Thanks to the efforts of technology giants such as Google, Amazon and Facebook, the two sci-tech terms of artificial intelligence and machine learning are now widely circulated. They are often used interchangeably, and many people use these terms to describe various smart appliances and replace human work robots. However, although artificial intelligence and machine learning are highly correlated, they are not the same.
Artificial intelligence is a branch of computer science that aims to develop a machine with intelligent behavior. Stanford University defines machine learning as: "Science that allows computers to take action without explicit programming instructions." Machines need the help of artificial intelligence researchers. But to make it truly intelligent, you need to hire a machine learning expert.
Large technology companies such as Google and Nvidia are currently working to develop such machine learning technologies. They are all trying to get computers to learn human behavior patterns in order to push the next technological revolution in the eyes of many people – let the machine “think†like humans.
In the past 10 years, machine learning has brought us driverless cars, practical speech recognition, effective web search, and a deeper understanding of the human genome. But how does it work?
Give a simple example. If you misspelled a word while using Google search, you'll see a prompt: "Do you mean...?" This is the result of Google Machine Learning algorithm generation. This system can determine your search intent within a few seconds after you enter a specific keyword.
Micro-neural computer
For example, if you want to search for "WIRED" but accidentally spell it "Wored", the system will probably find that you have entered the wrong word and returned the correct result after a few seconds: "WIRED". Google's algorithm will recognize your search intent a few seconds after you send out a search request, and keep this information in mind, in case the user makes a similar mistake later. In this way, Google "learned" to correct your mistakes.
While this is a very simple example, data scientists, developers, and researchers are using more sophisticated machine learning techniques to explore areas that were previously unattainable. Programs that can draw lessons can help people study how the human genome works, and understand consumer behaviors at an unprecedented level, and even develop practical systems for buying recommendations, image recognition, and fraud warnings.
Now that you have an understanding of the basic concepts of machine learning, what is the difference between it and artificial intelligence? We started a conversation with Intel's machine learning director, Nidhi Chappel, to clarify the issue.
"The essence of artificial intelligence lies in intelligence - how to give intelligence to machines. Machine learning is the calculation method that supports artificial intelligence. My idea is: artificial intelligence is science, and machine learning is an algorithm that makes machines more intelligent. â€
“That is, machine learning has made artificial intelligence,†she added.
Chapel then explained that machine learning is the fastest growing part of artificial intelligence, so we have only recently seen many discussions related to it. Although it is only a small percentage of today's total calculations, it is indeed the fastest growing area, and as such, all companies are working hard to develop this technology.
“For a simple example, when you come to a new place and search the Internet for the 'most important thing to do', the order of the content you see is arranged by machine learning. Their arrangement and scoring methods. Both use machine learning techniques," Chapel said. He added that the same approach would be used when ranking popular news.
“Artificial intelligence has been widely infiltrated into our lives, but we have not realized that it has been applied in many fields,†she added. “You may use this technology dozens of times a day, but it’s not feel."
However, in order to promote the advancement of artificial intelligence, machine learning must achieve a huge leap in performance, which is difficult to achieve in the traditional high-performance computing field, because in these areas, the problem has been clarified, and optimization work has been carried out for many years.
There is still room for improvement in machine learning algorithms, which is why many large technology companies are scrambling to use them as their core strategy. These companies are sparing no effort to upgrade the machine's intelligence, hoping to advance technology and achieve the next phase of innovation, including fully automated and 100% safe driverless cars.
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