- [Instructor] When people think
about advanced data work these days,
the mind is immediately drawn
towards artificial intelligence and machine learning
the way that the mind was automatically drawn
towards big data a few years ago.
But to understand the connection
between big data and these other fields,
because there is a connection,
it's worth reminding ourselves about what they all mean.
So for instance, artificial intelligence
is a field in data science and in computer science
and it's the field that focuses on techniques
that allow computers to do things typically done by humans
like play a game of Go or classify photos,
or evaluate MRIs, and it's usually done in a way
that shows adaptability to new circumstances
or it shows intelligence and flexibility
in responding to the data that's coming in.
Machine learning, on the other end,
is a collection, a rubric of algorithms
that can find patterns in data to predict outcomes
like, for instance, whose face is this
and is the person that owns the phone?
One of the things about machine learning
is that most of them improve over time
as new data comes in and especially
as there is more labeled data
where you know what the particular outcome is.
It can improve and develop over time.
And machine learning algorithms
go from the relatively simple like linear regression
to the amazingly complex
like a deep learning neural network.
And you can also look at the connection
between these fields by looking
at Google Trends search data over time,
so this is showing us the relative popularity
of searched terms over the last eight years
from 2011 to 2019.
The first one which we have seen before
is the pattern for big data which peaked in 2014
and it's gone down a little,
makes you think people don't care
about big data as much perhaps.
This green one is the curve for machine learning
and you can see it has skyrocketed
over the last couple of years.
And this third one is artificial intelligence
and truthfully I was surprised
that it was lower than machine learning.
But here's what you need to know
about the relationship between artificial intelligence,
machine learning, and big data.
It works this way.
I actually want to go back to the 1600s
to Isaac Newton who said, "If I have seen further
"it is by standing on the shoulders of giants."
And it turns out that if artificial intelligence
and machine learning have thrived over the last few years,
and they have, it is because they have stood
on the shoulders of big data.
So the relationship between these fields
is in addition to that machine learning
and artificial intelligence are in addition to big data,
not instead of it.
They rely on big data, they can't do their work without it.
I'll give you a quick idea about this,
AI requires massive data,
the theory of neural networks has existed since the '50s,
but it was only in the last few years
that there has finally been enough data
to make this work well.
And that has to do with the volume, the first V of big data.
Second, the data is streaming constantly
especially with social media data and sensor media data,
it's coming in tremendously,
that's the velocity of big data
that makes so much of the machine learning possible.
And then finally, AI and machine learning
involve novel data like images, and movies,
and audio, and so many other things
that don't fit into standard relational databases,
that's the variety of big data where AI has been so useful.
And so, artificial intelligence and machine learning
have thrived because of the contributions of big data,
the volume, the velocity and the variety
have made the extraordinary developments
within these respective fields possible,
and that is how big data is still central foundational
to the function of all of these elements
within the data science world.