The world is filled with 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 said, "Any sufficiently
advanced technology is indistinguishable from magic." I found machine
learning not to be magic but tools and technology you can utilize
to answer questions with your data.
This is Cloud AI
Adventures. My name is the neuron, and in each episode, we will explore the
art, science, and tools of machine learning. Along the way, we'll see just how
easy it is to create unique experiences and yield valuable insights.
WHAT IS MACHINE LEARNING
SIMPLE DEFINITION?
Machine learning is an application of artificial
intelligence (AI) that allows systems to learn and improve from experience without being explicitly programmed automatically. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves.
WHY IS MACHINE LEARNING IMPORTANT?
The value of machine
learning is only just beginning to show itself. Today, a lot of data is 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 of 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.
HOW IS
MACHINE LEARNING USED TODAY?
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 machine learning at play, it
may not be immediately apparent that recommending the following video to watch is
also powered by machine learning. Of course, perhaps the most prominent 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 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, recommendation systems, and text and speech systems.
These powerful capabilities can be applied to a wide range of fields, from
diabetic retinopathy and skin cancer detection to retail and transportation in the form of self-parking and self-driving vehicles.
WHERE IS
MACHINE LEARNING HEADED?
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 using machine learning in
their products. It's rapidly becoming, well, an expected feature.
Just as we hope 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
jobs that we could never have achieved on our own Thankfully; it's not hard to
take advantage of machine learning today. The tooling has gotten quite good. It would help if you had data, developers, and a willingness to take the plunge.
IS THE MACHINE LEARNING
For our purposes, I've
shortened the definition of machine learning to just five words-- using
data to answer questions. While I wouldn't use such a short answer for an essay
prompt on an exam, it serves a valuable purpose for us here.
In particular, we can split the definition into two parts--using
data and answering questions. These two pieces broadly outline the two sides of machine learning, which are equally important. We refer to using data as training while answering questions is called making predictions
or inferences.
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 serve 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, data is key to this entire process. 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 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.
