introduction
In recent years, the terms artificial intelligence and machine learning have popped up frequently in Technology news and on websites. Often the two are used as synonyms, but many experts argue that they have subtle but real differences.
And, of course, experts sometimes disagree about what those differences are.
In general, however, two things seem clear: first, the term artificial intelligence (AI) predates the term machine learning (ML), and second, most people consider machine learning to be a subset of artificial intelligence.
Artificial Intelligence vs. Machine Learning
Although AI is defined in many ways, the most common definition is “the branch of computer science devoted to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition,” essentially it is Idea that machines can have intelligence.
The heart of a system based on artificial intelligence is its model. A model is nothing more than a program that improves its knowledge through a learning process by making observations about its environment. This type of learning-based model is summarized under supervised learning. There are other models that fall under the category of unsupervised learning models.
The term “machine learning” also dates back to the middle of the last century. 1959, ArthurSamuel defined ML as “the ability to learn without being explicitly programmed”. And he went on to create a computer checker 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 fashion for a long time, but became popular again when the concept of data mining emerged 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 the behavior of its program based on what it learns.
One recent very popular application of ML is image recognition. These applications have to be trained – that is, people have to look at a bunch of images and tell the system what’s in the image. After thousands upon thousands of iterations, the software learns which bitmaps are commonly associated with horses, dogs, cats, flowers, trees, houses, etc., and is pretty good at estimating 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 buy, and when Netflix suggests movies you might want to watch, all of those recommendations are based on predictions derived from patterns in their existing ones data result.
Frontiers of Artificial Intelligence and Machine Learning: Deep Learning, Neural Networks and Cognitive Computing
Of course, “ML” and “AI” are not the only terms associated with this area of computer science. IBM often uses the term “cognitive computing”, which is more or less synonymous with AI.
However, some of the other terms have very unique meanings. For example, an artificial neural network, or neural network, is a system designed to process information in a way similar to how biological brains work. Things can get confusing as neural networks tend to be particularly good at machine learning, so these two terms are sometimes confused.
In addition, neural networks form the basis for deep learning, a special form of machine learning. Deep learning uses a specific set of machine learning algorithms that run in multiple layers. This is made possible in part by systems that use GPUs to process a whole lot of data at once.
If you are confused by all these different terms, you are not alone. Computer scientists continue to debate their exact definitions and likely will for some time to come. And as companies continue to pour money into artificial intelligence and machine learning research, a few more terms are likely to emerge that add complexity to the issues.
artificial intelligence
