Hello and welcome.
I'm Derik Stenerson a Program Manager, Microsoft Office.
Thanks for tuning in to this session
on Microsoft Search in Office.
In this session, we'll take a look
at how we're bringing the latest web search experiences
and large scale AI models
to power answer finding in your documents.
Let's start out with the demo to illustrate
what the experience is like today.
What does it feel like?
Okay, let's set the stage.
You begin your journey by locating the document
that you think has the answers you need.
Maybe Microsoft Search in SharePoint
gets you this far in the journey.
Then you have the document open
and you're ready to begin the next phase
of your quest using search in the document.
In this case, the document is a water quality report
and are looking for information
on fluoride treatment in drinking water.
So I'm going to go up and begin my search.
No results found.
All right, so I know it's for tooth decay prevention.
So let me try tooth.
No, that's not it.
All right, let me try, it's a dental additive.
Huh?
Still not finding it.
All right, well, I guess I can just scroll.
I'll probably see it in here soon.
They've got a table here.
Okay.
Elements in the water.
All right, fluoride.
All right, a water additive which promotes strong teeth.
Well, as you can see, that was pretty frustrating.
And quite frankly, it doesn't feel
like a modern search experience
that I've come to expect.
Yes as we saw in the demo, the struggle is real.
Finding those answers and completing
your search journey is difficult.
It's error prone to get the correct spelling,
the keywords, it's time consuming.
As a user, I often have to scan through documents
to find something because the document
doesn't use the exact words I search for.
It's especially hard in searching long documents
written by others.
In domains where I might be unfamiliar with the terminology.
We all struggle with this.
And yet as consumers, we experienced search
on the web quite differently.
So we regularly hear from customers,
"Why can't this just work
like when I search on the web?
Why can't I simply ask natural questions
and not worry about misspellings?"
The answer of course is this is how search
in your documents should work.
You should be able to search your documents
like you search the web.
Today, we're going to take a look at a set
of improvements to make this possible
via Microsoft Search in the Office apps,
starting with Microsoft Word for the web.
Before we dive in, let's take a few minutes
to understand what Microsoft Search is,
so we can understand the context
for how these improvements fit
into the search ecosystem across Microsoft 365.
Microsoft Search is more than just results.
It's answers, it's people, it's insights.
Wherever you're working, providing a consistent,
great experience across the apps
and services you use every day.
It's our vision that Microsoft Search
become one ubiquitous capability
available to every Microsoft 365 experience
and leveraging the collective knowledge
and technology that we've built up across Teams,
Microsoft AI, all into one platform.
When we think about the guiding principles
through this vision, for an intelligent search experience,
we think about how it should be ubiquitous.
A search that's consistent and familiar,
but contextual to the app you're using.
It should empower productivity through actionable results.
search that's intelligent, personalized to you
and your organization delivers more actionable results.
Flexible and trusted.
And should support tailoring to your organization's needs
and align with the same privacy
and trust principles as [mumbles] to Microsoft 365.
This is the core about what Microsoft Search is about.
Microsoft Search at its foundation
is based on a machine learning platform
that leverages the global perspective
that is available via Microsoft Graph and Microsoft AI,
With trillions of signals of data,
the solution makes use of hundreds
of machine learning models from thousands
of classifiers that have been developed over the years.
And these models are trained on a continuous basis
to improve the results and relevance.
To deliver that personalization
to your organization and its employees.
So what does all this mean in terms
of bringing search to your documents
like you search the web?
By bringing the well-established web search technologies
such as query and documents understanding,
indexing and deep learning-based natural language models,
we can handle a much broader set
of search queries beyond exact match.
And thus delivering relevant search results
to improve the user's workflow,
enabling them to work more efficiently.
So when we say beyond exact match, what do we mean?
We talk about semantic search, a means of understanding
the user's search intent more broadly.
Based on the underlying meaning behind the user's query.
For example, typos, when there's a misspelling
in the query, Search can now show the related matches.
Forms of words, when there are different forms of words
in the document and the query,
Search can understand this and find the related matches.
Same thing with synonyms.
Multi-word queries.
An example of this could be a query of oil price.
And in the content you may find matches
such as price of oil, prices of the oil, cost of the oil.
All of these can be surfaced
to the users as potential answers.
So let's take a look at what
this looks like in the next demo.
Okay, let's set the stage again.
I've located my water quality report
and opened it in Word for the web.
I'm going to use the Microsoft Search in Office
to find the information on fluoride treatment
and drinking water.
We're going to start out with my typo.
So I go to the search box.
I typed fluoride in the way that I think it's spelled
and you'll notice that we find the related matches
with the correct spelling now.
Well, that's sure makes it a whole lot easier
for me since I'm a pretty terrible speller.
Now you remember when we tried this last time,
we had to look for some other terms.
So let's give those a try.
Let's try tooth.
And in this case we find a different
form of the word, teeth.
Again, helping me find the information
that I was looking for.
Finally, let's try our multi-award query.
'Cause we know fluoride's a dental additive.
And once again, through better language understanding,
we're able to locate desired phrase
with the same underlying semantic meaning,
even if the words are in different order.
So this shows a glimpse of how we're bringing
web search techniques to power
the in-document search experiences.
Wouldn't you say that's a pretty dramatic improvement?
It is, isn't it?
But the team didn't stop there.
With the recent breakthroughs in deep learning techniques,
we can now go beyond common search term-based query,
and get answers to your questions
based on the document content,
opening up a whole new way of finding knowledge.
For example, when you're looking at a water quality report,
you can easily answer questions like,
how often is the water tested?
Well, it's tested between 10 and 100 times per day.
You can also answer questions like,
how to reduce lead in drinking water.
And again, by understanding the content document,
an answer is presented to me.
This new Q and A format is powerful
and it opens up whole new ways
for exploring your documents via a very natural
question and answer format.
So that's a quick look at how we're enabling you
to search your documents like you search the web.
That demo highlighted the power
of this new Q and A experience.
And I wanted to spend a minute
just to talk about the technology behind it.
All of this is made possible using
the Microsoft Turing model
for natural language representation.
It's a large scale AI model,
bringing the power of deep learning
to searching document content.
Specifically, there are two deep learning models
that are using the public Bing web data
to enable these capabilities.
There's an encoder model, which outputs vectors
that capture the semantic meaning of the text,
such that semantically similar texts
have similar numerical values.
So this provides a better understanding of the language.
And there's a question and answering model,
which is built using a state of the art
large pre-trained Turing language model,
the same model that powers
question answering in Bing search.
So again, we're enabling you to search your documents
like you search the web.
What about availability?
This capability is available in Word
for the web in English markets today.
And it's coming soon to Windows Desktop,
more platforms and more markets over the coming year.
So to sum it up, the experience shown here today
is the first step in helping users more effectively
complete their search journey to find the answers they need.
We saw how the new intelligent query understanding
makes finding easy, like searching the web.
We saw how Q and A gives you a brand new way
to find answers by asking questions in a natural way.
So as we go forward, we will expand
on these intelligent search experiences
throughout the Microsoft Search ecosystem
in the Office applications.
I hope you're excited to be more productive
with these two great experiences
to enable answer finding in your documents.
So before I close, quick shout out to make sure
that you catch all the other great
Microsoft Search sessions to learn more.
In particular, be on the lookout
for Search in the Office Productivity Applications
by Nathan Frayer for a comprehensive overview
of Microsoft Search across Word, Excel and PowerPoint.
Thank you for watching.