Difference between Artificial Intelligence and Machine Learning

By Vivek Kumar

Artificial Intelligence & Machine Learning

With the widespread popularity of big data and analytics, terms like Artificial Intelligence and Machine Learning started getting thrown around a lot, but what do they exactly mean, and how do these terms relate? If you’re a sci-fi fanatic, you probably already have some idea about Artificial Intelligence, though it may be way more sinister and dystopian than what it really is.

Essentially, Machine Learning is a subset of Artificial Intelligence, which can be seen as an umbrella term for any piece of computer code that does something smart. In other words, all ML is AI, but all AI is not ML.

If that last line confuses you, we might need to rewind a little. Let’s take an in-depth look into the history of these terms, and by the end of it, you’ll know exactly what we mean!

The history of AI and ML

Although Artificial Intelligence has been extensively explored since only recently, the term has a longer history than Machine Learning. The real birth of this technology, however, started fairly recently – as recently as the 50s and the 60s, when scientists from mathematics, computer science, psychology, economics, and political science put the idea of “creating an artificial brain” on the table. This resulted in AI becoming an academic discipline in 1956.

During this period, Alan Turing developed a Turing Test which speculates the possibility of making machines think. In order to pass the Turing Test, the machine must be able to conversate in a way that was indistinctive from the way humans carry out conversations. This was the very first breakthrough in the philosophy of Artificial Intelligence.

Over the years, the field of AI has seen numerous innovations and breakthrough, but this definition still lies at the core of it all. In essence, Artificial Intelligence can be a pile of if-then statements, or a complex mapping of data into various categories. The if-then statements are simply rules that dictate the system to take the route A if the input is X – these rules are explicitly programmed by human hands. These if-then rules are also sometimes called as rules engines, knowledge graph, or expert systems. The intelligence these rules engines mimic can be that of an accountant who takes the data you feed it, runs it through a set of predefined rules, and gives you the amount of taxes you owe as the output.

Clearly enough, Artificial Intelligence is, and always was, a field with an extremely broad scope. And like any field encompassing so much, the main problem at hand gets divided into a number of smaller sub-problems, which later went on to become separate fields of study. Some of the major such sub-problems of AI include:

• Deduction, Reasoning, and Problem-solving
• Knowledge Representation
• Planning and Scheduling
• Machine Learning
• Natural Language Processing
• Computer Vision
• General Intelligence (Strong AI)

As can be clearly seen from above, Machine Learning is a subset of Artificial Intelligence. That is, all machine learning comes under AI, but not all AI counts as ML. For example, the rules engines we talked about earlier, they fit very well in the definition of AI but they are far from being Machine Learning.

What exactly is Machine Learning?

At this point, it is important to clarify what separates these rules-based learning from ML. In extremely simple terms, the only aspect that separates ML from these rules engines-based AI is the ability to modify itself on being exposed to more data. That is, ML is dynamic, and not static or predefined, and does not require human intervention to learn – which makes it less brittle and less dependent on human experts.

Machines learn based on algorithms, and these algorithms are developed to detect patterns in the existing data, and keeping those patterns in mind, make future data-driven deductions when introduced to a new set of data. These algorithms evolve based on the empirical data, so they can accomplish way more than a set of hard-coded instructions. Thus, in a nutshell, ML can be defined as the techniques to “train” the machines to learn from the past and get better in the future.

Let’s take an everyday example to make you understand better. Every time you misspell a search query on Google, it automatically warns you saying “Did you mean *this*?”. This is a result of sophisticated machine learning algorithms at work. More often than not, when you misspell a search query, you quickly go back to correct the spelling and search again (at least this is what we did before the ML algorithms got super-sophisticated). At this point, the Google algorithms recognise that you searched for something after searching for something else, and keeps it in mind for any similar misspelled searches in the future. In short, they learn to correct you.

In addition to these algorithms, recommendation engines, page ranking, and virtual assistants can be counted as a few more examples of ML in action. Over the years, the applications of ML have become far more widespread and solve many complex problems from fraud prevention, risk analysis, to even medical diagnosis.

Job prospects in AI and ML

As organizations began accepting the magic of AI and ML and realised the relevance it holds for their business, it resulted in a widespread increase in the demand for skilled professionals in this domain. There are numerous opportunities for AI enthusiasts in the world today, with every other major organization always on a lookout. If you wish to get started in your journey of making the devices smarter, we recommend you go through online machine learning and artificial intelligence courses. These courses are designed to help you start from scratch and reach all the way to the top of the ladder.

About the Author:

Vivek Kumar

Vivek is the President of Consumer Revenue at UpGrad, an online education platform providing industry oriented courses like Artificial Intelligence in collaboration with world-class institutes, some of which are MICA, IIIT Bangalore, and BITS.

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