What is Machine learning? » AI Tutorial
Machine learning

What is Machine learning?

What is Machine learning?
Machine Learning triggers the technology craze worldwide for several years now. In scholarly boundaries, each year there are thousands of scientific articles on the subject. In the industry, from major companies such as Google, Facebook, Microsoft, the companies to start investing in machine learning. A series of applications that use machine learning is born on all aspects of life, from computer science to less relevant disciplines such as physics, chemistry, medicine, politics. Alphago, the computer with the ability to compute in a space with the number of elements in the more than the number of particles in the universe, more optimally than any other player, is one of the many great examples for the remarkable of machine learning compared With classic methods.
So what’s the matter, machine learning?
To introduce machine learning, I would like to rely on its relationship with the following three concepts:
1. Machine Learning and Artificial intelligence (the Artificial intelligence or AI)
2. Machine Learning and Big Data.
3. Machine learning and predicting the future.

Artificial intelligence, AI, a phrase that fits just as strange to us. Close because the world is playing fever with the technology labeled AI. Strange because a real one is still out of reach with ours. Speaking to anyone, each person will be linked to a different image. The recent few decades have a change in the appearance of one of the international films. Previously, film producers often put robot images into films (such as Terminator), aimed at the beginning of viewers thinking that artificial intelligence is a method of human mirror by machines. However, in more recent films on the subject, such as Transcendence by Johny Depp as the main role, we do not see the image of a robot. Instead of a giant brain computing command of the Wanson Nanobot, known as singularity. Of course both images are fictional and imaginary, but such changes also partially reflect the change of human opinions on who. Who is now viewed as invisible intangible, or in other words can carry any shape. Because talking about who is talking about a brain, rather than talking about a body, is software rather than hardware.

In the academic gender, according to General understanding, who is a science class that is born with the purpose of making the computer get intelligence. The goal is still quite vague because not everyone agrees with a unified definition of intelligence. Scientists must define some more specific goals, one of which is the work for computer fool to be the Turing test. Turing Test was created by Alan Turing (1912-1954), who is considered the father of modern computer science , in order to distinguish whether the opposite person is a person or not.

Who is showing a human goal. Machine Learning is a means of being expected to help humans achieve that goal. And in fact, machine learning has brought mankind far away on the way of conquering anyone. But there is still a far more distance than the need to go. Machine learning and who have a close relationship with each other but not quite a match as a target (AI), one side of the media (machine learning). Conquering anyone though remains the ultimate goal of machine learning, but currently machine learning focuses on short-term goals such as:
1. Make computers with basic human perception such as listening, seeing, understanding language, settlement, programming,… And

2. Assist humans in handling a massive amount of information that we face daily, or also called Big Data.
Big Data is not a mainstream science industry. It is a folk phrase and is about the media tossing corals to refer to the outbreak of the current data. It is also no different for phrases such as the “Industrial Revolution”, “software era “. Big Data is an essential consequence of getting more and more connected Internet. With the advent of social networks but Facebook, Instagram, Twitter, the demand for sharing human growth in a dizzy manner. Youtube can also be considered a social network where people share videos and comments about video content.

Booming information is not the only reason that leads to the advent of the Big Data phrase. Remember that Big data appeared new from the last few years but the volume of data accumulated since the Internet appeared at the end of the previous century nor small. But, then the man sits around a pile of data and does not know what to do with them beyond storage and copying. Until one day, scientists realized that in a pile of data actually contained a huge mass of knowledge. These knowledge can help us understand the human and society. From the list of an individual’s favorite films we can draw the person’s preferences and showcase the films who have never seen, but are consistent with hobbies. From our network Community search list We will know the hottest problem is being of interest and will focus more news on the issue. Big Data only really starts from when we understand the value of the information contained in the data, and there is enough resources as well as technology to be able to exploit them on a huge scale. And it’s no surprise that the learning machine is the key component of the technology. Here we have a relationship between machine learning and the Big data: machine learning more developed thanks to the increase of the data volume of the Big data; In contrast, the value of Big Data depends on the ability to exploit knowledge from machine learning data.


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