What are some of the reasons why machine learning is so important these days? We will introduce the reasons for this, based on the necessity and technical background.
Advances In Technology Prompted The Use Of AI
Research into AI has been going on since the 1950s. However, with the technology of the time, there was a limit to the performance of computers, and development did not progress as expected. In the 1980s, a new technology called an expert system was developed, and AI again attracted attention. However, an expert system must give the computer the knowledge that an expert has.
There are many items to consider in diagnosing a single disease, and a vast amount of knowledge and data on exceptions must be provided. Therefore, it lacked practicality, and the boom had passed for a while. After that, in the 2000s, it will attract attention again. The technological realisation of the machine learning approach has dramatically reduced the time and effort required for human input. It has made it possible for computers to learn independently, rapidly increasing development speed.
Technology Has Made It Possible To Process Huge Amounts Of Data
The use of machine learning has made it possible to process vast amounts of data. As a result, companies with massive amounts of data can benefit from streamlining data analysis and associated work and processing.
There have been advances in research that attempted to process vast amounts of data, but there have been problems with the technology currently being implemented. Today, advances in IT technology have improved the performance of servers and other devices, creating an environment that can process vast amounts of data.
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Improving Work Productivity And Efficiency
It can be said that there is an aspect that is expected to improve productivity behind the use of machine learning. For example, AI can be expected to improve the efficiency of operations that can be processed without human intervention, such as data entry and email processing, which have been performed manually until now, to reduce labour costs.
Currently, the automation and efficiency of operations using AI are progressing to the extent that some say AI will steal jobs.
Machine Learning Algorithm Operation
Machine learning is not limited to any industry and has already been applied in various fields. This section will introduce examples of how each technology is actually used.
Improved Prediction Accuracy
Machine learning is sometimes used for demand forecasting in marketing activities and analytical work. The use of machine learning has improved the accuracy of predictions and analyses. For example, you can automatically analyse by setting a decision tree algorithm. Also, if you increase the number of branch points in the decision tree, you will obtain more accurate information.
In machine learning, it is possible to quickly get more detailed results by setting the algorithm in detail at the beginning, which used to take time to enter manually.
Image Recognition Processing
Machine learning image processing recognition is used in various fields, such as detecting defective products in manufacturing lines, detecting stop lines from aerial photographs, analysing customer behaviour from video analysis, and detecting abnormalities from X-ray photographs at medical sites. It has been.
A large amount of image data is required for image recognition processing. A computer recognizes features from the image data and classifies them. The part is that the more significant the data, the more accurate image recognition becomes.
Speech recognition based on machine learning is used in customer support at call centres, creating minutes of meetings, data input using voice, and so on. Speech recognition also needs to learn much data, like image recognition processing. For example, when developing translation software, it is necessary to analyse the meaning of each word and the context before and after the word.
Even for the word “see” in one word, “see” means “seeing people walking around town,” “look” means “looking at him,” and “watch” means “watching TV.” If you want to memorise only the words, you can input them one by one, but to accurately grasp the sentences, you need to learn a lot, and for that, you need machine learning.
By using machine learning, it is possible to analyse a more significant amount of data than before. By using the results of data analysis by machine learning, it is possible to predict vehicle allocation in the taxi industry, for example.
Until now, drivers have independently expected taxi passengers based on their familiarity with the area and experience. However, by inputting data such as past sales, occupancy rate, boarding location, travel range, etc., we can find places with high occupancy rates and optimal travel routes.
In addition, it is also used in agriculture. It uses meteorological, environmental, growth, etc., to predict harvest times and yields. If you can make a prediction, you can smoothly secure the necessary personnel and distribution destinations and eliminate unnecessary costs. It is also possible to take measures to maximise profits by learning from data.