Deep Learning vs. Machine Learning: What Business Leaders Need To Know

Artificial intelligence isn’t the future — it is the present. Already, businesses are deploying AI tools in a variety of ways: improving marketing and sales, guiding research and development, streamlining IT, automating HR, and more. The more business leaders can learn about AI – Deep Learning vs. Machine Learning and how to leverage it, the better.

Yet, plenty of business leaders remain a bit baffled by the jargon of AI — particularly when it comes to machine learning. Machine learning is a branch of artificial intelligence tech wherein machines are given algorithms that allow them to learn from data and improve their processes. While some use machine learning and artificial intelligence interchangeably, the truth is that the former is a specific field within the latter.

Even more confusing, there is another term, “deep learning.” Is this merely a different way of saying “machine learning,” or is this yet another field of AI? Read on to find out.

First, a Deeper Dive Into Machine Learning

As mentioned above, machine learning is a branch of artificial intelligence that relies on algorithms to identify patterns from data and apply lessons from those patterns to future decisions. Machine learning is the source of a vast array of automated tasks across various industries.

A common example of machine learning is an on-demand video streaming service that tracks which videos a user watches and recommends similar videos by learning the viewing history of similar users. Business leaders in different industries can take machine learning short courses online or consult a machine learning development company to learn more about how to apply machine learning tech to improve processes within their organization.

In truth, machine learning is essentially a mechanical function, not unlike a flashlight or a phone screen. Machine learning programs cannot deviate from the directions given to them by their algorithms; they collect a certain type of data, analyze it in a certain type of way, and refine their processes in a certain manner before starting the sequence over again. This is in contrast to artificial intelligence, which aims to simulate human behavior outside of explicit programming.

This isn’t to say that machine learning isn’t complex. In fact, machine learning has the capacity to be superior to human learning as determined by the algorithms used to direct machine learning functions.

Deep Learning Is a Type of Machine Learning

A subfield of machine learning, deep learning uses layers of algorithms that form artificial neural networks capable of recognizing errors in their own analyses. In basic machine learning, programs require human intervention if predictions are inaccurate; in deep learning, the complex neural network provides checks and balances on the system, guiding it toward greater and greater accuracy without the need for human correction.

Artificial neural networks mimic the processes used in the human brain. A model of brain cell interaction first developed in 1949 describes how neurons grow together when consistently used, which is what results in accrued knowledge. Deep learning integrates nodes, which increase or decrease in relative weight as the program gains more information about a specific topic. However, because deep learning applications can process much more data at a much faster pace, programs can identify much more complex connections than the human brain. Thus, deep learning tools are exceedingly valuable.

However, deep learning is not without flaws. Just as different people can draw different conclusions from the same set of data — as exemplified by lateral thinking puzzles — deep learning programs can come to the wrong conclusions. To ensure accurate results, artificial neural networks require intense training, which involves tinkering with the mathematical functions guiding its algorithms to ensure that the right nodes have the right weights. Not everyone has the capacity to train a deep learning tool correctly; it is a task best reserved for those well-educated and experienced in the machine learning field.

AI isn’t just one concept — it is a vast field of technologies and ideas. Already, AI and machine learning tools are essential for certain business functions; soon, it is possible that businesses unable or unwilling to dabble in deep learning will get left in the dust. The sooner business leaders understand how to leverage the different types of machine learning, the better their place in the future of the industry.