These models-- you've got text-to-speech.
You've got, rather, I should say, text-to-image with Dolly.
You've got coding.
You've got language.
Your ChatGPT has completely changed
the conversation about AI.
I think those of us who have been on the inside of AI
definitely have thought about it as, of course,
it's changing the world, and we see it every day.
But I think ChatGPT made it real for millions of people
by showing them what it can do and how powerful it is,
and their imaginations are now freed.
How can this be really useful towards someone who may
have, say, limited mobility?
One of my colleagues, Christina, her arms are paralyzed,
and she types with her toes.
And watching her use this product is really cool
because she said, oh, I just have to type
one sentence rather than typing lots of sentences
to plan a trip, for example.
How do we set the right mental model
for a technology like this?
It's super impressive.
It can create.
It can cite information.
And so, we found that people tend to over trust or over rely
the kind of information it generates.
And so, a lot of what our team did
was, like, how do you set the right mental model for users,
reminding them, hey, this is from the internet,
just as reliable as the internet.
Some things are factual.
Some things are not.
So, hi, everyone.
Welcome to the Global AI Bootcamp 2023.
We welcome you as one of the attendees
of our yearly global event with more than 70 locations
around the world.
Sammy, do you know that this is already
the fifth event in a row?
Five already?
Yes.
OK.
It's a 5th anniversary.
Wow.
It's crazy.
Time really flies.
Yeah.
We started in 2018, together with the Global HRD Community.
And then, in 2019, it really exploded,
with lots of locations around the globe.
Exactly.
Then, we had a couple of years, which were virtual
and some were hybrid.
And now, we're back with our 5th, anniversary of big events,
with lots of locations around the world.
It's really a nice history already,
but I think there's a lot of people
also that we need to thank.
And actually, those people are, right now, together with you,
in the room.
And as the local organizers, they've
been working hard to find a venue to find speakers,
to find sponsors, or maybe even catering for some foods.
So please, thank you to all the local organizers.
Give them a random applause for all the work
that they've been doing.
I think they really earn it because
of doing all that kind of work.
Yes, thank you.
Yeah.
But there's also some other people who have been doing
a lot of work over the last five years, and one person,
specifically-- well, it's sad that you cannot see him,
but we can see him.
So give me a second.
Let me switch on my phone and my camera.
Here.
Do you see this guy here?
He's one of the founding fathers of the Global AI Community
and has been working for us for over five years, of course,
doing everything.
And now, somehow, he found a new passion
in producing videos like this.
So should we go inside?
Let's go inside.
OK, let's go.
Come on inside.
So, hi, John.
Welcome to the Global AI Bootcamp.
Hi, thank you very much for having me.
Yes.
And thank you for being here with me.
It's my pleasure.
Yeah.
Before we start, can you tell us who you are
and what you do at Microsoft?
Sure.
I'm John Montgomery, and I am the corporate vice president
of product, in charge of Azure AI, which includes the Azure
Cognitive Services, Azure OpenAI Service, Azure machine
learning, and our AI frameworks.
So if we look at the past year, what
is your most memorable moment in terms of AI advancements?
The last year has been amazing.
I tend to think about the world in terms of customer impact,
more so than like research impact or product advances.
And in the last year, I would say,
we've seen several interesting customer things happen.
One of the biggest--
I mean, I could pick--
so there are little ones, like the improvements
we've seen in our cognitive, like neural voices.
The quality is getting better.
It's taking less training data, and that's enabled customers
like Duolingo to add languages and to make
the interactions more personal.
We've done a whole bunch inside of Azure machine learning
to make it scale better, to make training better.
And that's impacted a bunch of customers,
ranging from big ones, like Chevron
to some of our newer customers.
I think, though, if you look back at the last year,
probably, the single biggest thing
is around Azure OpenAI service going general availability
and ChatGPT.
And those are, probably, the biggest things
in the last year.
ChatGPT has completely changed the conversation about AI.
I think those of us who have been on the inside of AI
definitely have thought about it as--
of course, it's changing the world, and we see it every day.
But I think ChatGPT made it real for millions of people
by showing them what it can do and how powerful it is,
and their imaginations are now freed.
Yeah, that's totally true.
I've used it for a couple of times now,
and it is really, really big.
And it's really game changing.
I have to ask you then, what's the coolest thing
you've done with ChatGPT?
Oh, I have written some social media posts for my podcast.
Cool.
It's very good at it, even better than I am.
It is very good at things like that, yes.
Yeah, totally true.
You also lead a team at Microsoft,
and that team focuses on building tools
that are used by customers to create their own AI solutions.
So can you share a customer story with us
that really stood out for you?
Wow.
So when I think about my customers,
I really kind of think about it in two cohorts.
Microsoft is actually my largest customer.
All of the AI services at Microsoft,
they're using Azure machine learning
for training or for inference.
They're calling our cognitive services.
They're using Azure OpenAI Service,
and that includes things like GitHub Copilot,
which is built on Azure OpenAI.
It includes things like Dynamics 365.
I think, though, from a first party perspective,
in the last few days, we've seen some really huge announcements.
It was one that actually happened just today,
where we're seeing the potential of ChatGPT,
like things in Bing.
We've also seen a massive announcement
around Viva Sales and Teams premium.
We've been working for years with the Teams team
to do transcription of meetings.
And that transcription can drive things like closed captioning.
But we haven't tapped the full potential
until Teams Premium came out.
And now, you can get meetings summaries sent to you
and action items assigned to you in Teams.
And that's just showing the power of what AI can do.
So from a first party perspective,
I think that one is super interesting.
From a a third party customer perspective,
I'm going to pick a totally different one.
And I would focus more on the impact
that some of our responsible AI tooling has had.
The National Health Service in the UK
has been completely inundated since the start of COVID.
And they have been using the responsible AI
tools inside Azure Machine Learning
to help drive clarity and confidence in some
of the machine learning models they've created to figure out
where surgery patients should go for their hip and knee
surgeries, whether they need to go to a major medical center,
or they can go to a smaller regional medical center
based on a risk assessment profile.
And so, obviously, helping customers is huge.
And that was just an amazing example of, basically,
doctors working with their data science teams
to help an immediate and local problem using
the tools my team creates.
What do you think are the three topics that people
should be educated in?
I'd call out three things.
And actually, it might be the same,
even if I were talking to my sister, who
is an educator in Germany.
The first thing is understanding responsible AI,
understanding safety, understanding regulation,
and understanding the current truth and the current lay
of the land around these large language models.
There is a huge amount of fear, uncertainty, and doubt
out there, being spread by everything
from competitors to folks who just don't know anything.
The regulations are changing very, very quickly,
and the technology is changing very quickly.
I think understanding responsible AI,
what it means, understanding what Microsoft is doing is,
probably, first and foremost, on the list and making it real.
Because it is going to change very quickly,
but that knowledge is the foundation
of answering a bunch of questions about things.
I think the second thing that I would point to,
as something everybody should know about,
is large language models and this evolution
we're seeing around what you can do when you can't actually
take a model and retrain it from scratch.
I think we've trained ourselves over the years
that the only way you can do powerful things with a model
is retrain it from scratch on your own data.
These models, it doesn't work that way.
I mean, these models take months to train on supercomputers,
right?
And I think, now, we're starting to give people
fine tuning APIs and more and more tools on top of it.
Understanding what you can do with these, understanding
prompt engineering and what's coming after that,
that is a major thing for everybody.
The third one is actually more a longer lead pair
of technologies.
The first one of that is reinforcement learning.
And then, the second one is privacy preserving
training or federated learning.
And these may be the nuclear fusion
of artificial intelligence.
We've been talking about them for years.
And we're starting to see point applications.
I think the ChatGPT's use of RLHF
is starting to cause a lot of more interest in reinforcement
learning.
And certainly, as we roll these models out more and more,
dealing with the questions about,
how do you actually fine tune a model without needing to take
the data back to the source is something
that we're going to have to think about more and more.
So knowing about those two categories of technology
is probably a useful background thread.
It's not immediately practical for most developers,
or data scientists, or IT admins.
But knowing what they are and basically how they work
is, probably, another good foundational thing
for folks to understand.
Sounds like lots of good things to dive into for IT pros,
developers, data scientists.
Absolutely.
Yes.
Thank you so much, John, for being here with me.
Thank you.
It's been great to be here.
Thank you.
Well, that was a very interesting conversation
you had with John.
He really showed us what the strategy Microsoft
has for implementing AI in their products and also the future
that they are looking into.
But also, he shared those three different topics
that can help us to learn more about AI.
Yes, that was really valuable.
And he also briefly mentioned OpenAI,
and that's what you're going to focus on together
with Bob, right?
Yeah, really looking forward to that conversation.
Yeah.
Thank you, Bob, so much for making time for a quick chat.
Could you introduce yourself for the people who don't know you.
Sure.
It's a pleasure to be here.
My name is Bob Breynaert.
I'm within the Microsoft AI Platform Engineering
Organization.
And myself and my team have the fun
of engaging with customers and partners all over the world
to bring them on to Azure OpenAI Service-- customers, partners,
MVPs.
Can you explain to people what OpenAI exactly is?
Yeah, so OpenAI is the latest in the series of models
that are produced by OpenAI.
They're large language models.
They're generative models.
They're transformer models.
And really, what that means is, instead of taking a smaller
purpose built model, something that
does text analytics, something that does translation,
it basically has multiple capabilities
built into the service itself.
So in the case of OpenAI, it has fantastic language capabilities
with the GPT 3 models.
Also, one of the cousins of that is
ChatGPT, which has really captured
the hearts and the minds of everyone around the world.
And there's also the ability to do
text-to-image the DALL-E model.
And then, last, we have codex-- extremely important,
really, to democratize software development
and providing people access to SQL databases
that aren't SQL devs.
So it just brings all of these different capabilities
together through a single API.
And it's super powerful because it
enables people to take what they would
have been using six, seven, eight pre-built task
specific models.
And then, use the OpenAI service to do a whole variety of tasks.
We call this model as a platform.
So it's really a platform to build applications on top of.
I can imagine, with the services now open to the public,
the information that is putting in by the public
is also very interesting to make use to improve new models.
Yeah, that's a great point.
Because with our friends at OpenAI,
yeah, when ChatGPT is learning, millions of peoples
are using it, that feedback goes right into training
the next instance of the model.
And it's really powerful because it
builds in real-time feedback.
And one of the common things we get with enterprise customers
is they don't want their data to go into the model.
I think I know where this is going.
And so, we do have that enterprise promise in Azure
that your data is your data.
Yes, you can use your data to train the model.
But in enterprises, company secrets and so forth
are not going to go back into the model training.
But so, we have the best of both worlds.
We have OpenAI continually retraining, and then
our enterprise customers, with that data security
value that we provide.
Which is very important, of course.
It's quite interesting.
Now, you've been talking about Azure OpenAI,
but it's also just OpenAI.
What exactly is the difference?
Yeah, now, that's a great question.
I always like to start off just by talking
about what is the same?
You know, first of all, every OpenAI model that's released
is trained on the Azure supercomputer.
So think of OpenAI is an Azure ISV
of sorts, very strategically important one.
But nevertheless, they're very similar to the other partners
we have, in terms of just sitting on that Azure
infrastructure.
And then, same with, of course, Azure OpenAI Service.
A completely separate instance of Azure
infrastructure, but still both sit on that.
So that's similarity number one.
Similarity number two, it's the same models.
Yeah, these models are stochastic.
You will get different responses,
if you ask the same question multiple times.
You'll get that with OpenAI, as well as across OpenAI and Azure
OpenAI.
But the models are exactly the same.
They're the same code base, the same instances of the models.
When you get Text-Davinci-003 on OpenAI, you can take that work,
reproduce it, again, within the stochastic characteristics
of the models, and get the same type of answers.
So we see customers working on OpenAI
and then directly transferring that knowledge over
to Azure OpenAI Service very seamlessly.
We have one customer, Tremont, a startup,
who moved from OpenAI to Azure OpenAI.
He said it was literally three lines of code
to change his app to be able to use Azure OpenAI.
A couple of things on the differences.
Customer preference is super important to us.
We love when customers work on OpenAI.
But what we see with the largest enterprise customers
and also ISVs that work with enterprise
is there are certain security features and enterprise
features that companies need to take something into production.
There's also regulatory compliance.
So if you look at from a security perspective,
we support the Azure networking, like express route
and private links.
We have this data security policy
that your data is your data.
Also, the service is HIPAA compliant for health care
company, SOC 2 compliant.
So regulatory compliance is also super important.
One of the other major differences is geodiversity.
We do offer Azure OpenAI service in multiple Azure regions
around the world.
So what you're actually doing, you have the OpenAI services.
You're building a nice layer on top of it, with extra security,
making sure it's in different regions,
actually bring it to an enterprise level.
So you've talked now how enterprise organizations
are already making use of these services.
Can you give us any example, a use case?
Oh, yeah, absolutely.
So we see, with use cases, the language
use cases are grouped into four buckets or broad categories.
The first, we call reason over structured and unstructured
data.
And at the beginning, we were looking
at all these different model capabilities.
And they're basically classification, sentiment type
of classification, search, and then entity extraction.
And we said, what are people actually doing?
Well, they're getting reasoning.
They're gathering reasoning over data,
over structured and unstructured data.
And then, that usually feeds into other model capabilities,
where a customer will take a call center record,
do those types of things, and then use
the summarization capability.
So summarization is the other use case category,
very common with call center transcripts,
large technical documents, et cetera.
The third one is conversational AI.
And I don't need to explain that.
You can look at ChatGPT and see the amazing capabilities
that we have there.
And so, the other one is writing assistance.
Companies that are using OpenAI to basically write blog posts.
A lot of people on this audience will
have children that are using it to write homework assignments.
It is done out there.
And then, also, we see a lot in marketing campaigns
for developing taglines, for email headers, et cetera.
So there are a few case studies that were out there.
I mentioned Trelent.
CarMax is a customer that's been using it to summarize thousands
of automobile reviews, down to a very coherent description,
saving thousands of hours of collating
all that information together.
And we have farmlands out of New Zealand, who's actually
using it to look at the sentiment of their agriculture
retail customers.
Really interesting use case.
I mentioned Trelent.
What they're doing is, they're using a codex model
to generate doc strings for code documentation for legacy code.
So there's a lot of really interesting customer
engagements out there and more to come.
Yeah, I can imagine that.
It's interesting times that are coming with all the new things
that are coming up.
Well, linked to that, how do you think
the future of OpenAI Services is going to be?
Yeah.
I mean, the future happens every minute.
I think we heard some announcements today
about incorporating OpenAI into being search.
So, yeah, it's a very, very fast moving space.
But there's really a few key areas.
One is responsible AI, and we'll cover that in a bit.
The second is, if you take a look at these models, right?
You've got text-to-speech.
You've got-- rather, I should say, text-to-image
with DALL-E. You've got coding.
You've got language.
Multimodal is something that we're looking at,
moving forward, where these multiple model capabilities
are all converging onto a single model, a single API.
So that's something to look forward to.
Also, being used for more forward-looking,
like external users, social media type things.
Right now, responsible AI is a bit of a concern,
just because these models are generative.
And we have to take the responsible AI
and really test it out over time.
But I think we're going to start seeing it much more
wide used in that, as well as in mission critical applications.
I mean, Azure, OpenAI Service is built
for those types of enterprise deployments.
But really, the big things are multimodal
and also some of the advancements in responsible AI.
Well, you already answered my last question a bit,
concerning responsible AI.
I think that's something we really need to look more
into because it's so important.
But, Bob, I can only thank you for this very nice
conversation.
And I'm really looking forward to what open OpenAI is really
going to bring in the next coming levels, next weeks,
months for Microsoft and also for the world.
Thank you very much.
Thank you for having me.
So Bob really told us a lot of exciting stuff about OpenAI.
This is really going to be a huge game changer in the tech
industry.
I'm really looking forward to everything
that this is going to bring in the upcoming years.
For sure.
I'm also looking forward to that.
But I think our next guest will really giving us an example
how OpenAI has been embedded in a business application.
Let's have a view also on that.
Let's do that.
Hey, Sharon, thank you for joining me today.
I hope you're doing well.
Yeah, thanks for having me.
Well, I've heard you've been working also
on the release of Bing OpenAI, the combination of it.
Can you just tell us a little bit more about it?
Yeah, it's been super exciting.
We've seen the buzz all this week.
And our team really came in towards the end of last year,
really thinking about the responsible AI
and how do we think about the societal implications
of releasing something like this, following the ChatGPT
launch and the excitement over it.
Yeah.
Well, since you are focused on the ethical part of AI, what
were the things you really had to look
into in releasing a product like that?
Yeah, I think the initial version was really
about understanding, how do we set the right mental model
for a technology like this?
It's super impressive.
It can create.
It can site information.
And so, we found that people tend to over trust or over rely
the kind of information it generates.
And so, a lot of what our team did was,
how do you set the right mental model for users,
reminding them, , hey this is from the internet.
Just as reliable as the internet,
some things are factual, some things are not.
And so, that was our initial step,
was really around education around user transparency.
But there's so many more places that we
want to go to in terms of what is
the future human-AI interaction model that we really
want to create with the technology like this.
OK, it's quite interesting.
Now, the tool that-- well, the model that
is used behind the service is ChatGPT, where it consumes
a lot of data to get trained.
How does it get curated, that the data is, for example,
unbiased?
Yeah.
So there's like a slight note, where it's actually not using
the ChatGPT 3 model directly.
It's actually using something called Prometheus, which
was a joint model that was created from Bing and OpenAI
to really tailor ChatGPT, but for search scenarios.
So for example, limitations of ChatGPT
is that it doesn't have current events, right?
If you use it on OpenAI, it's limited to,
I think, the end of 2021.
Prometheus now allows like actual grounding
that against Bing's existing search index.
So you have a grounding of that information.
You're able to actually have citations.
It's able to point to the sources it's referencing.
And so, those are the new things in terms of bias.
But like I said, this is based off of the internet.
And so, there's a lot of biases inherently woven
into the internet.
And so, it's a little bit--
it's inescapable for it to be able to replicate some of those
biases.
I would say, on the platform level,
there's been a lot of work in terms of safety,
like content moderation, how do we think about safe inputs
and outputs.
If users tend to ask Bing, hey, I got this prescription
from my medication.
Does that look right?
It will tell you, hey, you should consult
the medical professional.
And so, we had to think about those kind of safety scenarios
as well.
Yeah.
Now, I have a question also concerning
what a company needs to do to get an ethical AI strategy,
but I think it's a good idea to also get
a little bit more clear overview of what ethical AI exactly is.
Yeah.
That's a loaded question.
I think there's so many aspects to that.
I would say, with ethical AI, it's not about right or wrong,
right?
Because your ethics are different from my ethics,
and how do we really determine what that is?
Especially for a company like Microsoft,
we're an international company with international consumers.
And so, when we say ethics, we're
really about, how do we be really intentional about what
we build?
When we build something, how do you
think about not just its initial impact, but the second order
implications.
And so, again, it's that word implications.
How do you think about the deeper consequences?
How do we think about not just your direct customers,
but who is affected by your product,
even though they don't necessarily use it.
For example, back to the ChatGPT scenario.
If people are using it for medical professionals,
even if doctors aren't directly using it, it might affect them.
Because their patients might not be going to them and say,
hey, I got this prescription or this diagnosis.
What do you think?
And so, we think about those different layers.
And so, I think for a lot of companies,
for ethical AI strategy, it's investing in that longer tail
consequences.
Because those longer term things will affect you.
And just being more intentional about thinking
about the consequences and implications of what
you're building.
What advice would you be able to give
organizations that want to implement
AI in their solutions?
I honestly think I do think it's really about the innovation
part is what's captivating everybody,
but really thinking about not just the application,
but the employment implications.
And when you talked about your earlier question of how
do you build an ethical AI strategy,
I think, at the core of the culture and the leadership,
you need to believe that's important.
It's not always easy.
Because ethical AI, you might not see the bottom line impact
right away.
But on the long scale--
at Microsoft, at least, we take it
so seriously because we're not just
a company here for the next 10 years.
We hope to be around for a much longer time.
And to do that, we have to be building towards a better
society for that to exist.
OK.
Thank you very much for this nice answers
on all my questions.
Looking forward to see more of you and your team,
also read articles or see videos about it.
Thank you very much.
Thanks.
Well, that was a really interesting conversation
I just had.
I'm really looking forward to where
AI is going to be embedded in even more business
applications.
Yeah, me, too.
And there is also another field where
AI can have a huge impact.
OK.
That's accessibility.
Oh, yeah, of course.
Yeah.
So we're now at the Inclusive Tech
Lab, where we're going to have a chat with Dona
Sarkar about this.
So let's go inside.
So, hi, Dona.
Thank you so much for joining me today.
How are you doing?
Awesome.
Thank you so much for being here.
Yes, really great to be here and have a conversation together
with you.
So I'm going to ask you a couple of questions.
So we start with the first one?
Let's go.
Can you tell me who you are and what you do with Microsoft?
Absolutely.
I'm Dona Sarkar.
I lead the accessibility tech program
for the company, which means I look after the,
let's say, many thousands of products we
ship externally, all of our websites,
and all of our internal tools.
So we talked a lot about AI and how
it can help people in their daily lives and in their work.
Can you explain how AI can also help people
with an accessibility needs?
Definitely.
So I absolutely love both of these topics
and the intersection.
Because AI's magical and having quite a moment right now.
And accessibility, I think, is one of the most fascinating
tech areas because you're legitimately
innovating with a real purpose.
You're not making like Uber for fresh fruit, right?
It's got a real reason to exist, all this cool innovation.
And when you look at the intersection of the two,
you're looking at ways, where you're literally
solving the equity divide.
And there's three things that I've
seen Microsoft do in the last few years
that I think it's really important to call out.
The first one that we talk about a lot are captions, right?
These are Azure Cognitive Services, Azure Speech
Services, Derive Captions.
And they've been starting to make
their way into every product.
First was, of course, PowerPoint,
which, if you don't have captions on in PowerPoint
by default, do it.
Second one is Teams.
And the third one, and most recent,
and I think the coolest is Windows 11 because they're
available offline as well.
Yes, it's really built into the product,
uses the mic and then--
Yes.
Yeah.
That sounds really great.
So Captions is the first one.
The second one is totally nerdy, but I love GitHub Copilot.
I absolutely love it.
I'm a developer.
So having this machine generate the first draft of code for me
that I can then refine is pretty cool.
The part of GitHub Copilot that I don't think people know about
is GitHub Copilot Labs.
This is the experimental mode of Copilot,
where they test all these interesting features
and functionality before they go live to everybody.
They're part of their nightly insider builds,
and there's a brand new one called Explain, where
you can highlight a chunk of code,
and they'll explain on natural language what it does.
I use it when I'm getting familiar with the new code
base or some product that I've never worked with before.
The third thing that I really liked
is for neurodivergent folks, right?
I'm a neurodivergent person.
I have dyslexia.
And one of the things I have really appreciated
is this thing called Teams Meeting Recap.
So say, you and I have a Teams meeting, and we record it.
And then, I'm thinking, what did I agree to do in the meeting,
right?
Because you assigned me some homework,
and I promptly forgot because neurodivergent urge to forget.
But the recap meeting notes actually just call out
and say, action items assigned to,
action items assigned to Hank, assigned to everyone.
And what a good way to, first of all,
bawl and tell people to do work, but also a really, really solid
way to make sure that, whether you were in the meeting
or not in the meeting, you didn't
miss anything really obvious.
Pretty sounds amazing.
Yeah.
So those were the three that I've really loved recently.
Which project really stood out for you?
So, of course, we're looking at recency bias, right?
We just had the Bing Edge launch on Tuesday.
That was a big deal.
That was fun, though.
My team got to work closely with the product teams
to make sure that not only are these new generative AI
experiences accessible from a screen
reader or any other disability type point of view.
Like, yes, I can use them, but they're useful and usable,
right?
So, how can this be really useful toward someone who may
have, say, limited mobility?
One of my colleagues, Christina, her arms are paralyzed,
and she types with her toes.
And watching her use this product is really cool
because she said, oh, I know just
have to type one sentence rather than
typing lots of sentences to plan a trip, for example.
We're trying to plan a conference, an offsite.
And she's able to do research so quickly now compared to before.
And the same with me because I will get
distracted on anything.
So being able to plan just a wheelchair-friendly trip
to Europe with a whole bunch of takeaways and in line,
it's like, look up airfare.
Look up hotels.
It's just a pretty magical experience.
And I think we're only getting started
at what this thing can do.
And that's the big part.
The Edge part that I found very, very fascinating
was this feature called Summarize,
where using, I think, it's only in the Bing Edge
or, sorry, the Edge Dev build right now, which is you
copy in a whole chunk of text, and it'll
pull out a summary of it.
And we just did that with this document
that we were supposed to read.
It was a very long document.
It was 12 pages.
And I thought, maybe, we can just try the summary feature.
And I did the summary feature, and I
asked my legal friend, who's really good at reading
documents, is it accurate?
And they said, actually, it's pretty accurate.
So you can imagine, that's a game changer for people
because it gives you this easy button shortcut key toward not
having to read a large quantity of words,
if you are a dyslexic person like me.
Yeah, that's really a major step forward for including everyone
here.
That's right.
And just making life easier for all kinds of people.
Yeah.
That's really difficult without including everyone
to build these types of products, yeah.
Right.
Nearly impossible.
Oh, and I'll give you one plug before I leave you.
We have our Microsoft Ability Summit
coming up on March 8, which is our big accessibility
conference.
There will be a lot of surprises, a lot of new things
announced, a lot of really cool tech demos,
and there are some pretty amazing A-list guests
attending this conference.
It's one of my favorite things of the year,
so I highly recommend everyone check it out.
Just look up Ability Summit Microsoft 2023.
I'll see you there.
OK.
Thank you so much.
Thank you.