What is Machine Learning?
The world is filled with data. Lots and lots of data. Everything from pictures, music, words, spreadsheets, videos and more. It doesn’t look like it’s going to to slow down anytime soon. Machine learning brings the promise of deriving meaning from all of that data.
In this series, I want to take you on an adventure through the world of AI, to explore 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. We’ll start with high level concepts and then dive into the technical details.
Arthur C. Clarke once said:
“Any sufficiently advanced technology is indistinguishable from magic.”
At first ML may seem like magic, but once you dive in, you’ll see that it’s a set of tools to derive meaning from data.
Data all around us
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 rules, we will turn increasingly to automated systems that can learn from the data, and, importantly, changes in data, to adapt to a shifting data landscape.
Machine Learning is already everywhere
We see machine learning all around us in the products we use today, but it isn’t always apparent to us that machine learning is behind it all. While tagging objects and people in pictures is clearly machine learning, you may not realize that features like video recommendation systems are also often powered by machine learning.
Of course, perhaps the biggest example of all is Google Search. Every time you use Google Search, you are 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. When you search for “Java”, machine learning determines which results to show first, depending on whether it thinks you are 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 engines, as well as 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 of course transportation, in the form of self-parking and self-driving vehicles.
An expected feature
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 looking to use machine learning in their products. It’s rapidly becoming an expected feature. Just as we expect companies to have a website that works on our mobile device or 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 ML to make existing human tasks better, faster, or easier than before, we can also look further into the future, when ML 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. The tooling has gotten quite good; all you need is data, developers and a willingness to take the plunge.
Using data to answer questions
For our purposes, we can shorten the definition of machine learning down to just five words:
“Using data to answer questions”
This is, of course, an oversimplification, but it can still serve a useful purpose.
In particular, we can split the definition into two parts: “using data”, and “answer questions”. These two pieces broadly outline the two sides of machine learning, both of them equally important.
“Using data” is what is typically referred to as “training”, while “answering questions” is referred to as “making predictions”, or “inference”.
What connects these two parts together is the model. We train the model to make increasingly better and more useful predictions, using the our datasets. This predictive model can then be deployed to serve up predictions on previously unseen data.
Data is the key
As you may have noticed, the key component of this process is data. Data is the key to unlocking machine learning, as much as machine learning is the key to unlocking the insight hidden in data.
What’s next?
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 for inferring answers from data. In the future, we’ll aim to give you a better sense of what approach to use for a given dataset and question you want to answer, as well as provide the tools for how to accomplish it.
This is the first in a series of posts on Cloud AI Adventures. Next time, we’ll dive right into the concrete process of doing ML in more detail, going through a step by step formula for how to approach machine learning problems.
