The terms AI vs machine learning vs deep learning are not interchangeable. These areas of computer science have been confusing too many people for way too long.
In reality, there are quite large differences between these areas, even though they are parts of the same field. You can describe them as steps in the evolution of AI or its different branches. In this article, we’ll take a closer look at all three areas and establish the differences and the uses of each one.
AI has several definitions. The one we think describes this technology most accurately is the following: artificial intelligence is a branch of computer science dealing with the simulation of intelligent behavior in computers. In essence, it strives to introduce a degree of reasoning into the computing process.
At the inception of this technology, it was mostly in the form of a system of if-then statements. This approach is mostly a thing of the past. It is cumbersome and requires manual input of commands by hand. Nowadays, more often than not, they are statistical models processing raw data into set categories.
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Another example that perfectly illustrates AI in action is the computer beating a person at chess. The machine has a system of operations programmed into it that allows it to make the best decisions based on the position of the pieces on the board. The essence remains the same whether AI works on data analytics or anything else.
As you can see, this description is quite broad. It gets more specific in the next sections though. Machine learning is a subset of AI, while deep learning is, in turn, a subset of ML. As a result, we get a sort of Russian doll of artificial intelligence.
Machine learning (ML) is a dynamic form of AI that is able to modify itself when it has access to a wider range of data. It learns and gets progressively better. This results in a system that is more robust and adaptable than traditional AI solutions. In turn, this means that it provides you with more accurate information.
With this approach, systems aren’t required to be explicitly programmed by hand. It works through algorithms with a set objective function. The optimization process based on the likelihood of error and success is what is called “learning”. And yes, as you would imagine, it involves a lot of guesswork.
To continue with the tabletop games analogy, let’s look at a real historic experiment done by Arthur Samuel, the godfather of machine learning. He successfully taught a computer to beat him at checkers. The machine analyzed the moves that resulted in success and failure and eventually formed an algorithm that has beaten the state champion. It was one of the first examples of a self-taught machine, and that was all the way back in the middle of the previous century.
Since then, this technology has vastly improved, as one would hope. The range of its uses has grown far beyond checkers too. It benefits midsize companies and huge enterprises with its process automation, security, data analysis, optimization of sales, and even customer service solutions.
Even daily life is affected by machine learning. From predictive recommendations based on your browsing history to email filtering, and other ways to make people’s lives more convenient. And, of course, whether you like them or not, it’s essential for targeted ads.
Deep learning is a subset of machine learning that goes a step further. It has two subdivisions of its own: deep artificial neural networks and deep reinforcement learning. When discussing this branch of AI, most people think of the first one.
An artificial neural network is a multi-layered structure of algorithms that are designed to recognize patterns and make conclusions based on them. The composition is loosely inspired by the human brain. The data it processes may take numerous forms: from numbers and text to sounds, images, time intervals, and much more. The more data it has access to, the more patterns the AI can recognize. As a result, the system becomes increasingly more refined as it’s fueled with large amounts of information.
The less talked about deep reinforcement learning focuses on software to be able to take the best course of action to achieve its goals. In other words, it combines function approximation and target optimization. If tasked with maximizing the number of points achieved in a game, they can go from a blank slate to performance far beyond human ability.
The term reinforcement comes from the technique used to train pets by scolding them for failure and praising them for success. What’s also notable is that an RL machine can wait to achieve a greater reward, rather than getting an immediate smaller one. In other words, this approach focuses on maximizing the chances of success, rather than minimizing the chances of failure.
As far as board game examples go, Google’s AlphaGo has mastered the complex game called go. It is notoriously difficult to program a computer to play it adequately, because of the nature of its rules. There were unsuccessful attempts in the past but it was the introduction of deep learning that has finally resulted in victory.
There are several different labs currently pushing the boundaries of deep learning. Among them are DeepMind, Google Brain, OpenAI and many universities. With some of the brightest minds working on it, the future trajectory of AI’s development looks just as bright.
The public perception regarding this technology varies greatly. Some think it will maintain this pace and evolve into artificial general intelligence – sometimes called strong AI – that will save humanity from its mistakes and lead us towards a utopia. Others are convinced that it might just spell doom for us all. The reality will most likely be less radically black and white.
As we described it in our article on the technology trends of the near future, AI is one part of the so-called DARQ, which stands for a distributed ledger, artificial intelligence, extended reality, and quantum computing. What makes them connected is that development in one of these fields also propels and gives new opportunities for the growth of others. This means we will potentially see a rapid spike in the development and integration of these innovative fields in our daily lives fairly soon.
So as far as the future of AI goes, deep learning is not the end. The general AI we’ve mentioned a bit earlier seems to be the goal for now. It is a complete simulation of all cognitive functions a human brain is capable of and more. This type doesn’t exist yet. Chances are that AI technology won’t stop there, but that’s a topic for another day.
AI vs machine learning vs deep learning is not exactly a competition but rather multiple levels of the same wide branch of technology. All three elements are essentially a part of artificial intelligence with each area going deeper. Pun very much intended.
With each subsequent iteration, this technology simulates more complex aspects of the human brain. From analysis to learning, making conclusions, and recognizing patterns. This allows this technology to offer new solutions to businesses and individuals alike, and ultimately improve the quality of life.