Since the emergence of artificial intelligence in the first half of the twentieth century as a utopian conception of technology, it has evolved unthinkably. Currently, differentiating ideas and concepts have appeared to define not only artificial intelligence, but also specific concepts such as Machine Learning and Deep Learning, as they are based on the processing of information in large quantities, better known as Big Data. As an introduction, we could say that Artificial Intelligence is the most basic concept in the data process, passing to Machine Learning being an intermediate concept, which describes us as the possibility that the machines have a practically automatic learning. And finally Deep Learming or deep learning, with the difference of not only learning through information, but making decisions from them. In this opportunity we will see each one of them.
The initial investigation of Artificial Intelligence began in the 1950s exploring issues such as problem solving and symbolic methods. This initial work paved the way for automation and formal reasoning we see today in computers, including decision support systems and intelligent search systems that can be designed to complement and enhance human capabilities.
Within the industry, artificial intelligence is taking great importance, such as automating repetitive learning and discovery through data, adding intelligence to existing products, adapting through progressive learning algorithms and achieving incredible accuracy when analyzing data and deeper data.
This technology has become popular since the 1980s until this last decade. It was born from pattern recognition and from the theory that says computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to know if computers could learn from data.
Learning in this context means identifying complex patterns in millions of data. The machine that really learns is an algorithm that reviews the data and is able to predict future behaviors. Automatically, also in this context, it implies that these systems are improved autonomously over time, without human intervention. All of these things mean that it is possible to produce models quickly and automatically that can analyze larger and more complex data and produce faster and more accurate results – even on a very large scale. And with the construction of accurate models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
Deep Learning takes basic concepts of Artificial Intelligence and focuses them on solving real-world problems from deep neural networks that mimic the way our brain makes decisions. That is, it uses the data it knows to make decisions about new data. That is why it is the technology most similar to human brain functioning.
These networks consisting of logical instructions, the product of asking binary questions (true or false, yes or no) from which a numerical value is extracted; every time you incorporate a new data, you transfer it to that neural network, and classify it according to the answer to those questions, this classification allows you to process huge amounts of complex data.
In summary, Artificial Intelligence gave rise to and it could be said that it encompasses Machine Learning technology such as Deep Learning, coinciding in the search to mimic the way the human brain learns. Being its main difference, the type of algorithms that are used in each case, although Deep Learning is more like human learning because of its functioning as neurons, Machine Learning usually uses decision trees and Deep Learning neural networks, which are more evolved In addition, both can learn supervised or unsupervised.