(notification sound)
- Ah, nice.
I don't know about you, but I'd miss half my meetings
without automated calendar notifications.
It's the only way to cope with an overloaded to-do list.
(notification sounds)
I guess the age-old tactic is just drink more coffee.
Wouldn't it be nice if my coffee maker
could actually read my calendar
and know to make another cup?
Or better yet, read my facial expressions?
And know to make it stronger?
But for that, we'd need AI.
(upbeat music)
Where automation can only follow the instructions
that humans provide.
AI is a larger concept
for creating intelligent machines that simulate
human thinking and behavior to execute tasks smartly.
In other words, AI encompasses machine learning.
(keyboard typing)
Machine learning is a subset of AI
that allows machines to learn from data
without explicit programming.
Machine learning algorithms adapt themselves
to perform better
as they are exposed to more data.
And the ML model represents what's been learned
by a machine learning algorithm.
Everyone and their grandmother is a consumer of AI.
Whether you shop online, browse recommended videos
or run search queries,
AI platforms use data to determine what's most relevant.
The pace and growth of AI technology is already
making a lasting impact on the world.
Actually, I have a few examples bookmarked.
Let's see.
Oh, um, I don't know if it's quite ready,
but this might be fun.
Hat-E, can you show me a few cool AI use cases?
(computer sounds)
- [Hat-E] The Royal bank of Canada created
the Borealis AI platform
with the ability to run thousands
of simulations and analyze millions of data points
to deliver next-generation banking applications.
- All right. Not too bad.
What else have you got?
(computer sound)
- [Hat-E] HCA Healthcare created a real-time
predictive analytics product system
to more accurately and rapidly detect sepsis,
a potentially life-threatening condition.
- I'm actually pretty impressed.
(notification sound)
Ah, Sherard.
Let's see. What?
Oh, wait that's not...
Wait, what's happening?
- [Hat-E] You seem to have too many items
on your calendar, Chris.
I think you would benefit from some resting cycles.
(notification sound)
- Oh no, no come on.
(Chris grunts)
Hat-E, I think you need some more training.
Your sentiment analysis is working well
but your next recommended action,
we need to work on it a little bit.
- [Hat-E] No Chris, no-
(clicking sound)
- Hey Chris, I just saw canceled meeting on my calendar.
Do you need to reschedule?
- Oh man sorry. My bad, long story it's kind
of an AI thing gone awry
but, but I've been thinking about transparency
and understanding the decisions that AI models make.
Means you have to understand the data that goes
into those AI models and in there there's a whole discussion
around bias, but I really just want to understand
your thoughts around the role
that open-source plays in the AI model development.
- Yeah, I, I'm actually really glad you've been thinking
about that, Chris,
because it is an area that we've been focusing on.
If you look at the model without the data,
then it's very hard to understand
the decisions that an AI application may
may have made, but then if you have the data, but no model,
then you don't understand the innovation that went
into the model itself.
And then of course at the very end of it,
if you don't have open-source tools that allow you
to create and edit and, and, and really iterate
over the process over and over again.
You know, without any one piece of that,
you have a disconnected story
In an Open Data Hub,
what we're doing is bringing all
of those things together: the data, the tools, the models
and just doing it in a way that Red Hat knows best
which is in an open source community-based environment.
- I really like that.
I mean, I'm drawing an analogy
in my mind about open source development
and software and a lot of visibility that goes
in that software development process.
AI needs the same thing: data as the source,
building the models, using open source tools.
And I think one of the cool things
around Open Data Hub is you can actually surface a model
as a microservice available to application developers
and really bring that all together
from that innovation point of view.
- Now what we're looking at is how do we apply
that same fundamental concept
but now move it over into AI and ML.
And so, when we look at ways
in which we needed to do this, it really centered
on what we know best, which is democratization
and making things freely available for people to use.
- I have to admit, I always get a little hung up
on the word democratization
because it feels a little bit
like a cheesy marketing slogan
but the way you're using it here is actually
a perfect use, dictionary definition use
of the word democratization,
which is really about leveling the playing field
and ensuring everybody has access because,
that broad access creates fundamentally better outcomes.
- It's about openness.
It's about collaboration.
When we bring all of those things together:
the data, the models, the tools
and it's all done in an open, innovative way,
we're actually fueling innovation
and creativity and allowing organizations
to create things at a rapid pace.
- Sherard, I'm glad we kept the time on the calendar
and, the reason I'm thinking about this transparency
and models is, the stakes are really high.
When we think of things
like autonomous vehicles or medical recommendations
where, literally, it's a matter of life and death.
So we really have to shine a light
on the process of data and training and building models.
So thanks a lot, Sherard.
- Absolutely.
- Data is crucial to machine learning models
and those models are critical to using compute power
to gain insights and create innovation.
Transparency into how data is used
to train AI is not only important
for technologists developing next generation applications,
but also for consumers.
So they can be part of innovation.
Think about what's possible with more equitable open data.
That's why I like to say:
open unlocks the world's potential.
(upbeat music)