background. 
SPEAKER 2 0:02:02
Yeah, no, I think it's been super interesting. I think StableDiffusion is kind of a specific text to image model. As for me, let's say I started off as a computer science at uni, 
SPEAKER 1 0:02:10
enterprise developer, and then became a hedge fund manager and one of the largest video game investors in the world and then artificial intelligence. And I was doing that, it was a 
SPEAKER 2 0:02:18
lot of fun. And then my son was diagnosed with autism and they said there was no cure or treatment. 
SPEAKER 1 0:02:23
So I quit, switched to advising hedge funds and built an AI team to do literature review, all the autism literature, and then biomolecular pathway analysis of neurotransmitters to repurpose drugs to help him out. And it kind of worked. He went to mainstream school and was super happy. That's awesome. 
SPEAKER 2 0:02:39
It was kind of cool. Good trade, good trade. Then I went back to the hedge fund world, won some awards. It's boring. Then decided to make the world a better place. So first off, 
SPEAKER 1 0:02:47
took the global X prize for learning. That was a $15 million prize from Elon Musk and Tony Robbins for the first app to teach kids literacy and numeracy without internet. My co-founder and I 
SPEAKER 2 0:02:55
have been deploying that around the world. And now we're teaching kids in refugee camps, 
SPEAKER 1 0:02:59
literacy and numeracy in 13 months and one hour a day. And we're about to air the crap out of that. 
SPEAKER 2 0:03:04
In 2020-21, I designed and led the United Nations, one of the United Nations AI initiatives against 
SPEAKER 1 0:03:10
COVID-19, Kayak Collective and augmented intelligence against COVID-19 launched at Stanford, backed by the WHO, UNESCO and the World Bank. And that was really interesting because we were trying 
SPEAKER 2 0:03:20
to make the world's knowledge free on COVID-19 with Core 19. So there's a 500,000 paper data set, 
SPEAKER 1 0:03:26
freely available to everyone. And then use AI to organize it because it's really confusing. During that, lots and lots of interesting tech kind of came through, but I realized 
SPEAKER 2 0:03:37
these foundation models are super powerful. You can't have them controlled by any one company. It's bad business and it's not the correct thing ethically. So I thought, let's widen this and create open source foundation models for everyone, because I think it can really advance humanity. 
SPEAKER 1 0:03:50
And again, I think it'll be great to see these things proliferate. So we can have an open discussion about it and also have the value created from just these brand new experiences. That's awesome. And when did you get started down that part of the journey? 
SPEAKER 2 0:04:03
About two years ago. Stability has been going for about 13 months now. 
SPEAKER 1 0:04:07
Yeah. When I think about the lot of stable diffusion goes back to this latent diffusion paper, which was not even a year ago. 
SPEAKER 2 0:04:13
It's not even a year ago. I think the whole thing kind of kicked off with Clip released by OpenAI in January of last year. So I actually had COVID doing that time while doing my COVID thing. My daughter came to me and said, dad, you know all that stuff you do, taking all that knowledge and squishing it down to make it useful for everyone. Can you do that with image? It's like, well, we can. So I'll build a system for her based on VQGAN and Clip. So an image generating model. And then Clip is an image to text model where she created like a vision board of everything she wanted, a description of what she wanted to make. And it generates 16 different images. And then she said how each one of those is different and changed the latents. And then it generated another 16, another 16, another 16. And then eight hours 
SPEAKER 1 0:04:49
later, she made an image that she went on to sell as an NFT for $3,500. Wow. And donated the proceeds to India Code Relief. Okay. I thought it was awesome. She's seven years old. Wow. And then 
SPEAKER 2 0:05:01
I was like, this is transformative technology. Image is the one it's at. Language, we're already at 85%. We're going to get a 95% image. We're at 10%. We're not a visual species. Like the easiest 
SPEAKER 1 0:05:11
way for us to communicate is what we're doing right now. We're having a nice chat. Then text is the 
SPEAKER 2 0:05:15
next hardest. And image, like be it images or PowerPoints are impossible. Let's make it easy. This tech can do that. So we started funding the entire sector, Google Colab notebooks, models, all these kinds of things. Latent diffusion was done by the Confiz Lab at the University of Munich, 
SPEAKER 1 0:05:31
who are led on the stable diffusion one as well. Amazing lab led by Bjorn Jommer and led by Robin 
SPEAKER 2 0:05:37
Rombach, who was one of our lead developers here at Stability. And then there was work by Catherine Kraussen, Rivers Have Wings is a Twitter handle on clip condition models and things like that. And the whole community just came together and built really cool stuff. Then you had entities like Mid Journey, where we just gave grants for the beta that started operationalizing it. It's all come together now to the finality of stable diffusion that was released on August 23rd. So that was led by the Confiz Lab. And then ourselves at Stability, Runway ML, a Luther AI 
SPEAKER 1 0:06:05
community that we kind of helped run and lie on, we came together to put out 100,000 gigabytes of image-label text pairs, 2 billion images turned into a 2 gigabyte file that runs natively on yo...

Read more on Ryland's post - the Macro problem with Microservices!

Thanks to Remotely for our recording platform.