Hey everybody this TEDx Talk Analysis on MindStretch with Fletch
Today we're gonna be goin' through a talk from 2014 by a
Technologist called Jeremy Howard about Deep Learning
and The Future of Jobs.
Everybody has to do somethin' in order to eat and have
a place to lay their heads. Fer a long time, humans spent
most of their days doin' only that, just like a lot a
other beins'. But at some point, they figured out how to
grow and store food, enslaved certain animals, and learned
to build themselves their own spaces that cut'em off
from the wild to keep'em safe.
Once they did this, they had time to do other things besides
just trying to survive every day. Like playin' around, creatin'
new technologies, and even philosophising. And this extra time
allowed some people to get better at some things, and others
at other things. Creatin' a division of labor. Some skills are
more valuable than others to the rest of everybody.
So some people have more status and stuff than others.
But that's just how it works.
Then over the generations people got better and better at things
streamlinin the means of production and the exchange of goods
and services. In the 1700's there was a major breakthrough
called the industrial revolution. And this is when humans
started working with mechanical machines to make their stuff.
It was game changer.
And this went on for a while. Automating the process of
weaving clothes, cotton mills, and harnessin the power of steam.
Eventually humans figured out about coal and oil which broke
the level of production wide open. This cheap and
powerful energy allowed all kinds of inventions like engines,
the automobile, which finally freed most the horses and mules,
new kinds of home heatin' and lightin', distributed electricity,
and eventually got'em off the planet and into space.
Somewhere in the middle of all this, somethin' in a league
of their own was invented. The electronic computer. Oh man!
This thing changed and is changin' everything as we know it.
And up 'til recently, everything it could do had to be
programmed by a human. But somethin' fairly new called
"Deep Learning" has changed the game again.
And this is where the story meets up
on the TEDx Talk with Jeremy Howard.
In 1956, he wanted to get this computer to be able to beat him
at checkers. How can you write a program, lay out in
excruciating detail, how to be better than you at checkers?
So he came up with an idea: he had the computer play against
itself thousands of times and learn how to play checkers.
And indeed it worked, and in fact, by 1962, this computer
had beaten the Connecticut state champion.
So Arthur Samuel was the father of machine learning, and I have
a great debt to him,because I am a machine learning practitioner.
So from this vantage point, I was able to find out a lot about
what machine learning can do in the past, can do today, and
what it could do in the future. Perhaps the first big success
of machine learning commercially was Google. Google showed that
it is possible to find information by using a computer
algorithm, and this algorithm is based on machine learning.
Since that time, there have been many commercial successes of
machine learning. Companies like Amazon and Netflix use machine
learning to suggest products that you might like to buy, movies
that you might like to watch. Sometimes, it's almost creepy.
Companies like LinkedIn and Facebook sometimes will tell you
about who your friends might be and you have no idea how
it did it, and this is because it's using the power of
machine learning. These are algorithms that have learned
how to do this from data rather than being programmed by hand.
Now just let that sink in there fer a sec. I know this idea's
gettin to be old hat. But them sites is using information
that you're a feedin' it with, to figure things out on its own.
Just like humans do.
Deep learning is an algorithm inspired by how the
human brain works, and as a result it's an algorithm which
has no theoretical limitations on what it can do.
The more data you give it and the more computation time
you give it, the better it gets.
Since that time, a lot has happened. In 2012, Google
announced that they had a deep learning algorithm watch
YouTube videos and crunched the data on 16,000 computers
for a month, and the computer independently learned about
concepts such as people and cats just by watching the videos.
This is much like the way that humans learn. Humans don't
learn by being told what they see, but by learning
for themselves what these things are.
Like when you watch a little kid or a baby just a figurin
somethin out. No matter how many times you tell 'em somethin'
they will just touch that hot stove, fall down the stairs,
or forget what somethin' is 'til they learn and remember it
on their own. And that's how it works for all of us no matter
how old we get, we just get better at it with experience.
Google announced it last year that they had mapped every
single location in France in two hours, and the way they
did it was that they fed street view images into a
deep learning algorithm to recognize and read street numbers.
Imagine how long it would have taken before:
dozens of people, many years.
So what does it mean now that computers can see? Well,
it's not just that computers can see. In fact, deep learning
has done more than that. Complex, nuanced sentences like
this one are now understandable with deep learning algorithms.
As you can see here, this Stanford-based system showing the
red dot at the top has figured out that this sentence is
expressing negative sentiment. Deep learning now in fact is
near human performance at understanding what sentences are
about and what it is saying about those things. Also,
deep learning has been used to read Chinese, again at about
native Chinese speaker level. This algorithm developed
out of Switzerland by people, none of whom speak or understand
any Chinese. As I say, using deep learning is about the
best system in the world for this, even compared to
native human understanding.
So we can see now that computers can not only see but
they can also read, and, of course, we've shown that they can
understand what they hear. Perhaps not surprising now that
I'm going to tell you they can write.
And here is some text that an algorithm
out of Stanford generated.
Each of these sentences was generated by a
deep learning algorithm to describe each of those pictures.
This algorithm before has never seen a man in a black shirt
playing a guitar. It's seen a man before, it's seen black
before, it's seen a guitar before, but it has independently
generated this novel description of this picture. We're still
not quite at human performance here, but we're close.
In tests, humans prefer the computer-generated caption
one out of four times. Now this system is now only
two weeks old, so probably within the next year, the computer
algorithm will be well past human performance at the rate
things are going. So computers can also write.
So I'm very excited about the opportunities. I'm also
concerned about the problems. The problem here is that
every area in blue on this map is somewhere where services
are over 80 percent of employment. What are services?
These are services. These are also the exact things that
computers have just learned how to do. So 80 percent of
the world's employment in the developed world is stuff that
computers have just learned how to do. What does that mean?
Well, it'll be fine. They'll be replaced by other jobs.
For example, there will be more jobs for data scientists.
Well, not really. It doesn't take data scientists very long
to build these things. For example, these four algorithms
were all built by the same guy. So if you think, oh, it's
all happened before, we've seen the results in the past of
when new things come along and they get replaced by new jobs,
what are these new jobs going to be? It's very hard for us
to estimate this, because human performance grows at this
gradual rate, but we now have a system, deep learning, that
we know actually grows in capability exponentially.
And we're here. So currently, we see the things around us
and we say, "Oh, computers are still pretty dumb." Right?
But in five years' time, computers will be off this chart.
So we need to be starting to think about
this capability right now.
Now, just look at those faces. I've done watched me a bunch
of TEDx Talks and I ain't never seen concern like that
throughout the room. They more nervous than a long tailed
cat in a room full o rockin chairs cause they a realizin'
what's a comin'. Up 'til this moment, most of 'em had thought
that there'll always be somethin' people are better at than
computers. But they in a different place than ever before.
Now he's gonna talk about what I mentioned at the beginning
of this video about other leaps of technological advancement
from history and how this time... it's just different.
We have seen this once before, of course. In the
Industrial Revolution, we saw a step change in capability
thanks to engines. The thing is, though, that after a while,
things flattened out. There was social disruption, but once
engines were used to generate power in all the situations,
things really settled down. The Machine Learning Revolution
is going to be very different from the Industrial Revolution,
because the Machine Learning Revolution, it never
settles down. The better computers get at intellectual
activities, the more they can build better computers to
be better at intellectual capabilities, so this is going
to be a kind of change that the world has actually never
experienced before, so your previous understanding of
what's possible is different.
This is already impacting us. In the last 25 years, as
capital productivity has increased,labor productivity
has been flat, in fact even a little bit down.
So I want us to start having this discussion now. I know
that when I often tell people about this situation, people
can be quite dismissive. Well, computers can't really think,
they don't emote, they don't understand poetry, we don't
really understand how they work. So what? Computers right now
can do the things that humans spend most of their time being
paid to do, so now's the time to start thinking about how
we're going to adjust our social structures and economic
structures to be aware of this new reality. Thank you.
So's your mind stretched from that talk? I sure hope so.
Cause this impacts everybody and if you're a lookin' to have
a use on planet earth in the near future, you better think
about what you know how to do and whether a computer is gonna
be better at it than you very soon.
Course there's lots more to this that I can't cover
real quick like. But hopefully this gives ya somethin'
to think about. Just make sure that you're learnin' how to
develop your skills to be used in complement, not competition,
with artificial intelligence.
Or you aint gonna be worth a hill of beans.
Alright that's it for this video. I hope it stretched your mind
at least a bit. Just remember, "a mind, once stretched by
a new idea, never regains its original shape." See ya next time.
Be sure to subscribe or follow to join in on the MindStretchin'
conversation. And if you thought this video was worth seein',
please like and comment below to let me know what you thought.
And... share it to your social media so that everyone has
a chance to see it too. Thanks for tunin' in and if you're
up fer it, see ya on down the road.
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