Rise Of Machine Learning

Symbolic approaches and AI planning do great work for applications that have a limited number of matching patterns; For example, a program that helps you complete your tax return. IRS provides a collection of rules for reporting a limited number of forms and tax-related data. Mix the form and instructions with the ability to crunch numbers and some approval logic, and you have a tax program that can carry forward you through the process. For example, if you have earned money from an employer, you complete the W-2 form. If you have earned money as the only owner, you complete the schedule C. The limit of this approach is that the database is difficult to manage, especially when the rules and patterns change. For example, malware (viruses, spyware, computer worm and similar) develop very fast to manually update their databases for anti-malware companies. Similarly, digital personal assistants such as Siri and Alexa need to be consistently favorable for the unfamiliar requests of their owners.

To cross these boundaries, beginner AI researchers began to surprise whether computers could be programmed to learn new patterns. His curiosity gave rise to machine learning – the science of motivating computers to do those things that they were not specially programmed to. Machine learning began soon after the first AI conference. In 1959, AI researcher Arthur Samuel created a program that could play checkers. This program was different. It was designed to play against itself to learn how to improve. It learned new strategies from every game played by him and after a short time he started defeating his own programmer continuously. A major advantage of machine learning is that it does not require a specialist to create symbolic pattern and list all possible reactions of any question or statement.

Imagine that machine learning is applied in the use of the Chinese room. The computer will inspect the exchange of notes between the person present outside its and the room. After checking thousands of exchanges, computer identifies a pattern of communication and adds general words and phrases to its database. Now, it can understand the notes obtained using the collection of its words and phrases more quickly and collect the rapid response using these words and phrases instead of collecting the reaction from the collection of characters. It can also create your own dictionary based on these matching patterns, so it receives complete response to some notes it receives. Machine learning is still qualified as weak AI, because the computer does not understand what is being said; It only matches symbols and identifies the pattern. The big difference is that instead of an expert to provide patterns, the pattern is identified in computer data. Over time, the computer becomes “smart”.

Machine learning has become mainly one of the fastest growing areas in AI as the cost of data storage and processing has declined dramatically. We are currently in the era of data science and large data – very large data sets that can be manifested by computer analysis by computer analysis.

Organizations are collecting very large amounts of data. The biggest challenge is to find out what to do all this data. The answer to that challenge is machine learning, which can identify the pattern even when you really don’t know what you are looking for. In a way, the machine makes the learning computer capable of finding out what is inside your data and tells you what he has found. The machine learning overtakes boundaries with symbolic systems. Instead of remembering symbols, a computer system uses machine learning algorithms to make models of abstract concepts. It detects the statistical pattern using a machine learning algorithm on very large amounts of data.

So a machine learning algorithm sees eight photos of different dogs. It then breaks these pictures into different points or pixels. It then sees these pixels to detect patterns. It may be that it looks at the hair pattern of all these animals. Maybe it can see a pattern for the nose or ear. It can also see a pattern that humans do not understand. Collectively, patterns create the statistical expression of “dogma”.

Sometimes humans can help in learning machines. We can give millions of pictures to the machine in which we have already included dogs, so the machine does not have to worry about excluding images of cats, horses or plane. This is called supervised learning, and data, which includes the “dog” labels and millions of pictures of dogs, is called training set. Using the training set, a human being is teaching the machine that all the patterns he identified has the characteristics of “dogs”. Machines can also learn completely. We simply put a huge amount of data in the machine and let it find their patterns. This is called untrained learning. Imagine that a machine is checking all the pictures of people on your smart phone. He may not know that she is your husband, wife, lover or girlfriend. But it can create groups of people who find him closest to you.

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