Basics of AI II

Let's discuss deep learning, artificial intelligence, and the relationship between ML, DL, and AI in this lesson.

We'll cover the following

What is deep learning (DL)?

Deep learning is a technique for implementing machine learning. Essentially, in deep learning, we have so-called deep neural networks (DNNs). What this means is that the code structures we write are arranged in layers that loosely mimic the human brain, learning patterns of patterns.


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What is artificial intelligence (AI)?

AI is the science of making things smart i.e, “Human intelligence exhibited by machines.”

A machine does not mean just a robot; it could be just a software program too. Basically, AI is a broad term for getting computers to perform human tasks.

The myth about AI

The systems implemented today can do one or a few defined tasks as well, or even better, than humans. For example, they can recognize objects or gestures that we trained them to learn. Although AI might seem to possess some human-level consciousness, this is really just a form of very fancy statistics. These AI systems are not self-aware, even if they appear to be making smart decisions like humans. The robot playing chess is just some fancy math going on to output an action. Today, we still need code written by a human to create systems capable of learning from data. However, self-programmed AI is likely not too far in the future.

Some examples of technologies that enable AI to solve business problems are robotics, autonomous vehicles, virtual agents, and machine learning.

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Relationship between AI, ML, and DL

Putting it all together, we can see that the three terms are essentially a subset of each other. Deep learning drives machine learning which can then enable artificial intelligence:

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As surprising as it might sound, the idea of AI dates back to the 1950s! On the other hand, deep learning has become a major player in the game in just the last few years. A convergence of advances in algorithms, data proliferation, and tremendous increases in computing power and storage have been the major forces behind turning AI from hype to reality.