Artificial intelligence and machine learning are trending terms in the tech sphere that are often – and incorrectly – used interchangeably.
At the base level, artificial intelligence (AI) stems directly from the study of computer science whereas machine learning (ML) is a subset of artificial intelligence, thus making it twice removed from the parent field of computer science. The two overlap, but they are not one and the same.
To recap, AI is a component of computer science that deals with computer systems performing tasks with similar, equal, or superior intelligence to that of a human (e.g. decision-making, object classification and detection, speech recognition and translation). On the other hand, ML focuses on studying algorithms, statistical models, and pattern recognition that computer systems use to perform tasks without explicit instruction (programming). This allows machines to learn for themselves and continually improve from past experiences.
This article will cover the differences of and similarities between artificial intelligence and machine learning, what each does independently, and their parallels to one another.
The hierarchy of artificial intelligence and machine learning
As mentioned previously, AI and ML are inherently related, but not synonymous. In essence, all machine learning is artificial intelligence, but not all artificial intelligence is machine learning.
Both terms live within the parent term of computer science (CS), with AI being the parent to ML. For a simplified visualization, take a look at this diagram:
With this in mind, there are a few key differences between artificial intelligence and machine learning that need to be discussed before understanding how the two function together.
Artificial intelligence background and functions
Artificial intelligence dates back centuries, but first became viable in the 1900s – and really took off in the 1950s. When computer scientist Alan Turing developed the Turing Test, he created the first test of machine intelligence against that of a human counterpart.
After that, each decade saw advancements in AI from industrial robots in the automotive industry to interactive computer programs that could communicate with humans (i.e. the first inception of chatbots), sci-fi film depictions of humanized bots, and voice assistants in smartphones that are programmed with natural language processing (NLP) capabilities.
All of these advancements led to AI becoming commonplace in our lives, so much that we often overlook that many of our day-to-day processes are fueled by artificial intelligence.
TIP: Want to learn more about the history of AI? Read our comprehensive guide spanning hundreds of years.
Use cases of artificial intelligence
If we take a closer look at a few tangible uses of AI, it’s easier to understand how some applications of artificial intelligence overlap with machine learning, and simpler to distinguish differences between the two.
Though people mistakenly assume artificial intelligence manifests itself solely in robotics, it’s still true that robotics is an important subcomponent of AI. Some examples of AI-powered robots can be found in the retail sector and medical field. In retail, robots are being used to help stock shelves, take inventory, and report back to a human manager about their findings. In medicine, robots are being used to help surgeons perform high-level surgeries like heart surgery for a less invasive approach.
Edtech, short for education technology, is bringing AI into the foreground for advancements in classrooms, and for students and instructors. From SMART boards to intelligent tutoring assistant bots for children with learning disabilities or for those who simply need additional help, AI is acting as a positive force in keeping students on pace for success.
Chatbots are used on almost every website we encounter. Whether they’re used for customer service and answering basic FAQs about a product or service or to provide purchase recommendations on an e-commerce website, you can’t go more than a day without encountering one. Chatbots can even act as personal assistants by helping to set reminders about calendar events and appointments as well as helping schedule meetings.
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Machine learning background and functions
Machine learning’s roots can be traced to a similar timeline to that of AI, but that’s because ML couldn’t exist without artificial intelligence existing first. Still, there are a few key dates in the history of ML specific to its own timeline.
In 1949, computer scientist Arthur Samuel worked on IBM’s first stored program computer, the 701. Ten years later, he completed development on a computer checkers-playing program – the first to independently learn how to play a game by using a machine learning algorithm called alpha-beta pruning. He also developed a scoring function that measured the chance of winning for each player based on board positioning of the checkers on either side. It considered the number of pieces remaining, how many kings each player had, and the number of checkers close to being “kinged” first.
Samuel designed other ways to help his checkers program improve, including rote learning techniques. Rote learning is inherently the essence of machine learning; it is a learning technique based wholly on repetition and memorization. The point of rote learning is that the more a person (or in this case, a machine learning program) studies and memorizes something, the higher likelihood that the individual (or program) will remember what it has learned. Thus, consumption leads to remembrance and understanding, which makes way for building and improving upon whatever has been learned.
Because machine learning is based on algorithms that make predictions on next steps, Samuel used this to train the ML program to remember the positions it had seen on the checkerboard as well as the value of certain positions (e.g. proximity to being kinged, center vs. end of the board, etc.). To continue growing the accuracy of this algorithm, Samuel had it play against itself as an advanced training technique.
The computer checkers-playing program helped catapult machine learning into the forefront of continued artificial intelligence exploration.
Like AI, applications of machine learning currently exist – including those we use on a regular basis. The following list is not exhaustive but gives a good overview of some current ways ML is used.
Self-driving vehicles and GPS map data
Autonomous cars are a prime example of machine learning in action. ML algorithms use neural networks, computer vision, and AI to recognize the type of road they are driving on, what certain street signs mean, if there is a streetlight, if there are pedestrians or other cars on the road, and any other random obstructions the self-driving vehicle might come into contact with.
Plus, any time you use your phone for GPS directions, machine learning is in action. Just as we learn to drive based on practice and following the same route and motions (e.g. a right turn, a u-turn, a lane change), the ML algorithm learns from route patterns to boost its accuracy in navigation and adhering to traffic laws.
Machine learning is used in all aspects of biometrics. Biometric authentication is a way of security and identification based on physical characteristics (e.g. your eyes, fingerprint, or – you guessed it – face). Because ML algorithms are trained to recognize objects and patterns, facial recognition pulls from computer vision and ML to help systems recognize physical characteristics in order to authenticate that the person trying to access a device is actually the person who owns it.
Whenever you’re online and look something up, data is being acquired about your search terms, your demographic information, related search interests, and more. AI marketing uses machine learning algorithms to track patterns in your online habits (as well as those of others) and make assumptions about your purchase patterns, who you are, and how to best target advertisements to you. For instance, if you’re someone who’s really into handbags, you’ll probably get an ad for handbags on Amazon.
Artificial intelligence vs. machine learning – the cheat sheet
To concisely sum up the above information, here’s a cheat sheet to help you remember the basic differences between AI and ML!
Want to keep learning about artificial intelligence and machine learning? Check out our extensive glossary of AI terms.
Rebecca Reynoso is the Content Editor and Guest Post Program Manager at G2. She also works as a freelance editor and writer for a few small- and medium-sized tech companies. Outside of work, Rebecca enjoys watching hockey, cooking, and spending time with her family and cat. (she/her/hers)