What is the Difference Between Machine Learning and AI?

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What is the Difference Between Machine Learning and AI?

It is not the same, but the idea that sometimes it can lead to confusion. Therefore I think it will be a written assessment to explain the difference.

The two conditions often appear when this theme is “Big Data”, analyzes and extensive widespread use of technology in our world.

In the Early days

Artificial intelligence has long been: Greek intellectuals are involved in stories about artists that are designed to handle our behaviour. The first European computers were conceived as “logic machines” and play capabilities such as basic mathematicians and memory, engineers who saw their work first before attempting to create a technical brain.

As technology and, most importantly, our understanding of how our minds are spread, the concept of what our AI has been changed. Instead of increasingly stronger mathematicians, the field of intelligence is focused on taking on the process of human rights decisions and carrying out the process of human trafficking.

This natural scheme (tools designed to make it reasonable) is often described as one of the two basic groups: applied or general. AI applied a lot of common: Systems designed to deal with actions and actions in a rational way or to drive an independent car that would fall into this category.

General AI (the process or vision can do any work) is small, but this is where some of the most joyful developments today. It is also the place where Machine Learning develops. It is often referred to as the AI, the actual fact is better than the current state of the art.

The Rise of Machine Learning

Two significant improvements have led to the Machine Learning development of vehicles promoting the development of AI with the current speed.

One of these was the performance, which was recognized by Arthur Samuel in 1959, rather than a computer science program that they needed to know about the world and how to carry out activities, it is possible to teach them to learn themselves.

Secondly, recently, it was the emergence of the Internet and the huge increase in the amount of digital data created, stored and prepared for analysis.

When the new designs are available, the engineer realized that instead of computers and machines for doing everything, it would be very good and they voted to think about human beings, and then connect to the internet to provide them access to all World information.

Neural networks

The development of neural networks was key to teaching computers to think and understand the world in the way we do it, with the basic benefits that we have, such as speed, accuracy and uncertainty.

The Neural Network is a computer system designed to work by sorting information as well as a human brain. It can be learned to identify, for example, images separated according to their contents.

Basically, it works in a possible way: based on the submitted data, it can make a confession, decide or predict the degree of theoretical. The inclusion of a response circle allows “learning”: when you know or find information about whether your decision is correct or wrong, you will change the way you take the future.

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Machine Learning applications can read and decide if the person writes to file a complaint or greetings issue. They can also listen to a piece of music, decide whether or not someone is likely to create happiness or sadness and get other musical expressions that are moody. In some cases, they can even make their music on the same topics, or find out they are probably more likely to appreciate the supporters of the original sector.

These are all opportunities provided by ML-based systems and neural networks. Thank you for a great deal about science festivals, there was also the idea that we are able to communicate with other electronic devices and digital information, as natural as we can with other people. On the other hand, the other AI – Processing Language Processing (NLP) – has become a new and exciting new source in recent years, depending on most MLs.

Applications NLP try to understand the communication of the natural language, whether written or oral, and we communicate through shared use of the language of natural ML is used here to looga help machines to understand the essence of a wide range of human language, and to learn how looga answer to the listener to understand.

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A Case of Branding?

Artificial intelligence – especially today, in fact, ML has a lot to offer. With the promise of automation of ordinary jobs, as well as providing an innovative vision, industry from all sectors, from bank to health and production, eliminates the benefits. Therefore, it’s important to keep in mind that AI and ML are something else … they are selling products, consistently and profitable.

Machine Learning is definitely used to sell. After the confidentiality of the invention for a long time, it may begin to look like one of the “old helmets” even before it is impossible. There was a fraudulent argument about the “revolution of the AI”, and the Machine Learning community really gives a new, bright, and more firm, and firmly focused on here and now.

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The fact that we will ultimately develop AI like the human being is often treated with the care of the technicians. In fact, today we are almost always here and we are doing the same with the speed. Most of the exciting development we have seen in recent years is due to the fundamental change in the way we look forward to the AI work, which has produced ML.

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By | 2018-12-05T20:20:00+00:00 December 5th, 2018|Data Science|0 Comments

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