#139 Dmitry Shapiro: Breaking Barriers with No Code AI

0:00If you ask ChatGPT to summarize a document, it will instantly summarize it. But who is it summarizing it for? Like let's say the document's about astrophysics. And so is it summarizing it for an eighth grader that doesn't even know what astrophysics necessarily really means? Or is it summarizing for an astrophysicist? Well, if it knows, it will summarize differently. So that's just one data point in the sense that you could pass to it in the exercise of summarization. Human salesperson can do a better job because they can see what the other party is grokking, not grokking, paraphrase, do all of that.

0:32That's how websites should be. They shouldn't be sort of pre-created. They should be these interactive things that a human shows up, says, this is me, this is my mental model, this is the digitized me. I think I'm interested in your product. Explain to me why I need it. Hi, I'm Craig Smith, and this is Eye on AI. This week I speak to Dimitri Shapiro, CEO of UAI, a platform that allows users to create their own generative AI apps without needing any coding, and a platform for distributing those apps, either for free or for profit. Dimitri also talks about Mind Indexing, a system whereby users feed personal data to a large model to enable customized contextual interactions Shapiro predicts that digitally capturing the nuances of personal context and preferences will lead to exponential leaps and AI's benefits across education communication teamwork and more I hope you find the conversation as interesting as I did.

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3:42Oh, shopify.com slash I on AI. Oh, carry on. Yeah, so my name is Dimitri Shapiro. I am the CEO of UAI. Prior to this, I was at Google for four years from 2012 to 2016. For the first two and a half years of that, I was running product on three machine learning teams that were taking all of this implicitly collected data that Google has about people and trying to make sense of it. Prior to that, I was the chief technology officer of MySpace Music, for those folks that remember MySpace. Before that, I built two other venture-backed companies. One was a competitor to YouTube called VEOH Networks. It was a major competitor.

4:32I raised$70 million for that. Prior to that, I built a venture-backed cybersecurity company called Aconex Systems. So enterprise software, cybersecurity raised$34 million for that. And from 95 to 99, I built the web team at Fujitsu, a giant Japanese company. I started writing code in 1984 when I was in high school. I was 14 years old. I saw the movie War Games in the theater and came home and started in my school hacking around on a computer. I have a degree in electrical engineering from Georgia tech i've never done a day of electrical engineering uh i've always done uh software i'm a nerd i'm just an old nerd georgia tech i i you know i actually wanted to do their online masters in computer science and they said i needed more uh computer science courses on my on my transcript, which is 40 years, 50 years old or something.

5:33So yeah, I've never, I've never taken a computer science class or even one day of any computer science class. I'm completely self-taught as my co-founder here, Sean Thielen, who just started writing code when he was like eight years old and is infinitely better than I am actually at it. Yeah. Well, last time we spoke, you were telling me about there are kind of two sides to UAI. There's the AI marketplace side and toolkit that people can use to build conversational AI chatbots and then deploy them in the marketplace for free or for if they're paid to generate. rate revenue. And then there's the other side, which you guys are calling indexing the mind.

6:34So can you talk about those two sides, how UAI came to be and what those two sides are about? Definitely. So when I was at Google, as I said, from 2012 to 2016, I was on the main campus there and working with all of this data that Google had in order to be able to understand people and create better services for them. It became clear to me then that even though Google has an insane amount of data, being able to use that data and sort of creating real applications from it has a number of challenges. One of the major challenges that are there is that all of that data is quite ambiguous. And so it can be used for doing things like, you know, ranking ads and recommending YouTube videos.

7:32But it's really hard for you to use all that ambiguous data in creating Google Assistant, for example. And I believe this is the fundamental reason why Google Assistant at this time is still so weak is because Google doesn't have that sort of explicit disambiguated data to really be able to drive, you know, train and drive that assistant. And so I had this insight again in sort of late 2012. And now we thought was the right time to start to do some work on ideas like that. And so we've created what we call a mind indexer. Think of it. It feels like TikTok. It's a never ending feed of full viewport experiences that basically ask you for input.

8:14So it prompts you with various things, things like multiple choice questions, open answer questions, a big grid of images, and it says tap all the ones that seem yummy. So explicit signals coming out of humans voluntarily about all kinds of things, because you can sort of create prompts about all kinds of things. And so we launched that in May and invited some alpha testers to come and play. Over 5 ,000 people showed up and started indexing their minds. We've now collected over a million data points of, again, people's preferences and biases and beliefs and given sort of any scenario, how they would respond.

8:54And so that's that part of it, which is this mind indexing part of that. And sort of our belief is long term, that the right way to leverage all of the power of modern AIs, let's say these generative AIs, they're all neural networks. And neural networks are great at what? They're taking a bunch of data, and in today's world, a bunch of data that's quite unstructured, so like a data dump, and then being able to tease out statistically meaningful features and start to make predictions and all of that. And so we believe that if humans could sort of digitize their own minds and have a data set that we could then present to AI and say, this is me.

9:37You figure out what you should help me with and how you could help me. Instead of me trying to figure out how to type prompts in with my thumbs, maybe the AI could simply figure out what I need. I think one of our biggest problems as humans is we don't know what we don't know. And so how am I supposed to ask AI to help me with things that I don't even know exist? And I don't know that. I don't know it. But the AI could figure out that I don't know it. Like if you index your mind very quickly, this thing can start to figure out where you have gaps in knowledge, for example. Right. And then fill those in and then help you sort of think about things.

10:15So that's sort of that part. Yeah. In a related part. Well, actually, can we stop and talk about that for a minute? because does that alpha test or is that publicly facing, it's not on your website, is it? It is, yeah. Okay, I'll go back. At this moment, it's a bit, I think, hidden because we're promoting the Mind Studio part of the thing. Right. But it is still there, and I'll share a link with you so you'll be able to see it, and your audience can come play with it and do that. So we'll make sure the link is included. Okay. But once you've done this, say you've done it for a year and your UAI has this profile built up who I am, how then can I use that?

11:09Or how do you use that? Is there a function that's built on top of that? Yeah. So think near term and then long term. In the near term, what we can do with it, meaning what we can allow you to do with it, because you're in control of all this data. It's obviously encrypted. None of our employees have access to it, nor will ever have access to it. That data is simply used by you to be able to, in the near term, take that and pass it in as context into large language models or image diffusion models or any other models that will accept that could benefit from knowing a bit more about the user that is engaging with them.

11:52I'll give you an example. If you ask ChatGPT to summarize a document, it will instantly start summarizing it. But who is it summarizing it for? Let's say the document's about astrophysics. And so is it summarizing it for an eighth grader that doesn't even know what astrophysics necessarily really means, or is it summarizing for an astrophysicist? Well, if it knows, it will summarize differently. So that's just one data point in the sense that you could pass to it in the exercise of summarization or in the exercise of writing something for you. Write me a blog post, write me a response to an email.

12:29The more it knows about sort of you, the more it can take these things into account in its generation of content. And so in the near term, you get to sort of personalize your interactions with chat GPT, Anthropic Cloud, Llama, whatever, like any of these things. Longer term, sort of our plans with it is again, we're in a sense compiling this data set of individual people and also of many people. And we can take that data set and then we can start to either find two existing models or train new models on sort of these parameters that are represented in the output of humans, which is what these models have been trained on.

13:16All that stuff that we've written as humans that's on the internet, that's what these large language models have parsed and been trained on. But they don't have sort of any way of teasing out what created that because the data isn't there. But we think if we can take that data and merge it, we can do that. Again, that's sort of longer term vision for that. And we think long term, that's really powerful and important. And again, our sort of vision is that, again, we believe that the right way for humans to really leverage all of the innovation and information technology, because these things are going to start to accelerate.

13:56Ray Kurzweil points out information technology innovation accelerates exponentially because each new generation is built on the prior. Well, wow, what a massive step forward we've now taken to have these new models and to have transformers and to have new GPUs. All of this stuff is doing exponential. There is no way that humans can keep up with that and truly be able to leverage those things by typing words into a text prompt. That just does not make any sense to us. And so we think that, again, long term, the right interface, the right human to digital interface is of a digitized human, not of a human with fast fingers.

14:38Yeah. And I'm interested in that. The reason I'm sort of stopping you on it is I am very familiar with a company called Reed in Korea. I think I mentioned it to you last time that has a system that with knowledge tracing algorithms and recommendation engines and, you know, a collection of algorithms that as a student interface, interacts with, uh, it knows what the student is weak on and it can feed it content to, to, uh, improve on that. And, and through the knowledge tracing and the, these prediction algorithms, it can tell the student what its end score, if it's a test prep or a class is going to be in the student, the more it follows the instructions, the closer it gets to, uh, to its desired, to the student's desired score, it can see its predictive score going up.

15:45And what you're talking about sounds like it would have a tremendous, uh, human, I mean, uh, tremendous applications in education where a student, and this is the sort of thing that kids like, you know, swiping through and that it would gradually learn, you know, the student at a deeper level. So then when the student is interacting with it, it would, you know, be more efficient. It would know what kind of content recommend. Have you guys talked to anybody about using this for education? Yes. In our Discord, again, these early alpha testers, I think it's been clear to all of us that education is smack dab in the middle of all of this.

16:39That's one of the biggest things. As I said, we don't know what we don't know. All of, you know, a vast majority of things that we search for are, in a sense, could be considered as education, whether you're searching for a product or you're searching for, you know, whatever, other than like planning things that like executing on things. Um, and, and, and, you know, the education as it's always been, obviously it's evolved and it's, it's different now than it was, you know, a hundred years ago, but, but generally it's been the same where you have some curriculum that's been created and that curriculum has been created for a large group of people and, and sort of personalized learning is something we talk about, but personalized in, in sort of modern day means that it's a bit more finely tuned to a more limited set of people, but it's still generic.

17:23It's not personalized. Curriculum isn't created on demand for each individual based upon the things they know or don't know. But that's what a good tutor does. A good tutor understands by spending time with you what you know and what you don't know, and then can fill in the gaps. And those gaps are different for each one of us. And they're also obviously a function of time. And so the number one thing that we believe that, you know, our mind indexer will need to do is to, in a sense, be a mind reader that is trying to figure out, given this situation, what might you choose, right? Another way of putting it, if LLMs can be described as prediction machines predicting, you know, the next token, the next word or so, given some prior tokens, you could describe the TikTok algorithm in a similar way that it tries to predict what video it should show you next that you will engage with.

18:27You'll spend more time on, you will like, you'll comment on. Then what we need to do is, meaning our algorithm, means to predict, given any situation, what might your response be? And in learning, that's basically testing and nuancing and disambiguating and then discovering, okay, you know, Craig is weak on this. Great. Now I need to spend my time helping him understand this. But because I know that he knows this and this and this, I can use these three things, sort of as analogies, for example, to be able to then get him to see this dimension that he's missing. So education, we think, is at the heart of this.

19:09And again, if we're successful and other companies are successful in doing this, researchers, et cetera, is we'll have this incredible world where all of us are going to learn, again, exponentially faster than we've been sort of getting better at learning, because we're not going to get sort of repetitive stuff shown to us in various ways, but simply have all of those things filled in for us. Like, again, I like to think of it as like a jigsaw puzzle. And each one of us have different shapes of jigsaw pieces missing. Well, this thing should be able to figure out what the pieces should look like and simply fill them in for us.

19:45Yeah. Yeah. And by the way, that also then goes to, you know, to, you know, if you extrapolate from there, you could start to imagine that like websites are the same thing. Websites are created for the purpose of educating people. Like, buy my product. How do I get you to buy my product? I educate you on the value proposition of my product. And today they're all made for, you know, at best, you have different landing pages for different personas, right? But those are still generic. And again, this is why a human salesperson can do a better job because they can see what the other party is grokking, not grokking, paraphrase, do all of that.

20:20That's how websites should be. They shouldn't be sort of pre-created. They should be these interactive things. that a human shows up, says, this is me. This is my mental model. This is the digitized me. I think I'm interested in your product. Explain to me why I need it. I mean, that all goes unsaid. That's the expected relationship. And then the thing simply creates the content on the fly for me. And that's different than the content they would create for this person or that person. Still be about the same product, but using different words, different analogies, different level of communication, less sophistication, more sophistication, you know, regional constraints.

21:00There's all kinds of things that could be marvelous once the thing can figure out who is it talking to, like who's the user. I think that's the number one missing set of variables, who's the user. And then MindStudio, when did that project develop? It's from what I can see, the tools that allow people to build apps are leveraging generative AI. It's mostly chatbots. Is that right? No, I wouldn't say that. Uh, it's actually mostly things that are like, I think it was like AI driven apps where the logic of the app is AI, but the app itself might be like, there's a bunch of content generators or things that help you figure out how to name your YouTube video.

21:55You know, the output of them is text and sometimes images. And some of them allow you to have a chat window to continue chatting with it and disambiguate, for example, or get it to do other things. Other ones that are built with Mind Studio don't even have that. They just sort of end and you can start a new activity, like new document. And so and certainly you can obviously build chatbots with it as well. We now have over a thousand, as of this weekend, AIs that have been created in the last month and a half since we launched it. And if you just go to uai.ai, Y-O-U-A-I.ai, you can just browse through, you know, over a thousand of these AIs in categories in many different purposes.

22:39Yeah. So how we got to that is, again, once we got these alpha testers in to start to digitize their minds and started talking to them, again, people were excited about the long term vision that we've been discussing, but also super excited about like, what could we do with this kind of stuff right now? And again, one of the things that was clear is like, you know, ChatGPT, Claude, et cetera, can take contextual guidance, right? This is sort of part of the prompt or the system message to put them in the right frame of mind to be able to respond to us and get us what we want. And that consumers shouldn't have to be the ones typing this in.

23:20Meaning if I want to use ChatGPT to generate blog post content for me today, I would need to show up and start typing, you are, again, you can paraphrase this, but some people get extremely verbose. You are a professional blog post creator with 40 years of experience, blah, blah, blah, blah, blah. I'm going to give you titles and you will give me back a blog post. I will need it to be formatted, backspace, backspace, backspace, structured. This doesn't make sense for consumers to do. And so it became very clear that it would be awesome to simply build a system that would allow people that do want to do prompt engineering to do that and package that as a simple interface for consumers to show up and just create a blog post or just get help on naming YouTube videos or just get a parenting co-pilot or get a personalized storybook generator for their kids or get diagnosed with various ailments that they have.

24:16We have a thing called AI Medic or get their pet diagnosed with various ailments because We have an AI vet or figure out when to plant their tomatoes in Southern California versus Northern California because we've got AIs for gardening, right? And so all of those things have been created by, let's call them prompt engineers, motivated people that showed up, created a new project, very quickly created the preamble, the parameters, played around and chose the model. Again, we're model agnostic. We support today GPT-3, 3, 5, 4, CLOD 1 and 2. and we'll support many more models, create multi-step automations.

24:52And so we've created this Mind Studio. It's an integrated development environment that lets anyone, you don't have to have any real tech skills. You can watch a couple of YouTube tutorials and 15 minutes later, you're a professional AI creator. And you can sit down and create these things. Many of them are created in less than five minutes. Some of them may take an hour if you really putz around with them. And then you can publish them. And when you publish them, they're just a URL. So you can share them anywhere, you can embed them anywhere, you can offer them for free, or you can turn on monthly subscriptions, and people get a free tier.

25:27And then they're asked to, you know, to subscribe to this thing. Yeah. And on the back end, you know, I've browsed through and I've got a couple sort of permanently open on my computer that I find useful. if they seem i'm trying to think there was they seem to be uh fine-tuned for particular domains at least some of them like the medical one you mentioned uh how does a user of uai fine-tune a model for a specific domain do you guys have uh data on your side that that you can fine-tune these models on? We do not. So there are a couple of ways that we've seen people doing it. One way is really not fine-tuning at all, simply setting the right context for the model, for the interaction, for the AI to be able to respond to and explaining to it that it is a, you know, sort of a virtual physician and that it should use its knowledge of medicine to engage with the user in conversation and to ask the user about, you know, their various symptoms in other states and then sort of disambiguate for them what might be causing their ailments, sort of not providing any new data to fine tune the model and doing it in that way.

27:07And obviously, like many people may have seen videos on YouTube already where there are physicians that are playing around with, you know, ChatGPT and are quite impressed, as many of us have been on other fields, you know, like its ability to write code. It's actually quite good already at being able to diagnose and create treatment plans and things like that without any additional fine tuning. But creating it from a context standpoint, this thing is not a blog post generator. This thing is not something else. it's in the mind again of a physician. The interaction, the relationship becomes such.

27:42And so it stays in that lane in doing that. There's another one that's a vet. Really powerful.

27:50Some of my family members have been experiencing problems with their pet and been using that for that. And it's been extraordinarily valuable in being able to point them even better than two vets that they've seen, where now they found things that they felt were like more reasonable is what might be elements there. So we think that's valuable. Another way you can do it is, of course, you can inject additional data into these interactions via the prompts, the preamble system messages, etc. And when you deal with models like Claude, for example, that have extremely large context windows, like 100 ,000 tokens, what's quite a bit of data that you could add if you had it to your AI and get these models to take that into account and utilize that as well.

28:40Yeah. Well, and that was what I was heading toward. You know, I've talked to a lot of people about, obviously, about LLMs in the last six months and particularly about the hallucination problem and reinforcement learning with human feedback and fine tuning. And in particular for something like a medical advice where you really want to avoid hallucinations, the solution has been to build a vector database full of trusted source material. Is there any way with UAI to do that? I mean, as, as, you know, a dabbler, so I have a project and I've talked to Pinecone and some other people and they're like, yeah, it's easy.

29:35Build a vector database. But, you know, yeah, you know, I'm not. But if I had a no-code interface and it's like, you know, drag these websites or these or upload the text and the backend will vectorize it and put it in a database or in a partition database where I could have access to it, that would be fantastic. So that's my question. Not yet, but certainly in the near future, yes. We believe that's absolutely in scope. Again, you can think of MindStudio as being, even though we call it an IDE, an integrated development environment, which is typically a technical term, right? So for things like Visual Studio Code and Sublime and all the other things people have used before.

30:35Because, again, sort of AI has now made it much simpler to be able to just speak English and explain to it what you need done. That's making things much more accessible for everyone to be able to build things, whether it's using Mind Studio or it's using GitHub Copilot, right, as you're a junior developer developing something, etc. But we were really excited about enabling many more people to be able to create things, whatever they are. And so anything that falls into sort of the scope of doing that, including allowing them to basically sort of get access to vector databases and not have to understand tokenization and any of that stuff, it certainly falls in the realm of that.

31:21That's not there now. Yeah. Today, we're a month and a half into this. again with over a thousand AIs already, but as we move forward, there'll be lots of innovation. Same thing with like these automations that we have today. So we have like in this middle section of Mind Studio, there's a tab for a preamble, there's a tab for sort of model settings where you can control various parameters like temperature or what happens if you overflow the