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Difference Between Artificial Intelligence and Machine Learning

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In researching what is the difference between Artificial Intelligence and Machine Learning, there was an interesting article by Forbes and one by AdWeek. Great, credible sources. One said Machine Learning (ML) was part of generalized Artificial Intelligence (AI) and one said ML was part of applied AI. Both agreed there were two types of AI, generalized and applied, but couldn’t clearly define them well enough to explain which part ML came from. Confusing. When in doubt on data science related issues, visit Quora.com.

Quora.com is a data science community that is a question and answer site. Real people who use these applications day in and day out. A number of users were much clearer on how to define these hot terms.

AI is the capability of a machine to imitate intelligent human behavior. ML and NLP are subfields of AI, and really tools on the path to realize full Artificial Intelligence for machines.

Machine Learning uses a vast array of algorithms to iteratively automate the process of learning. It is a process of making computers learn by using observations, data, and or past experience. Resulting models can be predictive or descriptive. The development of neural networks paved the way for the utilization of machine learning.

Natural Language Processing (NLP) is about understanding the structure and meaning of language as used by humans, translating it into a machine and processing and generating language back. It bridges the gap between human talk and computer programmed understanding. Machine Learning is used within NLP.

Deep Learning, another term that comes up when talking about ML and AI, is a subset of machine learning to simulate human-like decision making. Ugh. Isn’t that AI? It isn’t clear how Deep Learning differs from AI, but the IBM Director of Product Strategy says this about Watson to describe the difference between deep learning and machine learning, “ When people ask how Watson is different than a search engine, I tell them to go on Google and type ‘anything that’s not an elephant.’ What do you get? Tons of pictures of elephants. But Watson knows those subtle differences. It understands that when feet and noses run, those are very different things.”

Two breakthroughs led to the emergence of Machine Learning:

1. In 1959 Arthur Samuel of IBM wrote the first game playing program for checkers and showed that rather than teaching computers everything they need to know, it might be possible to teach them to learn for themselves. The concept of intelligent machines can be traced back to Greek mythology but Samuel’s work seemed to kick off more active investigation.
2. The emergence of the internet and the huge increase in the amount of digital information available for analysis, and the need to find a way to process this data in an cost and time effective way.

It is an exciting time to be part of the statistical and computer science world. Great advancements and contributions are being made. Do you want to be a part of it? Contact the Data Science and Analytic Recruiters at Smith Hanley Associates.

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