thank you thank you
thank you so during World War two the
first computer was invented they cracked
the German communication cold and
ensured a successful Normandy landing
the father behind this unprecedented
machine Alan Turing wrote the paper
Computing Machinery and intelligence in
1950 and the paper opens with the words
I propose to consider the question can
machines think well today inspired by
his thoughtful question we’ll try to
answer the following how can we create
an intelligent computer and what will
the future look like with intelligent
machines
well in fact AI has been growing
exponentially in the past decade it has
already been touching our lives in ways
that you might not notice for example
every time you go on Google search some
kind of AI is being used to show you the
best results every time you ask Siri a
question natural language processing and
speech recognition is being used so
artificial intelligence will probably be
one of the biggest scientific
breakthroughs in a 21st century it will
give us the power to probe the universe
and our humanity with a different
approach
AI has the potential of forever changing
our humanity the backbone of artificial
intelligence is machine learning and I
think the term is pretty
self-explanatory we want to make
machines learn based on its knowledge
and make decisions
machine learning can be understood in
two major components one is to use
algorithms to find meaning in random and
unordered data and the second part is to
use learning algorithms to find
relationship between that knowledge and
improve that learning process so the
overall goal for machine learning is
actually quite simple is to improve the
machines performance on certain tasks
and that has can be predicting the stock
market to complicated ones such as
translating articles between languages
and the screenshot that you see right
now is actually a depiction of Google
Translate neural network whoo speaking
of translation anyone here speaks the
second or third language right that’s
awesome well I was born in China and I
speak Chinese and also speak English
plus a couple of programming languages
here on account that so when my family
and I travel around the world we often
need something called Google Translate
and by examining Google Translate
artificial intelligence we can actually
gain a greater understanding of how most
AI works well first of all have you ever
wondered how much data is a Google have
well it turns out Google hauls right
around 10 to 15 exabytes of data well
what does that even mean
let me put that into perspective for you
if one personal computer is 500
gigabytes then Google’s 15 exabyte would
be 30 million personal computers and
data turns out to be one of the fuels
that powers Google Translate of magical
technology so on the surface Google
Translate hasn’t changed since 2007 when
it first launched but will you my notice
is that the translator is getting faster
and more accurate so it turns out the
learning process for Google Translate is
inspired by our own we as humans get
better at doing things by practicing
just like what our math teachers and our
musical teachers always tell us it turns
out Google Translate can get better at
translating by reading more articles so
how do computer learn
can actually come up with this flowchart
that will give us a summarize will give
us a good picture of how artificial
intelligence actually works so it turns
out we have to use some training input
and put that into a learning algorithm
which will give us some knowledge and
that knowledge will be on a computer
knows about that specific subject and
you and me where the user right the user
will give the computer some input and
hopefully some alpha will come out so in
our case Google Google’s 15 X byte of
data will be the training input and
something you won’t translate is going
to be the user input and the output is
going to be something in a different
language so the most important part of
this whole entire process is actually
the learning algorithm this is what
powers computers to learn and be
intelligent so today we’re going to
focus on two parts one is image
processing and the second part is neural
networks so let’s begin by talking about
image processing we can talk about
computer vision without talking about
human vision right and visual signal
from our retina is relayed through our
brain to our primary visual cortex in
the back of our brain which is right
here and virtual information is
separated and processed in three
different processing systems one system
mainly processing information about
color second one about shape the third
one about movement location and
organization so with all of that in mind
today we’ll try to create an application
that will be able to identify a
coca-cola logo so first of all we have
to understand that most pictures that we
see on a computer screen
I mean of pixels tiny tiny things that
represent color which is also why Steve
Jobs names his company Pixar since every
person that world is made of pixels
which is great so the computer is trying
to understand this image it will first
separate them into different futures
objects that we can easily see in the
still image and then each of these
features will provide the computer some
information about that image and today
we’ll mainly focus on
area parameter and skeleton and some
details about these features so now the
computer has those things in memory so
when the user gives the computer some
input it will be able to process an
import and compare that with what is in
memory and then give you some output
whether the image match with the
template or not so here’s that
technology in action so I’ve created an
application on this iPad that will be
able to identify coca-cola logo and this
application is actually powered by open
computer vision and thanks to a great
framework so today we’ll learn a
coca-cola logo so let’s click on that
great we just learned this image
wonderful and as you can see the image
on top has little green rectangles and
squares around it and those are regions
that the computers are processing and in
the image below is one of the biggest
features in that image and in a table as
you can see there are details that
computer is remembered so let’s dismiss
that and click start tracking oh look at
that that’s pretty sensitive it
successfully tailed that the paper right
in front of me has a coca-cola logo on
it great and also this is life so you
know I’m not thinking anything by the
way so wonderful thank you
so now let’s recap we can summarize
everything we did with this simple
flowchart we had some input data and we
use some algorithm to find some meaning
in that data and in the future we’ll use
new networks to improve this whole
entire process and hopefully learn more
and more images and the pixel in our
case or the input data and the meaning
where things like area parameter
skeleton those you know details the
computer focused on and hopefully in the
future we’ll be able to classify any
image we want remember in the very
beginning we talked about there are two
parts of learning algorithms right the
second part is near networks so let’s
talk about that a little bit our brain
is made of gazillions and good zillions
of neurons and those tiny things
communicate with each other process
information and that’s how we become
intelligent it took thousands and
thousands of years of evolution and is
such an amazing price
so Times’s thought what happened if we
actually turn that and put that into a
computer so first of all Russ will
understand difference and similarities
between artificial neuron and a
biological one so on your left this is a
biological neuron and it has cell bodies
axons and terminal axons and dendrites
and stuff like that and those parts will
take in information and process them and
give you some output
similarly on our right as you can see we
have a bunch of axis and from our
algebra class you might know that X our
input in our case and f of X is a
mathematical calculation and why it’s an
output so this picture will represent
the basically the relationship between
neurons since we have so many of them
right and this by altering the
relationship between our neurons which
are called synapses we will be able to
learn and gain a better understanding of
things and synapses are represented as
lines on our right so this is an
animated version of what scientists
believe our neurons would look like so
back in the old days you know in the
1970s and before most of us were born
when scientists wanted to do something
like image recognition or speech
recognition what they had to do is
that’ll sit around a table and you know
they’d have to put papers and pens and
start doing math they had to create
lookup tables and this was a pain
because they took so much manpower and
it took a long time so scientists
thought what happened if we give the
computers its own power to learn that
would be magical because lookup tables
would never exist if we can just make
computers learn on his own instead we’ll
have computers all knowledge about a sub
Civic subject and this is what this
diagram represents the computers own
knowledge about something and this is
really empowering because scientists no
longer have to create lookup tables for
days and years what they have to do is
just write a simple program train the
computer and then they can do things
like image recognition
and speech recognition in a matter of
seconds so with help from Google cloud
platform we’re going to do another
demonstration showing the power of
combining image processing as well as
neural networks so once again this is
all life and we have a great audience
here tonight and we’re going to take a
picture take a picture of my phone let’s
say and to see what computer things oh
it’s a mobile phone it’s a products at
gadget that’s wonderful so what if we
take a picture of the audience it’s a
performance there’s audience and say hi
to the camera
great thank you so all of the things
that we just talked about are intangible
just like art music and language and all
of that but technology like that plays
such an important role in our daily
lives for example in Google’s
self-driving car project they use image
processing to be able to identify the
difference between a police vehicle and
a normal passenger car and this is
another picture from Google self-driving
car project they combine image
processing and also laser and ultrasonic
sensors to be able to form
three-dimensional models of the cars
surrounding so the car can navigate
safely without lag and this might
surprise you back in the 90s
scientists actually implemented these
technology on fisherman’s boats a well
trained computer can can identify the
difference between a tuna and a cod so
next time when dining how is serving you
fish you might appreciate the technical
journey the low fish took to be on your
plate so what’s next let’s try to answer
this question what will the future look
like with AI well actually jump back in
history and talk about one of the
biggest breakthroughs that we had with
AI and many of you might recall this
historic event between Garrick House
Burrell and the IBM computer
blew the IBM computer became the
first-ever program to defeat a chest a
world chess champion under tournament
rules in a classic game it was a very
significant victory it was a milestone
however later analysis actually played
down the intellectual value of chance as
a game that can be simply defeated by
brute force which means that if you had
enough calculation and enough computing
power chess can be defeated which means
that calculation does not equal to
intelligence and this is a very
important understanding however Google
took a different approach they created
alphago a program like to learn a game
of go as it goes I mean no pun intended
there um go is a program of far less
rules but requires far more intuition
you cannot just calculate what the
possibilities of go so google’s alphago
was able to defeat the south korean go
champion lee sedol in a 2016 game and
this was a breakthrough another
breakthrough because the program used
reinforcement learning as well as neural
networks which resembles our own
decision-making process so AI will not
only change our lives in small ways like
we talked about evolve it will likely to
bring us tremendous change change like
we saw 200 years ago with the Industrial
Revolution when humans first harnessed
the power of CO and steam engines change
like we saw in the 1990s when millions
and millions of computers reached homes
across the globe AI will give us
unprecedented amount of power as well as
the opportunity to change imagine
imagine 10 years from now when we’re
autonomously constructing a space
station on Mars your car is driving you
to work well you are talking to a friend
on the phone who works in Wall Street
and he doesn’t have to worry about stock
tears anymore because AI will ensure a
fair
and safe trading environment also in
hospitals across the globe scientists
are using AI to find mutations in human
DNA databases and also cures for
diseases and these are just some of the
possibilities and the sky is no longer
the limit the power and the freedom that
we have of artificial intelligence is
empowering but also humbling we as
humans are capable of creating machines
that can learn and think just like us in
the long run
AI will not replace biological
intelligence yet it will enhance our
lives it would enhance our future and I
believe that most AI researchers out
there will agree with me on that so
after all you and I and all of us are on
this journey together all of us have the
chance to witness and also decide how
artificial intelligence will shape our future thank you