Artificial Intelligence vs. Machine Learning: The Ultimate Guide

Working with artificial intelligence and machine learning

In accounting— even though the field is not defined by advanced technology—artificial intelligence can be used to automate certain tasks to find patterns and insights. For those who are looking to buy and sell financial investments like stocks and bonds, machine learning can help forecast trends and identify potential avenues for profit.

Artificial intelligence and machine learning are valuable tools for professionals in a wide variety of industries, and they are helping companies reduce inefficiencies and improve their overall organizations. But many don’t know what differentiates artificial intelligence from machine learning. In most cases, people don’t know in what events or situations one system or method can be more beneficial than the other, or even how these concepts or practices are used in everyday life.

Current and aspiring analytics professionals must understand the best applications of both artificial intelligence and machine learning and how to use these tools to benefit their teams and larger organizations.

What Is Artificial Intelligence?

According to Oracle, the global software company, “artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.” For example, artificial intelligence can be used to deliver a personalized ad on a social media platform based on a previous search or internet history. Or it could include technology in a car that detects if other vehicles are too close or if a driver is at risk. The term artificial intelligence was first coined in the 1950s according to the software organization SAS. Early applications of AI included an exploration into problem-solving, and in the 1960s, “the U.S. Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning,” SAS notes. As personal computers became more ingrained in daily life throughout the 1980s, so did the ability to have certain commands automated and made more efficient, thanks to rudimentary artificial intelligence. And with the emergence and evolution of the internet, the applications for artificial intelligence grew exponentially—and continue to expand. Today, artificial intelligence and machine learning can be used by a variety of individuals, businesses, and industries. Writing for Inc., Rebekah Iliff notes how AI can be used for the collection and analysis of data, improving the hiring process at a given company for instance, or increasing and enhancing marketing efforts. Specific AI tools that can help with these processes may include a complex algorithm or digital program that can analyze collected data. Other AI-based solutions may scan resumes for specific professional keywords, or deliver tailored online advertising experiences to customers depending on their unique tastes.

What Is Machine Learning?

According to IBM, “machine learning is a form of AI that enables a system to learn from data rather than through explicit programming.” In machine learning, algorithms draw insights from data without any external programming or guidance. For example, if a computer user wants to search online for the best restaurants in San Francisco, the list of answers to that inquiry can be autocompleted or auto-filled. This is an example of machine learning. The system behind the search engine has discovered that “best restaurants in San Francisco” is a popular and frequent search term. Machine learning acts upon insight and experience to offer the user the ability to autocomplete their search. In the 20th century, early examples of machine learning included computers in the 1940s that “hold their instructions (programs) in the same memory used for data,” according to the BBC. Mathematician Alan Turing was a pioneer of machine learning in the 1950s as he developed his theory for the “imitation game.” The game would challenge participants to guess if a person or a computer was providing responses to questions that were posed. The objective was to enable a computer to mimic human “thinking” as close as possible.

Additionally, in the 1950s, psychologist Frank Rosenblatt developed the first neural network for computers, which attempted to simulate the cognitive processes of a human brain, Forbes notes. Various advancements in machine learning were made throughout the decades, but one standout moment occurred in 1997 when the IBM computer Deep Blue defeated a chess world champion. There are different types of AI/machine learning that may be more beneficial or useful to organizations. According to IBM, supervised learning “begins with an established set of data and a certain understanding of how that data is classified.”

For instance, a system may be developed to analyze the total weekly transactions of a restaurant, with different data sections or segments such as days charged, prices of meals or types of payment used. Unsupervised learning is applied where large amounts of unorganized data occur. For example, a survey may be sent to 10,000 individuals who recently attended an event. Each person provides their own unique responses, so the data is highly unorganized. “Understanding the meaning behind this data requires algorithms that classify the data based on the patterns or clusters it finds. Unsupervised learning conducts an iterative process, analyzing data without human intervention,” IBM notes.

Writing for Forbes, Bernard Marr notes that “at the core of reinforcement learning is the concept that the optimal behavior or action is reinforced by a positive reward.” For example, a self-driving car can learn through reinforcement learning about what speeds are optimal and what speeds to avoid. Deep learning is a process, according to IBM, “that incorporates neural networks in successive layers to learn from data in an iterative manner.” For example, a building may require visitors to undergo a facial recognition scan before they are granted access. The actual facial recognition scanning could incorporate deep learning, where a person’s eyes, nose, mouth, ears, and other facial elements are analyzed and processed.

Artificial Intelligence Versus Machine Learning

Writing for the Oracle blog, Peter Jeffcock provides a succinct description of the difference between artificial intelligence and machine learning. “AI means getting a computer to mimic human behavior in some way. Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.” For example, an algorithm that analyzes large and unorganized data collected through a mass survey would be an example of unsupervised machine learning as well as artificial intelligence. Machine learning itself falls under the larger AI umbrella and mimics human thinking. Most everyone knows about Microsoft Excel. Its software can add multiple numbers to find a sum in a manner similar to a human. Machine learning can be more specifically an algorithm that combines those sums and data in Excel to find patterns.

Whether machine learning or another type of artificial intelligence is more valuable or beneficial depends on the circumstance or situation. A financial institution may want to calculate if an initial investment will grow over time and use artificial intelligence to determine that. That same institution may also want to determine why certain past investments were more profitable and while others were not, and machine learning techniques could be used to help provide those answers.

Begin Your Analytics Career

Machine learning is a concept and practice that operates underneath the larger banner of artificial intelligence, just as supervised learning is a practice underneath the larger banner of machine learning. There are certain situations where machine learning can be beneficial or necessary, and others where a different type of artificial intelligence may be more useful or practical. The applications of artificial intelligence and machine learning are vast and are generating new opportunities for rewarding careers. A 2018 report from the World Economic Forum noted how data analysts and scientists, as well as AI and machine learning specialists, were among the top 10 types of emerging and growing roles predicted in 2022. To obtain one of these positions, candidates must have a deep background in analytics, and an understanding of how AI and machine learning can be used to benefit organizations.

Artificial intelligence and machine learning can be used in many organizations and industries. They can eliminate ineffective operations and create new methods of conducting business. Professionals who possess a strong background and skillset in both artificial intelligence and machine learning are in high demand and have the ability to make a positive impact.