What is Machine Learning? (AI Adventures)


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. [MUSIC PLAYING]

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