(upbeat techno music)
- Hi, I'm Walter Sun, Vice President
of AI in the Business Applications and Platform group.
Across our business applications products.
You have been hearing a central theme of infusing AI
in particular, technology to derived from
foundational language models
to dramatically improve business processes.
The ease of using and interacting with ChatGPT,
coupled with the delightful results observed
has made enterprise customers more inclined to
apply similar capabilities to solve business problems.
This is a huge inflection point for
adoption of AI in our industry.
In particular, we see a shift in
how companies think about their digital transformation.
The lifetime diagram is what we've been executing
over the last decade at Microsoft,
when partnering with our customers
to answer the question, "how do I
use technology and software to
reimagine my business"?
What we see now is that this conversation is moving
to the flywheel on the right hand side
where we answer a slightly different question
of, "how do I become an AI company"?
Even for those who do not realize the benefits
this means to their business, everyone knows
that they want more intelligence
in the systems they build
and the systems they consume.
We so strongly believe
in the power and potential of AI
that we've anchored our plans
for the next few years
around the idea of re-imagining business applications
and low-code platforms using the latest AI technologies.
Let's talk about where we are at
and what all this will mean to you, our customers.
To understand the pace of recent innovations
let's take a quick look at the history of language models.
Since the fifties, we've seen gradual developments
in technology about building AI into applications.
The bag-of-words model
which is grammar and sequence independent
still has effective implementations
in applications like spam filtering.
It doesn't matter where
in a paragraph the words "rapid weight loss"
or "sweepstakes winner" or "urgent, buy now"
appear in emails.
Those type of terms usually indicate
a questionable source.
Advances leveraging word order, and even memory
across sentences starting with hidden Markov models
and more recently, the long short-term memory models
established stronger interword relationships
across the text sequence.
But only in the past six years
has transformer-based models
dramatically expanded what's possible.
These foundational models, known as
large language models when it involves natural
language processing, contract relationships
between all word pairs over a longer span
maximizing the computer's comprehension of text.
GPU's, which do an excellent job
processing tasks in parallel,
are perfectly suited to handle the much larger
complexity of the self-attention computation
between word pairs.
Our Azure computation ecosystem
coupled with our close partnership
with OpenAI's engineers, has enabled us to
build generalizable solutions with these large models.
As our customers have been investing
to bring together their fragmented data
and turning that into a strategic asset.
We have explored how these large language models
could take that data, and by bringing structure
and comprehension to it
reshape the way that data are leveraged.
We have been focused on this the past five years
and now we're at a point where AI is
a core growing part of what we have developed
delivered to you and our products.
So how does this work?
As Charles Lamanna mentioned
large language models form the base
of our AI solutions.
The GPT family of models are deep neural networks
which have hundreds of billions of parameters.
To learn all of these parameter,
large language models are trained on
massive amounts of data, in the trillions of text tokens.
Once trained, they can be deployed
to more specific tasks simply by providing context
or tuning, using small inputs known as prompts
which creates a response which is more precise
and informative to the question asked
or the task requested.
These large language models are like
a powerful sports car, difficult and expensive
to engineer, but once built,
this vehicle can be deployed
to serve a multitude of tasks
based on instructions from the driver.
Basically, a picture is worth a thousand words.
So to illustrate the value of larger models
let's look at a generational example
using natural language to images where
the input prompt is "teddy bears
mixing sparkling chemicals as mad scientists"
digital art.
At 350 million parameters here,
you can see that the image generated is
of average quality,
two bears sitting in front of bottles
and behind a white background
perhaps not matching the description.
As you increase the parameter count, though,
you can see a much better representation
of the requested input
ending at a rich appealing image
of two mad scientist bears
at 20 billion parameters.
I show this to demonstrate how
larger foundational models can solve
a broader set of problems in a deeper way.
As noted earlier, our Azure ecosystem
enables the compute necessary
and our partnership
with OpenAI unlocks the engineering necessary to train
and build very large models like GPT.
Few other companies have the resources to train
and host such a model,
and with our engineers and scientists
tuning the prompts and providing the necessary context
through retrieval augmented generation
we can create deep solutions
for a variety of different business scenarios.
Returning to language models, one example
of how Microsoft helps businesses
is through our GitHub co-pilot offering.
Released last year, GitHub co-pilot autocompletes
a user's code, saving time on syntax
and repeated code segments.
The AI suggests upwards of 30% of code
for people who use the capability.
Instead of the user writing the code
and Copilot fixing it, at its core,
the UI is designed so that Copilot can present
a suggested code and the user can then edit.
We're extending this co-pilot naming
into the Dynamics 365 family
when leveraging large language models
from different foundational models.
If you look at this tree diagram,
you can observe that content generation capabilities
we're offering in Dynamics can be built off of
the base GPT model,
such as trained on ordinary human language
for scenarios like email generational marketing content.
When ordinary language won't do
fine tune models have been built,
including Codex, which is trained on
billions of lines of source code
to enable capabilities like GitHub Copilot
We also have DALL-E, which is tuned toward
producing image outputs.
Furthermore, we have a few scenarios
which require the generation
of domain specific languages
like our Power Automate cloud flows.
To obtain the best performance
we go under the hood of our powerful sports car
which is GPT, and made some adjustments,
training on many cloud flow examples
to produce a tune model which can handle
natural language to cloud flow workflows
very well.
Left to right we'll prompt the appropriate
foundational model to build the best results for you.
By building on OpenAI's foundational models
we believe we are best positioned to support you
in becoming an AI led company.
The combination of our early access
to open AI's large language models
when coupled with our own assets, Enterprise
and Bing search capabilities, CRM,
and our suite of responsible AI tools,
means we have unparalleled experience
and tools to solve your problems.
Think again to the sports car analogy.
We have access to GPT, which is like owning
a valuable sports car, which is hard to obtain.
While anyone could drive it
why not have a professional driver
take you around in it?
GPT as the sports car is powerful for anyone to use
but it's even better when managed by a professional
and we believe we can maximize your experience
in this AI performance vehicle.
Before we jump into a few examples of
how our Copilot tech, leveraging Open AI,
can help you, I wanna call out two important factors
in our approach towards AI.
First, our Copilot tech isn't just run
in the background in supplementary to
a business process.
Rather it's real-time interactive assistance
which we hope brings high value to you.
Namely, our viewpoint isn't "AI stands
on the sidelines occasionally coaching our users".
Rather, we believe the view should be, "AI solves
the main problems jointly with users
on the business playing field, assisting
while also ensuring the user is always
in control".
We thus must start anew to build UI
and design solutions that are truly AI led.
In this example, you see how we use natural language to
help a user create a bot and power
a virtual agent where a AI suggestions can be accepted
or further iterated on by the user.
Secondly, I'm proud to say that
at Microsoft we are deeply committed
to responsible AI with generative technologies leading
to a larger surface area of machine created
responses and content.
it's vital that we ensure this AI
responsibly serves your needs.
At Microsoft, we have researched
and defined clear principles and approaches
to follow when building an AI-based solution.
This ensures our offerings respect
fundamental human rights, reflect the diversity of society
and are created and used in an ethical and safe manner.
Now let's look at a few examples
of AI in practice in our business solutions.
As mentioned earlier, our technologies
and business applications are building
upon the Copilot concept
which GitHub introduced last year
Where GitHub users saved time on co-creation.
Our business Copilot delivers time savings
in content creation, app building
and question and answer scenarios.
This example here shows how we've leveraged GPT
for Viva Sales.
Even if you saw this earlier
or watch more in a bonus content.
I won't repeat the "what" for this feature
but rather I'll explain the "how".
Before we thought about the idea
of tuned email content creation.
A volume seller spends a lot of time
creating emails to ensure they don't seem
like bulk form letters.
We realized that bringing together the richness
of a customer CRM and office data
with GPT's strong content creation capabilities
will result in customized, high quality email content
which a seller just needs to confirm
before sending, keeping the user in control.
Now, rather than spending hours on hundreds of emails
a seller can have Viva Sales provide a list
of people to connect with
and create the necessary content
to provide that outreach.
Viva Sales revolutionizes the way sellers work
whether it's meeting preparation,
meeting follow-up or email generation,
it lightens the load for the seller
and at the same time deepens their customer
engagement to strengthen their customer relationship.
In addition to content creation
we are also leveraging foundational models
to facilitate makers in creating applications.
Rather than go through the tedious process
of building a new application,
our Natural Language to Apps capability
allows the maker to describe the words
that you use to create
and we take care of the rest.
For instance, if you're a pizza shop owner
wanting an online presence, you could say
"build me a pizza app",
and you'll be given options
of different types of apps
for a restaurant which sell pizzas.
You can then go to the next step
of iterating and improving your application
through words such as
"please add a menu tab and an order online option
in the menu".
You describe it and we build it all
in a no-code manner using natural language.
These are just two examples
of AI solutions which we've built
to transport the potential of your business
into a whole new universe.
We've been exploring and investing in the
power of AI for the past few years
and we can't wait to partner with you
to help you become an AI business.
Thank you for watching.