Artificial Intelligence and Machine Learning: What’s the Difference?

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Continuing the theme of ai ml in retail theme, it is worth comparing these two technologies. But first, let’s see what makes them different.

Among the most often used terminology nowadays to characterize new creative goods and services are “artificial intelligence” (AI) and “machine learning” (ML). Machine learning and artificial intelligence are often used interchangeably, but they’re not the same thing: machine learning is a specialized application of AI. Artificial intelligence is defined as the ability of any machine to interact with its surroundings and make suitable judgments to accomplish its purpose.

What kinds of applications may and cannot be regarded examples of machine learning (ML)?

It all comes down to the level of initiative given to the machine: if the instructions are precisely coded and the machine does not have to learn anything on its own, then you are dealing with AI. However, you will not be dealing with an example of machine learning, because in the latter case the machine must instead learn the rules to be applied autonomously.

Let’s take a look at two AI-related tasks (and, above all, how you can teach a device to perform them).

Assume for a moment that we have two computers, one of which can play chess, and the other of which can identify Nelson Mandela in a sequence of photos. When it comes to the second duty, if it is easy for a human to do, it is almost impossible for a computer to do. As it stands, no computer can recognize a buddy better than a human, yet in 1997, the Deep Blue computer was able to defeat the then world chess champion Garry Kasparov.

By establishing the whole set of chess rules, Deep Blue was taught how to play the game by the programmers: this was feasible since there aren’t many rules and, most importantly, because they are extremely formalized (a horse moves to L, the bishop moves diagonally, etc.). If chess had laws that were difficult to articulate and codify, Deep Blue wouldn’t have existed.

As a result, it may be argued that whenever it was Deep Blue’s time to move, he weighed all the options.

A counter-move to this move would open up for the opponent; if he picked the other one, I would have these options open up to me, and so on – until the best choice was found. Playing chess is tough for a person because the mind rapidly becomes overwhelmed by all the repercussions and can’t absorb them correctly. Instead, the computer’s success is the result of well-defined algorithms. Deep Blue, on the other hand, lacked both intuition and inventiveness in its move-making, to the point that one of its programmers questioned whether or not the computer was an example of AI.

No, this was not a case of machine learning at work!

The ability to recognize and identify a person in a photograph

Classification is the ML term for a comparable job. However, in theory, one may attempt to implement formal rules similar to those used in chess, such as establishing that the eyes are blue, and so on. Even yet, it may not be able to explain each physical attribute in a few words in a clear and unambiguous manner, such as expressing the shape of one’s nose in a few words. But even seemingly instantaneous information can be misleading: if a photo shows someone wearing a costume for Halloween or sunglasses on a sunny day, a friend could still recognize them; conversely, if the machine is given incorrect information about the subject’s hair and eye color, the machine is likely to make a mistake.

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