(bright music)
- [Narrator] Innovation hinges on our ability
to see things differently.
Breaking boundaries and looking between the lines
in an effort to solve some of
the world's toughest challenges.
(inspiring music)
- Hi, my name is Rangan Majumder,
and I'm the general manager
for Microsoft's Search and Turing group.
Our goal is to help the world get smarter
by finding information more efficiently.
When Bill Gates started Microsoft Research in 1991,
he had a vision that computers would see, hear,
talk, and understand human beings.
Since then, Microsoft Research has been working really hard
to make that vision a reality.
In 2016, we were the first group
to get human parity on the object recognition part
of the Image Net challenge.
In 2017, we got human parity on speech recognition
on the Switchboard challenge.
A year later, we achieved human parity
on the Stanford Reading Comprehension test,
and then last year, we were able to get human parity
on the general language understanding benchmark,
but our goal isn't just to solve these academic benchmarks.
We really wanna empower every person
and every organization on the planet
to achieve more with this technology.
In order to do so, we had to have a massive transformation.
That transformation is what we call AI at Scale.
The problem we saw was that Microsoft groups
were working on AI independently.
We had Microsoft Bing,
which was working on AI algorithms for better search.
We had Microsoft Word,
which was working on better AI algorithms
for better reading and writing assistance,
and we had Microsoft Outlook,
which had AI algorithms for helping you
manage your email more effectively.
We saw an opportunity to collaborate, share,
and actually build from each other's work.
To accelerate this change we needed,
we took advantage of three AI trends.
The first is transfer learning.
Which transfer learning, you actually train an AI model
on one set of tasks, and you can re-use that same model
on a different set of tasks.
The second trend was large self-supervised neural networks.
The idea here is that you can train
a really large neural network on massive amounts of data,
pre-train it, and then fine tune it
for another set of tasks,
and it turns out, training a neural network in that way
was much better than just directly training
for that task to begin with.
The third trend was a cultural shift.
Product groups need to coordinate and work together more,
and that's a cultural shift that
Satya was pushing from the beginning.
The results so far have been phenomenal.
Three years ago, the largest AI model we could train
was about 10 million parameters.
A year later, we could get to 100 million parameters.
Last year, we trained a neural network
for about a billion parameters,
and earlier this year, at 17 billion parameters,
called Turing NLG.
Let's take a look at what this model can do.
What we do is we give the model a prompt,
and then the model just tries to
fill in the rest of the story,
so if you ask, "How was the universe created?"
It understands that the creation of the universe
tends to be related to the Big Bang.
This isn't something where the model is
copying and pasting from the web.
The model's read tons of web pages,
gigabytes of data, and then it has learned associations,
and whenever you start a sentence,
it tries to finish the sentence.
Let's try another example.
Let's ask the AI model for some advice on life quotes.
Once again, we never trained this AI model
to actually generate life quotes.
These are things that the model's generating all on its own
from just reading tons and tons of
high-quality information on the web.
I especially like that last piece of advice.
To make this possible, we had to build
large-scale infrastructure of AI accelerators,
such as NVDIA GPUs, and interconnect them with
blazing-fast high bandwidth networks,
like NVLink and InfiniBand, and this is available
on Azure for everyone to use.
The second thing we needed was software
to maximize utilization of the hardware,
so that's why I invented DeepSpeed,
and DeepSpeed has its own novel algorithm,
so unlike previous approaches, such as model parallelism
and data parallelism, which have trouble scaling,
DeepSpeed comes with a novel ZeRO algorithm,
which actually partitions the model states.
With this, we can train models bigger and faster
on the same infrastructure,
and we open-sourced this on GitHub for everyone to use.
It's the same software we use
to train our 17 billion parameter model.
Now that we can train really large
natural language transforming networks,
we can now bring them across all our products,
so we've pre-trained these large models once,
and then we can fine tune it for different tasks.
So we've used this in Microsoft Bing
to return better results and answer more questions.
We've used this in Microsoft Outlook
to power this feature called Suggested Replies,
which helps you save time by helping
respond to your email much more quicker.
It's found in Microsoft SharePoint,
in a feature called Inside Look,
giving you a summary of your document
so you know which document that you're looking for
in the list of files,
and it's also found in Microsoft Word's Smart Find,
which helps you search inside a document
as easy as it is on the web.
This is what we call AI at Scale.
Creating AI infrastructure at scale,
training large AI models at scale,
and shipping AI models across all our products at scale.
To learn more, go to the AI at Scale
innovation site and innovation lab,
download DeepSpeed library at GitHub,
and look at the AI at Scale companion app.
Thank you. (inspiring music)
(bright music)
- [Narrator] Innovation hinges on our ability
to see things differently.
Breaking boundaries and looking
between the lines in an effort to solve
some of the world's toughest challenges.