By Artificial intelligence (AI) has been all the rage for a while now. Tech companies promote their newest products and services as AI-based, and we all fall for them. The truth is most of them are actually using machine learning (ML). Are these two the same thing?
In reality, they are related, but the two terms are not synonymous. Artificial intelligence is a much broader term, and machine learning is only a subset of it.
The term AI has existed since 1965, so it has grown in scale and become somewhat elusive. However, it’s quite clear that machine learning is only one piece of a much larger puzzle of recreating human intelligence in a computer program.
Machine Learning Is Much Simpler Than AI
Machine learning is essentially the study of algorithms that help a program improve automatically through experience over time. This means that ML allows a program to become better at what it does without human intervention.
So you’ve probably wondered how Netflix recommends you shows based on what you’ve already seen. The answer to this is — through ML. As time goes by, and you spend more time watching different content on the site, the algorithm starts to recognize the patterns in what you tend to choose.
Spotify works in the same way, for example. It generates a system for recommending songs you might like based on what you usually listen to. The more input you feed the program (or in this case, the more music you listen to), the more accurate the algorithm becomes.
Chatbots, such as those by ServisBot, also use ML to become better at what they do and learn from experience with customers. However, they rely on other AI techniques as well, such as natural language processing (NLP) for better understanding human language.
Just like there are many aspects of human intelligence, there are also different elements of artificial intelligence, and machine learning is one of them.
The Concept of Artificial Intelligence Is Ever-Growing and Ever-Changing
Artificial intelligence is still a long way from what we hope (and somewhat fear) it will become — a robot mind smarter than any human. Humanoid androids are not likely to become a problem any time soon.
However, since its inception, AI has advanced more than its founders could have hoped for.
In its broadest sense, artificial intelligence is the science that explores the ways we can create intelligent programs that would be on a par with the human mind. Whether the end goal of this discipline will ever be achievable is, in part, also a philosophical question. If we teach a machine to mimic our emotions, does it mean it can feel?
The elusive nature of the subject is the main reason why the boundaries of AI are constantly being pushed. As we unfold and understand different functions of our mind, we become able to reproduce them in a machine.
For example, we once thought it impossible for a machine to beat a human in game strategy — until a computer program called Deep Blue beat the number-one chess player in the world in 1997.
Not so long ago, Deep Blue was the pinnacle of AI. Nowadays, you can play chess on any device and gadget, and we have Sophia, the world’s first robot citizen.
So How Do AI and ML Relate to Each Other?
As we’ve already explained, machine learning is just one aspect of AI. Even a complex robot such as Sophia uses this technology to advance her knowledge and understanding of the world. However, she is capable of doing much more with that knowledge than Spotify can do with your song preferences.
An artificially intelligent program or machine uses an integrated web of skills, functions, and data sets. It acquires knowledge but also learns how to further apply it. You can think of it this way — an ML-based program is a one-track mind compared to the creative AI one.
For example, if only ML powered them, the chatbots we have mentioned before would be able to advance to some extent. However, without the advancement of the understanding of the natural human language through NLP, the progress would be limited.
AI and ML will probably continue to be used synonymously though, i.e., companies will keep referring to ML as AI because the latter sounds more appealing to potential clients. In a way, they are not wrong, but that’s a matter of semantics.
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