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AI solutions are the most effective when you actually
custom-build them for very well-defined applications.
So we realized that in fact, there's
a huge opportunity here for somebody
to build a custom AI assistant that focuses entirely
on the teacher--
their workflows, what they're trying
to accomplish, and allow them to essentially carry out
their goals in a much more efficient manner.
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The New Reality,
a look at the future of consumer technology,
with your host, Poppy Crum.
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I'm Poppy Crum.
Welcome to the Arm New Reality series,
where we explore the future of consumer technology.
The biggest transformations in education
are about connectivity.
It's always about, how do we use technology to enable learning?
I spend a lot of time thinking about how
we bridge the digital and the human world--
how the advances that are happening
in machine learning and AI that are transforming the devices we
carry around every day can make our worlds better,
can connect us more.
When you think about how our technology learns about us--
and that's the important thing-- the brain is dynamic.
The brain is learning.
What we're trying to figure out as neuroscientists
is how those learning algorithms work,
what neural correlates are supporting
these different types of data that are coming together.
Suddenly you have this transformation
where the chips in our devices are
able to be even more efficient, in some cases, at what data
it takes in, how it's responsive to the human, how
it's responsive to each of us in our environments.
One of the things that's really transformed
how we use technology and use mobile devices in our education
is the quality.
It used to be the case that assets--
the things that you use, the videos you
show, the access to experts--
was limited and narrow.
And now the purpose of an educator
is to look across the global expanse
and know that access to all experts is there,
and to the way they think about a problem.
You can bring that sort of empowerment
to every student everywhere.
It's completely raised the bar in education, but in a way
that's scalable and shareable.
Merlyn Mind is using the power of AI
to create digital assistants that aid teachers
in running their classrooms.
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I'm Satya Nitta, CEO and co-founder of Merlyn Mind.
I love creating technology to make the world a better place.
Merlyn Mind is an AI company.
We like to think of ourselves as an AI solutions company,
moving the whole field of digital assistance
into the enterprise and into the workforce.
Our first product is focused on teachers, a teacher-focused AI
assistant that we call Merlyn.
It's actually a complex product.
The Merlyn assistant is a bit like Siri or Alexa,
but it lives in a first-party product
that we call Symphony Classroom, which is a hardware product.
It's an AI hub.
And it lives in the classroom as a piece of infrastructure.
It's meant to control the main display
as well as control all the other devices
in the classroom, like document, cameras, teacher laptops,
student laptops, et cetera.
And Merlyn lives within Symphony Classroom.
It also lives within teachers' laptops.
YASSI AFSHARNIVE: Currently have it connected to this TV.
But you can imagine that we're in an actual classroom
and it's connected to an interactive display
or a projector.
I also have it paired to my computer
and connected with an HDMI cable.
I've done this so I can control all of the teacher content
on my laptop with my voice and this remote.
Hey, Merlyn.
Set a timer for 15 minutes.
SATYA NITTA: The foundation for this company
actually comes from the work that we did at IBM Research.
So me and my co-founders were all at IBM Research,
and we were focused on trying to build
AI solutions for education.
AI today is really all about picking
very well-defined, well-scoped problems.
And what we are about is not about pushing the limitations
of AI, but about working within the limitations
and doing something really imaginative and really useful
with it to enable teachers to teach better--
to in fact enable teachers to have more time focused
on the mission of teaching their children
than on having to deal with things like technology
and switching between various applications
and various devices, et cetera.
The typical teacher's workflow consists
of coming into the classroom, hooking up to the display,
and then switching between various applications.
They are basically tethered to their desks
and tethered to their computers.
Using Merlyn has really changed
the dynamics of my classroom.
Before Merlyn, I would walk around
with like a wireless keyboard.
But now, just even the voice, if we're looking up something
like we wanted to find out how we say "you're welcome"
in sign language, because we say "thank you" in the classroom--
and we asked Merlyn, a video popped up,
we watched it really quick, and we moved on.
I want my students to have that visual.
So I don't need to prep beforehand,
because Merlyn does it for me.
Anything that just comes up, we have that answer with Merlyn.
I could just go with the flow, and it's very helpful.
The Symphony Classroom appliance that we built
is an edge AI appliance.
And what that means is we do processing at the edge,
on the device.
So it's not happening exclusively in the cloud.
We actually do a lot of AI processing on the device.
Doing the processing on the device
allows us to offer much better privacy, much better latency.
And it's a very critical aspect of our technology,
because this enables us to assure our customers
that their sensitive biometric data is never
being compromised.
And in fact, we don't monetize the data.
We delete it instantly.
And all of that is possible because we have an edge AI
appliance.
This is a fairly heavy workload that we
have to do on the device.
But we also had a very unique requirement,
which is, this is a device that has a far-field microphone
on the device.
But the moment you have far-field microphones,
any noise within the system itself, like a fan
to cool the processor, will interfere dramatically
with the ability of the microphone
to pick up voice commands from within the classroom.
So what was very critical for us then
is to be able to do these very heavy AI processing-- speech
recognition, natural language understanding--
all of these happening on the processor
without having to use a fan to cool the processor.
So this then forced us down the path
of looking at RISC processors, and Arm
has some of the best low-power processors on the planet.
And we realized that we can have all the processing that we need
while staying within the 20-, 25-volt power envelope,
which meant that we did not have to do any active cooling,
like a fan on the device.
Search YouTube for shark videos.
SATYA NITTA: We like to think of ourselves
as one of the companies that is pushing the whole field
of digital assistants forward.
So we feel this particular template
is something that's repeatable.
So we can go study deeply the workflow of nurses,
or IT professionals, or finance professionals, for instance,
and enable them to access the power of AI
with a single click or a single voice command.
And the AI then does multiple steps for them
and automates routine tasks for them.
This is a really interesting direction
to take digital assistants in, and we
feel that this is a necessary evolution of the technology
that Watson and Siri and Alexa pioneered.
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MICHAEL LU: Arm's mission is to deliver
secure and power-efficient technology wherever
compute happens.
We try to deliver sustainable technology, by which I mean
it needs to be human-centric.
It needs to help individuals perform their tasks quicker
and easier, to simplify the complexity of technology
nowadays.
We see the promise of AI everywhere
being a revolutionary promise.
I very much see AI as augmented intelligence, not
artificial intelligence.
What do I mean by that?
I think sustainable AI development very much,
as we mentioned before, needs to help individuals.
So one thing that Merlyn Mind mentioned that
resonates with me quite a lot, which
is that the future of work is more about digital assistants,
more about enabling teachers to perform tasks easier.
And that, I think, is the right way
to think about the future of machine learning.
With the advances of AI capability,
some of the things that we talked about in the past
will become commonplace.
So we, as humans, have interacted
over the last few million years using language,
using facial expressions, using body language.
And in order to be more relevant and to be
invisible in the way AI will help us, we can certainly see--
if not now, but in the very near future--
the ability to predict intent without us vocalizing.
In order to have a sustainable path to that vision,
the key not only is power efficiency,
because the amount of compute for data transfer, data
processing, and AI machine learning will increase.
The challenges of having the right data architecture,
the right security and privacy architecture
to ensure that our most private information and intent are
used for our own benefit.
So these, I think, are some of the challenges
that we are addressing in our latest
technological developments.
Thanks for joining us.
We'll see you in the next episode.
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