to 2007 george w bush was president
Rihanna’s Umbrella topped the Billboard
charts v Harry Potter movie had just
been released and reality star Kim
Kardashian had yet to rise to prominence
somehow 2007 can feel both like it was a
million years ago and like it was just
yesterday but what you might not realize
was that in 2007 one major invention
catalyzed a shift in the way we live
work and play of course on June 29 2007
the iPhone was released now the iPhone
was not the first smartphone invented
Steve Jobs didn’t invent the technology
he just came out with the form factor
that was most widely adopted and say
Qualcomm employee I would be remiss if I
didn’t mention the Qualcomm CDMA
smartphone released in 1999 teen 99 or
the much more recognizable and popular
blackberry released the same year but it
was the iPhone that began that laid the
seeds for a foundation of change now a
plot if you have a smartphone with you
a plot again if you’ve used your
smartphone today to navigate to the
speeder store your ticket or summon
transportation so I’m certainly not
bringing to your attention anything you
don’t already know you’re aware you have
a smartphone as do all of your friends
and family but what you might not fully
grasp is just how quickly and
ubiquitously we’ve adopted this
technology in just nine years we’ve
gotten to the point where over 65
percent of adult Americans use a
smartphone if that number seems low to
you it’s because among college-educated
and young adults that percentage is much
higher in fact we were able to adopt
this technology so quickly and so
universally that we’ve catalyzed a shift
in the way we do business we’ve shifted
our economy to the on demand or sharing
economy the on demand or sharing economy
is a model in which goods and services
are traded on the basis of access rather
than ownership this means for
participating in collaborative
consumption where someone will rent out
or sell something they’re not using to
their peers whether it’s their car their
house and item or even their time rather
than the b2c business models of the past
mobile applications have enabled trusted
let’s take a moment and talk about the
economic conditions that made this shift
possible the on-demand
economic revolution didn’t become
possible because the kind of consumers
who were willing to pay extra for
convenience and on-demand services got
smartphones those kind of consumers had
already had them for a few years it was
when everyone had a smartphone they were
so abit with us that even low-income
individuals had one that these services
became popular second of all these
services didn’t rise to popularity until
2009 2010 shortly after the traditional
economy came crashing down following the
Great Recession of 2009 both high
skilled and low skilled workers alike
found it difficult to find meaningful
labor when you think about rideshare it
doesn’t just enable on-demand access to
transportation it enables an on-demand
income something that was becoming
increasingly valuable for this kind of
economic model to work not only did both
sides of the equation
have to have access to this technology
but they both had to see a value
proposition in providing it so that’s
where we stood just a few years ago
smartphones were rapidly approaching
their peak penetration and there existed
a gap in the economy people were willing
to pay for goods and services and people
demanded them but there existed nothing
to connect them fast forward to the
present and online marketplaces have
become abundant and the on-demand
economy is thriving so where do we stand
today especially with respect to machine
learning and artificial intelligence we
find ourselves on the precipice of the
next economic revolution the shift to
the automated economy one half of the
rideshare value proposition is the
supply side and labor is extremely
expensive and volatile when we go back
to what if instead of using your phone
to summon someone to take you to your
destination the car could take you there
itself what if we cut out the middleman
entirely and use automation to provide
one example of artificial intelligence
is Siri at this point you’ve all likely
used or seen someone use Siri to access
data and in a lot of ways series very
intelligent she can understand humans
the way humans speak using
colloquialisms or idioms and she has a
bunch of built-in jokes when you ask her
an opinion based question she’ll respond
with a humorous quip or sarcastic pun as
we continue to interact with Siri in
more complex ways this anthropomorphize
an artificial intelligent robot we begin
to expect more and more from her and it
is this expectation and comfort with
this technology that will drive us into
the automated revolution everything you
do on your device your smartphone tablet
or computer is data beat to be collected
when we use the same device the same way
for an extended period of time the
computer or device can begin to
recognize our patterns and predict our
next move this is called longitudinal
data so even today when I get my car
after work my phone usually has a
notification waiting for me it tells me
the traffic between my office and my
home so every day between around 5:00
and 6:00 p.m. it knows I’m gonna get in
my car and make the same commute and it
tells me the same thing every day
traffic is heavy expect delays another
famous example of artificial
intelligence is IBM’s Watson Watson
famously beat the world’s best chess
player at his own game chess is a finite
game though each move creates a finite
series of possible other moves and so
forth
and the machines ability to see the
entire board and see every option is
incredible in some ways in this context
it makes sense that the machine is
smarter than humans because it isn’t
bound by the confines of human memory
and capacity but what a lot of consumers
fail to understand is that the machine
isn’t understanding it’s just parsing
through options
the next best move Watson’s brand of
interfacial intelligence took an extra
step though when it challenged jeopardy
Victor’s Ken Jennings and Brad Rutter at
their own game jeopardy is an incredibly
complex game which requires a really
deep understanding of the English
language including jokes and puns Watson
didn’t have access to the Internet
instead it use deep learning and some
downloaded data to sort through the
clues using after it learned this
excessive data
Watson displayed its most probable
answer on the game signaling device and
handily beat its human competitors we’re
now at the point that not only two
machines beat the humans at machine-like
games but machines can beat humans at an
inherently human game the machine is
let’s talk about some ways where this
technology will disrupt our daily life
and years to come the self-driving car
is an innovation that science fiction is
predicting been predicting for years and
I’ll be the first to tell you it’s not a
matter of if it’s when driving is a
really arduous and inefficient task for
humans to complete it relies on a vast
amount of human skill judgment and
reflex human mistakes can cause vast
another challenge that the self-driving
car presents is the determination of a
hierarchy when you’re driving there are
often situations that arise where you
have to make a split-second decision
deciding what the best move will be for
you next things can happen such as
there’s a biker in the road if you
swerve to hit it you’ll fall into a
ditch and possibly injure yourself as a
human we make these decisions really
quickly and we don’t always make the
right one but what is the right decision
when it comes down to self-driving cars
that answer will be programmed into the
computer raising a lot of ethical
questions unlike chess where there are
rules that everyone follows in driving
there are no rules there’s an infinite
possibility of things that could happen
an item can follow up a pickup truck a
deer could run out into the road or
someone could run a red light what’s the
best move as we move forward with this
technology this and many similar
questions we’ll have to find the answer
to another place where machines can cut
out human labor is in delivery right now
you can have food or items delivered by
a human in a car or on a bike or you
know any number of other transportation
methods depending on where you live but
in the next few years we’re going to
expect to see drones take over this task
a drone is an artificially intelligent
robot that can use sensors to create a
3d model of its environment can use then
use a trajectory to navigate through
this environment and arrive at its
destination as drone technology evolves
there will be no needs for humans in
this equation anymore lastly I’m going
to talk about another place where
machine labor can take over humans food
preparation for years robots were able
to complete any number of repetitive
tasks over and over but when it needs to
use artificial intelligence is when it
needs to create custom made to order
items like for example start out how to
San Francisco momentum machines created
a robot that can prepare a custom
made-to-order burger at restaurant
this is a pretty exciting proposition
co-founder Alexandra Sparta Costa said
it himself this machine is not meant to
make our employees more efficient it’s
meant to completely obvious them we
could debate the ethics of this
statement ad nauseam and believe me it
will be in the next few years but what
history has shown us is that when
technology can make humans work can make
Goods be produced faster and cheaper it
will prevail Google CEO sundar pichai
said it himself machine learning is a
core transformative way in which we’re
rethinking everything we’re doing so
what exactly sets humans apart from
machines it’s our ability to think for
ourselves to fully and truly understand
the world around us and to behave
rationally and at times irrationally
just as we saw smartphones penetrate the
economy and it shift the way we do
business into the on-demand sharing
model we can expect to have see machine
learning have similar results nine years
ago the iPhone was released where will we be in nine more thank you