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It’s very common to hear the terms “machine learning” and “artificial intelligence” thrown around in the wrong context. It’s an easy mistake to make, as they are two separate but similar concepts that are closely related. With that said, it’s important to note that machine learning, or ML, is a subset of artificial intelligence, or AI.
To understand these two concepts better, let’s first define each one:
Many definitions of artificial intelligence have popped up over the years, which is one of the reasons it can seem somewhat complicated or confusing. But in its simplest form, AI is a field that combines computer science and robust datasets to achieve effective problem-solving.
Today’s field of artificial intelligence includes sub-fields like machine learning and deep learning, which involve AI algorithms that make predictions or classifications based on input data.
AI is sometimes broken down into different types, such as weak AI or strong AI. Weak AI, which is also referred to as Narrow AI or Artificial Narrow Intelligence (ANI), is AI that has been trained to perform specific tasks. It is the most apparent form of AI in our daily lives, enabling applications like Apple’s Siri and autonomous vehicles.
Strong AI consists of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI is only theoretical at this point, and it refers to a machine having intelligence equal to humans. AGI would be self-aware and able to solve highly complex problems, learn, and plan for the future. Taking things even further, ASI would surpass human intelligence and ability.
One of the ways to understand AI is by looking at some of its various applications, which include:
Machine learning algorithms rely on structured data to make predictions. Structured data is data that is labeled, organized, and defined with specific features. Machine learning usually needs this data to be pre-processed and organized, or else it would be taken over by deep learning algorithms, which is yet one more subfield of AI.
When we look at the larger concept of machine learning, it quickly becomes apparent that it’s a highly valuable tool for businesses of all sizes. This is thanks in large part to the massive amount of data available to organizations. Machine learning models process the data and identify patterns that improve business decision making at all levels, and these models update by themselves and improve their analytical accuracy each time.
Machine learning consists of a few different techniques, with each one working differently: 
Now that we have separated the two concepts of artificial intelligence and machine learning, you’ve probably guessed that each one requires a different set of skills. For individuals looking to get involved with AI or ML, it’s important to recognize what is required for each.
When it comes to AI, the skill set tends to be more theoretical rather than technical, while machine learning requires highly technical expertise. With that said, there is some crossover between the two.
Let’s first look at the top skills required for artificial intelligence:
When we look at machine learning, the skills tend to get far more technical. With that said, it would benefit anyone looking to get involved with AI or ML to know as many of these as possible:
These are just some of the AI and ML skills that should be acquired by anyone looking to get involved in the fields. With that said, any business leader would greatly benefit from learning these skills, as it would help them have a better understanding of their AI projects. And one of the major keys to success for any AI project is a competent team of leaders that understands what is taking place.
If you want to learn more about how you can acquire some of these AI or ML skills, check out our list of best data science and machine learning certifications.
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Alex McFarland is a Brazil-based writer who covers the latest developments in artificial intelligence & blockchain. He has worked with top AI companies and publications across the globe.
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