Artificial Intelligence vs Machine Learning: The Difference

Anyone who’s seen The Matrix has a general understanding of [AI](https://www.merriam-webster.com/dictionary/artificial%20intelligence) (artificial intelligence) and maybe even machine learning ([ML](https://en.wikipedia.org/wiki/Machine_learning)). While that and many other sci-fi movies have given a broad group of people a basic understanding of the two, it’s also caused confusion.

Machine learning and AI are frequently used as interchangeable terms. However, they are slightly different aspects of the same concept. In fact, AI is the parent to machine learning. Today, we look at what these technologies are and a brief on “Artificial Intelligence vs Machine Learning”.

What is AI?

English mathematician Alan Turing created the phrase “artificial intelligence” in the 1950s. The goal was (and is) to build machines (computers) that we consider “smart,” and that can perform various tasks. Applied AI has led to systems that can trade stocks and drive an autonomous vehicle. With AI, we teach computers what to do and then let them carry out our instructions.

AI is software that can answer questions of sorts within the confines of a set of instructions. AI is bound by the limitations of its directives.

Evolution of Machine Learning?

AI is the broader concept. Machine learning is a subfield of AI and came into its own in the 1990s. As [Forbes](https://www.forbes.com/sites/forbestechcouncil/2017/08/07/what-is-machine-learning/#39df6a8179a7) defined it, “Machine learning is the ability for computer programs to analyze big data, extract information automatically, and learn from it.”

While in AI we teach computers how to do something. With machine learning, we now give them information and let them figure out how to do it themselves.

These days, companies both large and small are using massive sets of data. There is so much data that humans decided we should have the machines help us organize and analyze it. Computers, of course, can do this work far faster and more precisely. That’s AI.

By using algorithms, or mathematical formulas, computers can take the data and then make predictions based on it. Early versions were dependent on those algorithms to make decisions, but now, the machines can teach themselves and set their own rules. That’s machine learning.

AI vs. Machine Learning

Netflix recommending a new movie for you? That’s AI. Humans categorized those choices and told computers to recommend similar movies based on specific instructions.

Machine learning manages facial and photo recognition programs (Facebook telling you who someone is), speech recognition (think Alexa and Siri), translation, and more. Doctors are using IBM’s Watson to make decisions about cancer treatments. Machine learning is Google’s Deepmind beating the world champion of Go in 2016.

Machine learning is when the computer can take a dataset such as a dictionary and learn to read without anyone giving it instruction. Machine learning can go beyond the reach of human capacity and at a much faster rate; this is where we get the idea of killer robots taking over the world.

Where it gets confusing is that machine learning can perform the same tasks as AI and vice versa. Thus, one could program a killer robot with AI and machine learning could take your buying history and discover what you might want next, as Amazon has done with its product recommendations. The difference comes down to the level of human intervention needed. Machine learning attempts to mimic the human brain, while AI is more of a feature inside the software.

For example, consider facial recognition software. A programmer creates a set of instructions for the software to measure the specific features of a face. When the face appears, the software compares the feature measurements with those already stored to discover a match. The program gets better at matching the more times the same face is encountered. Thus, even if a person wears a hat, the system can still match the face to an individual.

With AI, we can feed the computer a training set of data that consists of multiple face types. The AI system can now assign faces to categories without noting unique observations. The system then stores the results in a database, making the probability of a future match much higher.

The fundamental concept is classification. Our AI software determines how to classify the face data to make the maximum probability of a future match. In machine learning, we give the system multiple pictures of people’s faces – usually millions. Instead of the instructions needed to match faces, the computer attempts to learn from the pictures, recognizing people in a similar manor to human memory.

The Future

With both machine learning and AI, things are just getting started. Machine learning, especially, has more and more uses in healthcare, industry, and even for business owners, which we will address in future posts.