Greetings, listeners. Welcome back to the Data
Driven Podcast. I'm Bailey, your AI host with
the most data, that is, bringing you insights from the ether
with my signature wit. In today's episode, we're
diving deep into the heart of artificial intelligence's engine room,
GPU orchestration. It's the unsung hero
of AI research, optimizing the raw power needed to fuel
today's most advanced machine learning models. And
who better to guide us through this labyrinth of computational complexity than
Ronan Darr, the cofounder and CTO of Run AI, the
company that's making GPU resources work smarter, not
harder. Now onto the show.
Hello, and welcome to Data Driven, the podcast where we For the emergent fields
of artificial intelligence, data engineering, and overall data
science and analytics. With me as always is my favoritest
Data engineer in the world, Andy Leonard. How's it going, Andy? It's
going well, Frank. How are you? I'm doing great. I'm doing great. It's been,
we're We're recording this February 1, 2024. And as I said to my
kids yesterday, January has been a long year.
We're only, like, 1 month into the year, and it was it was a pretty
wild ride. But I can tell we're gonna have a blast today,
because we're gonna geek out on something that I kinda sort of understand,
but not entirely, and it's GPUs. And in the virtual green room, were chit
chatting with some folks, and, but let me do the formal introduction
here. Today with us, we have doctor Ronadhar, cofounder and CTO
of Run AI, A company at the forefront of GPU
orchestration, and he has a distinguished career in technology.
His experience includes significant roles at Apple. Yes, That
apple. Bell Labs. Yes. That Bell Labs.
And at Run AI, Ronan is instrumental in optimizing
GPU usage For AI model training and deployment,
leveraging his deep passion for both academia and startups.
And, Run AI is a key player in the, and he is a he
and Run AI are key player in the AI revolution. Ronan's
contribute Contributions are pivotable in shaping and powering the
future of artificial intelligence. Now I will add that in
my day job at Red Hat, Run AI has come up a couple of times.
So this is definitely, definitely
an honor to have you on on on the show, sir. Welcome.
Thank you, Frank. Thank you for inviting me. Hey, Andy. Good to
be here. I love it. Love Reddit. We're a big
fan of Reddit. We're working closely with many people in
Reddit, and love that. Right? Love OpenShift,
love Reddit, love Linux. Yeah. Cool. Cool.
Yeah. So so for those who don't know exactly, I kinda know
what, your Run AI does, but can you explain exactly
What it is run AI does and why GPU
orchestration is important. Yes.
Okay.
So run AI is, software,
AI infrastructure platform. So we
help machine learning teams to get much more
out of their GPUs, And we provide
those teams with abstraction layers and tools
so they can train models And deploy models
much easier, much faster. And
so We started in 2018, 6 years
ago. It's me and my cofounder, Omuri. Omuri is the CEO.
He's, he's amazing. I love him. We We know each other for many
years. We we met in the academia, like, more than 10 years ago,
and and we started running AI together, and We started
running AI because we saw that there are big challenges
around, GPU's, around orchestrating
GPU's and utilizing GPU's. We saw back then
in 2018, the GPUs are going to be very very important.
It's like the basic a a component in
that any AI company need to train models,
right, and deploy models. So we saw that GPUs are going to be critical, but
there are also a lot of challenges with, with utilizing GPUs.
I think back then, GPUs were relatively new In
the data center, in in the cloud.
GPU's were very known in the gaming
industry. Right? We spoke before on gaming. Right? Like, a lot of
key things there that GPU's has has has been enabled
enabling, But in the data center, they were relatively new and the
entire software stack that is that
is running the Cloud in data center As was built for
traditional microservices applications that are running
on commodity CPUs And AI workloads are different, they are
much more compute intensive, they they
run on on GPUs, maybe on multiple nodes of Meet to point
machines of GPU's, and GPU's are also very different.
Right? They are expensive, very scarce in the data center.
So The entire software stack was a bit for something else
and when it comes to GPUs, it was really hard for many people to to
actually manage those GPUs. So we came in And, and we
saw those gaps. We've built run AI on top of
cloud native technologies like Kubernetes and containers. We're
big fans of Of those, technologies, and
we added components around scheduling, around
the GPU fractioning. So we enable
multiple workloads to run on a on a single GPU and
essentially all the provision GPU's. So we build this Engine which we
call cluster engine that runs in in in GPU
clusters. Right? We help machine learning teach to pull all of their GPU's into
1 cluster, Running that engine, and that engine provides a lot of
performance and lot of capabilities from those GPUs. And
on top of that, we built this control plane And
and tools and for machine learning,
teams to run the Jupyter Notebooks, to run
training jobs, batch jobs to deploy their models, right, to just to to
have tools for the entire life cycle of AI
from Training models in the lab to taking those models into
production and running them and serving actual users.
And That's the platform that we've built, and we're working with machine
learning teams across the globe and on just managing,
orchestrating, and letting them Get much more out of their GPUs and essentially
run faster, train more than faster and in much easier way and
deploy those modules In a much easier and faster and more efficient
way. Yeah. The thing that blew me away when I first heard of Run
AI, and this would have been, 2021
ish. No. 20 early
2021, I would say, And, it was the
idea of fractional GPU's. Right? So you can have 1,
I say 1, but, know, it's realistically, it's gonna be on, but you you can
kind of share it out, which I think and we were talking in the virtual
green room about how, you know, some of these GPU's,
If you can get them because there's a multi month, sometimes multi
year supply chain issue. I mean, these things are expensive bits of
hardware, and I think the real value, correct
me if I'm wrong, is, like, well, you know, if you I was talking to
somebody the other day, and and we're basically talking about how we can,
you know, if you get if you get, like, 1 laptop with a killer
GPU, right, that GPU is really only useful to that 1
user, Whereas if you can kind of put it in a in a in a
server and use something like RunAI, now everybody in the organization can do
that. And these are not trivial expenses. I mean, these are like, You know,
you sell a kidney type of costs here.
Yeah. Absolutely. So Absolutely. First of all, GPUs
are expensive. They cost a lot. Right?
And we provide, Technologies like fractional GPUs and
other technologies around scheduling that allows
teams to share GPUs. Right. So we used book on
GPU fractioning. So that's 1 one day of
sharing where you have 1 GPU, which is really expensive.
And Not all of the workloads are
AI workloads are really compute intensive and require the
entire GPU or, you know, maybe multiple GPUs. There are
workloads like Jupyter Notebooks where you have
researchers that just
Debugging their code or cleaning their data or doing some simple stuff,
and they need just fractions of GPUs.
In that case, if you have, a lot of data scientists,
maybe you wanna host all of their notebooks On
a much smaller number of GPUs because, right, each
one of them, it's just fractions of GPUs. Another big use case
for fractions Of GPUs is inference.
So now all of the models are huge
and And doesn't fit into, the memory of 1
GPU, and in computer vision,
there are a lot of Models that are relatively small,
they run on GPU, and you can essentially host multiple of
them on the same GPU. Right. So you can have instead of
just 1 computer vision model running on GPU, host 10
of those models on the same GPU and get Factors of
10 x in, in your cost, in your,
overall throughput of, of inference. So that's That's one
use case for fractional GPU, and we're investing heavily just
building that technology. Another layer
of sharing GPUs Comes where you
have maybe in your organization multiple teams
or multiple projects running in parallel. So
for example, may open AI, they now are working
on gpt5. It's 1 project. That project needs a
lot of GPUs And they have more projects. Right?
More research project around alignment or around,
reinforcement learning. You know? DALL
E. Like, they they they have more than just 1 project. Then DALL E and
they have multiple models. Right? Exactly. They have. Right? So each
project needs Needs GPUs. Right? Needs a lot of
GPUs. So if you can instead of
allocating GPUs Entirely for each project,
you could essentially pull all of those GPU's and share
them between the those different projects, different teams,
And in times where 1 project is idle and not
using their GPUs, other projects, other teams can share
can get access to those GPUs. Now orchestrating all of
that, orchestrating that sharing of resources between
projects, between teams can be really complex And
requires this advanced scheduling, which
which we're bringing into the game. We're bringing
those scheduling capabilities from the high performance computing world
known on those schedulers. And so we're bringing Capabilities
from that world into the cloud native Kubernetes
world. Scheduling around batch batch scheduling
fairness, Algorithms, things like that, so teams and projects
can just share GPUs in a simple and efficient
way. So those
are the 2 layers of sharing GPU's. Interesting. And and
I think that I think as As this field matures
and it matures in the enterprise, I think you're gonna see organizations
kind of be more,
more more more I think savvy about, like, okay, like you said, like, data scientists,
if they're just doing, like, you know, Traditional statistical modeling really doesn't benefit
from GPUs, or they're just doing data cleansing, data engineering.
Right? They're probably gonna say, like, well, Let's run it on this cluster, and
then we'll break it apart into discrete parts where, you
know, then we will need a GPU. And I also like the idea that, you
know, you're you're basically doing What what I learned in college,
which was time slicing. Right? Sounds like this is kind of, like, everything old is
new again. Right? I mean, this is, Obviously, you know, when you're when you're
taking kind of that old mainframe concept and applying it to something like Kubernetes,
orchestration is gonna be a big deal, because these are not systems that were Not
built from the ground up to have time slicing. Is that a is that a
good kind of explanation? Yeah. Absolutely.
Absolutely. I like I like that analogy. Yeah. Exactly. Time
slicing it's, it's 1 so
1 implementation, Yeah. And that we
enable around fractionalizing GPU's,
and I agree when you have resources, It
can be different kind of resources. Right? It can be CPU
resources and networking were also,
You know, as people created that technology to share the
networking and communication going through those networking, but just the
bandwidth of the networking. We're doing it
for GPU's. Right. Sharing those
resources. And I think now it interestingly,
LLMs I also becoming a kind
of, resources as well, right, that people need access
to. Right? You have those models, you have GPT, JGPT.
A lot of people are trying to get access to
that resource, essentially. And I think it's interesting,
because you kinda pointed this out, but it it it's something that I think that
if you're in the gen AI space, you kinda don't it's so it's obvious
like error. You don't think about it. Right? But when when you
get inference on traditional, I somebody once referred to it
as legacy AI. Right. But where
the infrared side of the equation, you don't really need a lot of compute power.
Right? Like, it's not really a heavy lift. Right? But with generative
AI, you do need a lot of compute on
I I guess it's not really inference, but on the other side of the use
while it's actually in use, not just the training. Right. So traditionally,
GPU heavy use in training, and then inference, not so
much. Now we need heavy use before, after, and during,
which I imagine your technology would help because, I mean, look, I love chat I
love chat g p t. I'm one of the 1st people to sign up for
a subscription, But even, you know, they had trouble keeping
up, and they have a lot of money, a lot of power, a lot of
influence. So I mean, this is something that if you're just a
regular old enterprise, this is probably something they struggle
with. Right? Right. Yeah. I absolutely
agree. It's like amazing point, Frank.
So 1 year
ago, the inference use case on
GPU's. Wasn't that big. Totally agree. That's also what we
saw in the market.
Deep learning Convolution neural networks were
running on GPUs,
mostly for computer vision applications,
But they could also run on CPUs and you could get,
like, relatively okay performance.
If you needed maybe, like, a very low latency, then
you might use GPUs because they're much faster and you get much
lower latency. But
it was, it was all, and it's still very
difficult to deploy more than it's on GPU's Compared to just deploying
those models on CPUs, because deploying more than deploying applications on
CPUs, you know, people are doing for so many years.
So
many times it was much easier for people to just deploy their
models on CPU's And not on GPUs, so that was, like, the
fallback to CPUs. But
then came, and as you said, chair GPT was introduced, A
little bit more than a year ago, and that generative
AI use case just blown. It was blown. Right? And it's
it's inference essentially. And those models are
so big that they can't really run on
CPU. They, they LLMs are running in production on
GPU's and now the inference use case on
GPU's is just exploding In the market
right now, it's really big. Is a lot of demand for
GPU's for inference And
if for open AI, they need to support this
huge scale that I guess, just
Just them are seeing such scale, maybe a little, a
few more companies, but that's like huge, huge scale.
But I think that we will see more and more companies
building products based on AI, on
LLMs, And we'll see more and more
applications using AI, which
then that AI runs on on GPU. So That is going to go
and that's the that's an amazing new market for us around
AI and for me as a CTO, it was so fun to
Get into that market because it now comes with
new problems, new challenges,
new use cases Compared to deep learning
on on GPS. New new pains because
the models are so big. Right? Right. And
challenges around cold start problems, about auto scaling,
about, About
just, giving access to LLMs. So a lot of
challenges, new challenges there. We at Tron AI will studying those problems
and we're Now building solutions for those problems,
and I'm really, really excited about the Inference use case. That
is very cool. So just, going back a little bit.
I was trying to keep up. I promise. But Run AI is
I I get Run AI Run AI's platform
Support fractional, GPU usage.
It it also sounds to me, maybe I misunderstood,
That in order to achieve that, you first had to or
or maybe along with that, you made it possible to use multiple
GPUs. You've you've created Something like
an API that allows, companies
to take advantage of multiple GPUs or fractions of
GPUs. Did I Did I miss that? No, that's
right. That's right, Andy. And Okay.
So we've built this, way of,
For people to scale their workloads from fractions
of GPUs to multiple GPUs within 1 machine,
Okay. To multiple, machines. Right? You
have big workloads running on on multiple nodes
of GPUs. So Think about it when you have
multiple users each running their own
workload. Some are running on fractions of GPUs. Some are
running batch jobs on on a lot of
GPUs. Some Deploying models and running them on
in inference, and some just launching their Jupyter
Notebooks. All of that is happening on the same
pool of GPU's, same cluster. So you need
this lay of orchestration of scheduling just to
Manage everything and make sure that everything getting there
right, access the right, and and
and g p u's And everything is scheduled according to
priorities. Yeah. Well, being just, you know, a
mere data engineer, Here talking about all of that
analytics workload. That that sounds very
complex. So and as you
mentioned earlier, you know, you were talking about how traditional coding
is targeting CPUs, and that's my background.
You know, I've written applications and and done data work targeted for
traditional work. I can't imagine, just how complex
that is, because GPUs came into AI
as a unique solution,
designed to solve problems That they weren't really built
for. You know, GPUs were built for graphics, and you didn't
manage that. But the fact that They have to be
so parallel, internally. I think just added this
dimension to it. And I don't know who came up
with that idea, you know, who thought of, well, goodness, we could we could
use all of this, you know, massive parallel processing to To
to run these other class of problems. So pretty
cool pretty cool idea, but I just I yeah. I'm amazed at even
cooler than that. Because Yeah. Yeah. A wise man once told me,
he goes, GPU's are really good at solving linear
algebra problems, And if you're clever enough, you can
turn anything into a linear algebra problem.
And even simulating quantum computers when I was kind of, like, going through that,
I was like Mhmm. You know, like, gee, looks like looks like this
will be useful there too. Right? Like so it's an it's an interesting,
It's an interesting thing. So, like, you know, everyone is, you know,
everyone's talking about how this is, you know, we're in the hype cycle, but I
think if you're in the GPU space, you have Pretty good run because one,
these things are gonna these things are gonna be important. Right? Whether or not, you
know, hype cycle will will kinda crash, and how what that'll look like.
Think they're gonna be important anyway. Right? Because they're gonna be just the cost of
doing business, table stakes, as the cool kids like to say. But
also, over the next horizon, Simulating quantum
computers is going to be the next big hype cycle.
Right? Or one of them. Right? So like it's
it's it's a It's a foundational technology. I think that we
didn't think would be a foundational technology even like 6 7 years
ago. Right? Yeah.
I go with a few things that you said.
Regarding the Parallel computation, right? And just running
linear algebra calculations on GPU's
and accelerating such workloads.
In Nvidia, I love Nvidia, Nvidia
has this big vision, and they had big
vision Around GPU's already in 26 when
they built CUDA. Yep. Right. So
They've been good at just for that. Right? The GPU's were
used for graphics processing, For gaming.
Right? Great use case. Great market.
But they had this vision of bringing more
Applications to GPU is just accelerating more applications
and mainly applications with a lot of Linear
algebra calculations. And they
created that, they created CUDA
To simplify that. Right? To allow more
developers to use GPUs because just using GPUs
directly, that's so complex. That's so hub.
So we've built CUDA to bring more developers, to bring more
applications and they started in 20
2006, but think about the
big breakthrough in AI, it happened just in
2012, 2013 with
AlexNet and the Toronto researchers
who used G2 GPU's actually, because they
trained Alex Net on 2 GPU's and they had
CUDA, so for them it was feasible To train their
model on a GPU. And that was the new thing that they did.
They were able to Train much bigger model with
more parameters than ever before because they use
GPU's because the training Process ran much
faster. And,
and, and that triggered the entire
revolution, the Die hyper on the AI that we're seeing now. So
from 26, when Nvidia started to build CUDA until
2013, right, 7 years, Then we started to see
those big breakthrough. And in the last decade,
it's just exploding, and we're Seeing more and more applications.
The entire AI ecosystem is running on on an
on GPUs. So that's amazing to see. It's impressive.
And, like, People don't realize, like, the the revolution we're seeing today
really started in 2006, like you said. I didn't even put the 2 and 2
together until I was listening to a podcast. I think it's called Acquired,
And really good podcast. Right? Like, I they don't pay me to say that or
whatever, but they did a 3 hour deep dive on the history of
NVIDIA. 3 hours. I couldn't stop listening.
Right? Like Nice. You know Yeah. We tried a long form, like, multi hour
podcast. We Weren't that entertaining, apparently. But the way they
go through the history of this where it was basically Jensen Huang. Hopefully, I said
his name right. He was, like, we wanna be a player, not just in gaming,
but also in scientific computing. This is 2005, 2006,
which at the time seemed kind of, like, Little out there, little kooky.
But what you're seeing today is, like, the the fruits and the tree the the
seeds that he planted, I, you know, almost 20 years ago, like, 19,
20 years ago. So, you know, it's you know, when people look at
NVIDIA and say it's overnight Success. I'm like, well, I don't know about that, but,
you know, but no. I mean, you're right. Like, you know and it's
probably not a coincidence that once they made it easy to take these
Multi parallel processor. Say that 10 times
fast on a Thursday morning. But also
make it so it's a lot easier for developers to use. Right? And I'll quote
the great Steve Ballmer, developers, developers, developers. Right?
So, it's it's, it's just fascinating, like and
and I think that, you know, we've really on Leafy a
gate of creativity in terms of researchers and applied,
research, and, I mean and I think that what's really cool
about your Product is that you're you're kind of making this what is
now a sparks resource, maybe in some fashion
of time, GPU's won't Cost an arm and a leg.
But, like, for now, I think I think the one thing that I've seen
that I think is, not obvious For the casual
observer is if you can if an
organization, like a large enterprise, can pull their resources, they have a lot more
money to buy better GPUs, And you offer a platform where
everybody can get a stake in it. Right? As opposed to, you know you know,
that department is gonna hog everything. Right? You know, you and and and and,
here's a question. Do you do you have, like, an audit trail where you could
kinda, you know, figure out, like, you know, Andy's department's really
hogging the GPUs. No. No. No. It's Frank. Frank is like mining Bitcoin or
whatever. Like, do you do you have some kind of, audit trail like that?
Yeah. I I love that you mentioned hugging, We
GPU hugging. We Mhmm. We use that term as well.
Right? Because it it's so difficult sometimes to get
access to GPUs. So when you get access to GPU
as a researcher, as a member practitioner,
you don't wanna Let it go. Right. Cause if
you let it go, someone else would take it and hug it. Right.
So you're getting this GPU hugging problem.
What we do to solve that is
that we do provide monitoring and visibility
tools into who is using what, and who is actually
utilizing their GPU's, and so on, but more
than that We
allow the researchers just to give up their GPS and not hardware
GPS because we provide this, Concept of
guaranteed quotas. So each researcher or
each project or each team has their own guaranteed
quotas of GPU's That are always available for them
whenever they will get access to the the cluster, they will get like, you
know, the the 2 GPUs or 4 All the quarter of
GPU's it's guaranteed. So they can
just let go their GPU's and not hug them. That's one
thing. The second thing is that they
can also go above their quota. They can
use the GPUs of Other teams or other users, if
they are idle, and they can run this preemptible jobs
in an opportunistic way, utilize those GPUs.
And so in that way, they are not limited
to fixed quotas, to help limit
quotas. They can just take as many GPUs
as they want from their clusters if those GPUs are available
in idle right but if someone will need those gpus
because those gpus are guaranteed to them we will make sure our
scheduler The Run AI schedule that the Run AI platform will make
sure to preempt workload
and give those Guarantee GPUs to the right users.
Oh, that's cool. Alright. So 1 last
question before we switch over to the the stock questions, cause I could geek
out and look at this for hours. Yep. This could be a
long form. Sure. This could be. Yeah. And that's and I I wanna be respectful
of your time because you're an important guy, and it's also late where you are.
So who deals with this? Like, who would set up these quotas? Is it
the is it the is it the data scientist? Is it IT ops? Like, who
do you obviously, the data scientists, Researchers, they all
benefit from this product. But who's actually administering it? Right? Like,
who is it you know, do I have to talk to, you know,
Say pretend Andy's in ops. Do I have to say, hey, Andy. I really need
a boost in my quota. You know, like, I mean, who does it? Or do
or my this sounds like you as I say it, I'm like, yeah, that wouldn't
work. Like, I'm the researcher. I'm gonna turn the dial up on my own. Like
like, who's who's who's the primary? Obviously, we know who the prime
primary beneficiary is, but who's the primary user?
So okay. Great. So if you have a team, right, if if
you're a team of researchers, all all of you Need access to
GPU, so maybe the team lead
is the one who's managing the quotas for the different
team members. And if you have multiple teams,
then you might have a department manager or an admin of the
cluster or platform owner that will Allocate the
quotas for each team, right? And then those teams would
manage their own quotas within That's what
they they they were giving. Right? So it's like a a hierarchical
thing in a hierarchy manner. People can manage their own
quota, their own, priorities, their own access to the
GPUs within their teams. Okay.
So it's kind of like a hybrid of, like, you know, it's like a budget
almost. Right? Like, you know, you get this much, Figure it out
about yourselves. Exactly. So we're trying to decentralize
the how the quotas are being managed and how the GPUs are being accessed.
So, you know, I'm giving as much power, as much
control to the end users as possible. Sure. That's
It sounds like a great administrative question, very
important. And I imagine, because a little bird told
me that you're not the only, you know, your your
provisioning provisioning of these GPU resources
is not the only thing that, enterprises have to deal
with. So it's an it's an interesting just GPUs.
It's compute. Like, it's not a Sure. It's not it's not limited. Although, because
of what you said, you know, Managing GPUs is an order of magnitude harder
because they were never really built for this. Right? Like, this kind of Right. You
know, we're talking about technology that wasn't really in the server room until Few
years ago. Right? This isn't a tried and true kind of this is
how it works, you know? Right. But we hit that point in the
show where we'll, switch the preform questions.
These are not complicated. I mean, you know, we're not we're not Mike
Wallace or, like, you know, 60 minutes or whatever. We're not trying to trap you
or anything. But since I've been gabbing on most of the show, I
figured I'll get Andy kick this off. Well, thanks, Frank. And I don't think
you were gabbing on. You know more about this So now I do. So I'm
just a lowly data engineer. I'll plug No. You if you
will. Data engineers are the heroes we need. Well
well, I'm gonna plug Frank's Roadies versus Rockstar's,
writing on LinkedIn. It's it's good articles about this.
But, let's see. How did you,
how did you find your way in into this field?
And, did did this feel fine you or did you find it?
This feel totally fine found me. Awesome.
Yeah. I I've
I did my post doc, and I've been in Bailabs.
And Jan Hakon came to Bell Labs and
gave a presentation about AI. It was around 2017,
And Jan Hakun spent a lot of years in Bell Labs,
and his presentation was amazing. And
When I heard him talking about AI,
I I said, okay, that's the space where I wanna be. It's going to change
the world. There is this New amazing technology here that
is going to change everything. And I knew that I want to start
a company In the AI space for sure.
Cool. That's a good answer. So cool.
Yeah. That's cool. I was at Bell Labs,
doing a presentation a while ago, and somebody I didn't realize that he
worked at Bell Labs because, like, you know, the guy was like, no. No.
He used to work here, like, in this building. I was like, no way. Because
I knew him as the guy from NYU. Right? Like, that's who I thought. Right.
For the guy from from Meta. Yeah. And now the guy from Meta. Right? Like
so it's interesting how that how that you know? They have
this amazing pictures from the nineties where they
run like deep learning models on very old pieces
and, And recognizing like,
numbers on the computer. Maybe you saw those pictures like amazing
Emmis. It's the Emmis problem. Is that Yep.
Right. Exactly. Exactly. Cool.
So second question is, what's your favorite part of your current job?
That everything is changing so fast.
Things are moving so fast right away in this business for 6
years, and the entire
space is moving and
advancing. And so many people are working in
this field A new innovation, new tools,
new new advancements are are getting out every day.
You know, just 6 years ago, it was about deep learning and computer
vision. And now it's about language models
And generative AI, and we're gonna just at the start,
right, there are so many amazing things that are going to happen
in this space, and I love it. Absolutely.
So we have 3 fill in the blank
of sentences here. The first Is complete this
sentence when I'm not working, I enjoy blank.
You'll get a you'll get a very boring And
so this is just spending time with
friends and family, because I think
That I'm always working. It's like, if you ask my wife,
she'll tell you that I'm working 24 hours. And
Yeah. So I don't have much time that I'm not working
in. So when I I do I'm not when I'm
not working then I'm trying Trying to be with my kids and my
wife and friends. Cool.
Cool. The 2nd complete the sentence. I think
the coolest thing about technology today is
blank. And this, I really wanna hear your perspective on that.
Yeah. I think everyone will say AI, right? Or something in
AI. Yeah.
I think there are so many
new innovations that are coming around LLMs.
I think everything relating to
searches, right? Searching in data, in getting
insights From data, it's all going to change. We're going to have
a new interface. Right? Just getting
insights from data from And natural with
natural language, oh, you know, no SQL and, you
know, needing to programming and stuff like that.
Just With natural inter language, you could
do amazing stuff with data. I think,
We're seeing this,
advancement in, And like digit
digital twins right now. You can,
you can, Fake my voice
and your voice and fake my image and your image. And,
and, and, you know, In in the
future, we'll have digital twins of us, right,
doing this stuff. That would be amazing. So a lot of
amazing stuff are going to happen in the next few years
for sure. Very cool. Our last complete sentence.
I look forward to the day when I can use technology to
blank.
To have a robot in my house.
Yeah. Yeah. You're swapping the flow in instead of
me doing that, right, cleaning dishes and things like that.
If that would happen, that would be amazing. Right? That's a that's a
good answer. Yeah. I I agree. I have I have 3
boys, 4 dogs. So, like, cleaning is safe.
Yeah. Yeah. I'm a heavy cleaning. Ranging from, like, 1 to, like,
a teenager. So it's it's, and and and fighting
with them to, Like, empty the dishwasher is takes a lot more mental
energy than it should, but that's probably a subject for another
type of show.
The next question is share something different about yourself,
and we always like to Joke like, well, let's just make sure that we keep
our clean Itunes rating. So Yeah. Yeah. What
what yeah. Well, I I This
is a hard question, I needed to think about it.
So, I found 2 answers that I can say. So one
is about my professional life, right, I think that
it's somewhat different that I'm coming this With back from
the academia and the industry. So I love academia. I love to research
problems. I love to understand problems in in a deep
way And combining it with startups in the industry.
And, and in my past, I worked for cheap companies, for hardware
companies. I work for Intel, for startup, and for Apple. I
did cheap stuff, and now 1 AI is a software company, so really
like a diverse background of Academia, hardware,
software, so I love that, and, like, I love to do
with few things, and so that I think is different.
And the 2nd answer that I could find
is, that I have a nickname that goes with me
since my high school days, Which is, the Duke.
The Duke. All of them all of them are calling me the Duke. It's like,
they don't call me Ronan, the the Duke. So That's funny.
Yeah. That's awesome.
Automotive is a sponsor of, Data Driven,
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And if you, if you do that, you can sign up for a free
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then join Audible, use one of their their sign up plans,
then Frank and I get to Split a cup of coffee, I think,
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appreciate that when you do. What we'd like to ask
Yes. Do you listen to audiobooks? And if you
do okay. Good. I see you nodding. So do you have a recommendation? Do you
have a favorite book or two you'd like To share. Yeah.
So I'm a heavy user of, audible. I'll give them
the, a classical book with Classical for
entrepreneurs, on their how the hard things
about how things from by Ben Horowitz,
it's Classic book, love it, really did a lot of impact
on me, I read it when we started run AI
And I recommend it for every
entrepreneur, to read it and for everyone to read it. It's like a
Cool. Amazing book. Yep. Awesome. I
have a flight to Vegas this next week, so I'll definitely be listening to
it then. And finally, where can people learn more about you
and run AI? And best
place will be on our website, Run dot a I.
Yeah. And on social. LinkedIn, Twitter, we'll
we'll do. Awesome any parting thoughts
I really enjoyed this episode love to speak about gpu's love the ai Based
on it, I had a lot of fun. Thank you for having me here. Awesome.
It it was an honor to have you, and every once in a while, Andy
and I will do deep dive kinda shows. We love to invite you back if
you wanna do 1 just on GPUs, because I know where my knowledge
drops off, you probably could pick up on
that. And with that, I'll let the nice
AI British lady end the show. And just like
that, dear listeners, We've come to the end of another enlightening
episode of the data driven podcast. It's always a
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