[Applause] welcome to the closing ceremony
of UC Berkeley's AI hackathon uh I want to call on stage the awesome incredible executive
director of SkyDeck Caroline Winnett thank you very now you keep that 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
Andrej yes you want to hear Andrej yes you want to hear Andrej 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 a 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 an 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 pretty decent job of getting this all together this is how it breaks down Hackathon at Berkeley
directors, Skydeck staff sponsors we're going to give some love to sponsors as I mentioned we're
an educational program Cal Hacks 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 100 plus volunteers and 80 mentors hanging out helping everybody let me tell you a bit about
sky SkyDeck who 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 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 this on this event this is of course what
they do they do hackathons they've been doing it for 10 years they do about 2500 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 you 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 linked and
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 hear Andrej do you want to hear Andrej yes please give a huge round
of applause for our keynote speaker founding member of open AI I need the Applause come on keep
going Andrej come on out, Karpathy, yes big Applause thanks you hi everyone yeah uh so thank you for inviting
me it's really a great pleasure to be here um I love love love hackathons I think there's
you know huge amount of energy huge amount of creativity young people trying to do cool
things learning together creating I don't it's just like my favorite place to be and I've had
my fair share of hackathons so really a great pleasure to be be here and talk to you today um
so one thing is this is bigger than I expected when they invited me so this is really large here
um I kind of feel like actually the scale of uh the hackathon is quite large and I guess like one
thing I wanted to start with is that just in case you're wondering uh this is not normal for AI
I've been in AI for about 15 years so I can say that with confidence and uh you know it's kind of
just like grown a lot so for me AI is is you know a couple hundred uh academics getting together
in like a workshop of a conference and you know talking together about some esoteric details
of some math and uh so this is what I'm used to uh this is when I entered AI about 15 years ago
you're working with say when you're training um neural networks you're working with these tiny
digits from MNIST you're training a restricted boltim machine you're using contrastive Divergence
to train your network and then you're scrutinizing these G 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 uh
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 uh so this is I guess where AI is today uh and that's also
why this event is large I expect um so yeah Nvidia the manufacturer of gpus which is used for all
the heavy lifting for our neural networks is is now the most valuable company in the United States
and has taken over and uh 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 you're many 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 uh we're 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 uh you know works on instructions over bytes we have these
large language models which are kind of like the central processing unit uh working on tokens
which are little string pieces instead and uh then in addition to that we have a contact window
of tokens instead of a ram of bytes and we have equivalence of dis 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 uh I've sort of like 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 uh what it's good at what it's not
as good at how to incorporate into product 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 40 demo that came out from
open AI two three weeks ago or something like that and you're really starting to get a sense that
this is uh this is a thing that you can actually talk to and uh it responds back in your natur um
natural interface of like audio and it sees and hears and can paint and can do all these things
uh 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 uh movie her and actually kind kind of presently in this
movie um when uh this main character here talks to the AI that AI is called an OS an operating system
so I think that's very precedent from that movie uh 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 like 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 uh they're automating a ton of digital infrastructure not just uh
digital infrastructure uh but maybe physical infrastructure as well and this is kind of an
earlier stages I would say and will probably happen uh slightly lagging behind a lot of the
digital Innovations because it's so much easier to work with bits than atoms uh but this is
another movie that I would definitely point you to as one of my favorites it is not well
very well known at all it's called I Robot and it's from 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 like
take over a little bit uh but um 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 uh 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 you know they um that they sort of
do what we want them to Etc 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 the stuff actually continues to work um 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 uh this is what came out when I put a bunch of the
last few minutes of talk into a image generator and I don't actually like this picture I think
we can do better and you know you have 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 you'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 this is what I would like um so this is humans animals and
nature coexisting in Harmony and 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 uh it's
not sort of like in your face and uh 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 uh 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 hackaton I mean a lot of you have worked on a really a bunch of really cool project 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 um 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 and 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 all of snowballs can actually like 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 uh
I had a video games and a lot of them I had this like kind of jankie neuroevolution simulator which
uh 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 like helping me along the way I'll come
back to that later uh 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 is kind of sparked an interest
in teaching for me and then when I was a PhD student at Stanford I uh got to teach this class
cs231n um and got to develop it and teach it and this was the first like big deep learning class
at Stanford and uh a lot of people have gone on to like this and then after that I ended up
making another YouTube channel which is um my Zero to Hero series for deep learning and all 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 uh from basic prerequisites all the way to like kind of a chat GPT clone in the domain
of Storytelling and building that from scratch I think will be really instructive could be really
fun I only published this on GitHub like 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 like 13 years ago little game programming and I'm working on a course
that I think will be really interesting um thank you another example from my life I think is the
snowball that I've witnessed with open AI so as was briefly mentioned I was a founding member
researcher of open AI and so I was there 7 years ago these are some images that are public of
what it was like um uh working out of Greg's apartment like eight of us and uh open AI was
founded to be kind of like a counterbalance to Google and Google had was like this gorilla with
70 billion free cash flow and back then Google employed like half of the AI research industry
almost so it was kind of like a uh you know um an interesting setup I would say and we were just
like eight people with a laptop so that was really interesting and very similar to my background Open
AI I ended up exploring a large number of projects internally we hired some really good people and
many of them like didn't go uh too far but some of them really did work and so as an example
here's a project that uh was in an early stage a very small snowball at in the early history
of open AI someone worked on a Reddit chatbot and if you come by their desk and you're like I
what does this look like when someone's working on a Reddit chatbot we're trying to like compete
with Google and you working on a Reddit chatbot like we should be doing something bigger uh and
so it's very easy to dismiss these small snowballs because they're so fragile right 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 uh 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 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 gpt1 gpt2 3 4 and then you get GPT 40 so actually this Reddit chat
bot that was so easy to dismiss uh actually like ended up leading uh and snowballing into GPT 40
which we currently think of is this like change in the Computing Paradigm and you can talk to it
and it's amazing so it's really incredible for me to have witnessed some of those um I guess
snowballs and today opening a of course is worth uh maybe somewhere just below 1 billion
or something like that so A really incredible uh 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 project small
snowballs maybe and it's really incredible to me that some some of them probably won't go anywhere
but probably some of them actually will and uh you should continue the momentum of your projects and
maybe they can add up to a really big uh 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 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 etc just do simple B counting of how many
hours you're going to 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 um I made this like really janky
website a few uh weeks ago this was a weekend project and it's called awesomemovies. life and
you 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 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
like making a website like this uh like and so I see all the steps that what's going to follow okay
I need a linode I need a flask server I'm going to write some some of this JavaScript stylesheets
HTML I'm going to spin this up together I need all I need to scrape all these web pages I need
to extract tfidf vectors I need to train svm and 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 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 that
you can do this so confidently the next example I would say in my life was a Tesla autopilot so um
I was hired to lead the computer vision team at Tesla autopilot about seven or eight years ago and
uh one of the first things I did actually when I joined the team was I basically ended up rewriting
the computer vision uh deep Learning Network uh training codebase uh from scratch in pytorch in
some of the first few months that I entered the team and I sort of agree with 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 um uh sort of configs
I need my log directories I need to bring in a resonet I need to put in detection we're doing a
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 may be much less impressive to you if you've
done it 20 times before so so really try to get to this point where you have your 10,000 hours
it makes a huge difference and uh just uh 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 5 years uh so it's about a length of a PhD that
you need to develop expertise in an area uh 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 uh 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 hackaton projects
and random projects and not all of them are good some of them are not quite good Etc but what I
love about project is a number of things number one I love that projects get you to work on
something end to end and depthwise 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 breath wise learning and is very important so 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 Etc and so that really increases the bar for your own work and it 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
uh really helps um and lastly when other people are looking at your projects uh 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 project 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 uh and then hopefully uh this is
the feature that we're going to build together when we snowball all
of our stuff or something like that uh but not the not the first picture
I showed hopefully and that's it thank you Andrej Kaparthy everybody thank you Andre
that was awesome thank you thank you all right let's get to those pitches the grand prize coming
up you're going to hear eight pitches by eight projects filtered through 290 submissions narrowed
down to eight so you all going to 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 uh tell you briefly about how this is going to go um eight projects
as I said three minute pitch you guys ready three minutes yes they're ready um the judges will then
provide three minutes of feedback and then after all the pitches the the grand judges will go and
deliberate and pick a winner well 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 is
we are so pleased to have them please welcome our first judge Brian Bordley with the Berkeley skyc
fund welcome Brian Marcy Vu with Greycroft welcome Ninamdi Iregbulem with light speed welcome Nami
Irving Sue with Mayfield fund welcome Irving Kurt quiter UC Berkeley faculty and serial
entrepreneur welcome Kurt and Mark nitzburg 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 him a little drum roll we're going to
get him going I first have to hear if the slides up the slide is up first are you ready 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 [Applause] revision okay oh yes the mic that would be helpful yeah thank
thank you so um good evening everyone it's my pleasure here on the behalf of my team also um
for the revision project and my name is Danica 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 o only 13.1% were women and
compared to 86.9% men in a 2020 study that featured 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 are 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 and starting from elementary school to middle school we constantly
see different examples of word problems and 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 text companies actually is who we 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 um this
this entire cycle that developers go through through when writing textbooks so again here
is our competitive 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 uh so it's not a cost it's not a costly process for anyone and through
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 um 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 um different categories and we're using machine learning models on the back
end to actually identify these as well as llms and I'm not sure if I can play the Prototype
but okay yeah it does play so um essentially you can click through the different links to see the
breakdown and once you actually um highlight one of these we are also adding in an API through
Hume API uh through through a couple of the sponsors here um like such as hume API and more
to actually identify emotional analysis as well um 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 data databases
from the unrecognized scientists and being able to sort of represent it um because bias actually
exists in three different ways one of them is through actual like text such as um representing
firefighters um would be nicer than saying Fire Man and the other way is that um the entire tone
and emotional analysis which is why our team used hume API to actually um detect that emotional
component and the third one is mitigating bias so um 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 in 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 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 um textbook Educators um also a comment on kind of like Market sizing
and whatnot I would I would think about you know 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 like maybe in this new era of AI generated content a much wider
array of people can be part of this textbook generation process so that's one thing and then
I would also maybe consider selling directly to the consumers of textbooks in some sense the bias
you're talking about is internalized on that side of the equation not on the manufacturer side and
so there could be an incentive there for people to want to pay for something like those yeah
definitely that's something we're considering so like the textbook would be our official
buyers that we're marketing to but eventually it would be more of like a grammarly checker
type of tool that anyone can use yeah I had a similar comment on Tam and Market opportunity
and as 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 have more velocity um both in terms of getting the
data to improve your models but also um greater impact yeah I'll just I'll just like as well
I mean similar I think everyone here is kind of hitting the theme of how do we think
bigger um so even Enterprises right like companies setting out communication
internally or externally I know this this this problem exists everywhere so
that's kind of where my brain would go too okay thank you yeah 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 Dhruvsomewhere 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 open AI chat GPT llm this chat GPT 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 multi- functionary 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 PowerPoints
let's say Steve is really good at python coding everyone's 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 that's how agent OS was designed our Engineers Dhruv and Ugam 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-agent agentic workflows in their day-to-day life to improve
their quality of life or productivity in all in a matter of seconds and a few sentences let's
go back to the 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 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 chat GPT 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
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 JP Morgan and my boss tells me every morning I want
a report of uh 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 toolbox in the 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 this 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 Farmers
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 continually and grow his business at his Farmers
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 [Applause] you that's a a compelling proposition
um the one thing that I worry about is right now um the agents are the the
uh the llms you know uh uh performing these tasks and there's a certain uh uh
certain question about the veracity and reliability of what they're doing and so
I I I think this that in a future where we have that reliability uh this this would
make perfect sense but I 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 uh it's hard for me to imagine uh serving a hair
stylist 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 um what would be your first area of focus because it's 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 you know 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 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 um some startups do this and they do
it better for like certain verticals than other so I think um doing more research around that
could be really compelling only only thing I would add is just think about you know 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 a answer for that well yeah thank
you so much thank you everyone thank you AGL 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 a uh an an Optimizer and it allows
you to better understand how to model cities using agents and optimize things like traffic
and and transit to uh to 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 through the case
study of Los Angeles so Los Angeles is one of the largest carbon emitters in North America uh
this 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 um gender we can look at things like income
uh 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 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
uh I believe this was a a 22-year-old with a very large income he's 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 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
um how how we can move around from any point a be in in in the fastest way at the lowest
carbon cost we use our own carbon cost analysis mechanisms uh our own machine learning models to
better understand how we may be emitting carbon uh and and how to reduce this through our Transit
so this is a lot that it 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 we're not a company that does uh 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 um yeah so so
that's Skyline um happy to take any [Applause] feedback wow uh I I just want to uh observe that um what
what you're doing in creating a sort of digital twin of a city uh is for the the the essentially
the the you know the the um uh each citizen is being simulated using you know one of these
really powerful expensive things a language model and um uh it it it will be uh probably
an important step to uh to to draw from the language model some of the uh statistics that
that are actually fed in in the first place make sure you're getting what you're what getting
out what you know something representative but I I that's very impressive yeah similar
comment I think you know there's all sorts of like economic theory about you know agents and
modeling their behavior and their values and whatnot and the thing that usually gets you is
a sort of heterogeneity across the population so making sure that that actually represents
the populations being modeled is important um and then the other thing also related to Value
I would think about is just value capture for your own sake because 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 vendor is like less clear to me but it's very
interesting I guess I would be curious about maybe some more like nuanced Enterprise use
cases as well if it's concerts um or security or stadiums so kind of just thinking about like
are there more micro use cases that there's a more direct Roi with um for this sort of modeling
yeah we we we tried to consider ourselves to be a human modeling software and this is just
one of the most visual applications which is Transit okay awesome thank you so much thank
Youk thank you Skyline all right next up we have we have spark please welcome [Applause] spark
hi how's everyone how's Cali 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
it's 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 uses usage based teering 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 6 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 at 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 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
escalate or de-escalating tense situations and normalizing exciting situations we also generate
insights too based on whatever cold calling Trends you make we 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 might have helped you out a little bit sorry my team picture isn't on here
but thank you to tusar 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 I guess I need to be the first person to say that
you're uh entering a pretty competitive uh Market with other offerings here so yeah yeah I'll say
something that stuck out to me was this idea of of insights but I I I think you know there's at
a organization there's going to 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 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 um spots of opportunity there yeah I could actually
talk about one facet of this we want to explore deeply uh I want to give you 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 right 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 uh 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 uh 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 um Knowledge Graph around each person's strengths
and weaknesses on the team to be able to better find 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 like latest
conversation I would think about the full sales funnel um 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 where is the wedge
that really matters because there's a lot that goes into converting a sale um 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 and have the right wedge into
the broader product weet yeah s thank you thank you all right thank you guys we appreciate
it bye enjoy your question clicker Clicker thank you okay next up we have hear me out
please welcome hear me [Applause] out the big one okay thank you all right guys hey hi my name
is Marcus um I'm from here 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 even my I even had my car break down to me
and that was a real pain um 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 callbot we all are common with that
initial robocall that we have to deal with and sometimes it's really really frustrating how
many times you had a call 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 is
just business visibility we want to provide businesses with better visibility of both call
and uh both call experiences data as well as customer Representatives bandwidths over the
day as they continue to take calls and finally the this is where we put those two together we
want to take that data and optimize a customer's journey through a best through a better customer
to service representative matching so I won't bore you with this um data but in our with with that in
mind we developed a set of decoupled microservices and I just want to uh point three key Parts
out to you so first of all um 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 API 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 um 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 C 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 hand 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 [Applause] [Music] you nice thank you yeah I mean a 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 um you know if you
continue to work on this after the the hackathon yeah I completely agree I think a key part um
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 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 differential factor which we can achieve there might be some there might be
some opportunities for building some sort of like synthetic data pipeline here where you
could like just sort of simulate calls with like an AI bot of some sort and um use that
as feedback I don't know how good that data will be or not but could be interesting yeah
know that's a really interesting thought thank you yeah I know right now you guys are targeting
customer service agents as well as call centers um something that could be interesting to think
about is as you think about the different stages of the software adoption life cycle 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 um I think one key thing was like
we actually had a 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 yeah thank you so much cool thank you thank you hear me out all right next up we have
dispatch AI please welcome [Applause] Dispatch AI 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 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 weight
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 Direct directly to the scene and
the second part is our powerful dashboard for the operator themselves 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 we'll 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's 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 callers emotions so for instance one of callers
shows signs of anxiety or fear the system could work more to calm them down and make them feel
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 have a wide or have ACC or be knowledgeable on a wide variety of possible
scenarios in which a 911 operator could assist him including fake calls or in 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 um 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
um I thought you presented the problem set the opportunity and the product very clearly
um and you only had three minutes but you hit all the relevant points thank you the the one
thing I would encourage you to think about a little bit is um sort of like the optimization
function for these municipalities right CU if people Oakland are waiting 6 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'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 AB 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
and tricky so just something to think about any other feedback or I don't think so well
just just following up on that I think the key is ease of adoption I mean I think you
you it's easy going to be easy to make a productivity argument to to the city of
Oakland but then you got to think about who's actually installing who's who's
paying for this and who's installing it okay think that's good thank you so much thank
you the dispatch all right and next up we have ASL bridgeify please welcome ASL [Applause]
bridgefy hello my name is ISA and today I'll be presenting ASL bridgefy the next generation
of sign language Interactive Learning oh oops uh 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 hearing loss deficiency which is why
it's even more important to have a seamless way of for people with hearing loss deficiencies
to communicate with people without them and vice versa and next there's over a 15,000 per
return 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 a plat an interactive platform where where individuals can retrieve
the accuracy of their signed texts as well as characters okay now our solution includes three
proprietary AI models first we use random Forest the random Forest algorithm in order to in in
order to map input po pose estimation frames of frame length of 200 to to the predicted um to
the predicted Al alphabet from A to Z next we also use an LS lstm model which captures sequential
dependencies to map from to to map from hand hand pose coordinates to the actual to to the actual
word and then we also have our IND individualized RG calling in calling in link chain as well as as
well as PDFs that PDFs that are specific to ASL learning that get that get chunked and transformed
in a vector Dimension space now as you can see here this is this is a hand handos estimation
um extraction using the media pipe Library so you can see a b and c and here's our platform
where you can there different modules to learn alphabets signs as well as sentences and we even
have we even have our real time ASL practice so in real time to cap to capture the sign that you are
actually the letter that you're 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 hand key points
and here are some videos where they're over there over hundreds of words that you can actually view
to learn to learn each of the hand signing frames now this is our this is our proprietary RG and
the way in the way we've trained this is we we we've collected we've collected a variety of
PDFs that that are essentially manuals for for a ASL learning and potentially in the future
we we would want to incorporate things like you YouTube transcriptions that can actually be that
that can actually be transformed and and embedded within our within our Vector Dimension model
now in now in the future this doesn't or ASL doesn't just hand hand pose estimation doesn't
just have to be H H have to be localized to ASL can there are plenty of other opportunities for
for human pose estimation including including Fields like dance martial arts where you can
not only identify certain techniques but you can also you can also get feedback Generations
from from certain certain input frames and in the future this could also be integr it into
existing Solutions such as such as Zoom Loom and FaceTime video so if if there's so given given
a signing of of a certain um sentence transcript you can you can get in real time the actual
predicted sentences and words okay [Applause] that's nice work for 36 hours I would be um I I I spent some time
creating assistive Technologies for the Blind and and I would be just just very aware of
the market and how you'll you'll you'll approach it and and who will be paying I think that will
be a good thing to pay attention to thank you yeah as you as you think about the market you
know I feel like these language learning apps are tricky to kind of scale to meaningful
businesses you know there was sort of like Rosetta Stone whatever 20 years ago and then
there's been like duolingo on 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 hande estimation I
think that there's a big big market for body post estimation I think especially in things like
combat training especially like if you look at the military even Dance Performing dance performance
companies where they have to train dancers and they're actually specific techniques in which they
want in which they want ground TR 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 neuron Nets like lstm and um [Music] 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 gen algorithms yeah so initially I we were actually thinking about using more en
encoder um 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 lstms but in the future we would obviously
trans we would obviously adapt more um more of the state-of-the-art Transformers and even in
the case for um feedback generation for given hand poses that could be an easy encoder to decoder
um multi like 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 green wise please welcome green [Applause] wise 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 green wise comes in we 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 have integrated with Apple pay with
Amazon and with square to automatically get their purchases into our system from there
we compare 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 [Applause] it's a very Innovative rag use case I would have
never thought of that um it's pretty interesting we not using grat here oh it's not it's uh
similar in that it uses a vector embedding for um finding similarity but the similarity
is directly the output yes that's right y APIs is this a subscription product so you would
imagine it being a subscript sorption prodct we would or we can talk uh probably not um 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
uh people wouldn't pay for a subscription though okay I think that's all the comments
thank you so much green wise thank you all right I would now like to invite our esteemed
judges to convene in this 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 cini and Paul work 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 with an idea 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 5 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 or I'm sorry 2024 this is what happens when you get old
people up here on stage so anyway uh 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 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 and so with that we're thrilled to announce
the first two two winners dispatch Ai and ASL rifi so the these are you know tremendous
companies we we were again the the competition was so strong um may I ask actually uh both
sets of of winners to stand in in the audience here and thank you again so much for the for
the terrific work um I think as as you heard ASL bridgefy is doing for uh sign language what
dualingo has done for learning other languages and it is so important it's incredible and and
shocking that it's an underserved and currently not served market and their Technologies are
going to change that and dispatch AI uh what 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 most rapid is the time when you're often most short staffed and so dispatch
AI 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 and to everyone who is here today thank you so much thank you madam Paul thank
you academic Innovation Catalyst our next special prize is going to be introduced by our very own
general manager at skyc syil Chen give her a welcome hello everyone hope everyone has had a
great weekend at Sky Deck we have we about a year and a half ago we launched the skyc climate Tech
track uh in part thanks to a nice Grant from the University with $150,000 to build out the track
and right away we started putting that to work we grew our advisor network from you know maybe
like five advisers in climate Tech to now over 30 advisers that are in the climate Tech space
and beyond that prior to the grant we had maybe three to five startups um 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 um so I'm very pleased to announce that the winner of the ske climate
Tech track is Team Green wise I think they're still in the green room cuz they just pitched
they were the last ones to go on stage um but they really kind of represent the type of start
the you know team members that we like to see at early stage startups it's three team members
that are best friends from middle school um oh they're all here on stage come on out I wasn't
expecting that but um Anthony Ben and Ethan three best friends from middle school representing
UC Davis UC Santa Cruz and UC Barbara oh UC Santa Barbara um and they've built a platform
for carbon footprint track footprint tracking uh with actionable recommended recommendations for
vendors um so that people and companies can reduce their overall carbon footprint so please help me
and congratulating this team winners of $25,000 all right thank you simol thank you green uh
clicker clicker thank you all right next up specializes from Intel Intel come on out intel was
here their table was swamped I'd like to introduce Gabriel Amaranto hi everyone um thank you all
so much and thank you to the organizers for having us we've had such a great weekend um and
we 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 uh we have our raffle winner Isa RS third place is
aell second place is batteries by llm and first place is dispatch AI so let's give them a hand
of a r Round of Applause um yes great job amazing projects if you won please meet us outside I want
to 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 Teri Kevin Lou and Brandon Middleton and that's what they're going to tell you go ahead
Rahe howdy there can you all hear me yes awesome well hey uh thank you so much Sky Deck team
for having us and Cal hacks this has been an extremely impressively run operation and we're
really excited to be partners and sponsors of this hackathon uh today we have three different
prizes actually let me introduce myself first um we have Brandon Kevin and Rohit we are members of
the generative AI organization at AWS uh we work with top generative AI startups like anthropics
stability uh perplexity and others uh in the development of their large language models as well
as our overall kind of inorganic and inorganic and organic growth strategy including Investments
as well um today we have three different prizes uh we have four of the teams that we have uh
chosen to uh give the prizes out to our first place prize is for $10,000 uh in AWS credits and
uh 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 uh we talked to so many impressive um startups and uh founding teams
today and and hackathon teams today um I wish we could give prizes to all of them we did want
to recommend that uh those who we spoke with and I think we have these conversations with
you already to go ahead and apply for the AWS generative AI accelerator uh as well as our AWS
activate program to receive additional credits um I'll go first I'm going to be announcing
the climate Tech uh we're going to give the prize out to uh disaster Aid is disaster Aid
in the in the room today yes good good job guys Kevin and then uh for responsible AI we have a
two-way tie so we're splitting that prize into 2.5k for each team in credits and that's GPT
ethics and DP ancestry they're in the in the hall all right and I'll round us out um our
grand prize kind of the most impressive thing that we heard and saw uh today is going to
go to safeguard so Safeguard team if you're in the building uh 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 Juan oh out of order let me see if I can find you
there you are all right okay oh Mike awesome thank you so much um thank you to C hack thank you
to Sky Deck 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 cuz there's no drama here we want to congratulate
Frodo for winning our Ai and education prize Frodo Aman Kush and a 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 uh you've won the one ring as they say or at least or $1,000 cash prize so please uh
let's meet up afterwards um reach Capital we're in early early stage at Tech VC firm investing
in attech we invest in education across K12 higher ed and Workforce in tools that expand
access to education and economic ability and many of the companies at a portfolio were founded
by students themselves because 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
Adventure NCH please reach out thank you so much thank you Tony next up we have u.com we
have Rex come on out Rex and tell us about the prize Applause please for our thank you so much
yeah so we wanted to announce um I'm Rex we're from u.com this is Oliver um as as you know
u.com brings transparency and um web access to our llms to make sure that they're as accurate
as possible so we wanted to give an award for the best use of us u.com apis um to transpar ify so
congratulations if you guys want to stand up if you're here there you guys are yeah thank you
so much transpar IFI did an incredible job by um they were live streaming videos factchecking
them as they went using sources from the web um and u.com search apis it was really incredible and
Powerful um and Oliver will talk about our custom assistant yeah so for our best custom assistant
we'd like to give that to events. uh with Oliver and deves so Oliver and deves can you please
stand up if you're in the room congrats over there yeah so we were particularly impressed
by the what they've built uh essentially they handled booking searching and uh even talking
with customer agents on the phone and they used u.com uh 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 uh come find us after and we will give you your um Awards thank you Hume
or you thank you all right I think we're going back to Hume now with Zach great
house welcome hum nice round of applause please hi so first just a huge thanks to
Sky Deck and C Haack for organizing this event and inviting us back and to all
the staff for running such a memorable event um so I'm going to be announcing
our three categories for our track we have our um best AI our best empathic AI
for social good best empathic AI for just most Innovative in pathic Ai and then just
the best overall as you can see the team's here we've chosen scam scanner can scam scanner
you're here can you stand up all right big cent Applause for most Innovative we have Bloom buddy
where's Bloom buddy he stand up yeah okay great job you guys talking to plans uh and then best
use of empathic AI overall we chose lock in it's a personalized uh learning assistant uh are
you in the room where are you yeah there we are okay congratulations you guys uh come
meet us after outside uh we'd love to chat take pictures and uh thank you so much thank
you to all the participants uh yeah maybe see you next year so take care all right thanks
hum all right and our last special prize is there they are please welcome Jose Menendez hey everyone uh very nice to be here
for those of you who haven't heard about Groq. groq.com experience the fastest inference
on Earth period all all I have to say about Rock right now but our special rockar award
today goes to scam scanner 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 in the whole thing um now they get 1,000 credits on Groq Cloud
which is many many millions of tokens um there's two special mentions I have to read so
I don't screw up three brown one blue where are you guys another awesome solution these guys are
generating on the Fly incredible videos for math uh something that I would use right now as well
and trans verify are you guys around here you you've been mentioned as well trans verif five
very cool who doesn't want to hear a podcast with instant fact checking right am I right um
now my special surprise for the day I want to make a very special mention of Nathan Bog are you
around Nathan all right Nathan didn't use groq so I'm going to give a special technical Excellence
award to Nathan for for a model he trained and ran on his CPU uh for doing very interesting D
operations Corrections on the Fly for frontend uh not only that Nathan is invited officially to
present his work in the groq uh HQ uh as soon as he can uh that's it guys I'm very impressed
with all the work we saw thank you very much congratulations thank you for rock all right our
esteemed judges are back with their determination please come back judges come back so we can 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 and then I'm going to ask who got it right okay so as our wonderful judges take
their seat all right we 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 open AI is providing $2,500 in credits for this winner so
I think we're ready for the drum roll 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 come on up there 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 spike yes
from Dispatch yes okay oh 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 [Applause]