All right. 1
Let's get started by tackling a big topic. 2
What exactly is machine learning, 3
and why is it important? Every day when you open the newspaper or your favorite blog, 4
you see this word everywhere. 5
Machine learning, machine learning, machine learning, but nobody really explained to me what exactly is 6
the significance of this thing and why we should all care. The first time I really understood this, 7
it was huge, 8
and I'm going to try and explain it right here in less than five minutes. 9
OK let's start the clock. To start, 10
we have to look back at the history of the computer. The development of the computer started with one 11
human urge. 12
It's not a desire for understanding the universe. 13
It's not from wanting to further the frontiers of science and it's not even because we wanted to organize 14
the world's information. 15
Computers exist because some guy, let's call him Bob, decided that he wanted to spend more time chilling 16
at home rather than working hard. 17
The fundamental urge that drives any programmer is laziness. 18
And we invented various forms of computing so that we can work less and spend more time watching Netflix. 19
The first thing that could be considered remotely close to a computer was the abacus. Now ignoring the 20
racy picture, the abacus is useful because it helped us calculate and keep track of the steps in our 21
calculations. 22
But this still required us to know the times tables and we programmers were too lazy for that. 23
So we built a calculator. 24
Well there are quite a few steps in between some code breaking and a lot of movie fodder but essentially 25
we are here. 26
We figured out transistors and we could use them to perform logical tasks. 27
So we now had something that could be vaguely programmed. Just replace the Apple logo with Texas Instruments 28
and you're basically looking at the hype around the calculator. 29
But we obviously wanted to be even lazier and that required our computers to do more. 30
So we invented programming languages. 31
We got rid of ticker tapes and started using CRT screens. 32
We were able to use a vaguely human readable language like Fortran or COBOL and converted into ones 33
and zeros that the computer could understand. Our programs could now be executed by the computer, 34
one line at a time. If we told it to go left, it wouldn't dare to go right. 35
We specified the conditions and based on those conditions, the computer checked to see what it should 36
do. 37
So now we can play games, send email and use Excel instead of an abacus. 38
But what if we could get the computer to do even more of our work? Work that we previously thought could 39
only be done by humans, like buying gifts for your husband or your wife. 40
Okay, just kidding. 41
We're not quite there yet, but what about things like identifying the unripe fruit from our farm? 42
What would you tell the computer? What would your program look like? If object color is red then keep 43
it. 44
What if next week it's chili harvesting time and you only want to keep the green ones? 45
Well, I guess you'd have to rewrite your code. 46
Wouldn't it be great if we could actually teach the computer what a tomato is? 47
Maybe we could refine our program. If something is round, red and around 100 grams, 48
it's probably a tomato. Problem - 49
it's more subtle than that. 50
You wouldn't tell your kids that everything red, round and 100 grams is edible, would you? 51
So, how do kids learn? 52
Maybe we'd have to show them lots of examples of tomatoes. Tomatoes from different angles. 53
Tomatoes of different sizes. 54
Tomatoes of different shapes. 55
Now we're getting close to machine learning. 56
It's similar to kids learning, but with machines. In the previous example of teaching kids to classify 57
tomatoes, we gave them lots of different examples and taught them that they are each a tomato, even if 58
they might look different. 59
When we do the same thing for our computers, it's called supervised learning. By providing labeled training 60
data we can slowly teach our computers what a tomato is. 61
And once we have a trained machine learning model that knows what a ripe tomato looks like, then we can 62
do this. And teaching computers is way more fun than teaching kids, because even if you teach your kid 63
what a tomato is, they won't work on your farm 24/7 sorting your tomatoes, but your machine learning model 64
will. 65
So I hope you're now as excited as I am about machine learning.