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- The main thing that we provide
is an artificial intelligence platform
to supercharge image sensor performance.
Now this has many different applications
but, being the Robotics and AI sessions coming up
in the context of that,
robots need to be able to see their environment
in order to understand their surroundings.
A lot of the image sensors that go on to these robots
don't see very well in the dark.
One of the ways that
we can go about improving how they see,
and we're talking about robots,
we're also talking about AIDUS systems
and autonomous vehicles.
All these things have difficulty
sensing in the dark right now
with traditional camera-based sensors.
But there's a lot of information that's still in there
that we're not able to access
with the current way that we process
the raw data coming off the sensor.
What we do is actually mimic the human brain
in terms of, how we interpret the raw signals
that are coming in to our eyes.
- Human beings are better at seeing in the dark
than a self driving car?
- Yeah, exactly.
Even though your retina has
five to 10 times worse sensitivity
in terms of quantum efficiency
than your CMOS sensor does
even on a cheap cellphone.
We're able to do much better
in terms of interpreting those signals
and see a much clear image.
- We can connect the dots
in a way that they can.
- Exactly. What we do, is we take that...
The way we're able to do that
is because, we go through this amazing process
called Perceptual learning
where we're born blind
and over the first few months of our lives,
we actually learn how to see our brain trains itself
how to again interpret these raw signals
that are very, very noisy coming in from the eyes.
We do that because we have real world experiences right,
we interact in the real world and understand the edges,
the patterns and the textures
that come up over and over again.
We get incredibly efficient biological encoding
of these visual features.
That's really what's missing right now
in terms of how our robots and camera systems in general
see the world.
What we do is,
we capture that process,
put it in to artificial neural networks,
establishing of digital brain behind the image sensor
and thus dramatically improving it's capability .
- It's cloud connected
or are all these machines going to have to
sort of re-learn how to see in the dark
one by one?
- Yeah, to answer the 1st part of your question,
there's just too much bandwidth that would be necessary
in order to do this on a cloud.
These are incredibly uncompressed raw frames
coming through 30 frames a second.
It needs to...
All the computation needs to happen
on the edge locally.
Each camera model...
Each of them have their own manufacturing processes
and circuit design, which you basically require
a new neural network to be trained for that model.
While there is some sort of re-training
that would need to happen,
we wouldn't be at the sort of individual unit level,
it would be at the sensor level.
- How big of your user base do you anticipate
being these robotics applications?
- Right now we're actually doing a paid pilot
with a very large robot manufacturer.
The volumes aren't huge,
just generally in the robot space.
But there certainly
a great application show the importance
of being able to see well
in visually challenging scenarios.
We're also focusing a lot on the Smartphone market
as well as the automotive market.
- And you can't say who that partner is?
- At this point I can't.
- Thank you so much for joining us.
- Okay, great. Thank you Brian.
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