• AIP Team

What's the Difference Between Machine Learning vs AI?

Updated: Aug 13



As new technologies continue to bloom around us, it can be hard to keep track of exactly what each new thing is and what it does. And nowhere is this confusion more common than in the discussion of machine learning vs AI.


Often talked about as if they’re the same thing, these two similar (but markedly different) technologies have, in the past decade, become much bigger players in conversations surrounding the future of tech and what it can do for our industries. Today, we’ll break down what each of these things are—and aren’t—and what they may look like in the future.


Machine learning vs AI


Artificial intelligence isn’t a monolith. Rather, it’s more useful to think of AI as an umbrella term that encompasses several different interconnected and related technologies and capabilities. In terms of its relation to machine learning, consider AI the tree from which ML branches out of.


What is AI?


Artificial intelligence (AI) is the practice of mapping human intelligence and abilities onto computer algorithms. First developed in the 1950s, AI started out as simple programs that could do small tasks—like beat a human player in chess. Throughout the decades, artificial intelligence has evolved in complexity, creating a rich field of different applications and capabilities spanning from quality control in manufacturing, to customer service chatbots and many things in between.


How does AI work?


Data scientists construct an algorithm and then “feed” it a set of specific rules and stipulations through which the algorithm can complete tasks. This ability to follow rules constitutes the “intelligence” of the algorithm. AI is typically classified into 2 distinct groups:

General AI: General AI-powered algorithms can solve problems given the right set of data and rules to follow.


Narrow AI: These types of AI are much more limited in capabilities. They are most often constructed to handle one specific type of task.


There are a few more distinctions here, which we write about in length here.


What is ML?


Artificial intelligence isn’t machine learning, but machine learning is a type of AI. What sets machine learning (ML) apart from its predecessor is that its ultimate goal is to go beyond the mimicry of human intelligence that AI provides.


Built on models that allow for continuous improvement, machine learning algorithms are fed a vast quantity of data and are allowed to explore different outcomes, coming up with recommendations and helping make decisions based on the data lakes they’re able to tap into. Unlike AI, machine learning programs can change over time to be able to make better decisions and pick up patterns quicker. Where AI can help us take a step forward in understanding the data around us, machine learning helps us take a leap.

Capabilities and uses of AI vs Machine learning


While machine learning is markedly more advanced than AI, that doesn’t mean that either doesn’t have their place in today’s business world. And oftentimes, one or the other is best suited to solving the issue at hand.


Artificial Intelligence


So many of the tools we use in our daily lives are built on artificial intelligence. To our phone’s virtual assistants to chatbots we talk to when shopping online, AI has proven to be a valuable resource not just within organizations, but in our daily lives. Nowadays, the scope of AI’s capabilities are almost infinite, with new use cases being programmed every day.


Thanks to the relative democratization of artificial intelligence technology in the past few years, it’s easier and more affordable than ever for businesses to take advantage of it and adapt it to their own needs.


Machine learning


On the other hand, machine learning is still an emerging tech that has yet to reach its full potential. Still, there are a variety of applications that machine learning is being used for today.


From learning to predict and assess risk factors in insurance, to helping researchers identify and monitor emerging infectious diseases. ML’s promise is that it will always get better with more time and data. Still, it’s capabilities are limited to a point, especially for businesses that don’t have the manpower and funds to continually train and test these algorithms.


Why it’s important for businesses to distinguish between the two


As these technologies become more widely known and available, it’s become increasingly important for businesses to invest in one or both to keep up with the competition. What has often happened though, is that businesses, either knowingly or not, have used AI and ML (along with other similar terms) interchangeably, using whatever sounds best to advertise their products. As we’ve shown though, the two perform distinct functions, and can’t be grouped together.


As the field continues to grow and these technologies evolve, it will be increasingly important for companies to do their due diligence when advertising the capabilities of their products. Investors and consumers alike are more savvy than ever, and over-promising on the capabilities of a particular piece of tech could lead to reputational problems.


Deep learning and the future of the field


As machine learning is a subset of AI, deep learning is a subset of ML. Characterized by taking the capabilities of machine learning to its highest potential, deep learning is where data scientists are hoping to take the field.


Inspired by how the human brain works, deep learning aims to imitate the ways our brains make decisions. Unlike machine learning, deep learning can automatically discover the features that will be used for classification within a data set, machine learning programs have to be fed those features first.


Researchers have just scratched the tip of the iceberg when it comes to deep learning, and it will likely be several more years before any big advances are made. Like machine learning, this new subset of methodology requires a lot of structural support, high-end machinery, and time to get results. But, this technology could transform the way we use machinery to help us work.


Instead of using algorithms to make recommendations and simple decisions, teams in the future could use deep learning to mimic natural speech and assist with high-level decision making in real time.


This is just one of the many ways businesses might be able to adopt the future of machine learning and AI into their daily operations. For what you can do now, there are plenty of options. We’d be happy to discuss what options exist for you today—from marketing to logistics.



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