Can you give Can you give
some specific examples of applications of AI? Certainly. So we have a fairly large
collaborative
robotics program. So the cobots we work on are primarily targeted
at the moment at manufacturing applications, manufacturing,
warehousing, logistics, these types of applications
where normally you may have a person doing a job
that can be dull, it can be dangerous, and having robotic support or having a robot
actually do the job may make it much safer, more efficient, and
more effective overall. So we work on a lot of
those types of applications, particularly where
the robots are trying to interface directly
with people, as I said. So the robot may help an individual to
lift a heavy container, or help to move
items on a stocking, on a shelf stocking purposes, so all these kinds
of applications, where I think we'll see
collaborative robots move first, and then hopefully one day and maybe into your
home
to help you with the laundry and dishes in
the kitchen. Hopefully. For example, in oil and
gas, there's a company, a pretty large oil
and gas company called the Abu Dhabi
National Oil Company, and one of the problems that any kind of oil company
has to deal with is, where's the best place for
them to drill for oil? So they have to find these rock samples of
all these different places, for this place and in
this place, and that place, and maybe hundreds of different places for
them to drill oil. From these rock samples, now you have
all these fine sheets of rock in maybe hundreds
or thousands of them, and it's up to these oil
companies to be able to classify these using they're trained
and expert geologists. But to train
geologists to properly classify these sheets of
rock can be quite difficult, it could be time-consuming, could cost a lot
of money as well. So one way to help augment the capabilities of humans is to be
able to use
computer vision, to classify these rocks
samples to be able to identify which of
these locations are the best to drill for oil? That's in oil and gas. Imagine
before this,
if there was a very, very rare form of cancer experienced by a doctor in Dubai, and
if there were
another case in New Zealand, how do you think they would have actually figured out
that, "Hey, we're both dealing with this very rare case since we
work together." That wouldn't have been
possible in the past, but now with machine
learning technology being able to aggregate
knowledge from so many different sources into one centralized Cloud
and understand it, and provide that information inaccessible, intuitive,
implicit way. Now, that New Zealand doctor
can actually go ahead and use this machine learning technique to say, "Hey, just a
few days ago there was a doctor with a very similar case," even though it may
not be the exact same thing. Sure. So we work with a number of startups and the
number
of enterprises, and I'll just bring
a couple of examples. So what they like to
talk quite a bit about is company out in California
called Echo Devices. What they've done
is they've taken a simple device which
is stethoscope, something we see around
the neck of every physician, nurse, and the health
care professional, and they taken that device and basically have transformed
that into first, into a digital device by cutting
the tube on stethoscope, inserting a digitizer into it
that takes an analog sound, transforms it into
a digital signal, amplifies it in the process, makes it a lot easier
for people to hear, it's amplified sound,
the sound of your heart, or your lungs working. But what it also allows us to do is
that allows us to take the digital signal and sent it via Bluetooth to a smart
phone. Once it's on a smart phone, they're able to graph it, which allows the
physician
to better understand, not just through audio data but through an actual graph of
how your heart is working. But because the information is now captured in
the digital world, it can now be central
machine-learning algorithm, and that's what they do. A machine-learning algorithm
can actually learn from that, apply your previous learnings
from the human doctors, cardiologist, and now assist a physician who is using the
device in
their current diagnosis. So it basically not replacing a physician in
any way, shape, or form, it is assistive technology
which is taking the learnings of the previous generations
of human cardiologist, and helping in the diagnosis
in the current state. To me, that's a perfect example
of taking the X, which is in this particular case
as a stethoscope, and then adding AI to that X. I have a really nifty
name for that, they call it Shazam
for Heartbeats. (Music)
