The words artificial intelligence and machine learning have started to appear regularly in news and blogs about technology over the past few years. The two are sometimes used as synonyms, but other experts believe that they have slight but significant distinctions.
And, of course, even the experts disagree among themselves about what those disagreements are.
In general, though, two things seem clear: first, the term artificial intelligence (AI) is older than the term machine learning (ML); and second, most people find that machine learning is a subset of artificial intelligence.
Artificial Intelligence vs. Machine Learning
While AI is characterized in many ways, the most generally accepted concept is “the computer science area dedicated to solving cognitive issues typically associated with human intelligence, such as learning, problem solving, and pattern recognition,” in essence, it is the idea that machines can possess intelligence.
The heart of a program focused on Artificial Intelligence is it model. A model is nothing more than a program which improves its knowledge by making observations about its environment through a learning process. A type of model based on learning is grouped under Supervised Learning. There are other examples in the unsupervised learning software category.
Even dating back to the middle of the last century, the term “machine learning” In 1959, Arthur Samuel described ML as “the ability to learn without explicit programming,” and he went on to develop an application for computer checkers which was one of the first programs that could learn from their own mistakes and improve their performance over time.
ML fell out of vogue for a long time, like AI science, but it became popular again when the data mining idea started to take off around the 1990s. Data mining uses algorithms in a specified collection of knowledge to check for patterns. ML does the same thing, but then goes one step further-it modifies the actions of the software depending on what it knows.
One recent application of ML which has become very common is image recognition. Such systems must first be educated-that is, human beings need to look at a bunch of pictures and tell the program what’s in the picture. After thousands of repetitions the algorithm learns what pixel patterns are commonly associated with horses, dogs, cats, flowers, trees, buildings, etc., and it can make a pretty good guess about the content of images.
Most web based companies are now using ML to drive their recommendation engines. Of instance, when Facebook decides what to include in your newsfeed, when Amazon highlights items you may want to purchase, and when Netflix recommends movies you may want to watch, all of those suggestions are based on assumptions that come from trends in their current data.
Artificial Intelligence and Machine Learning Frontiers: Deep Learning, Neural Nets, and Cognitive Computing
“ML” and “AI” are of course not the only concepts associated with this field of computer science. The term “cognitive computing,” which is more or less synonymous with AI, is also used by IBM.
Many of the other words, however, have very special significances. For example, an artificial neural network or neural network is a device designed to process information in ways similar to the manner in which biological brains function. Things may get complicated because in machine learning, neural networks tend to be particularly strong, and these two concepts are often conflated.
Furthermore, neural nets provide the basis for profound learning, which is a particular form of machine learning. Deep learning uses a collection of multi-layered machine learning algorithms. In addition, devices that use GPUs make it possible to process a whole lot of data at once.
If all these various words annoy you, then you’re not alone. Computer scientists are still discussing their exact concepts, and are likely to come for some time. And as businesses start to pour money into artificial intelligence and machine learning studies, a few more concepts are likely to arise to bring even more complexity to the problems.
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