What is the Difference between Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning are very hot recently. In 1997, DeepBlue of IBM defeated Gary Kasparov, the world champion of chess. At the moment, artificial intelligence started to gain attention from the public, while not much of them really understood the power of the engine (or AI). In 2016, AlphaGo of Google DeepMind defeated Lee Sedol, one of the best GO player in the world, by 4-1. After almost 20 years, the advanced AI technique once again showed its strength to the world by defeating a top human chess player. Many of those computer scientists are seriously thinking how to deploy such technique to other areas to help human make better decisions.

About Artificial Intelligence (AI)

 In 1950, a paper called “Computing Machinery and Intelligence”, published by Alan Turing, discussed a groundbreaking issue that whether machines can think. He also proposed the renowned “Turing Test”. The Turing test was designed to be a primitive way to determine whether or not a computer counts as “intelligent”, by asking a human judge to tell whether he was interacting with a human or a machine.

After a few years till 1956, John McCarthy organized an academic conference dedicated to the topic of AI. At the end of the conference, various participants recommended to further study "The conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." That was the first few clear definitions of artificial intelligence.

For the recent decade, the study of AI has been moved from the realms of science fiction to the realm of scientific fact. We have some great stories including IBM’s DeepBlue and Google DeepMind’s AlphaGo, which raised the public consciousness of artificial intelligence. One of the types of AI that we most often interact is machine learning.

About Machine Learning

The term “Machine Learning” also dates back to the middle of the last century. In 1959, Arthur Samuel defined machine learning as “the ability to learn without being explicitly programmed”. He then continued to develop a computer checkers application that was one of the first programs that could learn from its own mistakes and improve its performance over time.

One of the very popular applications of machine learning algorithm adoption is image recognition. These applications must be trained before use. Human has to prepare a set of images and assigned a label (category) to each of the image as the training dataset, after a certain rounds of training iteration, the application will then learn the patterns or characteristics of those images and be able to associate or classify them into different categories like flowers, trees, dogs, cats etc. With the technology nowadays, the result is very accurate.

Many companies are using machine learning to power up their recommendation engines. For example, Facebook shows you some relevant newsfeed, Amazon and TaoBao show you some products you may like, Netflix shows you some movies you may want to watch etc. These recommendations are based on machine learning to make predictions from patterns in the existing (historical) data.

As the nature of machine learning is related to statistics, data mining and predictive analytics, some of those computer scientists argue that it should be classified as a separate category from artificial intelligence. Applications can exhibit AI features like natural language processing or automated reasoning without having any machine learning elements, and machine learning engines do not necessarily need to have any AI features as well. However, machine learning has been part of the discussion of artificial intelligence form the very beginning, the two are often used and deployed together in various applications in the market today.

Conclusion

“Artificial Intelligence” and “Machine Learning” are two closely associated terms in the field of computer science. There are even a few more in the market, for example, IBM uses “Cognitive Computing” to describe its Watson engine, which is more or less an alias term of AI. While there are some terms with unique meanings. “Artificial Neural Network” (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes - or learns, in a sense - based on that input and output. Well, ANN is particularly good at machine learning…

There are no clear definition or specification on how to categorize those terms and fields. Computer scientists continue to debate the exact definition with their own point of view, and it probably takes some time to come to a consensus. No matter how the result is, the key is to select the right algorithm for your problem and to maximum the outcome for decision making.

Original Link: http://datasciguru.com/2017/03/24/what-is-the-difference-between-artificial-intelligence-and-machine-learning/