Machine Learning And Artificial Intelligence - Know The Real Differences

Here are the main differences between machine learning and artificial intelligence


Is there any difference? Really? Because most of us have been using the terms artificial intelligence (AI) and machine learning (ML) interchangeably since their inception. They are not quite the same thing, which made us write this blog so that we could clear the air of misperceptions among the users as well as the business groups and enterprises. You’ll be surprised to know the thin-line differences that these two phenomena hold in the tech world. So, to begin:

The analogy between ML and AI

This has been a sensible practice among us that if you really want to mark out differences between the two related things, it’s important to understand how they are related and share the common ground. Let’s look at them.

Looking at the image, above we can conclude that AI is a much wider field of study than ML. Instead, machine learning is a subtopic within AI. Ever since processes and lifestyles went online, we started producing a lot of data every day. Typically, machine learning and artificial intelligence have come up as solutions to deal with such massive amounts of data and putting this random, unorganized data to intelligent use.

In today’s modern technological era, the most important general-purpose technology that we have is artificial intelligence, machine learning in particular. Machine learning is the ability of a machine/computer to output or perform some actions that it wasn’t programmed to do. In this case, the machines do not require humans to explain to them exactly how to accomplish a task. The scientists along with tech experts have been capable of building systems that perform tasks on their own. Those machines are called a ‘learned machines.’ All in all, machines access the data and develop and use self-learning algorithms.

As we move on to relating machine learning with business, we end up learning about a much-advanced concept of artificial intelligence. Poised to make a superb transformational impact, AI is the ability of a machine to perform a task in a way that is considered ‘smart,’ especially for businesses. In a nutshell, AI transforms an enterprise’s core processes and business models to take advantage of machine learning. You can call AI the next phase of machine learning.

How are they different?

The differences are tough to explain because both ML and AI go hand-in-hand. We hope you’ve understood the meaning of these two terms and how they are related to each other. So, as mentioned, AI encompasses a wider perspective in the society, in the business while machine learning is limited to machines performing actions on the basis of algorithms. The latter is more of numerical solutions, which may further help organizations to make their business decisions.

Though there are differences, you really can’t choose between the two. It’s like what level of solution does your problem demand. If you want to solve simple a pattern-recognition problem, machine learning will be enough. However, if you want to use those patterns and related data for targeting the right users or maybe to find gaps between the data patterns, you need to go a step further and implement an AI-based system. You’ll need the much more inquisitive and brilliant methods and brains to do that for you.

What can AI do today?

Like other technologies including AR/VR and IoT, artificial intelligence has also generated unrealistic expectations in every industry- be it insurance, education, retail, entertainment, health, transportation, finance and virtually every other industry. Essentially, it has touched a large set of data and made businesses and enterprises believed that they can do some crazy stuff by using the power of AI. Image recognition, voice recognition, vision systems have improved tremendously in today’s time. Additionally, the cognition and problem solving have improved and beaten the finest human players in the world.

We’re witnessing all sorts of AI- deep learning and neural networking chips. However, AI development is still in infancy. There’s a lot left to experiment. Companies and tech geeks are building new packaging infrastructure and architecture for solving more complex problems and algorithms. According to a Tractia report, AI-driven consumer services will be worth $2.7 billion by the end of 2017 (as against $1.9 billion in 2016).

'Artificial Intelligence will become the ultimate version of Google. It would understand exactly what you wanted, and would give you the right thing.' We visualize a time when AI-based systems will surpass human performance at a given task. And then, they will be much likelier to be used by most organizations worldwide.

For deeper insights into the future of machine learning, deep learning and, Artificial intelligence and its growing role in an enterprise, attend this year's Machine Learning Innovation Summit in San Francisco May 


Read next:

Why We Need Data Visualization To Understand Unstructured Data