 Welcome to the closing ceremony of UC Berkeley's AI Hackathon. I want to call on stage the awesome, incredible Executive Director of Skydeck, Caroline Winnett. Thank you, Rene. Hi everybody! How you doing? Awesome! You ready to hear who won the hackathon? Yes, you are. How many hackers here? How many in the audience? Oh, nice. Very good. All right. We're going to get started because I think you want to hear Andre. Yes? You want to hear Andre? Yes, you want to hear Andre. All right. Let's quick run through. You want to hear some cool facts about what has been happening? This is what we're going to do today. We're going to get to our pitches soon. This is some pictures. All you hackers, did we have fun? Did we have a good time? I had an absolute blast. And yes, there were llamas, for sure. I was there most of the time. I was not there at 3 a.m. But I was so impressed with all of you. You hacked your hearts out. And I'm so proud of all of you. Whether on stage or in audience, you're all completely awesome. All right, how many people it took to make this happen? This giant number, 371, UC Berkeley Skydeck, which I represent, and Cal Hacks, educational program and student organization, so I think we did a prettyacks Educational Program and Student Organization. So I think we did a pretty decent job of getting this all together. This is how it breaks down. Hackathon at Berkeley directors, Skydeck staff, sponsors. We're gonna give some love to sponsors. As I mentioned, we're an educational program. CalHacks is a student organization. This is all because of the sponsors. So we're going to give them a ton of love when they come up on stage. You with me? Awesome. Okay, 140 judges, a hundred plus volunteers, and 80 mentors hanging out helping everybody. Let me tell you a bit about Skydeck. Who hasn't heard of Skydeck? Anybody? A couple of you. Skydeck, UC hasn't heard of Skydeck? Anybody? A couple of you. Skydeck, UC Berkeley's flagship accelerator. We host 250 startups a year. Our accelerator track gets $200,000 in investment from our dedicated venture fund. Pretty cool. Let me tell you about Berkeley Skydeck fund, our dedicated venture fund, investing in about 40 startups a year. That's a lot of startups for dedicated venture fund, investing in about 40 startups a year. That's a lot of startups for a venture fund, by the way. The 200K investment. And who wants to apply to Skydeck? July 16. I want to see all of your startup applications coming in. That's in a month. And hackathons at Berkeley are amazing student organization, truly extraordinary people who helped put us on this event. This is, of course, what they do. They do hackathons. They've been doing it for 10 years. They do about 2,500 students a year. And of course, they reach a ton of universities. How many people here not from Cal, hacking not from Cal? Fantastic, welcome. Berkeley is a place where we bring great talent in. How many people here not from Cal? Hacking not from Cal. Fantastic. Welcome. Berkeley is a place where we bring great talent in. Y'all are great talent. We brought you here. That's what we do. That's what Berkeley Hackathons does. Come to their 11th big hackathon in San Francisco in October. Check them out on social media. Get on that LinkedIn, all of that. Okay? Who's coming to San Francisco y'all coming yes okay fantastic all right thank you to our partners all of you who brought your hackers here including our friends down in the South Bay thank you for joining us and all the other great universities fantastic really happy to have you you want to have you. You want to hear Andre? Do you want to hear Andre? Yes! Please give a huge round of applause for our keynote speaker, founding member of OpenAI. I need the applause. Come on, keep going. Andre, come on up. Carpathy. Yes, big applause! Thank you. Hi, everyone. So thank you for inviting me. It's really a great pleasure to be here. I love, love, love hackathons. I think there's a huge amount of energy, a huge amount of creativity, young people trying to do cool things, learning together, creating. It's just like my favourite place to be and I've had my fair share of hackathons, so really a great pleasure to be here and talk to you today. So one thing is, this is bigger than I expected when they invited me, so this is really large here. I kind of feel like actually the scale of the hackathon is quite large, and I guess like one thing I wanted to start with is that, just in case you're wondering, this is not normal for AI. I've been in AI for about 15 years, so I can say that with confidence. And it's kind of just grown a lot. So for me, AI is a couple hundred academics getting together in a workshop of a conference and talking together about some esoteric details of some math. This is what I'm used to. This is when I entered AI, about 15 years ago. Say when you're training neural networks, you're working with these tiny digits from MNIST, you're training a restricted Boltzmann machine, you're using contrastive divergence to train your network. And then you're scrutinizing these on your first layer to make sure that the network trained correctly. And I know none of that makes any sense because it's been so long ago. But it was a different vibe back then, and it was not as crazy. I think things have really gotten out of proportion to some extent. But it is really beautiful to see the energy. And today, 15 years later, it looks a lot more like this. So this is, I guess, where AI is today, and that's also why this event is so large, I expect. So yeah, NVIDIA, the manufacturer of GPUs, which is used for all the heavy lifting for our neural networks, is now the most valuable company in the United States and has taken over. And this is the day that we live in today, and why we have so many hackathons like this and so on, which I think is quite amazing, but definitely unprecedented. And this is a very unique point in time that many of you maybe are entering the AI field right now. And this is not normal. It's super interesting, super unique. There's a ton happening. Now, I think fundamentally the reason behind that is that I think the nature of computation basically is changing. And we kind of have like a new computing paradigm that we're entering into. And this is very rare. I kind of almost feel like it's 1980s of computing all over again. And instead of having a central processing unit that works on instructions over bytes, we have these large language models which are kind of like the central processing unit working on tokens, which are little string pieces instead. And then in addition to that, we have a context window of tokens instead of a RAM of bytes, and we have equivalence of disk and everything else. So it's a bit like a computer, and this is the orchestrator, and that's why I call this like the large language model LMOS, and I've And I've tweeted about this in some more detail before. And so I see this as a new computer that we're all learning how to program and what it's good at, what it's not as good at, how to incorporate it into products, and really how to squeeze the most out of it. So that I think is quite exciting. And I think maybe many of you have seen the GPT-4 demo that came out from OpenAI three weeks ago or something like that. And you're really starting to get a sense that this is a thing that you can actually talk to. And it responds back in your natural interface of audio. And it sees and hears and can paint and can do all these things. I think potentially many of you have seen this movie. If you haven't, I would definitely watch it. It's extremely inspirational for us today, movie Her. And actually, kind of presently in this movie, when this main character here talks to the AI, that AI is called an OS, an operating system. So I think that's very present from that movie. And it's a beautiful movie, and I encourage you to watch it. Now, the thing is that in this movie, I think the focus is very much on the emotional intelligence kind of aspects of these models. But these models, in practice, in our society, will probably be doing a ton of problem-solving in the digital space. And so it's not just going to be a single digital entity that kind of in some weird way resembles a human almost, in that you can talk to it, but it's not quite a human, of course. But it's not just a single digital entity. Maybe there's many of these digital entities, and maybe we can give them tasks, and they can talk to each other and collaborate, and they have fake Slack threads, and they're just doing a ton of work in the digital space, and they're automating a ton of digital infrastructure. Not just digital infrastructure, but maybe physical infrastructure as well. And this is kind of in earlier stages, I would say, and will probably happen slightly lagging behind a lot of digital innovations, because it's so much easier to work with bits than atoms. But this is another movie that I would definitely point you to as one of my favorites. It is not very well known at all. It's called iRobot, and it's from 2004. Will Smith, amazing movie. And it kind of explores this future with like human robots doing a lot of tasks in society. And kind of spoiler alert, it doesn't go so well for these people in this movie, and the robots kind of take over a little bit. But I think it's kind of interesting to think through, and I definitely would encourage you to also watch this movie. And this movie takes place in 2035, allegedly, which is 10 years away. And so maybe in 10 years, you can definitely squint and think about that maybe we are going to be in a place where these things are walking around and talking to us and performing tasks in physical world and digital world. And what does that look like? What does that mean? And how do we program them? How do we make sure that they sort of do what we want them to, et cetera? So when you put all this together, I think the feeling that people talk about often is this feeling of AGI, like do you feel the AGI, quote, unquote. And what this means is that you really intuitively understand the magnitude of what could be coming around the corner if this stuff actually continues to work. The amount of automation that we can potentially have in both the digital space and the physical space. Now, I don't know about you, but I actually find this picture kind of bleak. This is what came out when I put a bunch of the last few minutes of talk into an image generator. And I don't actually like this picture. I think we can do better. And, you know, we have a few thousand people here. You're about to enter the industry, and you're going to be working on a lot of this technology, and're going to be shaping it and you'll have some active sort of power over it. So I don't know, maybe we want this to look something like this. I mean, this is what I would like. So this is humans, animals and nature coexisting in harmony. But secretly, this is actually a high tech society and there are robots and quadcopters and there's a ton of automation, but it's hidden away and it's not sort of like in your face. So maybe this is something that we want instead. And you should feel a lot of agency over what you want the future to be like because you're going to build it. So maybe we can agree right now that this is better than the previous picture. But I don't know about you, but I would hope so because I'm going to be living in that future I think. So the question for this hackathon, I mean a lot of you have worked on a bunch of really cool projects over the last day or two. And the question is, how do we go from hacking to actually changing the world and building this future, whatever that may be for you? And so what I thought I would do in this talk is go over maybe like my last 15 years or so in the industry. And I think I had a bit of a window into how projects become real world change, and I have some takeaways and things like that that I maybe wanted to talk about. So the first thing that I find really incredible is how projects that are sometimes very small projects, like little snowballs, can actually snowball into really big projects, and just how incredible that is to watch. So as an example, I have my fair share of hackathons, like I mentioned. These are some projects from a long time ago that I worked on over the last 15 years or so. So I had a little Rubik's Cube color extractor. I put up some game programming tutorials on YouTube like 13 years ago and tried to teach people programming for games. I had video games and a lot of them. I had this like kind of janky neuroevolution simulator, which was kind of interesting. And unsurprisingly, not all of these projects actually go on to Snowball. A lot of this is just exploration. You're tinkering. And so actually, these three projects didn't really go anywhere for me. I wouldn't say that it was really wasted work. It was just like it didn't add up to and didn't Snowball, but it was still helping me along the way. I'll come back to that later But the game programming tutorials actually ended up snowballing for me in a certain way Because that led me from game programming tutorials to a bunch of Rubik's Cube videos actually that became kind of popular at the time And this kind of sparked an interest in teaching for me. And then when I was a PhD student at Stanford I Got to teach this class cs231N and got to develop it and teach it. And this was the first big deep learning class at Stanford, and a lot of people have gone on to like this. And then after that, I ended up making another YouTube channel, which is my Zero to Hero series for deep learning and LLMs. So a lot of people like that as well. And then on top of that, continuing the snowball, the project I'm currently very interested in is this next class and what it could look like and how I can make it better. And I'm calling that LLM 101N, and it's about building a storyteller, something like kind of a chat GPT that you can work with to generate stories. And the idea is you build everything from scratch, from basic prerequisites all the way to like kind of a chat GPty clone in the domain of storytelling. Building that from scratch will be really instructive, could be really fun. I only published this on GitHub two or three days ago, so it's pretty raw and still very much in the early stages, but I'm really excited for it, and this for me is an example of a snowball. It started with 13 years ago, a little game programming, and I'm working on a course that I think will be really interesting. Thank you. Another example from my life, I think, is the snowball that I've witnessed with OpenAI. So as was briefly mentioned, I was a founding member, researcher of OpenAI, and so I was there seven years ago. These are some images that are public of what it was like working out of Greg's apartment, like eight of us. Open AI was founded to be a counterbalance to Google. Google was like this gorilla with 70 billion free cash flow. Back then, Google employed half of the AI research industry almost. It was kind of like an interesting set-up, I would say. We were just like eight people with a laptop. So that was really interesting. And very similar to my background, OpenAI ended up exploring a large number of projects internally. We hired some really good people. And many of them didn't go too far, but some of them really did work. And so as an example, here's a project that was in an early stage, a very small snowball in the early history of OpenAI. Someone worked on a Reddit chatbot. And if you come by their desk and you're like, what does this look like when someone's working on a Reddit chatbot? We're trying to compete with Google, and you're working on a Reddit chatbot. We should be doing something bigger. And so it's very easy to dismiss these small snowballs because they're so fragile. These projects are so fragile in the beginning. But actually, this Reddit chatbot, and by the way, don't read too much into the specific details. These projects are so fragile in the beginning. But actually this Reddit chatbot, and by the way, don't read too much into the specific details. These are kind of like random screenshots, just for illustration. But this was a Reddit chatbot and it looked naive. But actually, Reddit chatbot, what is that? It's a language model and it happens to be trained on Reddit, but actually you could train a language model on any arbitrary data, not just Reddit. And when the transformer came out, this was spun into something that worked much better. And then the domain was expanded from just Reddit to many other web pages. And suddenly you get GPT-1, GPT-2, 3, 4, and then you get GPT-4.0. So actually this Reddit chatbot that was so easy to dismiss actually ended up leading and snowballing into GPT-4.0, which we currently think of as this change in the computing paradigm. You can talk to it and it's amazing. So it's really incredible for me to have witnessed some of those, I guess, snowballs. And today, OpenAI, of course, is worth maybe somewhere just below $100 billion or something like that. So really incredible to see some of these snowballs in practice. So I would say a lot of you over the last two days have also worked on small projects, small snowballs maybe. And it's really incredible to me that some of them probably won't go anywhere, but probably some of them actually will. And you should continue the momentum of your projects, and maybe they can add up to a really big snowball, and that's really incredible to watch. The next thing I wanted to briefly talk about is this concept of 10,000 hours that was popularized by Malcolm Gladwell, I think. I actually am quite a big believer in it, and I think that to a very large extent, success comes from just repeated practice and just a huge amount of it, and you should be very willing to put in those 10,000 hours and just literally just count. Don't be too nervous about what am I working about, am I succeeding or failing, et cetera. Just do simple bean counting of how many hours you're doing, and everything adds up. Even the projects that I failed at and didn't snowball into anything, those add to my counter of number of hours I've spent developing my expertise and getting into an empowered state of being able to take on these projects with confidence and getting them to work. So a few examples of that. I made this really janky website a few weeks ago. This was a weekend project, and it's called awesomemovies.life. And you can visit it. I think it still works. I'm not 100% sure. I wouldn't recommend you go there. It's trying to be a movie recommendation engine, because I was trying to figure out what to watch on that Saturday, and then I was like, okay, I need to build myself a movie recommendation engine. So, I put this up, and one of the tweets that was a reply to mine was, wow, that's so cool that you got this to work in a weekend. And I was kind of reflecting on that at the time, because it wasn't as amazing to me. And the reason for that was that what this person is not seeing is that this is my 20th time making a website like this. So I see all the steps I was going to follow. I need Linode, I need a Flask server, I'm going to write some of this JavaScript style sheets, HTML, I'm going to spin this up together. I need to scrape all these web pages, I need to extract TF-IDF vectors, I need to train SVM. And all these things are things I've already done before 20 times. I already have code snippets lying around from previous projects. And I'm just remixing what I have, and I've already done before 20 times. I already have code snippets lying around from previous projects. And I'm just remixing what I have and I've already done all of this. And so remixing everything into a new form isn't actually that much work and allowed me to put this up over the weekend. And it's not that crazy. And this only comes from expertise. This only comes from having done it 20 times. And you can do this so confidently. The next example I would say in my life was a Tesla Autopilot. So I The next example I would say in my life was Tesla Autopilot. So I was hired to lead the computer vision team at Tesla Autopilot about seven or eight years ago. And one of the first things I did actually when I joined the team was I basically ended up rewriting the computer vision deep learning network training code base from scratch in PyTorch in some of the first few months that I entered the team. And I sort of rewrote the whole thing from scratch in PyTorch in some of the first few months that I entered the team. And I sort of rewrote the whole thing from scratch, and that ended up being a kernel of what it is now. And I think to some extent to some people that looked impressive at the time, but for me it wasn't because I was coming from my PhD and I spent five years doing stuff like that, and I knew exactly what needs to go into there. I need my training set, my evaluation sets, I need my training loop in PyTorch, I need my sort of configs, I need my log directories, I need to bring in a ResNet, I need to put in detection, we're doing regression, classification. And so the whole thing, like I'm anticipating all the steps, and that only comes from experience, that only comes from having done it 20 times before. And so I think this makes a huge difference. And things that look impressive are maybe much less impressive to you if you've done it 20 times before. And so I think this makes a huge difference. And things that look impressive are maybe much less impressive to you if you've done it 20 times before. So really try to get to this point where you have your 10,000 hours. It makes a huge difference. And just... Yeah, that's it. By the way, 10,000 hours, if you're doing six hours per day, I think this works out to about five years. So it's about a length of a PhD that you need to develop expertise in an area. So I think this works out to about five years. So it's about a length of a PhD that you need to develop expertise in an area. So I think it's roughly correct that that works out to about a PhD length. The other thing that I found is actually quite useful is to keep the dopamine flowing. Be aware of your psychology, your brain, how it works, and what it needs to keep going and how to keep inspired. And so in particular, your brain is a reward machine, and it wants rewards, and you need to give it rewards. So what is a good way to give it rewards? And in my practice, it is by doing projects and work on projects and continue publishing them. And so here I have a web page snippet of some of the projects I have worked on in the past. And these are hackathon projects and random projects, and not all of them are good. Some of them are not quite good, et cetera. But what I love about projects is a number of things. Number one, I love that projects get you to work on something end-to-end and depth-wise. Like, normally when you go to classes, you're learning in a breadth-wise fashion. You're learning a lot of stuff just in case you might need it in the future. Well, when you're working on a project, you know what you need, and you're learning it on demand, and you're just trying to get it to work. So I think it's a very different mode of learning that really complements the breadth-wise learning and is very important. So I 100% encourage people to work on projects. The other thing is putting them up is actually also like a really good Jedi mind trick in my experience. The reason for that is that if you're going to put something up, you're thinking about all the people who are going to be looking at it, your friends and teammates and family and future employers, et cetera. And so that really increases the bar for your own work. And it makes you work harder because they're going to be looking at it and you feel shame if it was crappy. And so you work much harder and you're going to go that extra mile to make it really good. And that really, really helps. And lastly, when other people are looking at your projects, you're going to get that reward because they like it, they appreciate it, they fork it, they work on top of it, and so that feels good to your brain. And so the way that this comes together is you are getting your dopamine, you feel good, that way you can build up to 10,000 hours of experience, and that's what helps you a lot, snowball your projects from a small snowball all the way to a really big one and actually make change in the world. So in summary, that's I think how it works like on a high level, and the message is just keep hacking. That's it. And then hopefully this is the future that we're going to build together when we snowball all of our stuff, or something like that, but not the first picture I showed, hopefully. And that's it. Thank you. Andre Kaparthi, everybody! Thank you, Andre. That was awesome. Thank you, thank you. All right. Let's get to those questions. So, we have a couple of questions. So, we have a question from the audience. So, we have a question from the audience. So, we have a question from the audience. So, we have a question from the audience. So, we have a question from the audience. So, we have a question from the audience. That was awesome. Thank you, thank you. All right, let's get to those pitches. The grand prize. Coming up, you're gonna hear eight pitches by eight projects, filtered through 290 submissions, narrowed down to eight, so y'all are gonna see some cool stuff. The grand prize is $25,000 investment, an actual term sheet from the Berkeley Skydeck Fund. They must commit to hacking all summer on their project, and they must appropriately form of course a legal entity. How do you get money otherwise? All right. I would like to now tell you briefly about how this is going to go. Eight projects as I said. Three minute pitch. You guys ready? go. Eight projects, as I said. Three-minute pitch. You guys ready? Three minutes? Yes, they're ready. The judges will then provide three minutes of feedback. And then after all the pitches, the grand judges will go and deliberate and pick a winner while we show you some other cool stuff. All right. I would like to introduce now to great applause everybody, please, because we have an incredible panel of judges. We are so pleased to have them. Please welcome our first judge, Brian Boardley with the Berkeley Skydeck Fund. Welcome, Brian. Marcy Vu with Graycroft. Welcome, Marcy. Nnamdi Iregbilem with Lightspeed. Welcome, Namdi. Irving Suh with Mayfield Fund. Welcome, Irving. Kurt Koitzer, UC Berkeley faculty and serial entrepreneur. Welcome, Kurt. And Mark Knitsberg, Berkeley faculty and director of the UC Berkeley Center for Human Compatible AI. Thank you, judges. All right, we got eight startups warming up backstage. Let's give them a little drum roll. Let's give them a little drum roll. We're going to get them going. I first have to hear if the slide's up. The slide is up first. Are you ready? Are you ready? Yes! Please give everybody a warm round of applause. They've been up all night hacking, and they're ready to share with you. Please welcome the first project, Revision. Come on out. Come on out, Revision. Oh, yes, the mic. That would be helpful. Yeah, thank you. So good evening, everyone. It's my pleasure here on behalf of my team also for the Revision project. And my name is Danika, I'm a rising senior studying computer science at UC Berkeley. We have masters of design students as well as data science students on our team and we're really excited to tell you about our project. So our project we're focusing on building an AI co-pilot tool for STEM textbook authors capable of detecting and mitigating bias in textbooks to create inclusive education content. And there is a reason why we're doing this. When considering overall representation of scientists across textbooks, only 13.1% were women and compared to 86.9% men in a 2020 study that features seven of the most frequently used biology textbooks within the US. And on average, people of color only appear every 320 pages of text, while white figures observed every 24 pages across 10 college STEM textbooks published between 2016 and 2020. So we thought about this problem deep and hard and it has been something that I've seen from my personal studies. Starting from elementary school to middle school, we constantly see different examples of word problems in other situations where text is always there. And it's not always reflective of the actual true history. And this research has been done by numerous scientists who have gone through this process of identifying people, creating databases, but there is just no current fix that, and no one is really hoping to create this problem, but there is no current fix that helps address this problem. So the textbook companies actually is who our team identified as our buying customer. The current revision process actually takes six to 12 months of a committee of five or more full-time employees working on bias checks. And the issue here is that employees are actually not experts on their topic. They also bring in their personal biases as well. So our tool would come in right in between the writing and revising part of this entire cycle that developers go through when writing textbooks. So again, here is our competitor analysis. I'm sure many of you have used Turnitin or Grammarly when you're submitting even essays. And we really think that there needs to be an additional check here for bias and checking gender, racial, political, and other biases and making this process affordable and automatic. So it's not a cost-leave process for anyone. And throughout this process, we're addressing supply chain diversity. So starting from a younger age, the elementary school students could be able to use textbooks that truly reflect the true history as well as themselves. And here is our prototype. So we have our text box here on the left side of the screen where you get to show in real time the examples of like some sort of text that a writer is creating at the moment. And on the right we have an overall score and the bias checks for different categories. And we're using machine learning models on the back end to actually identify as well as LLMs. And I'm not sure if I can play the prototype. But... Okay, yeah. It does play. So, essentially, you can click through the different links to see the breakdown. And once you actually highlight one of these, we are also adding in an API through a couple of the sponsors here, like such as Hume API and more to actually identify emotional analysis as well in the textbook writing. And in addition to this, we're hoping to build a chat bot that can actually help you also get bringing in databases from the unrecognized scientists and being able to sort of represent it because bias actually exists in three different ways. One of them is through actual like text such as representing firefighters would be nicer than saying fireman. And the other way is the entire tone and emotion analysis which is why our team used Hume API to actually detect that emotional component. And the third one is mitigating bias. So we also considered adding in the chat bot. So say for example if you want to highlight scientists that are like, for example, contributing to physics, you wouldn't just say, list a few male scientists and call it a day. We would also suggest equivalent contributions of female scientists as well. So please join me and our team in revisioning our future of education and work. Thank you. So I think everybody in communication today, nonprofit or profit, is concerned about diversity. So it seems like you have a much larger market than just textbook educators. Also a comment on market sizing and whatnot. I would think about potential ways you could expand the market here, because the number of people who are involved in writing textbooks is a relatively small group. But one way to think about it is maybe in this new era of AI-generated content, a much wider array of people can be part of this textbook generation process. That's one thing. And then I would also maybe consider selling directly to the consumers of textbooks, because in some sense, the bias you're talking about is internalized on that side of the equation, not on the manufacturer's side, and so there could be an incentive there for people who want to pay for something like this. Yeah, definitely. That's something we're considering. So the textbook would be our official bias that we're marketing to, but eventually it would be more of a grammarly checkered type of tool that anyone can use. Yeah, I had a similar comment on TAM and market opportunity and as you think about just how a textbook gets put into production that if you actually had it as a tool for whether it's news or other areas, you would have more velocity, both in terms of getting the data to improve your models, but also greater impact. I'll just flag as well. I think everyone here is hitting the theme of how do we think bigger. Even enterprises, like companies sending out communications internally or externally, I know this problem exists everywhere, so that's kind of where my brain would go to. Thank you. Thank you. Whoops. Agent OS. Please welcome Agent OS. Hey there, everyone. My name is Shashank. I'm here today with my friends, Agam and Dhruva Huja, somewhere in the crowd over here. We built today Agent OS. Picture this. You work at a hair salon, and you guys are bombarded every single day and every single year by your accounting and tax preparation qualms. These are things that are very hard to deal with, and you've heard of tools like OpenAI, ChatGPT, LLM this, ChatGPT that, everything, but you have no clue where to start using these technologies. And that's no fault of your own. The current state of the technology right now is very bad at multifunctionary tasks. More so, it's very hard as an individual developer, sometimes even non-technical, to even get started with even the simplest automations or workflows or tools with such LLMs. Even engineers with years on years of experience in this space take tens of hundreds of hours and even thousands and thousands of dollars to even get started to build something. This is where Agent OS completely transforms the landscape. With Agent OS, you're able to create multi-agent workflows in a matter of seconds from natural language. What does that even mean? Take your average corporate org structure. You have your managers, you have your workers, and sometimes you even have your interns. Everyone is really good at what they do. They have their tools, their skills. Let's say John is really good at charting and making PowerPoint skills. Let's say John is really good at charting and making PowerPoints. Let's say Steve is really good at Python coding. Everyone is really good at what they do. In this, you have a very collaborative working together to create this common solution for someone coming from higher up. That's how AgentOS was designed. Our engineers were able to replicate this human collaborative process programmatically using LLMs. What does this do? This allows everyone from the common Joe all the way up to enterprise clients to be able to interact and use these multi-agentic workflows in their day-to-day life to improve their quality of life or productivity. All in a matter of seconds and a few sentences. Let's go back to the case study of the hair salon. In the process of doing your taxes and accounting, you have multiple steps. You have your collection from your receipts and your invoices. You have calculating your cash flow, all the calculations you have to do. You have to manage your workers. And then you also have to do your general summary of what about your insights for the year, how you were spending, what you were spending on. And you have to also do a lot of clustering and analytics on this. This is a very complex workflow that's nearly impossible for modern day LLMs at the current state to do right now. You can take ChatGPT, you ask it a question for even more than three things, it'll forget what the first thing was by the time you're at the second. It doesn't work that way. With AgentOS, this completely changes where you're able to have these complex workflows. way. With Agent OS this completely changes where you're able to have these complex workflows. Let's dive into another demo. So let's say I'm an analyst at JPMorgan and my boss tells me every morning I want a report of XYZ stock in the morning, a detailed report on paper. How do I do that? I use Agent OS. On the screen you can see a bunch of other complex use cases of multiple agents working together collaboratively, but in the toolbar, in the search search bar you can see the use case of the analyst. Here I have to do market research, live stock data, I have to search the internet, go on Yahoo Finance, then I have to create my analysis, technical analysis, qualitative analysis. Then I have to do what my boss is telling me to do. And after all of that I have to create charts, graphs, and visualizations. Here, you can build tools using natural language like the one right there that says, write me a tool that fetches the meta stock price from Yahoo Finance. In a matter of seconds, the common Joe or anyone is able to create that tool, connect them to workers. You can think of workers as your everyday employees, agents, people that perform these actions using the tools, and then connect them to super teams. And these teams are able to, on the screen you see four, but you can scale this up to 40, 400, basically complex vertical and horizontal organizations that are able to perform complex decision making and complex analyses for anyone, from enterprise to consumer. What does this do? With the multi-agent, multi-team framework, this completely opens the landscape up for anyone and everyone to take on the power of LLMs into their own hands from natural language. Take your average farmer at a farmer's market. He's trying to create his marketing campaign for the upcoming farmer's market this Sunday. He has no clue where to start looking at his metrics, looking at the customers, looking at the weather, and creating these brochures, papers, pamphlets, and whatnot. With one line and one minute using Agent OS, he can create all the documentation he needs in order to enact this stuff and be able to perform successfully and continue and grow his business at his farmer's market. Things like this are completely opened up with Agent OS, and we hope to completely democratize the process of using LLMs at all scales, at all geographies, and all use cases within sentences and seconds. Thank you. That's a compelling proposition. The one thing that I worry about is right now the agents are the LLMs performing these tasks, and there's a certain question about the veracity and reliability of what they're doing. And so I think that in a future where we have that reliability, this would make perfect sense, but I would want to add a kind of tandem subject matter expert, maybe looking over the shoulder of each of the agents. I think next time I hear this pitch, I'd love to hear about the one market you're going to crush. It's hard for me to imagine serving a hairstylist one day and a Morgan Stanley analyst the next. This is a huge opportunity and a big, bold mission that you have. I would want to dig a bit deeper into your tech staff and the people you have on your team because these are really complex problems and issues. And also agree that what would be your first area of focus because it's pretty broad and wide. because it's pretty broad and wide. I'll say I kind of like the broad focus and there's a lot of individual startups tackling each of these individual problems. If it's invoicing or research, so it might be interesting to figure out how to like loop in all these other tools that are out there and really kind of just be like an interface layer and let these other companies solve the technical challenges. Yeah, I think the value proposition of creating multi-agent workflows in a matter of seconds is really compelling. I think the next step would be trying to figure out how can you go from simply performing these tasks to becoming the best at these tasks. So, for example, going after the outliers, sort of the thesis around coaching networks. Some startups do this, and they do it better for certain verticals than others. So I think doing more research around that could be really compelling. The only thing I would add is just think about enterprise security and how you solve for that. There's a lot of authentication and authorization you're going to have to do for all these agents, so just have an answer for that. Well, yeah. Thank you so much. Thank you, everyone. Thank you, Agent OS. All right, next up, Skyline. Come on out, Skyline. Hey, everyone. Hey, so my name is Rajan, and I'm a first-year student at the University of Waterloo, and I study software engineering. And I fundamentally believe that cities shouldn't be static. They should optimize and scale for the people who inhabit them. And so we built Skyline. Skyline is an optimizer, and it allows you to better understand how to model cities using agents and optimize things like traffic and transit to inherently increase mobility and reduce things like carbon emissions. So this is a very weird problem that we solved, but I want to walk through the case study of Los Angeles. So Los Angeles is one of the largest carbon emitters in North America. This is mostly because of their transit, because of the amount of cars. And so what are ways in which we can optimize this? Well, let's look directly at the people who inhabit Los Angeles. We can extract census data, things like age. We can look at things like gender. We can look at things like income. We can find population density graphs. And using this information, we can look at things like income, we can find population density graphs. And using this information we can start to find patterns. Specifically what we did is we created 500 distinct agents. Each agent is a different citizen with different interests and what we can do is we can give them each their own chain of thought. Each person here has their own day in their life. For example, this person is a very young, I believe this was a 22-year-old with a very large income. He has a long day at work and after work he goes to the gym. We can now reason about what this person may do and now model this on a map. Now once we have how these different agents are moving around, what we can do is we can try and optimize things like transit. So what we do here is we have our own proximal policy analyzer, and this allows us to create simulations on what we believe to be the best way to understand how we can move around from any point A and B in the fastest way at the lowest carbon cost. These are our own carbon cost analysis mechanisms, our own machine learning models to better understand how we may be emitting carbon and how to reduce this through our transit. So this is a lot that is through you and I think the best way for me to represent this to you is through a video. I hope this video loads. It's possible to play the video? So what we first do is we have an agent-based simulation. These are 500 distinct things in parallel that are running. Now they each go around throughout their day and what we can do is we can find patterns in how they move around. Now the best part is what we can do is now that they're all back in their original position, we can start a generation of transit. And we're using these patterns to now generate live, different transit systems that we believe to be the most optimal. So what Skyline is, we're not a company that does analysis of transit. We are a human modeling company, and that allows us to better understand and better predict how things around us will change and how we can optimize them using these patterns. Yeah, so that's Skyline. Happy to take any feedback. Wow. I just want to observe that what you're doing in creating a sort of digital twin of a city is essentially each citizen is being simulated using one of these really powerful, expensive things, a language model. And it will be probably an important step to draw from the language model some of the statistics that are actually fed in in the first place and make sure you're getting out something representative. That's very impressive. Similar comment, I think there's all sorts of economic theory about agents and modeling their behavior and their values and whatnot. The thing that usually gets you is the heterogeneity across and whatnot. And the thing that usually gets you is the sort of heterogeneity across the population. So making sure that that actually represents the populations being modeled is important. And then the other thing also related to value I would think about is just value capture for your own sake. I feel like this is like a category of software where like, yeah, the economic impact of this could be massive, but how much of that do you get to capture as a software renderer is like less clear to me, but it's very interesting. I guess I would be curious about maybe some more nuanced enterprise use cases as well, if it's concerts or security or stadiums. So just thinking about are there more micro use cases that there's a more direct ROI with for this sort of modeling? We try to consider ourselves to be a human modeling software and this is just one of the most visual applications which is Strenzy. Awesome. Thank you so much. Thank you. Thank you, Skyline. All right. Next up, we have... We have Spark. Please welcome Spark. Hi. How's everyone? How's Kali? We are Spark, and we're giving a voice to new entrepreneurs, young entrepreneurs. So, let's admit it. Cold calling is really hard. I mean, resources are hard to get. It's a steep learning curve, and getting attention is hard. If you've cold called someone, you know they don't have time. They'll say, oh, sorry, call me back later. I mean, they're busy. Everyone's busy. We have things to do, so we have to figure out how can we earn the time of working people. There's existing solutions. It's long and arduous for trial and error. It's expensive for a sales coach. And finally, if you have a sales partner, chemistry isn't easy if you're just meeting them, right? Well, we have a process. You upload a transcript to our software. We go through and analyze the emotion. We aggregate this data, and we give you productive feedback. Who's our target market? Well, look around. You guys are our target market. People who are engineers, people who love to build. And say this weekend you made some sort of product you want to sell. You don't have much experience with sales or outreach. With our software you can record your cold outreach and we can tell you what you've done right and how you can improve to hopefully land your product where it needs to be. And later on we want to expand to call centers and sales staffs because we think we can spread this across an organization and it can be highly profitable. We have usage-based tiering, so 75 cents a minute for 1,000 hours going up to 40 cents for 10,000. So this is our software. And I want to tell you guys a story. I started being an entrepreneur around six months ago, and we made an AI body cam analysis startup. So I did 100 phone calls, 100 cold calls. I got no clients. 200, 300, 500, and 700, no one was responding to me. So by 800, I got actually three, and I realized something. The human brain is pretty amazing. We're able to pick up on patterns, but the same time it's kind of inefficient because it took 800. Here we look at the emotion between every single sentence and we figure out spikes of emotion and decreasing emotions. We see that when we talk about security and data privacy with police officers it shows an increase in their interest. And this was a trend among many conversations we had. So in our analysis page, we see in the top left that mentioning AI accuracy and efficiency, increased officer safety, and discussing cost savings really helped us when we were talking to officers. Because we're some college students, right? We're dealing with some pretty confidential data. Bringing this up early really helped improve our rates. And the four things you see here in the corners are the different triggers we generated automatically based on the cold calls we had. So one is positive reactions, negative reactions, de-escalating tense situations, and normalizing exciting situations. We also generate insights too, based on whatever cold calling trends you make. We also have a RAG, so you can upload your company knowledge, your target audience, and your pricing information. So if you make a mistake, don't worry, we got your back. We'll tell you, hey, maybe instead of saying this, you could have said this because it might have helped you out a little bit. Sorry, my team picture isn't on here, but thank you to Tushar and Nick and Krishna. You guys were a great team, and I'm honored to be here representing you guys today. I'm open to feedback. Thank you. and I'm honored to be here representing you guys today. I'm open to feedback. I guess I need to be the first person to say that you're entering a pretty competitive market with other offerings here. Yeah. Yeah. I'll say something that stuck out to me was this idea of insights. But I think, you know, there's, at an organization, there might be a sales team and a marketing team and an online web team, and those teams don't really talk to each other. So maybe it's interesting to think about how do you pull insights from like one channel of sales or marketing and actually bring that into like another channel. So maybe the insights from cold calling are actually influencing what's going on the website maybe there's some interesting spots of opportunity there yeah I can actually talk about one facet of this we want to explore deeply I want to be an example say we have three founders in the company right I have a first cold call with one person and later on my second co-founder wants to set up a warmer call in the future, and then my third founder wants to set up a third call. We want to build a profile for this client as they go along so we can truly understand them. And also, we want to develop a profile on ourselves, too, so we can learn more about ourselves as we go and how we're behaving, make sure that we're learning as we go. So we're thinking, if we develop a CRM on top of this data that we leverage, then we can connect multiple teams and enable cross-functional benefit. Yeah, I had a similar comment. I think it would be really game-changing if, in addition to some of the real-time analyses you guys are doing around sentiment, where you can see the system with information on prior calls or this person's particular strengths and weaknesses and how they complement those of the other people on the team and to really build the CRM this knowledge graph around each person's strengths and weaknesses on the team to be able to better fine-tune the system yeah thank you you guys saw the analysis but also there's a long list of past conversations. You can actually go into every single conversation you've ever had and look at it deeply the same way you did in the latest conversation. I would think about the full sales funnel. This is pretty deep down in it. And as you think about where are you really going to be able to convert or where is the wedge that really matters? Because there's a lot that goes into converting a sale and it's not just the cold call. So is the issue are you actually calling the right people or is the issue like are you actually speaking to the right decision makers? So just thinking more broadly about that funnel and where you might actually be able to have the most impact and have the right wedge into the broader product suite. Yeah. Thank you. Thank you. All right. Thank you guys. We appreciate it. Bye. Enjoy your questions. Clicker. Clicker. Clicker. Thank you. Next up, we have Hear Me Out. Please welcome Hear Me Out. The big one. Hi, guys. Hi, my name is Marcus. I'm from Hear Me Out. And what we've built is an AI-driven customer service pipeline optimizing call matching and visibility. So that might leave you scratching your head. So let's just talk about the problem. So let me give you a bit of context. I'm an exchange student. And when I first came here, I had to deal with so many things. I had to deal with banking. I had to deal with insurance. I had to deal with deliveries. And I even had my car break down on me, and that was a real pain. In short, I was overwhelmed by the sheer number of customer service calls, because for each of these things, I had to make so many calls just to get things done. And I think a lot of you guys can relate to that. We've all had our fair share of bad call experiences where we're upset, the customer service representative is also upset, and nothing gets done. We've also had good experiences as well. And I think that's the core of what we're trying to tackle here. We want to create a pipeline that tries to provide optimal matching and provide visibility on emotional data to businesses. So we also did the research, and I think the numbers speak for itself. This is a key problem with a sizable market, and a sheer number of people are affected by this as well. And this is our problem statement, which is, how might businesses which offer customer service calls provide a better experience for their customers? So we think we can tackle this in four key components. First of all, an improved call bot. We all are common with that initial robo call that we have to deal with and sometimes it's really, really frustrating. How many times have you had a call about to talk to you and it just doesn't direct you to the right person? I think we've all experienced that before. Second and third, and I think this comes hand in hand, is just business visibility. We want to provide businesses with better visibility of both call experiences data as well as customer representatives bandwidth over the day as they continue to take calls. And finally, this is where we put those two together. We want to take that data and optimize a customer's journey through a better customer-to-service representative matching. So I won't bore you with this data, but with that in mind, we developed a set of decoupled microservices. And I just want to point three key parts out to you. So first of all, we want to assess customer agreeability with an initial robocall. But this won't just be your normal robocall. We want to use Hume's EVI to manage the robocall in an empathetic manner, such that it measures the customer's emotions as they go through the call, and eventually outputs an agreeability score for the customer. Second of all, we have a call analysis feedback loop and that's that whole thing on the right that goes down below. And what this does is once you have a call connected between a customer and a representative, it takes in multiple factors of data such as the call duration, the emotional change over the call, and the overall call outcome. Using Hume's emotional measurement API, we can then also generate a call score. Finally, and this is the third and key part to this, it's the matching API. Using the two things that I just mentioned, we can best match a customer to a customer service representative, which matches their vibe, their energy, and their emotions based on how our custom model is developed. So what's the outcome of all of this? As a representative goes through their day, their state changes depending on how their calls go, and their bandwidth adjusts accordingly. This affects the subsequent customers which they are matched to in a positive manner, and creates a better experience for both parties. So there's a lot more which we can build with what we have, but with this foundational pipeline, we believe we effectively tackle the problem that needs to be solved. That's all I have for today. Thank you. Nice. Thank you. Yeah, I mean, a little bit of feedback similar to the last company as well. Just there's a lot of companies working in this space too. So I would just continue to think through, you know, how to find that core differentiation if you continue to work on this after the hackathon. Yeah, I completely agree. I think a key part that we thought was really exciting was just what you can achieve with custom models. What we're doing by developing a feedback loop is we're creating something where we can create, in a sense, a model which trains itself. We can assess how calls might improve or get worse after the matching, and that feedback feedback loop is we're creating something where we can create, in a sense, a model which trains itself. We can assess how calls might improve or get worse after the matching, and that feedback gets fed straight to the matching API so that it knows whether or not it's done a good job or not. And we find that really interesting, and we think that that's a key differentiating factor which we can achieve. There might be some opportunities for building some sort of synthetic data pipeline here where you could just sort of simulate calls with an AI bot of some sort and use that as feedback. I don't know how good that data will be or not, but it could be interesting. Yeah, no, that's a really interesting thought. Thank you. I know right now you guys are targeting customer service agents as well as call centers. Something that could be interesting to think about is as you think about the different stages of the software adoption lifecycle as you go from your early adopters to your early majority and then your late majority who's eventually going to justify your evaluation in terms of like what those ideal customer profiles are going to look like down the line. Yeah, thank you for that. I think one key thing was like we actually had a judge come to us and talk to us about how they're doing something similar for sales representatives as well and we found that really interesting. So happy to figure out how we can pivot if that need arises. Thank you so much. Thank you. Thank you. Hear me out. All right. Next up we have Dispatch AI. Please welcome Dispatch. Hi, everyone. My name is Spike, and I'm with Dispatch AI. In the United States, over 80% of 911 call centers are critically understaffed. This means that in a crisis, people with real emergencies aren't able to get the support they need because all the human agents are busy, and they're often put on hold. This is particularly an issue in our neighboring city of Oakland, where last year the average wait time was over 60 seconds to pick up a 911 call. Now, in an emergency, every second counts, and this could be literally the difference between life and death. This is unacceptable, and that's why we built Dispatch AI, the world's first AI 911 call operator designed to eliminate these wait times. Our system is powered by two key components. First is the voice AI. The voice AI will step into calls when all human agents are busy, and it will work with the caller to evaluate their situation, extract the location, and optionally dispatch first responders directly to the scene. And the second part is our powerful dashboard for the operator themself. So the operator will have access to a bird's eye view of all of the ongoing calls, which will be automatically triaged by the AI into different forms of severity or priority. Further, they'll see that the AI will automatically extract the location and will provide a live transcript of the call so that they can quickly see what's going on and even step into the call once they're available. Further, they have buttons that allow them to directly, with just one click, because the location is already fetched, dispatch police, firefighters, or paramedics. All of this is done from a human-centric angle. And the way how we achieve this is by taking into account the caller's emotions. So, for instance, when a caller shows signs of anxiety or fear, the system could work more to calm them down and make them feel at ease before taking the next safe step. This system is fully designed with ethical safe guides in mind. And part of that was fine tuning a model on over 500 911 calls so that it could understand the proper protocols and be knowledgeable have a wide or have access or be knowledgeable on a wide variety of possible scenarios in which a 911 operator could assist in, including fake calls or instances where it may not need assistance. This is all powered by our innovative tech stack that utilizes a variety of AI components, including the voice AI, the emotional analysis, and of course, a key component of this, the fine tuning itself. Our mission is to make requesting emergency services more effective and efficient. Thank you. I'll go first. I thought you did a great job. I thought you presented the problem set, the opportunity, and the product very clearly. You only had three minutes, but you hit all the relevant points. Thank you. that you hit all the relevant points. Thank you. The one thing I would encourage you to think about a little bit is sort of like the optimization function for these municipalities, right? Because if people in Oakland are waiting 60 seconds to get their 911 call answered, like there's a reason for that. I don't know what that is, but somehow these municipalities have decided that that's how they want it to be. And so I would just think about like, you know, as you bring in AI to this problem, doing the like potentially difficult A-B test of making sure that whatever it is that these municipalities are actually optimizing for is actually improved by this. Because it seems like a no-brainer when you first say it, but like, clearly it's this way for some reason that is probably nuanced and tricky. So just something to think about. Any other feedback? I don't think so. Just following up on that, I think the key is ease of adoption. I mean, I think it's going to be easy to make a productivity argument to the city of Oakland, but then you've got to think about who's actually installing, who's paying for this and who's installing it. Okay, that's good. Thank you so much. Thank you. We dispatch. All right. And next up we have ASL Bridgeify. Please welcome ASL Bridgeify. Please welcome ASL Bridgeify. Hello, my name is Isha and today I'll be presenting ASL Bridgeify, the next generation of sign language in interactive learning. Oh, this? Okay, sorry. So what was the inspiration behind this? Well, ASL is the third most studied language after Spanish and English, and over a billion people are projected to have some sort of hearing loss deficiency, which is why it's even more important to have a seamless way for people with hearing loss deficiencies to communicate with people without them and vice versa. And next, there's over 15,000% return on investment over a 10-year period, demonstrating the value proposition. And existing platforms like Duolingo surprisingly do not take into account ASL learning, which is why it's important to build an interactive platform where individuals can retrieve the accuracy of their signed texts as well as characters. Now, our solution includes three proprietary AI models. First, we use the random forest algorithm in order to map input pose estimation frames of frame length of 200 to the predicted alphabet from A to Z. Next, we also use an LSTM model which captures sequential dependencies to map from hand post coordinates to the actual word. And then we also have our individualized RAG calling in as well as PDFs that are specific to ASL learning that get chunked and transformed in a vector dimension space. Now, as you can see here, this is a hand-posed estimation extraction using the media pipe library. So you can see A, B, and C. And here's our platform where you can, there are different modules to learn alphabets, signs as well as sentences and we even have a realtime ASL practice so in realtime to capture the sign that you are actually, the letter that you are actually signing and give you the accuracy for that. So here's an example of us using the media pipe library to actually extract all of the hand key points. And here are some videos where there are over hundreds of words that you can actually view to learn each of the hand signing frames. Now this is our proprietary RAG. And the way we've trained this is we've collected a variety of PDFs that are essentially manuals for ASL learning and potentially in the future we would want to incorporate things like YouTube transcriptions that can actually be transformed and embedded within our vector dimension model. Now in the future, this doesn't, ASL doesn't just, hand pose estimation doesn't just have to be localized to ASL. There are plenty of other opportunities for human pose estimation, including fields like dance, martial arts, where you can not only identify certain techniques but you can also get feedback generations from certain input frames. And in the future this could also be integrated into existing solutions such as Zoom, Loom, and FaceTime video, so if there's, so given the signing of a certain sentence transcript, you can get in real time the actual predicted sentences and words. Okay. That's nice work for 36 hours. I would be... I spent some time creating assistive technologies for the blind, and I would be just very aware of the market and how you'll approach it and who will be paying. I think that will be a good thing to pay attention to. Thank you. Yeah, as you think about the market, I feel like these language learning apps are tricky to kind of scale to meaningful businesses. You know, there's sort of like Rosetta Stone, whatever, 20 years ago, and then there's been like Duolingo in this most recent gen, but there aren't like that many that get to meaningful scale. So might be worth just thinking about that market and what are the kind of success drivers. I think even as I mentioned previously, apart from just hand pose estimation, I think that there's a big market for body pose estimation. I think especially in things like combat training, especially if you look at the military, even dance performance companies where they have to train dancers and they're actually specific techniques in which they want ground truth feedbacks for, I think those are also potential markets that could be ready to penetrate into. You chose more traditional machine learning algorithms and early neural nets like LSTM, and that may be the right answer. That's not obvious to me, but I think you would, for today's audience, we need to explain why you're not using more contemporary genii algorithms yeah so initially i we were actually thinking about using more and encoder encoder based transformer models but um we also we ran into some struggles so we just ended up we ended up settling on the lstm on the lst, but in the future, we would obviously adapt more of the state of the art transformers, and even in the case for feedback generation for given hand poses, that could be an easy encoder to decoder multi self-attention model that you could train. Okay, thank you so much. Thank you. Thank you. All right, our last contestant for the grand prize is Greenwise. Please welcome Greenwise. Please. When I was 14, I stopped eating all meat. I lasted about two months. Now, even though I still eat meat, there are a lot of small changes you can make that have a huge impact on your carbon footprint. For example, by switching from beef to chicken, you cut the carbon footprint of your meals by six times. What we do is we help you make that switch from beef to chicken for everything, for your shoes, your shirt, household supplies, food, everything has a carbon cost and a carbon footprint that we can mitigate. So how does a consumer analyze all their purchases and the carbon footprint of anything and try to make all these very difficult decisions and research about how they should change their actions? Well, this is where GreenWise comes in. Well, this is where GreenWise comes in. We seamlessly integrate with existing customer purchase models to basically input what the consumer is already doing, for example, through receipts or through emails, and we integrate with Apple Pay, with Amazon, and with Square to automatically get their purchases into our system. From there, we vectorize their purchase and compare it to our vector database. This database has all the carbon footprints of over 10,000 products that we've analyzed and made sure that these are accurate carbon estimates. Additionally, by using the vector embedding, we make sure that these similarity scores are very accurate. It's not an LLM that can hallucinate. These are real accuracy scores and real carbon predictions. From there, it directly can tell them an alternative product that is very similar but has a less carbon footprint. Additionally, this presents a lot of room for scaling. When other businesses want to analyze their carbon footprint for their products or for events and other bigger venues. So, from good intentions to reality. Let's make it happen. It's a very innovative rag use case. I would have never thought of that. We're not using rag. Oh, it's not? It's similar in that it uses a vector embedding for finding similarity, but the similarity is directly the output. Yes that's right. Yeah. Is this a subscription product? So you would imagine it being a subscription product? We would imagine. Probably not. We ideally we'd integrate with existing businesses like Instacart or Safeway so that they can show uh our results on how green or how uh the carbon footprint of certain products is on their uh app. Um but it also works for consumers to use on their own as demonstrated here. People wouldn't pay for a subscription though. Okay, I think that's all the comments. Thank you so much. GreenWise, thank you. the comments. Thank you so much. GreenWise, thank you. All right, I would now like to invite our esteemed judges to convene in the secret room where judges make their decisions. And we are going to have the special prizes. So as I mentioned, a bunch of sponsors came to make this all happen. We're an educational program, and it is entirely the support of these sponsors. And they're not just providing support, they got cool prizes! So let's bring them on. In just a minute you're going to hear from each one. These are the sponsors for today. And I also want to thank our community sponsors. These are startups, very cool startups who hung out and helped our young hackers with their needs and their cool tools. All right. So our very first special prize is going to be announced by a very special campus partner. I'd like to welcome the academic innovation catalyst. I'd like to welcome out here Matt Cincini and Paul Wuerck to tell you about AIC, one of our newest campus partners doing very cool stuff. Please give them a welcome. Thank you. Thank you so much, Caroline. It's just a thrill to be here. So my partner and I, Paul and I created Academic Innovation Catalyst, or AIC, to release more innovation from academic research for positive social impact. And we're focused initially on the areas of climate tech and AI for good, which is why we're here today. How do we do this? Well, we make proof of concept grants, so no strings attached, non-dilutive grants to academics with an idea, and then we help them take that idea, carry it through commercialization to scale and sustainability. And so that's what we do. We're thrilled to be here today, and we'll be making two awards to the most compelling business plans or innovations involving the use of AI to make the world a better place. And we couldn't be more excited to announce them in five seconds here. I'll just say that we met with many amazing teams. It's been an extraordinary weekend. Thank you so much for including us. We had to narrow it down to two. It was tough, but I think you'll see that they're well-deserving. So with that, let me hand it to my partner, Paul Work, to announce the winners of the AIC AI for Good Awards in 19, I'm sorry, 2024. This is what happens when you get old people up here on stage. So at any rate, we are really thrilled to be here, as Matt said, and we're especially thrilled with the fact that so many of you are putting your talents to work for such great causes and for the betterment of humanity. And AI has so much potential in so many realms, but among the most important is to make the world a better place and to make a social impact. make the world a better place and to make a social impact. And so with that, we're thrilled to announce the first two winners, Dispatch AI and ASL Bridgeify. So these are tremendous companies. Again, the competition was so strong. May I ask, actually, both sets of winners to stand in the audience here? And thank you again so much for the for the terrific work. I think as you heard ASL Bridgeify is doing for sign language what Duolingo has done for learning other languages and it is so important. It's an incredible and shocking that it's an underserved and currently not served market, and their technologies are going to change that. And dispatch AI, what can you say? I mean, it's such an important issue to be able to get emergency response, to be able to get a response when you need it. And, of course, the reality is, when we have, unfortunately, too many mass catastrophes, the time when you need the response is that you're often most short-staffed. And so dispatch AI is using artificial intelligence and a variety of technologies to speed that process up and to help both the dispatchers and the people that the dispatchers are helping. And so can I ask the dispatch AI team to stand up as well and be recognized? It's a great job. Congratulations to all of you and to everyone who is here today. Thank you so much. Thank you, Matt and Paul. Thank you, Academic Innovation Catalyst. Our next special prize is going to be introduced by our very own general manager at Skydeck, Sybil Chen. Give her a welcome. Hello, everyone. Hope everyone has had a great weekend. At Skydeck, about a year and a half ago, we launched the Skydeck Climate Tech Track, in part thanks to a nice grant from the university, $150,000 to build out the track. And right away, we started putting that to work. We grew our advisor network from maybe like five advisors in climate tech to now over 30 advisors that are in the climate tech space. And beyond that, prior to the grant, we had maybe three or five startups every batch that were in climate tech. And now we average 15 startups per batch in the climate tech space. And we really hope to see that grow. So I'm very pleased to announce that the winner of the SCADIC Climate Tech Track is Team GreenWise. I think they're still in the green room because they just pitched. They were the last ones to go on stage. But they really kind of represent the team members that we like to see at early stage startups. It's three team members that are best friends from middle school. Oh, they're all here on stage. Come on out. I wasn't expecting that. But Anthony, Ben and Ethan, three best friends from middle school representing UC Davis, UC Santa Cruz and UC Barbara, UC Santa Barbara. And they've built a platform for carbon footprint tracking with actionable recommendations for vendors so that people and companies can reduce their overall carbon footprint. So please help me in congratulating this team. Winners of $25,000. All right. Thank you so much. Thank you, Green. Clicker. Clicker. Thank you. All right. Next up, special prizes from Intel. Intel, come on out. Intel was here. Their table was swamped. I'd like to introduce Gabrielle Amaranto. Hi, everyone. Thank you all so much, and thank you to the organizers for having us. We've had such a great weekend, and your projects are so amazing, so thank you to everyone who joined our track. As you can see, the winners behind me. Congrats to all the winners. We have our raffle winner, Isla Arres. Third place is Asel. Second place is Batteries by LLM. And first place is Dispatch AI. So let's give them a round of applause. Yes, great job, amazing projects. If you won, please meet us outside. I wanna hand you your prizes. We have them with us. So please meet us outside so we can take pictures and hand you your prizes. We have them with us. So please meet us outside so we can take pictures and give you your prizes. Thank you. Thank you, Intel. All right, next up, AWS. Come on stage, AWS. We've got Rohit Tauri, Kevin Liu, and Brandon Middleton. And that's what they're going to tell you. Go ahead, Rohit. and that's what they're going to tell you. Go ahead, Raheet. Howdy there. Can you all hear me? Yes? Awesome. Well, hey, thank you so much, Skydeck team, for having us and CalHacks. This has been an extremely impressively run operation, and we're really excited to be partners and sponsors of this hackathon. Today we have three different prizes. Actually, let me introduce myself first. We have Brandon, Kevin, and Rohit. We are members of the generative AI organization at AWS. We work with top generative AI startups like Anthropix, Stability, Perplexity, and others in the development of their large language models, as well as our overall kind of inorganic and organic growth strategy, including investments as well. Today we have three different prizes. We have four of the teams that we have chosen to give the prizes out to. Our first place prize is for $10,000 in AWS credits, and we have two other prizes, one for climate tech and then one for responsible AI, which are 5,000. I did want to say that we talked to so many impressive startups and founding teams today and hackathon teams today. I wish we could give prizes to all of them. We did want to recommend that those who we spoke with, and I think we had these conversations with you already, to go ahead and apply for the AWS Generative AI Accelerator, as well as our AWS Activate program to receive additional credits. I'll go first. I'm going to be announcing the Climate Tech. We're going to give the prize out to Disaster Aid. Is Disaster Aid in the room today? Yes. Good job, guys. Kevin? And then for Responsible AI, we have a two-way tie, so we're splitting that pride into 2.5K for each team in credits, and that's GPT Ethics and DP Ancestry. They're in the hall. All right, and I'll round us out. Our grand prize, kind of the most impressive thing that we heard and saw today, is going to go to Safeguard. So Safeguard team, if you're in the building, stand up real fast. Let's give you a round of applause. I don't see them, but God bless you and keep doing what you're doing. Thanks so much, guys. Thank you. Thank you, Intel. Our next amazing partner, Reach Capital. Please come out, Tony Wan. Oh, out of order. Let me see if I can find you. There you are. Okay. Mike. Awesome. Thank you so much. Thank you to CalHacks. Thank you to Skydeck. It is such a delight and pleasure to be with all of you today. And thank you to everyone for being here from across the country, from across the world. My name is Tony, and I'm from Reach Capital. And let's just cut to the chase, because there's no drama here. We want to congratulate Frodo for winning our AI and Education Prize. Frodo, Aman, Kush, and the team, if you are here, please stand up. Please stand up. All right, you are right up front. You are in the right place. Thank you so much. You've won the one ring, as they say, or at least our $1,000 cash prize. So please, let's meet up afterwards. Rich Capital, we're an early-stage edtech VC firm investing in edtech. We invest in education across K-12, higher ed, and workforce in tools that expand access to education and accountability. And many of the companies in the portfolio were founded by students themselves. You know, like what better place to find great ideas and great talent than to go to places like this where students are living that experience. So if you're building a venture in ed tech, please reach out. Thank you so much. Thank you, Tony. Next up we have you.com. We have Rex. Come on out, Rex, and tell us about the prize. Applause, please, for our sponsors. Thank you. Thank you so much. Yeah, so we wanted to announce I'm Rex. We're from you.com. This is Oliver. As you know, you.com brings transparency and web access to our LLMs to make sure that they are as accurate as possible. So we wanted to give an award for the best use of U.com's APIs to Transparify. So congratulations. If you guys want to stand up. If you're here. There you guys are. Yeah. Thank you so much. Transparify did an incredible job by live streaming videos, fact checking them as they went, using sources from the web, and u.com search APIs. It was really incredible and powerful. And Oliver will talk about our custom assistant. OLIVER CHIEN PANZERALEKKE- Yeah, so for our best custom assistant, we'd like to give that to events-.ai with Oliver and Devesh. So Oliver and Devesh, can you please stand up if you're in the room? Congrats. Over there. Yeah, so we were particularly impressed by what they've built. Essentially, they handle booking, searching, and even talking with customer agents on the phone. And they used u.com in a way to actually find these events. So we were incredibly impressed by them and can't wait to see what they do in the future. Yeah, come find us after, and we will give you your awards. Thank you, Hume. Or you, thank you. All right, I think we're going back to Hume now with Zach Greathouse. Welcome, Hume. Nice round of applause, please. Hi. So first, just a huge thanks to Skydeck and CalHacks for organizing this event and inviting us back. And to all the staff for running such a memorable event. So I'm going to be announcing our three categories for our track. We have our best AI, our best empathic AI for social good, best empathic AI for just most innovative empathic AI, and then just the best overall. As you can see, the team's here. We've chosen Scam Scanner. Can Scam Scanner, are you here? Can you stand up? All right, big, absurd applause. For most innovative, we have Bloom Buddy. Where's Bloom Buddy? Can you stand up? Yeah, okay, great job, you guys. Talking to plants. And then best use of Empathic AI overall, we chose Lock-In. It's a personalized learning assistant. Are you in the room? Where are you? Yeah, there we are. Okay, congratulations, you guys. Come meet us after outside. We'd love to chat, take pictures. And thank you so much. Thank you to all the participants. Yeah, we'll maybe see you next year. So take care. All right, thanks, Hume. All right, and our last special prize is Grok. There they are. Please welcome Jose Menendez. Hey, everyone. Very nice to be here. For those of you who haven't heard about Grok, grok.com experienced the fastest influence on Earth, period. All I have to say about Grok right now. But our special Grok star award today goes to ScamScanner. Where are you guys? So these guys have a product that I want my mom to use today, right? Monitor your call for potential scams. Who doesn't want that for your mom, your uncles, and the whole thing? Now they get 1,000 credits on GrokCloud, which is many, many millions of tokens. There's two special mentions. I have to read so I don't screw up. 3brown1blue, where are you guys? Another awesome solution. These guys are generating on the fly incredible videos for math. Something that I would use right now as well. And Transvaryfy, are you guys around here? You, you've been mentioned as well. Transvaryfy, very cool. Who doesn't want to hear a podcast with instant fact checking, right? Am I right? Now, my special surprise for the day, I want to make a very special mention of Nathan Buck. Are you around? Nathan! All right. Nathan didn't use Rock, so I'm going to give a special Technical Excellence Award to Nathan for a model he trained and ran on his CPU for doing very interesting DOM operations corrections on the fly for front-end. Not only that, Nathan is invited officially to present his work in the Grok HQ to present his work in the Grok HQ as soon as he can. That's it, guys. I'm very impressed with all the work we saw. Thank you very much, congratulations. Thank you, Grok. All right. Our esteemed judges are back with their determination. Please come back, judges. Come back so we can all enjoy the grand prize are you guys ready do you have a guess is there a voting do we have voting tally taking bets everybody I want you to guess the top your top two choices for grand prize and I'm then I'm gonna ask who got it right. Okay, so as our wonderful judges take their seat. All right, we got some shout outs going here. Any other shout outs? Okay. All right, this audience is into it. So as a reminder, the grand prize is a $25,000 investment from the Berkeley Skydeck Fund. Also, a golden ticket to our PAD 13 program at Skydeck. And a special prize. We are happy to announce that OpenAI is providing $2,500 in credits for this winner. So I think we're ready for the drumroll. Take your guesses. Only the judges know. I don't know. We're all about to find out. It's Dispatch AI! Dispatch, where are you? Come on up. There's stairs right there. Come on. Come to the front stage. There you go. Thank you, judges. I want to invite, while we invite Dispatch up, I want to thank all of you for coming. I want to invite Spike from Dispatch. Okay, here's the team. There we go. Dispatch AI, grand prize winners. Well done. Well done. I'd like to invite the Skydeck staff to come out and the Berkeley Hackathon staff to come out. Come on out. They've been working all weekend. I think some of them did not sleep at all. Please give everyone who joined to make this a huge round of applause. Thank you, everybody. Thanks for joining us. We will see you next year.
Andrej Karpathy's Keynote & Winner Pitches at UC Berkeley AI Hackathon 2024 Awards Ceremony
At the 2024 UC Berkeley AI Hackathon's Awards Ceremony, the atmosphere was electric as Andrej Karpathy, founding member of OpenAI, delivered an inspiring keynote. Out of 371 projects, the top 8 teams took the stage to pitch their groundbreaking AI solutions. After intense deliberation by our esteemed judges, the big reveal came: up to $100K in prizes were awarded, celebrating innovation and creativity in AI for Good. Missed the live ceremony? Relive the excitement and watch the future of AI unfold! 

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Chapters:
0:00 Welcome
0:19 Caroline Winnett
4:05 Andrej Karpathy Keynote Speech
22:20 Pitch Overview
23:29 Judge Introductions
24:43 Revision
31:23 Agent.OS
38:54 Skyline
44:32 Spark
51:35 HearMeOut
57:05 Dispatch.Ai
1:02:04 ASL Bridgify
1:08:57 Greenwise
1:13:35 Special Prize 1
1:17:24 Special Prize 2
1:19:30 Special Prize 3
1:20:45 Special Prize 4
1:23:15 Special Prize 5
1:24:27 Special Prize 6
1:26:00 Special Prize 7
1:27:24 Special Prize 8
1:30:10 Grand Prize Winner




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