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Artificial Intelligence and Machine Learning

 

Through the past few years, the terms artificial intelligence and machine learning have begun showing up often in technology news and websites. Typically the two are used as synonyms, but many consultants argue that they have subtle however real differences.

 

 

And of course, the specialists typically disagree among themselves about what these differences are.

 

 

Usually, however, two things appear 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 lots of ways, essentially the most widely accepted definition being "the field of computer science dedicated to fixing cognitive problems commonly related with human intelligence, similar to learning, problem fixing, and pattern recognition", in essence, it is the concept machines can possess intelligence.

 

 

The center of an Artificial Intelligence based system is it's model. A model will not behing but a program that improves its knowledge by way of a learning process by making observations about its environment. This type of learning-based model is grouped under supervised Learning. There are different models which come under the class of unsupervised learning Models.

 

 

The phrase "machine learning" additionally dates back to the middle of the last century. In 1959, Arthur Samuel defined ML as "the ability to be taught without being explicitly programmed." And he went on to create a computer checkers application that was one of the first programs that would be taught from its own mistakes and improve its performance over time.

 

 

Like AI research, ML fell out of vogue for a very long time, but it turned widespread again when the concept of data mining began to take off across the 1990s. Data mining makes use of 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 conduct based on what it learns.

 

 

One application of ML that has turn into highly regarded lately is image recognition. These applications first should be trained - in other words, humans should look at a bunch of pictures and inform the system what is in the picture. After thousands and thousands of repetitions, the software learns which patterns of pixels are generally related with horses, canines, cats, flowers, trees, houses, etc., and it can make a reasonably good guess about the content material of images.

 

 

Many web-based corporations additionally use ML to power their suggestion engines. For example, when Facebook decides what to show in your newsfeed, when Amazon highlights products you may want to buy and when Netflix suggests movies you might want to watch, all of those suggestions are on based mostly predictions that arise from patterns in their present data.

 

 

Artificial Intelligence and Machine Learning Frontiers: Deep Learning, Neural Nets, and Cognitive Computing

 

 

Of course, "ML" and "AI" aren't the only phrases related with this field of pc science. IBM frequently makes use of the time period "cognitive computing," which is more or less synonymous with AI.

 

 

However, among the different terms do have very distinctive meanings. For example, an artificial neural network or neural net is a system that has been designed to process information in ways which might be just like the ways organic brains work. Things can get complicated because neural nets are typically particularly good at machine learning, so these phrases are sometimes conflated.

 

 

In addition, neural nets provide the foundation for deep learning, which is a particular kind of machine learning. Deep learning makes use of a certain set of machine learning algorithms that run in a number of layers. It is made possible, in part, by systems that use GPUs to process an entire lot of data at once.

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