AI FAQ Series: How Are AI and ML Related?

Welcome back to my weekly FAQ Series on all things AI and the entrepreneur. 

Another question that popped up in a networking Zoom I attended as it pertains to AI is -- what is the actual difference between AI and Machine Learning (ML)?

It struck me. Entrepreneurs hear these terms all the time now, and it might be worthwhile to spend a little time on what these mean and the differences. 

According to Concepta, 71% of businesses surveyed report they plan to expand their use of predictive analytics and other AI applications over the next year.

This includes predictive analytics and machine learning.

So, let’s take a look at these terms.

We’ll tackle these this week as the next topic on the weekly FAQ Series on Artificial Intelligence. 

The Question

How Are AI AND ML Related?

BRIEF ARTIFICIAL INTELLIGENCE EXPLANATION 

Many folks think AI is a relatively new term, but Artificial Intelligence has actually been around since the 1950s. 

It can be thought of as a subset of Data Science in general. AI essentially represents the overall idea of simulated intelligence in machines, eventually running through enough iterations to anticipate decisions and probabilities on their own.

The aim is to eventually build machines that are capable of thinking like humans. Now, the layperson’s attention might now drift to sci-fi movies like The Terminator, but that’s not really what AI is.     

The notion we see in movies would be classified as Artificial General Intelligence, which has not been achieved. No, not even Alexa in your smart home set-up. 

The type of AI in 2020 that we see deployed in work settings and in the home is Narrow intelligence – that is, it’s AI that can perform repetitive, repeatable tasks that allow humans to spend time on higher-value work.

HOW ABOUT MACHINE LEARNING (ML)?

Machine Learning came along in a little later in the 1960s. It’s the practice of getting machines to learn with being explicitly programmed. 

ML is a further subset of AI and Data Science. The main aim is to make machines learn through data so they can solve problems. 

You’ve heard the term ‘algorithms’ – Machine Learning are collections of these all processing (hopefully) enormous sums of data to provide highly probably insights and solve problems. 

Lots of math!

ANY OTHER RELATIONSHIPS YOU SHOULD KNOW?

Let’s mention the term Deep Learning here. You may have heard this one in discussions on AI, but not necessarily know the relation.

Deep Learning originated a little later, in the 70s, and takes ML to another – deeper – level. This is a further subset of ML, AI, and Data Science.  

Think of a brain, with all of its neurons firing and its networks and sections all functioning every split millisecond to help humans navigate every decision and body function 24/7. 

Deep Learning seeks to create Artificial Neural Networks, similar to how a brain looks, to solve very complex problems and automatically discover patterns for feature detection – very quickly. 

IN CLOSING..

As computing has gotten better, so has the transformation into deeper learning and AI solutions. 

This has been more of a basic introduction into differences in these terms and technologies, but I thought it relevant given some of the general confusion in the marketplace when it comes to AI.

It is definitely possible for SMBs to afford and take advantage of available AI technology right now. 

It starts with data. Then, with trying one project in an area where you have that data, a desire to simplify and automate.   

Keep all the great questions coming!

Stay patient and enjoy the AI Journey..