KATE COOK: Hi, everyone.
I'm Kate Cook, and I'm a Program Manager for Project Turing.
YOUNGJI KIM: Hello, my name is Youngji Kim
and I'm a Program Manager for Project Turing.
Today with Kate, we will present
how we are applying AI at Scale with Microsoft Search.
You might have heard of AI at Scale before
from our previous talks or our blogs.
But just in case, let me do a quick introduction.
At Microsoft, we have invested heavily in AI
for a long time.
And now with our AI at Scale initiative,
we are pioneering a new approach to AI.
It will bring next generation of AI capabilities
that are scaled across Microsoft products, and AI platform.
So what does it entail?
It includes a few major components.
First, we are developing a new class of large
and centralized AI models.
And when I say large, I mean really, really large.
In AI, we use a number of parameters
to represent the size of an AI model.
You can think of a parameter loosely equivalent
to a connection between neurons in our brain.
So the higher number of parameter
is where the higher number of connections between neurons,
means more advanced, and more capable AI model is
for a task.
Our Turing Natural Language Generation model,
or TNLG model has 17 billion parameters.
And with 17 billion parameters,
Turing Natural Language Generation model
is the largest publicly available AI language model
in the world.
Second with the large AI models,
existing infrastructure doesn't really work anymore.
So we are creating the state-of-the-art infrastructure
in the Azure to power this new class of models.
As our CTO, Kevin Scott shared at Built 2020,
Microsoft Cloud hosted supercomputer has 285,000 CPUs,
10,000 GPUs and high bandwidth network
that interconnect those components.
Not only that, we have the software stack
including Azure Machine Learning, ONNX Runtime
and other Azure AI services to help manage it.
Third, we are really making a culture shift.
Instead of each product group such as Word, Outlook,
Teams and SharePoint, to have their own AI team
and develop a separate AI models,
we really had to come together and collaborated
to build a large and centralized AI model,
which are scaled and specialized
across different product domains later on.
So with this high ambition to enable next generation
of AI experiences, we had to decide which experience
we should tackle first.
There are so many possibilities.
And we have started by teaching AI
how to speak human language,
and we have named them Microsoft Turing Models.
Turing Models are our own set of large
and centralized AI models that understand human language.
In other words, capable of natural language understanding.
Once we developed Turing Models, we scaled
and specialized the Turing Models
across different language understanding tasks,
such as search, summarization,
contextual prediction and question answering.
Also, we apply them across different products of Microsoft
such as Bing, Office and Dynamics.
So let's see Turing Models in action
across different products in Office.
Over to you Kate.
KATE COOK: Thanks, Youngji.
Now that you've heard a brief introduction
to our AI at Scale initiative, let's see
what this actually looks like in Microsoft products.
Today, I'll be highlighting a few key ways
we've implemented Turing large scale models
in Office 365 specifically text prediction,
and in-document semantic search in Word,
at-a-glance summarization in SharePoint,
and suggested reply in both Outlook and Teams.
Now imagine I'm a Facilities Manager at Contoso Electronics.
With the current COVID-19 pandemic, my team has transitioned
to focus on remote workplace initiatives
for our Contoso employees.
It looks like two of our initiatives were just approved,
and my manager has asked that I update this information
on our employee facing guidelines doc.
At the bottom of this email,
I can see a Outlook suggested reply feature.
This feature is powered
by Microsoft Turing Language models,
which identifies the top three most relevant responses
based on the content of my last email.
Suggested reply allows me to quickly respond to Irvin
without pressing any keys on my keyboard,
saving me time, and effort on a repetitive task.
This feature is already available to Outlook on the web
and Outlook mobile users, and will be available
on Outlook desktop in the coming months.
To update the document,
I will go to My Team's SharePoint folder,
which as you can tell, has a number
of facilities related documents.
I can hover over the document title
to see a-at-glance summaries,
which provide a glimpse into the documents content,
allowing users to quickly identify the document
they're looking for, without actually needing to open it.
This feature, is built upon the same Turing power technology
as the suggested reply feature,
except in this case, the model is fine tuned
to extract the most important sentences
from a given document.
This feature is currently to SharePoint
and OneDrive customers worldwide.
Based off of the summaries, it looks like
this is the document that I'm looking for.
Now that I have my document open,
I'll scroll down,
create a header for my new initiatives,
and begin typing.
You'll notice as I type,
our text prediction feature
is providing AI generated suggestions
from our Turing Language Model.
I can easily tab over to accept a suggestion,
allowing me to make updates to my document quickly
and easily while staying in the flow of my writing.
These predictions are generated based on the content
of my document to ensure each suggestion is relevant,
and to minimize the number of incorrect
or distracting suggestions.
Another exciting benefit of text prediction
is that correct spelling and proper grammar
are built into the feature reducing the amount of time
and effort required to go back and edit the document.
Just within this single paragraph,
I have saved a considerable number of keystrokes.
This will translate to millions of keystrokes saved
for our customers every month.
The Turing powered texts prediction feature
will be released to our Word customers in the coming months.
Now imagine I'm a Contoso employee
who is reading this document and trying to identify
the information that is most relevant to me.
With Turing powered in-document semantic search,
we have extended the capabilities
of the control find experience to go beyond exact match
and keyword search, allowing users to search
with natural language queries, synonyms,
and even misspellings.
Now let's try this out.
As the Contoso employee, one of the questions
I'm most curious about, is when we will go back
to the office.
[indistinct] type in this search into the document,
and you'll notice Turing model is using semantic search
to find the answer to my question.
It understands that we, in this case is referring
to Contoso employees and going back,
is a synonym for returning.
I can also search for our Parental Leave Policy.
Now I've accidentally misspelled my search,
but our semantic find feature is able
to identify the relevant section of the document
that I'm looking for even with this misspelling.
The in-document semantic search feature
is available worldwide for our Word online customers.
We are also in the process of enabling this feature
across our other Microsoft products.
Now, the last technology I would like to demo
is the same suggested replies feature I showcased
in Outlook, but this time on Teams.
Now I'm currently in My Team's Mobile app,
and I see Irvin just pinged me
to ask if I've made the edits to the guidelines doc.
I'm away from my computer, but I can easily respond
to his message on my phone with just a single click.
Irvin responds and I now see new suggested replies
based on the content of his last message.
I'll again, leverage these suggestions for my response.
Now just to reiterate, this feature is powered
by the same Turing technology, leveraged in Outlook,
highlighting just one example of what it can look like
to implement AI at Scale.
This suggested replies feature is rolling out soon
to our Teams mobile users worldwide.
These are some of the exciting ways
Microsoft's AI at Scale initiative
is powering the next generation AI experiences
across Microsoft and in this case,
across our Office applications.
I will now hand things back to Youngji, who will demo some
of our additional Turing powered capabilities.
YOUNGJI KIM: Thank you, Kate.
In addition to what Kate just demoed,
we have semantic search in SharePoint,
also powered by Turing models.
Semantic search in SharePoint enables you
to find the most relevant document,
even if you're not using the exact keyword specified
in the document.
For example, I am querying for a Tesla charging station,
and our semantic search found the most relevant document,
Electrical Vehicle Charging Point Station.
Although this document does not include word Tesla.
The Turing powered semantic search really understood
that Tesla is an electrical vehicle,
and was able to find the most relevant document.
This is fundamentally different approach
to the traditional keyword matching based search.
Also Turing powered semantic search
can understand the language to figure out our users intents.
Which comes really handy when I type in a question
like this.
How many floating holidays do I get?
Semantic search gets what I'm looking for
by understanding my question query.
And it even highlights a portion of the passage,
which directly answer my question.
So for call to action,
we have the private preview open
for the Turing powered semantic search for SharePoint
you guys just saw.
And we are especially looking for partners
who want to try out semantic search for non-English content.
You can sign up by to aka.ms/semanticsearchpreview,
or using this QR code.
Also, if you want to learn more about the Turing models,
you can check out our talk by following this link.
Thank you for your time today.