What is Machine Learning?
YUFENG GUO: The world is filled with data, a lot of data--
pictures, music, words, spreadsheets, videos, and it
doesn't look like it's going to slow down anytime soon.
Machine learning brings the promise
of deriving meaning from all of that data.
Arthur C. Clarke famously once said,
"Any sufficiently advanced technology is
indistinguishable from magic."
I found machine learning not to be magic,
but rather tools and technology that you
can utilize to answer questions with your data.
This is Cloud AI Adventures.
My name is Yufeng Guo, and each episode,
we will be exploring the art, science,
and tools of machine learning.
Along the way, we'll see just how easy
it is to create amazing experiences
and yield valuable insights.
The value of machine learning is only
just beginning to show itself.
There is a lot of data in the world today generated
not only by people, but also by computers, phones
and other devices.
This will only continue to grow in the years to come.
Traditionally, humans have analyzed data
and adapted systems to the changes in data patterns.
However, as the volume of data surpasses
the ability for humans to make sense of it
and manually write those rules, we
will turn increasingly to automated systems that
can learn from the data and importantly,
the changes in data to adapt to a shifting landscape.
We see machine learning all around us
in the products we use today.
However, it isn't always apparent
that machine learning is behind it all.
While things like tagging objects and people inside
of photos are clearly machine learning at play,
it may not be immediately apparent
that recommending the next video to watch
is also powered by machine learning.
Of course, perhaps the biggest example of all
is Google search.
Every time you use Google search,
you're using a system that has many machine learning systems
at its core, from understanding the text of your query
to adjusting the results based on your personal interests,
such as knowing which results to show you first when searching
for Java depending on whether you're a coffee expert
or a developer-- perhaps you're both.
Today, machine learning's immediate applications
are already quite wide-ranging, including image recognition,
fraud detection and recommendation systems,
as well as text and speech systems too.
These powerful capabilities can be
applied to a wide range of fields,
from diabetic retinopathy and skin cancer detection to retail
and of course, transportation in the form
of self-parking and self-driving vehicles.
It wasn't that long ago that when a company or product had
machine learning in its offerings,
it was considered novel.
Now, every company is pivoting to use machine learning
in their products in some way.
It's rapidly becoming, well, an expected feature.
Just as we expect companies to have a website that
works on your mobile device or perhaps an app,
the day will soon come when it will
be expected that our technology will
be personalized, insightful and self-correcting.
As we use machine learning to make human tasks better, faster
and easier than before, we can also
look further into the future when machine learning
can help us do tasks that we never
could have achieved on our own.
Thankfully, it's not hard to take advantage
of machine learning today.
The tooling has gotten quite good.
All you need is data, developers and a willingness
to take the plunge.
For our purposes, I've shortened the definition
of machine learning down to just five words--
using data to answer questions.
While I wouldn't use such a short answer
for an essay prompt on exam, it serves a useful purpose for us
here.
In particular, we can split the definition into two parts--
using data and answer questions.
These two pieces broadly outline the two sides
in machine learning, both of them equally important.
Using data is what we refer to as training,
while answering questions is referred to as making
predictions or inference.
Now let's drill into those two sides briefly for a little bit.
Training refers to using our data
to inform the creation and fine tuning of a predictive model.
This predictive model can then be
used to serve up predictions on previously unseen data
and answer those questions.
As more data is gathered, the model
can be improved over time and new predictive models deployed.
As you may have noticed, the key component
of this entire process is data.
Everything hinges on data.
Data is the key to unlocking machine learning, just
as much as machine learning is the key to unlocking
that hidden insight in data.
This was just a high level overview
of machine learning-- why it's useful
and some of its applications.
Machine learning is a broad field,
spanning an entire family of techniques when
inferring answers from data.
So in future episodes, we'll aim to give you
a better sense of what approaches
to use for a given data set and question
you want to answer, as well as provide the tools for how
to accomplish it.
In our next episode, we'll dive right
into the concrete process of doing machine learning
in more detail, going through a step-by-step formula for how
to approach machine learning problems.