Module 4 - Introduction

In this module, you will learn about the current thinking on the future with AI, as
well as hear from experts about their advice to learn and start a career in AI. You
will also demonstrate AI in action by utilizing Computer Vision to classify images.

Module 4 - Learning Objectives

 Discuss how AI experts view the future of AI.
 Describe how to approach beginning a career in AI

Our Future with AI

The original AI researchers were very
interested in games because they were
extremely complex. Huge numbers of
possible positions and games were
available, yet they're simple in a
certain way. They're simple in that the
moves are well-defined, the goals are
well-defined and so you don't have to
solve everything all at once. With chess
in particular and the work on Deep Blue
at IBM, what became apparent what
computers could do on a problem like
that was bring a massive amount of a
compute resource to do deeper searches
to investigate more options of moves in
chess that then was previously possible.
Watson defeating Jeopardy, so this was
another crossover point in sort of the
development of AI and cognitive
computing. The questions that IBM was
able to answer with Jeopardy were
questions that weren't simply looking up
in a database and you know finding the
answer somewhere. Rather, it required
information retrieval over lots of
different information resources and then
the combining of these together, using
machine learning that could arrive at
answers that went beyond what was
simply
written somewhere. Now our technology
is
so much better and so much more
advanced
that we're really ready to move on and
to tackle much more challenging
problems
that have this kind of ill-defined or
kind of messy nature. Every industry from
oil and gas, to healthcare, to media and
entertainment, to retail are just being
swamped by a tsunami of unstructured
data that can be multimedia. It
can be images. It can be video.
It can be text. And it's really the ability to
understand that data that is becoming
critical. One of the most valuable
applications of cognitive computing is
in the health domain. Medical providers,
physicians, nurses, assistants face
enormous challenges leveraging all of
the available information that's out
there. The medical literature increases
by about 700 hundred thousand articles
every
year and there's already millions of
journal articles out there. Today's
imaging technologies produce very rich
amounts of information. In fact, a
particular scan might have five thousand
images in it. You combine the image
analysis with natural language
understanding and text analysis,
leveraging the medical literature,
leveraging the patient's medical history.
The physician has got a lot more
information and knowledge at their
disposal to help them make the best
diagnosis possible.
Clearly, there's this intersection of
what the computer can do and
what people are able to do, that
gives you something that's better than
each of them individually. What is going
to be truly interesting is to
see what is the best way for them to
have really symbiotic type of
interaction; taking the advantage of each
other's strengths to collectively solve
a problem. Watson looks at another
aspect of intelligence and a much more
difficult aspect of intelligence, that is,
language. You have to be able to
interpret the questions and come up with
the right answers, no matter what the
topic. So I think the ideal scenario for
AI in the modern world is not to try
and develop a system that completely,
autonomously handles every aspect of a
problem but have a collaboration
between
machines doing what they do best and
people doing what they do best. And I can
imagine that combination will do better
than either one by themselves. We're
constantly here looking for
what's the next grand challenge problem
we can take on, that's not just around
the corner or a year away, but it's going
to take a multi-year effort. And when we
get there, we'll have something that's
valuable for the world.
Reading: What's next for AI
In Q&A sessions, IBM gathered the opinions of AI visionaries to consider
what the future might hold for AI.
Click each name to discover more, and then return to the course to continue.

Kevin Kelly, Tech Author and co-founder of Wired

Kevin is optimistic about the effect of AI on jobs, and believes that a key role of
AI will be to transform big data into something businesses can use.

Mark Sagar, Oscar-winning AI engineer

Mark combines his knowledge of computer graphics, human physiology and
artificial intelligence to develop emotionally responsive avatars for businesses.

Chieko Asakawa, Accessibility research pioneer & IBM Fellow

One of the world’s premier accessibility researchers, Chieko is excited about the
progress of Ai in helping blind people experience and explore the world.

Yoshua Bengio, Preeminent deep learning researcher

Describe as a founding father of deep learning, Yoshua sees unsupervised

learning and natural language processing as being the areas in which AI will
develop very quickly.

Margaret Boden, Veteran AI & cognitive scientist

Margaret is interested in how AI will help us understand human creativity, and

champions a multidisciplinary approach to AI.

Future with AI
RAV AHUJA: What does AI have in store for us in the

future? LEON KATSNELSON: My crystal ball is a little

cloudy, so I don't know if I have a

prediction that I would bet money on.

What I do know is what we have seen

the way AI has progressed. It starts

fairly slowly, but then it gains steam

exponentially. One good example is

Deepmind put the system together

that won in the game of Go. It's a

2500 year old game, which won against the

human opponent. But what's most amazing

is while the first system was able to

outdo 2500 years of human history in the

game, the second generation of that

system was able to outplay the first

generation of the system in less than

one year. And it only took about 40 hours

of training for it to be able to achieve

that level of proficiency in that game.

It won a hundred games out of a

hundred. So what we do know is the pace

of which the technology is accelerating

is just breathtaking and it's not

something we can predict very well. So if

there was one prediction I were to

make, it's gonna get faster. It's

gonna get better. It's gonna get cheaper

and it is gonna happen very very rapidly

within a very short period. STEVEN WASLANDER: it's

gonna be an interesting world. It's going

to evolve very rapidly because these

technologies not only can perform tasks

that we've never seen automated before,

but learn as they do them and continue

to improve. So what we'll see is we'll

deploy systems and every year they'll

get incrementally better at the tasks

they're trying to do. So we won't have to

drive our own cars anymore. Hopefully we

won't have to put away our own dishes.

You know there'll be a lot of simple

things in our lives that become

automated and become eliminated from our

daily task list. It's a revolution that

we've been through before, you know, the

first washing machines the first

dishwashers, etcetera.

All these things have helped us to you

know enriched our lives and simplify the

way we live and increased our comfort.

I think we'll see many more such

evolutions as we watch the AI

world unfold. MICHAL PRYWATA: particularly in healthcare

I think we'll see faster recovery times.

We'll see better patient outcomes. We'll

see people spending less money and time

in hospitals and in in various care

centers. AI has been game-changing for

for healthcare. There's so much

information that we can take out of

a particular system and apply it to

another. We can build all sorts of

models that have been and

can be hugely beneficial to the

long-term care. So that's

something that I'm very excited about

and seeing that evolve. JONATHAN KELLY: Looking into the

crystal ball I'm always apprehensive to

make long-term predictions, but my hope

is that we are able to indeed deploy

these collaborative robotic systems,

including self-driving cars, to make

people's lives better.

That's the idea; to use AI and

robotics technology to improve the

quality of life for the whole spectrum

of society. So I would

be looking forward to a future in

which AI plays a central role in

freeing us from the dull, the mundane, the

dirty, the dangerous jobs and hopefully

providing us with more time to spend

with our families, and better health,

potentially better health care, through

data analysis. And so I think if

I envision the future that I hope will

will come to pass in a number of

years, we'll be really leveraging

 these technologies to make our lives

better and to free ourselves from

dull, dirty, dangerous work.

Reading: What will our Society look like

when AI is everywhere?
Writing for the Smithsonian magazine, Stephan Talty and Jules Julien
paint a fiction picture of how life might be in a society where AI is
almost everywhere. The full article can be read here: What Will Our
Society Look Like When Artificial Intelligence Is Everywhere?
AI has come a long way since the seminal “Dartmouth workshop” at Dartmouth
college in June 1956. A new discipline was being discussed, so new that “People
didn’t agree on what it was, how to do it or even what to call it,” said Grace
Solomonoff, widow of one of the workshop attendees. In recent decades, AI has
developed to be present in our everyday lives, to an extent that has caused
some to wonder: how far can this go, and what will happen then?

Read the article and then return to continue the course.

Reading: Lesson Summary

In this lesson, you have learned:

 AI experts have varied views of the long-term future of AI.

 Understanding and generating natural language is likely to be the next big
growth area for AI, along with vision systems for the blind.

Optional:

If you want to learn more about the future of AI, watch:

 IBM Think 2019 - Chairman's Address: Building Cognitive Enterprises

 IBM Think 2019 – The Future of Infrastructure

Your Future with AI

RAV AHUJA: What advice would you give to someone

who wants to learn AI or perhaps even

get into a career in AI? LEON KATSNELSON: I believe

artificial intelligence is going to

permeate every sphere of human endeavor.

So the first thing I would say is: I

would congratulate the person who

has chosen to study and apply AI as

having made the right choice. The

second part I would say is because

the technology is moving so fast,

because we have not

yet discovered what it is we're

gonna discover, I think it's really

important to keep an open mind. It's

important to keep an open mind and not

get too attached to any particular

technology, any particular technique, any

particular implementation but to really

think forward and think outside of the

box. Think about what may...

think about the art of the

possible. Think about what may come.

The number one advice I would

give is: apply what you learn. Don't make

it academic. Make it practical. JONATHAN KELLY: The first

thing I would say is take a look at a

great Coursera course, like this one, and

grasp the basics of AI. Get a fundamental

lay of the land and understand what is

involved in various types of AI systems

and perhaps find the niche that you're

most interested in. Then of course

look at academic programs that can help

you to learn more about that specific

area. For students who are coming up

through high school for example, I would

say definitely spend time focusing on

math and science. Certainly all

disciplines are valuable but your

knowledge of mathematics and science

will certainly pay off when you are

working on AI systems because there are

tons of opportunities and you'll need

mathematics to really fully understand

how the AI systems operate. So take

your mathematics and your science

seriously, then look at Coursera and

beyond to other educational

programs that can

give you the basis you need

to really jump into the industry. JOSEPH SATARCANGELO: The

field of AI has changed a lot in the

last five years. You no longer require a

PhD in some kind of advanced mathematics

or know some obscure programming

language. You just have to know how to

use the software API's and understand

the problem. For example you can use

Watson and you just have to understand

the problem and understand how the API

works, but you still require some

advanced knowledge if you like to build

your own algorithm. TANMAY BAKSHI: Artificial

intelligence machine learning, this is a

very important field. It is the future of

technology because it essentially opens

up this whole new world of interaction

with computers. A world that's so far we

barely even knew existed and it enables

us to interact with computers in an

implicit way. But what I would say is

that while machine learning technology

is important, it's not something that you

can learn in isolation. Machine learning

technology is not its own standalone

subject.

It's another algorithm in the toolbox of

a plethora of algorithms that

programmers will use in their

applications, albeit a much more

intelligent, or a much more powerful

algorithm than most, but still it's

another algorithm. So before you learn

machine learning technology, it's very

important to understand the actual

programming and the technology that goes

behind machine learning technology

specifically because machine learning

actually requires this kind of next

generation of programming. A hardware

acceleration is so much more that

regular programming and regular

algorithms don't necessarily require.

I would really recommend making sure

that first of all you are passionate

about technology itself. If you are,

continue. Learn about programming. Learn

about coding. Learn how to actually speak

the computer's language. Learn about the

computational thinking behind code and

then go ahead and learn about machine

learning from the very very basics. Start

off with an API like IBM Watson to help

you get an idea of what it's capable of.

Then move on to more advanced custom

techniques and the math behind them.

STEVEN WASLANDER: AI is a fascinating field but it's

built on a huge number of foundational

domains or foundational fields of study.

So you really need to know your

mathematics. You really need to know your

probability theory and statistics, your

optimization. And you have to be able to

program. You have to be able to take

advantage of the tools that are out

there to train these networks and

understand how they work. So AI is a

really broad field that requires a lot

of specialists and a lot of

specialization in different areas. One

of the best things you can do is to get

started quickly. Start playing with some

simple tasks. Try to identify

digits or try to find cats on internet

pictures, things like this. These

are wonderful challenges that can get

you going and understanding what you

need to learn about that field. MICHAL PRYWATA: Getting

into AI now is like getting into

anything to do with internet 20,

30 years ago. It's kind of the

Renaissance of software right now. It's

possible now. It wasn't possible 10 years

ago. We have the computing technology. We

have the computing power. We have the

knowledge and this is a field that will

only grow. I think you need to

get in yesterday.

Hotbeds of AI Innovation
JONATHAN KELLY: My latest poll of top cities for AI,

which is primarily myself just looking

at what's coming through my own inbox in

terms of opportunities. It's

fairly widespread actually, there are a

lot of opportunities in a lot of

different places. There's of course

the San Francisco Bay Area which

continues to be a hotbed for startups

and AI and robotics, and I think will

continue to develop. There's

Boston in the United States which also

has a large number of startups. There's

Seattle and then here in Canada we've

been doing a great job in both Toronto

and Montreal of actually developing

quite a robust and thriving AI ecosystem

that I think is going to also continue

to develop and grow. I think we're

quite excited about that. And then

farther I feel there are a number of

locations in Europe, Asia, some

opportunities in China. Certainly China

and India as well, absolutely.

So lots if you're located somewhere

next to a major city center. I think in

any of these areas you're well

positioned to launch your AI career.

STEVEN WASLANDER: Toronto is exploding. It's been amazing

to watch and be a part of. We

have Geoffrey Hinton, obviously one of

the three pillars of the Turing

Award for major contributions in

deep learning. And the whole vector

Institute and ecosystem has come up

around the wonderful progress that came

out of the University of Toronto over

the last few decades. What we're

seeing is wonderful incubators and

startups in the area investing

heavily in AI technology and

capabilities. We're seeing large

companies bringing in research labs and

and putting those right next to the

University. And we're seeing a

wonderful amount of funding coming in

for research specifically in AI in

Toronto. So I think we're competitive

with any of the best places to learn and

work on AI in the world. JONATHAN KELLY: I think

Canada is actually doing very well.

We've done

an excellent job overall in terms of

centralizing some really fantastic AI

talents. In Toronto and Montreal in

particular, and also in Vancouver, but

Vancouver, Edmonton a number of different

cities in Canada have really become very

strong AI centers. And they continue to

grow. We're attracting a lot of great

AI talents and I believe that we'll just

continue. So Canada is shaping up to

be a great place to come and do AI

research and work in AI.

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