- Symbolic reasoning and planning AI worked great
for machines whose matching patterns were limitive.
You could easily use this approach for creating
a program to help you with your taxes.
The IRS only has a limited number of rules,
and you can go through the paperwork step by step.
You could even create many if-then statements.
If you have a dependent, then fill out x form.
If you live in this state, then fill out the z form.
The challenge with this approach
is that if the list of possibilities gets too long,
then the system is difficult to manage.
Let's say you wanted to create an AI program
that identified animals from a database of images.
You wouldn't want to create a list
of these matching patterns,
that would have too many variables.
Imagine all the different lists of size, eye shapes,
number of legs, or other distinguishing features.
You also wouldn't want to create something like
if it has fur, then check to see if it has whiskers,
if it has whiskers, then check the shape of its ears.
A list like this would become way too long
if you wanted to match many different kinds of animals.
There are also way too many opportunities
where the system could get stuck.
Maybe you couldn't see the whiskers
in a low resolution photograph.
Then you have an unidentified image.
That's why early AI researchers started to wonder
if instead of planning out matching patterns,
a computer could be programmed to learn
the new patterns by itself.
They call this idea machine learning.
Machine learning got its start very shortly
after the first AI conference.
In 1959, an AI researcher named Arthur Samuel
created a program that could play checkers.
This program was different from
any of the other symbolic systems.
It was designed to play against itself
so it could learn how to improve.
After each game, it would learn new strategies,
and after a short period of time,
it consistently beat its own programmer.
This was a breakout discovery.
For one, you didn't need an expert
to create symbolic patterns.
So it wasn't like an expert system that needed a doctor
to list out all the possible responses.
The machine would create the expert list
and also look for the matching patterns on its own.
Think of it this way.
The Chinese Room Argument had the computer
match the incoming notes with a book of Chinese phrases.
Then the computer looked through the phrases
and taped together a response
from the Chinese characters on the floor.
That's a tedious job, yet the really tedious job
was creating the phrase book.
Imagine having an expert Chinese speaker guessing
through all the notes that might come through the slot.
Instead you could try to use machine learning.
Here you have a program watching notes being passed
between two native speakers, almost like it was processing
email conversations going between two people.
After looking at many thousands or even
many millions of conversations,
the machine could start to pick up patterns.
It might notice that conversations always start
with similar greetings and responses,
so it would create its own book
based on these matching patterns.
Something like if the note says this,
then be sure to say this as a response.
You still run into the same challenge
that John Straw pointed out
with the symbolic systems approach.
The machine doesn't exactly know what's being said,
it just matches the symbols and identifies patterns.
The big difference then is instead of having
an expert speaker list every phrase,
you have the machine learning the patterns
by looking at the data.
One of the big advantages to this approach
is that the machine can continue to learn.
If the machine finds out new patterns,
it can rewrite the book to make it more accurate.
With planning AI and the symbolic systems approach,
you're stuck with the book that was written by an expert.
In the past few years, machine learning
has been one of the fastest growing areas in AI.
That's partly because it's now much cheaper to store
massive amounts of data.
In fact, sometimes machine learning is used interchangeably
with the terms data science and big data.
If you think about this, it makes a lot of sense.
Organizations are collecting vast new amounts of data.
So now, the big challenge is to figure out
what to do with it.
Machine learning can help you identify patterns
without even really knowing what you're looking for.
In a sense, you have artificial intelligent machines
learning what's in your data
and letting your organization know what it has found.