Application of Machine Learning in Artificial Intelligence
Artificial intelligence is known as one of the most exciting topics today. Simply it’s the automating capability of machines on decision making, reasoning and performing actions.
The extreme demand on improving accuracy and performance led toward the evolution of artificial intelligence. Currently, it is incorporated in every field of science and technology.
One of the key domains in Artificial Intelligence is Machine Learning. The next set of questions you may have is, what is Machine Learning? And How it really works? Do you really interest in Machine Learning? Keep on reading. You will find the answers through this article.
Machine learning is the process of creating computer programs(algorithms) that are capable of learning by example data sets and creates models to perform a certain task by machine itself. It is a field of computer science that evolved from studying pattern recognition and computational learning theory in artificial intelligence.[1]
Deep learning is a subset of machine learning where algorithms are created and function similar to those in machine learning, but there are numerous layers, attempt to imitate the function of the human neural networks of the brain.
Supervised Learning is a basic machine learning process which involves the learning process under human supervision. The two major categories of supervised learning are classification and regression. Classification leads to discrete/qualitative, whereas regression leads to continuous/quantitative targets. Supervised learning carries with a set of labeled data according to the human knowledge to identify the correlation between the input parameters. The advantage of supervised learning is, it requires minimum number of data sets compared to the unsupervised learning strategies to get the expected outcome. This Machine learning techniques is commonly used in business state predictions, whether forecasting predictions, clinical event predictions etc.
Unsupervised learning is a self-learning process of the machine without the intervention of human. The machine itself learn the correlations and patterns of the given input data in order to perform decision-making. The beauty of unsupervised learning is that, it can identify the hidden patterns of data which are difficult to identify with human cognition. It has the potential to unlock previously unsolvable problems and has gained a lot of traction in Artificial Intelligence. Those are widely applied in anomaly detection, fraud detection in communication networks etc.
Reinforcement Learning, is a machine learning paradigm for a sequential decision-making process under uncertainties [2]. Reinforcement algorithms usually get the optimal outcome through trial-and-error mechanism. Therefore, that approach can be applied in developing the intelligent Agents for game developing, path finding, obstacle avoidance etc.
Now, AI applications are more focused on Deep learning (DL) as a huge step forward for machine learning. It is based on the way the human brain process information and learns. There are numerous layers of these algorithms- each providing a different interpretation to the data it feeds on.
In deep learning, the term Artificial Neural Network (ANN) is used interchangeably with the following:
• net
• neural net
• model
If an ANN has more than one hidden layer, the ANN is said to be a deep ANN. Different layers perform different kinds of transformations on their inputs. Data flows through the network starting at the input layer and moving through the hidden layers until the output layer is reached. This is known as a forward pass through the network. Layers positioned between the input and output layers are known as hidden layers.
Consider the number of nodes contained in each type of layer:
• Input layer - One node for each component of the input data.
• Hidden layers - Arbitrarily chosen number of nodes for each hidden layer
• Output layer - One node for each of the possible desired outputs.
Deep learning is widely used in image and lexical analysis because the complex feature extraction and pattern recognition cannot be performed well with supervised learning process. But these techniques have been criticized because there is no way of representing causal relationships (such as automate reasoning programs and explaining how the result has been achieved). Moreover, it requires massive amount of data (approximately 1 million records) to complete a successful learning process.
The performance of the machine learning depends on two major facts. Data set and the algorithm which has been used. In pursuit of better predicting performance, configurations of machine learning tools should be adapted to the task with input data, which is often carried out manually and the best learning and boosting algorithms. Generally, the data sets are split as training and testing to test the performance of the trained model. Based on the result, model finetuning techniques are used to increase the accuracy of the models.
To conclude this article, I will use the following quotes:
“We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.” ~Amy Stapleton.
As the community we need to be prepared for the future and bear the responsibility of handling it with care, as intelligence is huge; so are the dangers.
References
1. N. Silaparasetty and N. Silaparasetty, “An Overview of Machine Learning,” Mach. Learn. Concepts with Python Jupyter Noteb. Environ., no. January, pp. 21–39, 2020, doi: 10.1007/978-1-4842-5967-2_2.
2. C. Liu, X. Xu and D. Hu, "Multiobjective Reinforcement Learning: A Comprehensive Overview," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 3, pp. 385-398, March 2015, doi: 10.1109/TSMC.2014.2358639.
3. Q. Yao et al., “Taking the human out of learning applications: A survey on automated machine learning,” arXiv, pp. 1–20, 2018.
0 Comments