TheNavigator_Vol1Issue1_v1.5_digital-singles

36
Here are the choice-cuts from their call…
Dean Graziano: You’ve come a long way since the Visible
Technologies days. It was my first start-up. We’ve
been family ever since. It’s still one of the top social
media monitoring analytics platforms, so we did good.
Miles Ward: Plus, I met my wife there.
DG: I mean, I don’t think there’s anything
better than that happening.
MW: What we were trying to do is an appreciable
fraction of what Google tries to do, right?
DG: Right.
MW: Take and construct a full-text searchable, indexed
slice of the internet. If you ask core Google
engineers, “How does Search work?” They say,
“Well, you make a full-text searchable index of an
appreciable slice of the visible components of the
internet.” I’m like, “Dude, I did one of those, but I
was trying to do it in Sequel server at Visible!”
DG: Now data’s commoditized, right? You can get the
fire hose of data. When I tell people how we used
to collect data, they’re like, “What?” It’s insane.
MW: Yeah, in the salt mines, with pick axes.
DG: It was data as unstructured as you
can get and still go grab it.
MW: You’d had to find somebody willing to pay the extra
mile to get the insight that’s on the other side of some
gnarly thing [mountain of data]. Then technology
shows up and says, “Oh you figured out a way to
extract value by putting this in an organized pattern?
Awesome.” And then the machinery of development
kicks in and zip—[extraction] is trivial.
Isn’t that how technology works? It hunts down
opportunities for value creation and systematizes
them. From—Maybe we should print this picture—
to—Maybe we should print these pictures bigger—
to—Why don’t we just get our own printer? That’s
scalability—that systematic approach, increasing
the throughput that delivers value. That’s what
all these businesses are hunting down.
DG: I think all the stuff we see in movies is right
around the corner. You and I are both in the
AI space, where’s the future going?
MW: It’s easy to get swept up in the visual and visceral
and say “I want to shake hands with a robot! We’ll
go on a hike together!” Narrow application of these
technologies is powerful enough. Being able to use
modern prediction and extrapolation in modern
Machine Learning is going to make predictions more
accurate. A lot of the risks can be reduced in a lot of
places, and where there’s too much manual labor—that
stuff gets a lot easier, really quickly.
That’s not to say that there isn’t a huge amount of
problems left to solve, but you’re going to solve them
using slightly different tools in slightly different ways,
hopefully to much more scaled effect.
I had a good conversation with a [Google] customer
in banking. I proposed to the CIO and CFO that
they can’t really groc what AI can do for their
business if until they start some projects right
now and figure it out. But they were thinking in a
scaled way already. They said, “I don’t want to be
able to execute a single project. I need to build a
machine which executes thousands of projects.”
Every little part of my business, I expect to have-
DG: Automated.
MW: Right. You’ve got to ask: How many parts of our
business currently use statistics? Who’s likely to
walk through our front door? Who’s a risky bet to
make on an investment or give a loan? How do we
communicate to our regulators about our liquidity? In
all these places, predictions are based on a statistical
math that’s 200 years old. And it’s not that a machine
running the math is that much better, it’s just by using
so much more data, you make predictions that are
materially more accurate.
The Google example that’s most mind blowing for
me is our data centers. We consume about a fifth of
the X86 processor cores manufactured on a yearly
basis. It’s a gigantic physical facility. They have to
run those things efficiently. Shave a little off here
and that’s big bucks that go back into the coffers.
We’re on our trillionth revision of the software that
manages power and cooling inside of these facilities.
Last year, we turned all that software off. Take the best
thinking, by the best engineers at Google, working
on the most instrumented data center facilities in
the world, doing everything they could do to figure
out the very best way to control those tools. Instead,
build a Machine Learning model that digests all the
same inputs that they see, and all of the changes
they’ve made over the last ten years affecting the
turning on/off of the coolers. And our system’s
not 4% more efficient, it’s 40% more efficient.
DG: That’s amazing.
MW: These are parts of the business where 1% is
monumental for a lot of different manufacturers—
folks in oil and gas and energy, any businesses
where outputs are already at scale, where single
digit percentages matter, and where they’re using
statistics—90% of businesses on the planet. By
applying machine money, they get a step function in
accuracy. That’s giant. It’s literally a multi-trillion-dollar
opportunity.
To capture that opportunity, you’ve got to do the
leg work of having the data organized and having
your systems ready to go. You can’t just phone
it in and poke the ML button on the side of the
spreadsheet and purr… out it goes, but there’s a lot
of spots where companies have a big opportunity.
DG: Dude, I love you, great talking to you. It warms
my heart to see the impact you’re making.
MW: Dean, you were a big part of the family that
I have in technology, so I really value your
participation as a partner as we plug in to
some of these incredible new things.