- Deep learning and machine learning have been supercharged
with the availability of massive amounts of data.
If you wanted to create an AI program
to identify pictures with cats,
then you'll typically have access
to millions of these images online.
You could feed your network and help it learn.
It's the same with other types of data.
You can easily get digital art, music, and documents.
Just a few decades ago,
it was extremely difficult
to get a few thousand digital images or music.
Now it's pretty trivial to get access to all types of data.
This new treasure trove of data
has really given machine learning a huge advantage
over symbolic reasoning.
It's now much easier to have a neural network
chew on terabytes of data than it is
to have experts hard code reasoning and answers.
Remember that machine learning feeds on data
as a way to learn new things.
The more data you feed into the network,
the easy it will be for the machine to find subtle patterns.
In some ways, it's similar to how technology evolved
on the World World Web.
When the web first started out in the early 1990s,
there were just a few sites
that created directories of the entire web.
One of the most successful was Yahoo.
In many ways, Yahoo was like an expert system.
Experts had to hard code each new site
to fit into their large directory.
In the early days of the World Wide Web,
you would submit your website to Yahoo
and then work with them
to put your site in the best category.
It was a labor-intensive process
because human beings had to create the categories
and then manually add new sites.
Sometimes Yahoo had to break down
categories into subcategories,
again a human task prone to error and time consuming.
This process works fine when you have thousands
or even hundreds of thousands of websites.
It starts to break down pretty quickly
when there are billions of new webpages.
Human beings just couldn't keep up
with that influx of new information.
So, in the mid 1990s,
Yahoo started working with a smaller company called Google
that would use machines
to find its own categories and connections.
When people clicked on a link,
Google would increase the weight of the link.
The Google algorithm also put more weight on websites
that were heavily linked.
That way, Google could create stronger connections
between popular destinations.
This is the same way that you should see the connections
between neuron strengthening
in an artificial neural network.
Each time a visitor clicked on a link
that correlated with their search,
they would make the Google network
just a little bit more accurate.
It's no coincidence that Google is one of the companies
that's most enthusiastic about AI.
In many ways, their whole business has been built up
using machines to interpret massive amounts of data.
Rosenblatt's Perceptrons could only look through
a couple of grainy images.
Now you have processors
that are at least a million times faster
looking through millions more images.
Plus, the new deep learning architecture
allows machines to find patterns in the data
that just a few decades ago
would have been nearly imperceptible.
You have many more layers of players in your marching band.
These deep learning artificial neural networks
look at so much data and create so many new connections
that it's not even clear how these programs
discover their patterns.
It's like a black box where swirls of computation and data
are mixed together and agree on what it means to be a cat.
No human knows how the network arrived at their decision.
Maybe it's the whiskers.
Maybe it was the ears.
It could be something about all cats
that we humans are unable to see.
In a sense, the deep learning network creates its own model
for what it means to be a cat,
one that humans can only copy or read
but not necessarily understand or interpret.
So, if you're starting your own program in AI,
you have to be comfortable with the fact
that your network might be sensing things
that humans are unable to perceive.
Artificial intelligence is not the same
as human intelligence.
And even though we might reach the same conclusions,
we're definitely not going through the same process.
This might be fine if you're looking for images of cats,
but it becomes far more of an issue if your AI program
is designed to decide who gets a loan to buy a house.