- [Instructor] So let's talk a little bit
about AI and machine learning,
as these terms often get used interchangeably.
AI is the ability of machines to perform tasks
normally requiring human intelligence.
This includes things like visual perception,
decision making, speech recognition,
and translating between languages.
Now AI as a field includes machine learning
and deep learning, but also includes several approaches
that doesn't involve any learning.
For example, when AI first kicked off in the 1950s
many experts believed that human intelligence
could be achieved by creating a sufficiently large
rule base and then using this to manipulate
any input data.
This was known as Expert Systems.
Machine learning is a relatively newer field of AI,
requiring learning.
Typically when a programmer has to solve a problem,
they take a zip of the data
and then based on a set of rule that they create,
they arrive at the answer.
With machine learning, we turn this on its head.
Given the data and the expected results,
we get a machine to determine what the rules should be.
So machine learning is different from regular programming,
in that the system is trained
rather than explicitly programmed.
Deep learning is a popular subset of machine learning
where the focus is on learning rules,
via several successive layers.
Modern deep-learning networks typically have
tens if not hundreds of layers.
Let's use this diagram as a representation
of a deep-learning network.
So what's actually happening in these layers?
Each layer is learning certain characteristics of the image.
This could be the edge of corners or textures.
In this example, the final layer then outputs the prediction
that the original image is
a representation of the number four.
There is a feedback loop which allows the model
to learn and get more accurate.
And you can normally get better results
with training with more images.
There's been a lot of hype around deep learning
and rightly so.
This is because we've had breakthroughs in areas
that have been historically very challenging.
Deep learning models are used in self-driving cars,
image classification and handwriting transcription.
They have near-human-level speech recognition
and can accurately translate between languages.
Now that we've had a quick over of AI,
let's look at why we should even bother
with introducing AI to the organization.