Role of Machine Learning in Artificial Intelligence
In the modern world, technology plays a vital role in diverse areas like education, health, transportation, finance, agriculture and constructions. In order to reach the peak of these dominant sectors, technological concepts like artificial intelligence laid out the basement. Since the brain whispers “What is Artificial Intelligence?”. I’ll go ahead with a small briefing about artificial intelligence and machine learning. Artificial Intelligence simply is the process of imitating the cognitive functions of humans. It is a broad area composed of several sub-sectors like machine learning. Machine learning is not as complex as AI but it is a subset that relies on algorithms and is trained through the experiences automatically. Machine learning basically aims in developing software programs that can train from data and use it for future proceedings.
The machine can be learnt through past experiences and input data records and can be used in future for decision making through the patterns generated by the machine itself. The ultimate goal of this is to make the computers (machines) learn by themselves without any intervention or guidance from mankind. Machine learning algorithms can be categorized into 4 main categories as supervised, unsupervised, semi-supervised and reinforcement.
Supervised machine learning algorithm is the way of learning through past experience, training dataset labelled data to predict results of instances. After adequate training, the algorithm can result in more accurate outputs. Then we can generate the errors caused due to predicted outputs and actual outputs in order to calculate the model accuracy. This is quite a contrast with unsupervised machine learning algorithms because data used for training are not labelled or classified. Unsupervised learning basically describes the hidden patterns and relationships from unlabeled data. Semi-supervised machine learning algorithms acquire a hybrid nature from both supervised and unsupervised learning. They carried out the training process with both labelled and unlabeled data. This approach improves learning accuracy to a considerable extent. Reinforcement machine learning algorithms is another ML learning approach that deals with its associated environment by finding plus and minus points. Delayed reward and trial and error are the most specific features of reinforcement learning.
The most beneficial feature of machine learning is that it can handle massive amounts of data and generate accurate results in a fast manner to identify advantageous opportunities or disadvantageous occurrences, though it requires resources and time to train it effectively.
Machine learning can be applied to many manual processes, industrial approaches, security, the health sector, and education in order to implement AI-based results generation. As an example, it can be elaborated like we can apply Machine learning techniques for eLearning to get maximum benefits from study materials in a personalized format to each and every individual. Basically, students have different capacity levels so if a system can specifically concern each and recommend the approach of studying, then it would be a massive achievement of AI-related to education.
In addition to it, most of the companies are now looking into Machine Learning approaches to set up their future goals and plans through past experience. Once they are deployed, it will be beneficial to both customers and companies. ML related techniques can be used in prediction, classification, decision support systems, clustering, and product anomaly detection for the companies. Further machine learning can be used for data security purposes as well. ML-based data security tools have the capability to reduce the risks and overcome cyber-attacks to data security. Since these kinds of systems are trained with experience, they adapt themselves through automation algorithms and detect vulnerabilities and convey them to the responsible authorities.
These are some of the examples that elaborated the contribution of Machine learning in the artificial intelligence sector. Since this is a vast area, we can think broadly about this topic but in this context, I try to give a basic idea about machine learning with its example applications.
