Applications of Machine Learning in Artificial Intelligence
Introduction
Machine learning is a sub set of artificial intelligence. Artificial Intelligence is defined as that “Artificial Intelligence which enables machine to imitate human behaviour”. Although the concept of Artificial Intelligence became in very past, the concept becomes very popular today because earlier we had low amount of data and the amount of data was not enough to predict the accurate result. But today there is the tremendous amount of data and we have advanced algorithm with high end computing power and storage that can deal with such large amount data as a result.
“Machine learning is a subset of Artificial Intelligence which use statistical methods to enable machines to improve with experience or act and make data driven decisions to carry out a certain tasks.” These programs are algorithms are designed in a way that they can learn and improve over time when expressed to new data. Relationship between machine learning and artificial intelligence can be shown as below diagram.
Machine learning in Artificial Intelligence
The Machine Learning has a significant role in Artificial Intelligence researches. Machine Learning has been spread through various kinds of Artificial intelligence branches such as computer vision, intelligent robots, expert systems, automated reasoning, natural language, understanding and so on. As well as there are some specific fields of application such as the search engine, stock market analysis, medical diagnosis and so on.
There are several applications of machine learning. The learning method on learning strategy can be explained as below. The environment and the learning compose the learning system.
Learning strategies is on the basis of the classification criteria for converting electronic message students to achieve the necessary degree of difficulty reasoning and how to classify and follow simple to complex, the small to multi-order divided into the following basics.
Rote learning
This is the most simple machine learning method. It is the memory. That is the new knowledge is stored, the supply and demand wants when retrieves transfers, but doesn’t need to calculate and the inference.
Explanation- based learning
According to the operational guidelines and teacher the domain theory, concept examples and the goal concept are provided.
Learning from instruction
The students gains the information from the environment, transforms the knowledge into the expression form.
Learning by deduction
After the logical transformation the reasoning embarks from the axioms and infers the conclusion.
Learning by analogy
Analogy is one kind of effective inference method which can be used to describe the similarities between objects.
Inductive learning
Inductive learning can be highlighted as the most effective symbol of machine learning method.
There are three main aims of machine learning researches such as general learning algorithm, the cognitive model and the goal of the project. In general learning algorithm, there is no limit and algorithm type is not necessarily to similar method in human. In cognitive model direction is a studying human’s learning computation theory. The goal of the project is used to solve the special actual problem and to develop project system.
Applications of the Machine Learning in Artificial Intelligence
Some of the examples in the machine learning in artificial intelligence from our day to day life can be mentioned as Tesla self-driving cars’ applications, email spam, malware filtering and online fraud detection, Apple series playing computers, traffic predictions, virtual personal assistants and speech recognition. These all examples are also based on deep learning and natural processing.
Some applications can be described as below.
1. Tesla self-driving cars’ applications
At the driving mode of such kind of cars it can detect all the objects and humans nearby car by using the device which applicate machine learning of artificial intelligence.
2. Email spam, malware filtering and online fraud detection
To ensure the updates, receive mails and to make sure the online transactions by detecting fraud transaction machine learning is playing a major role. Machine learning helps to detect the fake account, fake identities and steel money. As well as if new mails are received then it is filtered by the machine learning application into three main categories such as normal mails, important mails and spam mails.
3. Traffic prediction
With the help of the google map concept we are able to travel anywhere searching the most suitable route for our travel. For it google map predict traffic condition. To do it uses two main prediction readings as the real time location and the average time has taken on past.
4. Speech recognition
Speech recognition is also an application of machine learning. Search by voice is an option for searching in the google search engine which is a popular application of machine learning.
5. Virtual personal assistants
According to the voice instructions we can find out the information what does we want to search such as google assistant, Alexa, Siri and so on. This is also an application of the machine learning in Artificial Intelligence.
Conclusion
Artificial Intelligence raises Machine Learning into the intelligent level with too many applications. Continuously the applications of Machine Learning are growing close to humanity’s intelligent level.
References
M. Xue and C. Zhu, "A Study and Application on Machine Learning of Artificial Intellligence," 2009 International Joint Conference on Artificial Intelligence, Hainan, China, 2009, pp. 272-274, doi: 10.1109/JCAI.2009.55.
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