FIRAT TEKINER: Welcome to AI, "The Future of Us,"
a podcast style video series that explores the ways
AI is shaping our future and how we
can prepare for the challenges that lie ahead.
I'm your host Firat Tekiner from Google Cloud,
and today, we are joined by Donna Schut, who
is head of applied analytics and AI solutions at Google Cloud.
Thank you for being here today, Donna.
DONNA SCHUT: Thanks for having me, Firat.
FIRAT TEKINER: So could you please tell us
a bit about your background and expertise in the field of AI?
DONNA SCHUT: Sure.
So actually, as of today, I lead a technical solutions
management team focused on generative AI.
And prior to this, I led applied analytics and AI solutions
as well as worked in our professional services
organization, helping some of our most advanced customers
and actually internal teams as well build their machine
learning platforms.
FIRAT TEKINER: That's awesome.
How did you become interested in AI
and what do you find the most fascinating about it?
DONNA SCHUT: So growing up, I played chess
at an international level, and that really
sparked my passion for how we design
AI systems to help humans.
And AlphaZero is a great example of that.
So in chess, when you evaluate a position,
you look at three things material, which is--
they're points given to pieces, space, and then piece activity.
And what AlphaZero essentially did
is it changed the way that we evaluate those three things.
So placed more emphasis on space and piece activity
and introduced new concepts.
And that really is a good example
of what I love about AI, is that it
pushes the boundaries of our thinking,
and it can be applied to so many different problems
that you're always learning.
FIRAT TEKINER: Wow.
Great.
I didn't know that actually.
And one of the funny things that I heard recently
is that LLMs are not good at playing chess as well.
So I'll definitely be catching up with you on that.
So I want to look into a bit of the challenges
around it, how we can close the gaps in between the data
and machine learning as well.
And in your opinion, what are the main overlaps
that you see traditionally for two separate disciplines?
So for data analytics and data engineering and machine
learning based on your experience.
DONNA SCHUT: That's a great question.
I think for me it always starts with a business problem.
So whatever problem that you're looking to solve.
First of all, it depends on what is the right approach
for that problem.
It could be analytics or it could be machine learning.
And then as you mentioned, I think
we've seen them traditionally be separated,
but ideally, you want a common vocabulary between those two
teams, where the business metrics that the analysts are
reporting on are the same as the features
that data scientists use.
So an example of that would be, for example, conversion ratio
within the marketing domain.
And then I think when you look to data engineering and ML
engineering, I think building an ML-enabled solution
is an iterative process between data engineering,
machine learning engineering, and actually also
app engineering, where the ML engineering team really
leverages the data sets and features produced by the data
engineering team.
And then the ML engineering team will feed back
those data requirements to the data engineering team.
So really machine learning models
are only as good as the data that's provided.
FIRAT TEKINER: Thank you.
And that overlap is--
I totally agree.
And just to expand on that a little bit,
how do you see DataOps and MLOps with LLMs
in mind evolving in the next few years?
Because we are seeing a lot of applications.
We are seeing lots of new approaches coming through
as well.
Where do you think that will be leading to?
DONNA SCHUT: So I think LLMs really allow
for faster experimentation, in many cases,
without the need for heavy data preparation, which
was needed to train machine learning models previously.
We're still in the very early stages of generative AI,
with many customers experimenting,
whereas automation is an advanced characteristic
of a mature technology.
And so this is still very much an evolving field.
However, we expect generative AI to take a similar trajectory
to traditional machine learning when
emerging machine learning efforts
matured into ML engineering.
So for example, we currently see a lot
of ad hoc prompt engineering and very nascent prompt evaluation
and prompt management processes.
At the same time, we see experimental approaches
applied to other methods of model adaptation,
like model tuning.
And this is similar to the early stages of ML workflow
implementations before the introduction of frameworks
like TFX and Kubeflow.
We also already see the divergence
from established MLOps practices to what's
optimal for working with LLMs.
Most customers will likely adopt foundational models
to their requirements through prompt engineering
or optimized tuning techniques, like parameter
efficient tuning, reinforcement learning from human feedback,
distillation, where knowledge from a larger model
can be learned by a smaller model
will become an important aspect of preparing
models for efficient deployment and surveying.
And then integration of LLM models
with external sources of knowledge
will also become a common pattern.
So organizations will need to evolve their MLOps processes
and tooling to better accommodate
these new techniques.
FIRAT TEKINER: And that's really interesting insights, Donna.
And I think we'll be seeing lots of different personas taking
some of the tasks that you mentioned as well.
It'll be interesting to see whether this will rely on
with the data scientists, ML engineers, data
analysts, or engineers or even business analysts as well.
But I would like to focus on the challenges
on applying and using generative AI.
According to you, what are the examples
of how GenAI is currently being used effectively
across the different industries?
Because I know for the fact that you talk to a lot of customers.
What are the things that you are seeing?
I would like to get a bit more insight on that.
DONNA SCHUT: So I think with generative AI,
we've really entered a new era, which
is defined by intuitive interaction between humans
and technology.
And areas where we see a lot of interest
is, for example, in marketing, so being
able to very quickly generate content based off
of trends that marketers are seeing,
being able to generate content for audiences
that's much more personalized.
We see it being used in customer service.
So for example, to create chatbots
that are more conversational to their customers
or assisting support agents by summarizing their case notes,
indicating the next step, or generating an FAQ where
they can look up answers.
It's helping engineers by generating code
or suggesting the next lines of code,
or it can be through a code bot, where developers
can talk to and ask questions, or for debugging purposes.
So those are a few, not all encompassing,
but it shows the wide range of impact that it can have.
FIRAT TEKINER: I think certainly, we'll
be seeing lots of innovative applications in this area,
but within that as well, what value does it
bring to the organizations?
So there are a lot of interesting applications coming
through, but it's sometimes challenging to identify
where the value really lies.
And what do you feel around that?
DONNA SCHUT: So I think areas where
we've seen it create value is it enhances the customer
experience, so the ability to reach larger client bases
by making these interactions more
natural and conversational, improving or accelerating
the time to value.
So it's easier to search and understand
large amounts of complex data, and then
assisting employees or boosting their productivity.
So by generating text code image, some of the examples
that we just gave, very quickly and multimodally.
Those are a few of the areas where
we've seen it create value.
FIRAT TEKINER: And definitely, multimodal data processing
is something that we have been speaking
around a lot within the past decade or so.
And I think this is the moment that we'll
be seeing more and more, with the LLMs and generative AI
solutions helping us to tap into the potential
of the particular unstructured data, whether it is text
or it comes in different forms as well.
And I'd like to look ahead a little bit with AI and the data
analytics as well.
So what do you believe is the most
exciting potential application of AI that we have yet to see?
Because we are seeing a lot.
There's lots of noise as well.
And I'm sure that what we are speaking now will not be what--
it is very difficult to predict the future,
but what do you think around that?
DONNA SCHUT: Well, I think personally,
I'm most excited about solving the big problems
around the intersection between people and technology,
so around health, education, and then sustainability as well.
So I'm looking forward to AI helping
us to discover new drugs, create more tailored education
programs to really help get the best out of people.
More accurately, predict floods and wildfires
to help keep people safe in a changing environment.
FIRAT TEKINER: Yes.
I think there's still lots of potential there to tap into.
And my last question, and by all means not the least,
so we spoke a little bit around it, different type of personas,
but what advice would you give-- you are very experienced
in this area--
to individuals or other organizations
looking to prepare for a future where really AI
plays a significant or more significant role?
Because if they don't get the value,
and if competition comes and does it before them,
it will be challenging for them.
Where do you see that?
Where do you see AI plays even more role
within the organizational processes as well?
DONNA SCHUT: I would probably recommend
by starting to identify use cases for an organization,
where they expect good ROI and that are relatively low risk.
And you can brainstorm by tasks that LLMs
are good at, such as search or summarization, generation,
rewriting, and then start experimenting.
There's a lot of tools right now that
make AI much more accessible.
And so I would recommend to think about the approach
or mix of approaches that you would want to take and then
hire accordingly.
So for example, you might use out-of-box product
functionality to quickly build immersive experiences
for which you would need application developers.
You might adapt foundational models to your own needs
and to differentiate for which you would need data scientists,
or you might even train and build
your own customized large models using specialized AI
infrastructure.
And for that, you would need ML researchers and engineers
that are very specialized.
FIRAT TEKINER: Thank you.
Yes, and I agree actually.
It is really a multi-objective optimization problem
at the end of the day, whether you should be building
or whether you should be buying the solutions,
whether you should be using the foundation models
or whether you should be going and developing your years
and training years, whether you should bring your own data sets
or your existing data sets.
I think there will be lots of things that we'll see
and organizations need to juggle around as well.
But I think you are spot on, what
are the key parameters that gets you the highest ROI
and where you would get the most value is the key.
Thank you for joining us, Donna.
It has been incredible--
DONNA SCHUT: Thanks for having me.
FIRAT TEKINER: --to hear from you.
And we look forward to seeing how AI will really continue
to shape the world around us.
And hope our viewers will be inspired
to embrace its potential opportunities.
Thank you very much.
DONNA SCHUT: Thanks.
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