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Thought Leadership

What is Machine Learning (and How Does It Work)?

The rise of AI-enabled applications from tech companies like Google has seen a corresponding rise in interest around the science that drives it. That’s why you suddenly hear the term machine learning thrown around a lot in various blogs and at practically every tech conference.

Many developers still haven’t had a lot of exposure to this branch of programming. Most lay people get confused and think it’s all crazy robots and just want to know how to configure their latest IoT device.

First, let’s clear up some of the confusion around machine learning. Then we’ll get into the science and math (yes, there will be math) behind the concepts. Finally, we’ll dive into some common applications of machine learning that we’re starting to see not only reshape the way companies are doing business but also permeating the daily lives of ordinary people.

Definition of Machine Learning

What is machine learning?

Machine learning isn’t artificial intelligence in and of itself. And it’s much more than automating a bunch of simple tasks. It’s a specific branch devoted to helping computers learn from humans and how to interact with us in a human-to-human like manner. Sounds simple, right?

If it was, scientists wouldn’t still be devoting so much effort into making it happen. Great strides have been made over the past century in getting machines to accurately interpret requests from humans and provide us with what we need in response.

Those requests aren’t happening naturally, not yet anyway. There’s a lot of work that goes into getting Alexa to pull up your favorite playlist on demand.

How Machine Learning Works

The artificial intelligence driving most modern applications is due to rigorously designed algorithms created by developers and computer engineers. Tons of data sets get built and rebuilt until they’re ready to go. Machines then use them to help anticipate different aspects of human behavior.

Done correctly, the calculations drive artificial intelligence to gauge what it’s being asked to do and use those same algorithms to figure out where to get the information needed to accomplish its goal.

Every day brings hundreds of new algorithms from eager AI enthusiasts, so there’s no way to calculate how many are out there. All algorithms contain some combination of the below concepts:

  • Representation – The language the computer uses to understand us.
  • Evaluation – How the computer interprets our requests.
  • Optimization – How the computer arrives at the correct path for responding to what it’s being asked for.

It doesn’t matter if you choose to code your AI application in R, Python, or any other language. The important thing is providing it with the correct data sets to anticipate behavior from humans.

Challenges of Machine Learning

Most algorithms follow a set pattern. Humans don’t often do this, which is where scientists still struggle in developing truly autonomous AI.

Think about it. Human speech patterns can vary depending on what part of a state you were born in. Each region has its own slang and dialect that’s easily understood by those familiar with it.

But what if you’re a machine and someone asks you to find a fat Firebird anywhere for sale near them? People would translate the fat to the slang “phat” and know that the search is for a really enticing car.

How does the machine know when and how to switch between the two terms? That’s the difficulty in getting machines to accurately predict human behavior without significant help and consistent updates from engineers.

Tasks and problems involving lots of ambiguity aren’t what AI is best suited for now.

Prerequisites for Working with Machine Learning

Companies that want to leverage the capabilities of machine learning have a few options: 1) hire a top-notch developer or engineer who can design the company’s applications, 2) engage a third-party vendor offering the tools and capabilities you’re looking for, or 3) get a qualified team member to learn the machine learning ropes. In most cases, companies that don’t have the internal resources to devote to such complex development projects opt to enlist a third-party vendor to provide the capabilities they need. This is often the most practical option, as those vendors likely have a robust team consisting of data scientists, developers, engineers, and other tech geniuses who spend all day, every day building machine learning applications and tools – meaning they bring a wealth of expertise to the table.

We’ve already mentioned some of the common computer languages to learn for anyone interested in getting into the behind-the-scenes details of machine learning, but a good understanding of linear algebra will also come in handy. The most important thing you should be doing is establishing good habits around defining the problem you’re trying to solve, building adequate datasets, and thoroughly testing them out.

It’s important to constantly refine your datasets for responses to new information your machine might not be aware of. Life isn’t static, so the algorithms your processes use shouldn’t remain that way, either.

It also helps to have a good understanding of the following:

  • Regression (Linear and Polynomial)
  • Decision trees
  • Markov Chains
  • Support Vector Clusters

Applications for Machine Learning and AI

Problems with a clearly definable outcome are what machine learning handles best. Image recognition, finding patterns in missing data, and insight from clear, unambiguous language are things AI can do well.

It’s also used quite frequently to find discrepancies in financial transactions, make predictions based on past data patterns (think stock market), and recognizing when someone’s sending you some type of spam or fraudulent email and marking it as such.

The hopes are that deep learning, an aspect of machine learning that models itself after the neural networks of a working brain, will help bridge the gap that exists in getting machines to respond to unfamiliar data or input. That should provide more opportunities for companies to find innovative ways to apply AI not only to their business processes, but in forward-thinking ways that can assist ordinary consumers in their day to day lives.