Artificial Intelligence and Machine Learning Basics
During the past few years, the terms artificial intelligence and machine learning have begun showing up frequently in technology news and websites. Often the two are used as synonyms, but many experts argue that they have subtle but real differences.
And of course, the experts sometimes disagree among themselves about what those differences are.
In general, however, two things seem clear: first, the term artificial intelligence (AI) is older than the term machine learning (ML), and second, most people consider machine learning to be a subset of artificial intelligence.
Artificial Intelligence vs. Machine Learning
Though AI is defined in many ways, the most widely accepted definition of being “the field of computer science dedicated to solving cognitive problems commonly 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 an Artificial Intelligence based system is it’s model. A model is nothing but a program that improves its knowledge through a learning process by making observations about its environment. This type of learning-based model is grouped under supervised Learning. There are other models which come under the category of unsupervised learning Models.
The phrase “machine learning” also dates back to the middle of the last century. In 1959, Arthur Samuel defined ML as “the ability to learn without being explicitly programmed.” And he went on to create a computer checkers application that was one of the first programs that could learn from its own mistakes and improve its performance over time.
Like AI research, ML fell out of vogue for a long time, but it became popular again when the concept of data mining began to take off around the 1990s. Data mining uses algorithms to look for patterns in a given set of information. ML does the same thing, but then goes one step further – it changes its program’s behavior based on what it learns.
One application of ML that has become very popular recently is image recognition. These applications first must be trained – in other words, humans have to look at a bunch of pictures and tell the system what is in the picture. After thousands and thousands of repetitions, the software learns which patterns of pixels are generally associated with horses, dogs, cats, flowers, trees, houses, etc., and it can make a pretty good guess about the content of images.
Many web-based companies also use ML to power their recommendation engines. For example, when Facebook decides what to show in your newsfeed, when Amazon highlights products you might want to purchase and when Netflix suggests movies you might want to watch, all of those recommendations are on based predictions that arise from patterns in their existing data.
Artificial Intelligence and Machine Learning Frontiers: Deep Learning, Neural Nets, and Cognitive Computing
Of course, “ML” and “AI” aren’t the only terms associated with this field of computer science. IBM frequently uses the term “cognitive computing,” which is more or less synonymous with AI.
However, some of the other terms do have very unique meanings. For example, an artificial neural network or neural net is a system that has been designed to process information in ways that are similar to the ways biological brains work. Things can get confusing because neural nets tend to be particularly good at machine learning, so those two terms are sometimes conflated.
In addition, neural nets provide the foundation for deep learning, which is a particular kind of machine learning. Deep learning uses a certain set of machine learning algorithms that run in multiple layers. It is made possible, in part, by systems that use GPUs to process a whole lot of data at once.
If you’re confused by all these different terms, you’re not alone. Computer scientists continue to debate their exact definitions and probably will for some time to come. And as companies continue to pour money into artificial intelligence and machine learning research, it is likely that a few more terms will arise to add even more complexity to the issues.