Andrew Ng - Why Data Engineering is Critical to Data-Centric AI
Summary
TLDRIn this podcast, Andrew and Joe discuss the paradigm shift from model-centric to data-centric AI. They explore how data engineering is pivotal to successful AI implementation, especially with the rise of generative AI and large language models. Andrew highlights the importance of curating high-quality data for training and fine-tuning models. The conversation also touches on emerging AI applications in education and the future of work, emphasizing the potential for generative AI to transform these sectors.
Takeaways
- đ Data Centric AI is gaining momentum as a shift from the traditional model-centric approach, emphasizing the importance of data quality and engineering.
- đ The process of training AI models involves a significant amount of data handling, from selection to fine-tuning, which is often more labor-intensive than model engineering itself.
- đ Data issues are prevalent in generative AI, affecting training, fine-tuning, and practical deployment, highlighting the need for robust data strategies.
- đ An example of data curation in AI is Meta's use of an earlier model, llama 3, to generate coding puzzles that trained the subsequent model, llama 3.1.
- đ Licensing and procurement of data are significant challenges, especially when aligning data with human values and labeling schemas.
- đ§ Data engineering is mission-critical for AI, with decisions around data storage, cloud services, and database schemes being vital for performance.
- đą Companies are urged to invest in data infrastructure with specific use cases in mind to avoid over-optimization and to build a strong foundation for AI applications.
- đ€ Agentic workflows, where AI models engage in a process of thinking and refining their outputs, are emerging as a key strategy to enhance AI output quality.
- đ The intelligence of AI models is largely derived from the data they've been trained on, suggesting that data curation is paramount for achieving intelligent behavior.
- đ Generative AI has the potential to transform education, making coding companions and customized courses more accessible and effective.
Q & A
What is the main topic of discussion in the transcript?
-The main topic of discussion is Data Centric AI and its significance in contrast to the traditional model-centric approach in AI development.
What is the significance of data engineering in Data Centric AI?
-Data engineering is critical to Data Centric AI as it involves structuring the data effectively to build successful AI systems, including error analysis, data curation, and making trade-offs between data quality and quantity.
How does Andrew view the shift from model-centric to data-centric AI?
-Andrew sees the shift as a practical evolution where focusing on data engineering and curation is more fruitful than solely engineering the mathematical models or AI algorithms.
What is the role of data in training large language models?
-Data plays a significant role in training large language models, with a large fraction of effort dedicated to acquiring the right data to feed into these models for effective training and fine-tuning.
What is an agentic workflow in the context of AI?
-An agentic workflow in AI involves a series of steps where an AI model generates content, reflects on it, possibly does additional research, and iteratively improves the output before finalizing it.
How does Andrew perceive the current momentum in the Data Centric AI community?
-Andrew finds the momentum in the Data Centric AI community quite exciting, noting the increased focus on data issues across various stages of AI model development.
What is the importance of data infrastructure in building AI applications?
-Data infrastructure is mission critical in building AI applications as it provides the foundation for data storage, management, and accessibility, which are essential for developing and deploying AI models effectively.
What challenges do companies face regarding data in AI, according to Andrew?
-Companies face challenges such as deciding how to store data, choosing cloud services, database schemes, and making trade-offs between cost and performance. Additionally, they struggle with architecting data for specific AI purposes.
How does Andrew suggest companies should approach improving their data infrastructure?
-Andrew advises companies to invest in data for specific purposes, using AI wins as a way to drive data architecture improvements, rather than attempting to fix all data issues at once.
What is Andrew's perspective on the future of AI and its impact on education?
-Andrew anticipates a transformation in education with generative AI playing a significant role, potentially acting as a coding companion or customizing courses, but he emphasizes the need for people to understand coding concepts to effectively use these tools.
What are Andrew's thoughts on the current state and future of Transformer architectures in AI?
-While Andrew acknowledges the dominance of Transformer architectures, he also expresses interest in alternative models like SSRM and diffusion models, suggesting that the field should continue exploring new architectures.
Outlines
đ€ Introduction to Data Centric AI
The paragraph introduces a conversation between Andrew and Joe about Data Centric AI. Andrew explains the shift from model-centric AI, where progress was made by inventing new models and algorithms, to data-centric AI, where the focus is on curating and engineering data to improve AI performance. He discusses the importance of data in training large language models and how data issues persist through various stages of AI development, including fine-tuning and deployment. Andrew also highlights the Meta Llama 3.1 paper, where an earlier model was used to generate coding puzzles that trained the next version, showcasing the potential of using AI to create training data.
đ The Role of Data Engineering in AI
In this section, Andrew and Joe discuss the critical role of data engineering in AI. Andrew emphasizes the importance of data infrastructure and how it affects the success of AI systems. He mentions the challenges companies face in architecting data and the need for a purpose-driven approach to data improvement. The conversation also touches on the talent shortage in data engineering and how companies should approach investing in data for AI. Andrew suggests a cyclical approach where initial AI successes fund further data infrastructure improvements.
đ§ Exploring Agentic Workflows and Data Centric AI
The discussion continues with agentic workflows and their impact on AI. Andrew explains how agentic workflows, where AI models are prompted to think and refine their outputs iteratively, can lead to higher quality outputs compared to direct generation. He also talks about the use of these workflows in various applications and the importance of data curation to avoid model collapse when training AI on machine-generated data. The conversation highlights the need for a thoughtful approach to data generation and usage in AI training.
đ Generative AI in Education
Andrew shares his thoughts on the potential of generative AI in education. He envisions a transformation in education where generative AI could serve as a coding companion, making coding easier and more accessible. He suggests that learning to code with the assistance of AI could align educational practices with the future of work, where coding companions are commonplace. Andrew also addresses the challenge of teaching students to use AI tools effectively while understanding the fundamentals of coding to avoid common errors.
đ The Future of AI Applications
In the final paragraph, Andrew expresses his excitement about the future of AI applications. He believes that while foundational work in AI, such as training models and improving data engineering, is essential, the real value will come from practical applications of AI. Andrew notes that despite the high costs associated with training foundation models, the economics at the application layer are favorable for innovation. He anticipates a surge in AI applications that will drive the field forward.
Mindmap
Keywords
đĄData Centric AI
đĄModel Centric
đĄData Engineering
đĄFoundation Models
đĄGenerative AI
đĄAgentic Workflow
đĄData Infrastructure
đĄModel Fine-tuning
đĄData Curation
đĄTransformer Networks
đĄEducational Transformation
Highlights
Discussion on the shift from model-centric to data-centric AI.
Importance of data engineering in the practical application of AI.
The role of data in training large language models.
Data curation challenges in training AI models with machine-generated content.
Innovative use of an earlier version of a model to generate training data for its successor.
The significance of data in driving the intelligence of AI models.
The necessity of error analysis in data-centric AI systems.
The trade-offs between quantity and quality of data in AI applications.
The impact of generative AI on the future of work.
The role of data infrastructure in supporting AI applications.
The potential of agentic workflows in improving AI output quality.
The use of AI in education and the transformation it might bring.
The importance of understanding coding concepts when using AI coding companions.
The economic implications of AI applications and the potential for capital efficiency.
The future of AI and the focus on developing useful applications.
The potential of diffusion models for text generation as an alternative to Transformers.
Transcripts
hi
Andrew hey good to see you Joe good to
see you too how's
things uh the uh exciting times
say awesome well yeah uh thanks for uh
joining the show today it's good to see
you um yeah so we're here to talk about
a topic I think near and dear to you uh
which is data Centric AI uh but also how
data engineering um is critical to data
Centric AI so um I guess to back up uh
walk us through the beginnings of your
of your um thoughts around data Centric
AI because I I think when before that
there was a more of a model Centric
world we were in yeah so I think for
many decades um AI progress was driven
it feels like primarily by people say
downloading data sets off the internet
and then spending a lot of time trying
to invent new map or invent new models
to make it do better on that data and
that's fine nothing's wrong with that
because of that recipe AI made a lot of
progress but I think many practitioners
of AI including you and me and many
others have known that if we're trying
to build something you know practical
ship it um sometimes uring the data is
much more fruitful than trying to engine
in the math or the model and so I wanted
to uh creating and trying to popularize
this term data Centric AI to coales a
lot of the already ongoing work um on
entering the data rather than the model
and it's been quite uh exciting actually
to see how much momentum to dat the
centri AI Community
has I think we we have to ask because I
mean this is the main thing in the air
right now generative AI does data
Centric AI have something to do say
about how we train large language models
for example what are your thoughts on
that I think very much so in fact um uh
everything from training the foundation
model to you know post training maybe
fine-tuning to even some of the uh
deployment usage seems to keep on
running into data issues I know that in
the popular press people talk a lot
about scaling laws and building you know
bigger Transformer networks or whatever
to train even more data on and that is a
key part of it but when I talk to my
friends they're involved in you know the
actual dayto day of how do you get these
models to work um a large fraction of
the efforts I'm tempted to say more than
50% but L very large fraction of their
fers is actually thinking through how to
get the right data to feed into these
Foundation models um and then of course
you know even after someone else has
trained a large language model large
Foundation Model A lot of data workers
go into fine-tuning it and then also in
Practical deployment and usage um you
know if you're doing a few shot learning
lot lot of data oriented thinking there
as well so not everything is data
Centric AI but even in gen and
Foundation models a much larger fraction
of it is then I think people wiely
appreciate I actually talk about this
for a long time but you guys are oh no
please do keep going yeah we're here to
listen oh so maybe one one fun thing
when uh in the Llama 3.1 paper uh The
Meta release I think one of the coolest
things you a lot of cool things in that
paper but one of the coolest things was
meta used an earlier version of the
model used llama 3 um and then an
agentic workflow to basically use llama
3 to generate coding puzzles that were
then used as training data to train
llama 3.1 and I think this has always
been one of the puzzles of how you get
synthetic data work to train Foundation
models and um using an agentic workflow
we use the early model but let it think
for a long time iterate over something
over and over to come over good result
and you train the Next Generation model
to come over the equally good answer
very quickly rather than need to think
of over and over I thought that was um
one really nice recipe um uh you know
for for creating data to train
Foundation models and really when I
think when I talk my friends training
you know some of the very large
Foundation models um a lot of the head
space is boy can I sign the right
licensing deals with the Publishers to
get the data and of all these datas
which one do I invest dollars in to buy
um or for the preference tuning be rhf
or you know DPO to to line it with human
values what's the labeling schema how do
I get that data and then it turns out
that while there's certain Innovations
on trading Transformer networks and all
that it feels like uh there's there's at
least as much maybe even more hot to
compare that there Sly a lot of thinking
dayto day on um how to get the data to
chain these
models when you came up with the um I
think the original uh article on um data
Centric AI um and you're thinking around
that that was in the time before chat
GPT and I and I think that um how how do
you differentiate between uh or is there
a differentiation between data Centric
AI um and maybe more classical um I I
kind of let deep Lear into that to like
classical like three gen Ai and then um
gen AI is there is there a difference in
how you approach data Centric um AI or
is it all sort of the um same thing at
the end of the day I don't know maybe
are related to set of techniques on the
Continuum um feel like you know data CI
for vision is different than for text
it's different for audio it's different
when is a input modality like structure
data that humans kind process that well
ourselves so I think it is very I think
of is different for um different types
of data and types of modality but then
there are underlying principles um
really how do you systat engineer the
data to build a successful AI system so
things like error analysis to figure out
where are the gaps um in order to try to
get more data as well as what the
techniques for datation to get high
quality data and then the trade-offs
between you know small amounts of high
quality data versus large amounts of
slightly lower quality data I see I see
these themes that seem to be pervasive
um Al the way from you know training
computer vision models to the way we are
trying to figure out which small handful
of examples to put into a large language
model prompt because we're doing F short
learning um yeah I I think and then of
course um underlying that I see with you
know supervised learning and generative
AI I see a lot of businesses um actively
thinking about how to get the data
infrastructure sorted out and gen
certainly given Tailwinds to a lot of
companies and boards and cosos wanting
to get that data infrastructure right
and I think that's been a that's been a
positive change actually just increased
urgency when people say we got got to
sort out our data so that certainly has
uh create a lot more urgency you know to
to to the kind of stuff that you guys do
I I I imagine it would been good for
sales of your book as
well very much so yeah we we came off of
one hype cycle and now we we're kind of
riding another
yeah but it's it's it's interesting
though because I feel like the
um yeah I feel like there's a focus on
the fundamentals and making sure you can
invest in the the foundation to enable
um you know analytics and and certainly
machine learning and AI uh that was um
but I feel like it's a hard realization
for companies to get to because it's
like I think initially it's a uh
especially in the 2010s it's like well
let's just jump straight into doing
machine Learning Without uh much data at
all um and I think all of us kind of saw
how that worked out and so um yeah so
with data engineering I guess is ends up
being I guess fairly important to to all
this so understanding it a bit
um I guess what are your thoughts on uh
the the role of data engineering um with
uh with AI these days Andrew I think as
Mission critical I am seeing uh
significant you know uh Talent shortages
uh as in I I I you know travel around
the US travel around to different
countries is now than usual to visit
sometimes very large very profitable
companies um not not tech companies
sometimes sometimes tech companies
sometimes not tech companies and um
there are really smart people doing some
business but kind of struggling to Think
Through how the architect the data and I
think it is difficult you know I think
the number of decisions we have to make
um in terms of how to store the data
which cloud services to use you know
what what's the right database scheme
what are the trade-offs between cost and
performance um and then also one of the
other challenges is most companies it
turns out don't want to spend you know
whatever a million dollars to just to
improve the data because you you want to
improve the data for a purpose and so
hoping businesses get in that cycle of
when you can hopefully start to deliver
wins on the AI machine learning
generative AI site but concurrently even
as you're building valuable applications
um use that as a way to Drive how you
improve your data architecture so that
your foundations to keep building on top
of become even better so so I have say
there are there are some cosos that have
said great let me invest a lot of money
to fix all my data and then it'll be
beautiful and then I'll have wonderful
Ai and I think you would both advice
people like please don't do that right
you do need to invest in the data but
the data is usually built out for a
specific purpose because otherwise there
too many things we can optimize you make
it faster speedier more distributive
more robust what whatever it's two IND
decisions but if you have one or a
handful of use cases that you're driving
toward then that helps the data team um
make the right priorization decisions uh
in order to improve the data
infrastructure and then that creates an
amazing foundation for lots of people to
build lots of exciting applications on
top of but I know I I feel like this is
advice I give to kind of companies quite
often I I I'm I'm guessing you give very
similar advice to many businesses all
the time we do yeah and it's yeah it is
interesting I suppose but you know I
think every company we talk to has to
have an AI story right now so um whether
you like it or not you're going to have
to figure out a way as a company to to
um you know start working with AI on
that note too I would like your your um
thoughts on what you're seeing or
hearing when you talk to your friends
about uh small language models and also
AI agents um what are you seeing out
there so I think uh agentic workflows is
is one of the most exciting um
directions for AI uh so you know I think
the way a lot of people use the large
language model is we prompt it and then
we expect it to write an essay for us on
whatever we ask and that's a bit like
going to a person and saying hey buddy
please write an essay for me by typing
the essay from the first word to the
last word all in one go without ever
using back space and you know maybe you
can write like that but most of us don't
do our best writing that way in contrast
to agentic workflow might ask the large
language model to um brainstorm and
outline and then ask if uh you need to
do any online research if so go download
a few web pages and put them into the
large language model context then write
the first draft they read the first
draft in critique it and and and so on
um and so uh we're seeing with many
agentic workflows the quality of output
is much higher than you know anyone than
than possible with just having with
direct Generation Um and I'm seeing this
useful for for many applications so
actually you know de AI interally as
some agentic workflows AI fund which
also lead um has many projects in kind
of healthcare you know legal compliance
processing various types of complex
documents where we couldn't do the job
without an agentic workflow and then the
most interesting thing I think um uh was
it uh GP 401 uh uh preview uh just
released recently and then also
anthropic also been doing something
related for months uh which is a
fine-tune the large language model um to
generate you know thinking tokens um
along the way and I think this
incorporating an agentic workflow where
the Lun langage model is um pre-trained
or sorry fine tuned to do sort of Chain
of Thought reasoning so it spends more
time thinking uh before it outputs you
know the final answer I think
incorporating this directly into the LA
langage model is exciting Direction
actually I think for a few months uh uh
I I I I I think it's been you know
jailbroken for months right so that's
what we know anthropic uses a tag um XML
tag and thinking I think but it's you
know various people have jailbroken it
to make it basically review the tag and
show his internal thinking dialogue so
that this so so I I think this is public
I don't think I'm reviewing new already
the internet so I thought I thought
actually really clever but I think the
open eyes taken it to a whole other
level with this new release model and
then I think it was just one or two
weeks ago there was a um overhyped
reflection 70b model with the initial
claims turned out not to be quite
accurate but that was also exploring a
similar technique and even though the
hype and the inaccuracy of the initial
results was unfortunate you know the the
underly technique it seemed like an
interesting very interesting Direction
takes well so I think this is in the air
um agentic work those that people can
implement but in also Al uh that is
possible to fine-tune a large language
model to basically do Chain of Thought
reasoning internally and maybe use some
tags to have some thinking you know that
may or may not need to be reviewed to
the end user so I I think there's a
fascinating direction oh and then of
course uh a lot of the work to do this
is you know come of the data set right
to show it how to Think Through what's
the right thinking process different
types of tasks so but so so exciting
times that's interesting I guess what
are you gonna ask Matt oh I I was going
to ask and this is kind of related to
what you're talking about I think one of
the problems that's in the air right now
is so theoretically if you take the
output of a model and then retrain on
that output you degrade the weights
basically right it's like recording a
signal that has extra noise in it and
then there also been research papers
that have shown this experimentally um
what what are your thoughts on how we
solve this problem especially as more
and more of the content on the web that
we're using to train is machine
generated and deep learning
generated yeah good question so I think
I think the the data curation of uh
selecting high quality data off the
internet I think that's an ongoing thing
um and I think so it turns out if you
use a large language model to generate
text and you train a different model on
that um that is a good idea if you're
applying model distillation if you have
a large model generating very thoughtful
text you want to train a small model to
mimic the thoughtfulness of a big model
you know that's that works that's model
distillation but I think as you're
saying Matt what doesn't work is if you
train a model use that to generate data
and then use that data to try to train
an even better model in fact a few
researchers have shown that if you do
this process enough times generate data
train a new model have the new model
generate data use that to train the next
model then you actually end up with
model collapse right where where the
model starts generating very
uninteresting things but where this
technique does Really Work Well is If
instead of using direct generation
instead of copy pasting one model's
output into the training data of the
next model if you instead use this type
of agentic workflow where the first
model you might have it write an essay
reflect on it do some web search you
know then critique it and improve it so
it does a lot of work to come up with a
pretty good essay and then you try to
get the next generation of model to
generate that essay directly with much
less work than the first model had then
that does seem a whole maybe analogies
of human thinking are always dangerous
so I was nervous about making that but
you know I remember when um when I was a
kid you know practicing for math
competitions or whatever right I would
spend a long time trying to solve some
math problem but having solved that
myself I go oh next time maybe that's a
shortcut I could use to solve the next
problem so training on your own thinking
is okay if you learn to do quickly what
took you yourself a long time to do and
and so it's been so this does seem to
work for L languish models as well
inter so it's almost like a
self-governing approach that you're
looking for not just training on data
but actually critiquing and then like
you're saying an agentic almost
thoughtful process you go through yeah
yeah yeah so and I think um yeah and and
and maybe just I I I think uh lot of the
large companies training you large
Foundation models um kept lot the
details of what they do right some of
proprietary but I definitely you know uh
get a strong sense talking to multiple
people from multiple companies a lot of
the head space is not just in tuning the
foundation model and making sure the
gpus are reliable whatever there is a
lot of that too but a lot of head space
isn't freeing out the
data I think I think you know a AI right
AI models is uh the model Plus data and
in fact if you know if um actually
common experience for a lot of people if
you go through the math of what a
Transformer neuron network does you know
go through the attention mechanism right
blah blah blah um I've had a really
interesting experience a lot of people
learning that from the first time they
go what I don't get it how could you
know like a few lines of math
demonstrate this weird intelligent
behavior of a large language model and I
think the answer is um the magic is is
not only from the Transformer neuron
Network which is very clever you know a
lot of the intelligence of the L Anish
model comes not from the neuron Network
architecture but it comes from the data
and this is why when people wonder I
want understand lar langage models let
me study the Transformer neuron Network
very common reaction is okay I finally
worked through all this math but I don't
get it it doesn't make sense why this
MTH would be so intelligent and I think
the the Gap is lot that code
intelligence comes from these models
having sucked in massive Text data sets
you know generated by mostly humans we
hope uh and that's what's that data is
what's creating a lot of the
intelligence or appearance of
intelligence in these
models I guess you
um uh you have a lot of friends in the
space are you seeing um maybe an
evolution of something outside of the
Transformer architecture right now or
are we uh or are we stuck with this for
a while you know it's a great question
um I I I I think uh transform
architecture seems to have strong
Tailwinds uh the what ssrm stasis models
have been kind of around for a little
bit they have not really taken off yet
uh uh but you know still enough
researchers working on it I think it's
fascinating we keep an eye on scaled to
very long input context very interesting
ways uh and then uh the other one that I
don't never will take off is the
diffusion models for text generation uh
really
just very recently I see my paper on
this fascinating uh today I think the
dominant model for image generation or
diffusion models where you generate a
blurry image and then repeatedly kind of
quote remove noise to sharpen up the
image and so um uh I think Stephano man
and some folks came a way to generate a
quote blurry piece of text and then
slowly sharpen it up and and when
trained to be gpt2 size it seems to
outperform gpt2 but you know JW's out on
on what will happen as as this scills
out so I I don't know if it'll work but
this I think if we're stuck with
Transformers and nothing else for a long
time I think we'll be fine but you know
with ssrm and diffusion models and
others trying other things I think you
know collectively our community has a
few shots at coming up with something
even better no pun
intended um kind of Switching gears a
bit so education's also a big passion of
yours um you started companies around it
before
um and where do you think uh generative
AI fits into uh education these days say
I want to learn a topic um you know I'm
teaching a topic where do where does it
fit in yeah so I think I feel like there
is a coming transformation of Education
maybe that I don't feel like I know
exactly what it will be yet um you know
there stuff that um a few companies have
done you know I think uh K Academy built
kigo which seems to work well Cera has
CER coach which actually works really
well uh people haven't tried it I've
used a bunch it's actually you know
surprisingly good um but I think that uh
that's just one product idea um uh corer
uses gen for course Builders so quite a
lot of companies are using cuse Builder
to customize causes for specific
Enterprises needs so you know there's a
bunch of ideas like this um like a aita
and so on I think that they could be
bigger transformation in education
coming and I don't feel like I know
exactly what it is um but maybe uh when
I when I chat of uh um you know
University leaders one other thing I
often talk end up talking about is the
future of work uh because one challenges
maybe take coding as as an
example I think we should just all learn
to code with generative AI as a coding
companion um I know that some schools
are still debating what are not to ban
you know chat gbt for the programing
causes uh but honestly I think it's
clear the future of software engineering
will be coding alongside the coding
companion and we can decide you know is
it GitHub co-pilot or cursor or copy
pasting directly from gb4 or CLA or
Gemini which I still do a lot of
actually um but you never have to learn
to code alone and so I think aligning
the way we teach programmers um with the
future with not where the field has been
but where the feud is going I think we
need to do that and that's just computer
science I think in education you know
thinking through what will future
chemical engineer do what a future
doctor do I think that's actually a big
challenge of academic institutions but
on coding specifically I think every I I
would like I would like to see pretty
much everyone learn to code uh because
with a coding companion with Jenny I
hope coding is easier than ever before
um and the value of someone being able
to write a little bit code is higher
than ever before so I'm seeing you know
soft engineers get meaningful
productivity boosts we just do a lot
more right when you use geni but then
also among among my teams I'm seeing
kind of marketers and investors and kind
of people who job Ro is not software
engineer um write just a little bit of
codes uh to download web pages
synthesize it get insights and I find
that people that know just a little bit
of code um can do a lot more than often
do a lot more so that's why we we we
release this free sequence of causes uh
ai ai python for beginners to to help
people you know uh learn coding for the
first time so so one of the complaints
I'm seeing from open source maintainers
is that you have people who will use
like chat gbt to generate code and then
they try to run it and it doesn't work
correctly and it turns out there's a
really basic error like a variable is
named inconsistently right maybe it's
even capitalization or something and so
the variables don't match so it doesn't
work and they they know no coding and so
they don't know how to debug those
errors errors so how do we like teach
students to use the tool but also you
know have enough insight into how coding
actually works so they're actually
learning the the minutia that they need
to know to write good code yeah I know I
know social media tends explode with the
you know code and then why I build this
thing that's very cool that they did
that uh and I think at least for now
those are the exceptions and I'm seeing
that but hopefully there'll be more and
more of these exceptions but I think
I find people get a lot more traction
with low code than no code and if you
know just a little bit about what does
the word even exception mean you know
and and there these Concepts um I think
I I think for quite a long time someone
that knows a little bit of programming
Concepts uh will be to do a lot more
than someone that you know doesn't know
coding at all and just prompting um the
the the boundary is Shifting because of
Improvement in technology I think
anthropics um artifacts was really
clever right helping take Bas take
things to deployment and I know kind of
replate you know also making it easy
reducing friction deployment I uh uh and
I think um uh I personally build a lot
of um streamlet apps because it turns
out you know gbd4 is really good at
writing streamlet code so I don't worry
about the syntax just thrill stuff up in
minutes on on like a cloud you know or
whatever um so I think I think yeah I I
but but by find that people that
understand the bit of the coding
Concepts um you just get much further
much quicker and it's less like less
likely to hit a dead end I
think we have about three minutes left
um what are you most excited about over
the next
year
um excit about many many things but they
made me pick one it would be um
applications uh I think that uh there's
a lot of foundational work to be done um
including training Foundation models
better technology tons of work to be
done in data engineering but where I
think many people you know will get the
most value out of it will be the
applications um and we do need to work
on the foundations the shortage of
people that understand how to build the
data the foundation models the
infrastructure got to keep on working on
that and then I think ultimately you
know our field would be judged by our
success um at delivering useful
applications so I spend a lot of my time
focusing on applications uh but it
actually work on applications that then
you know causes me to sometimes have a
strong view maybe right maybe wrong that
boy we really need to get this data
infrastructure right or boy we really
need to get this orchestration later
right but but I I I think that we're
actually starting to see a rising flood
of applications oh and one one one
interesting thing about applications as
well I know people read in the news
about these you know billions of dollars
more than single digit billions of
dollars spent on gpus to train
Foundation models and people think doing
AI is really expensive right but it
turns out that because someone else has
spent you know these tens of billions of
dollars um is now very Capital efficient
to start to work on some data
infrastructure to start to work on some
application so I think at the
application layer the economics look
very favorable to to people you know
building and want to deploy stuff
it's
awesome Andrew it's uh great to talk
with you as always um thanks for thanks
for joining the podcast for taking the
time great yeah thank you great time you
guys thank you Joe thank you man yeah
cool right take care
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