Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward Humanities and Social Sciences Communications
While AI refers to the general attempt to create machines capable of human-like cognitive abilities, machine learning specifically refers to the use of algorithms and data sets to do so. Machine learning is a method of data machine learning importance analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
The strength of ML resides in its capacity to learn from data without need to be explicitly programmed (Samuel, 1959); ML algorithms are autonomous and self-sufficient when performing their learning function. Further to this, ML implementations in data science and other applied fields are conceptualised in the context of a final decision-making application, hence their prominence. In the data mining literature, many association rule learning methods have been proposed, such as logic dependent , frequent pattern based [8, 49, 68], and tree-based . An ANN is a model based on a collection of connected units or nodes called “artificial neurons”, which loosely model the neurons in a biological brain.
Importance of Machine Learning
Innovation—in applying ML or just about any other endeavor—requires experimentation. When researchers experiment, they have protocols in place to ensure that experiments can be reproduced and interpreted, and that failures can be explained. Yet, as supply chains become increasingly more complex and globally interconnected, so too does the number of potential hiccups, stalls, and breakdowns they face. To ensure speedy deliveries, supply chain managers and analysts are increasingly turning to AI-enhanced digital supply chains capable of tracking shipments, forecasting delays, and problem-solving on the fly.
Much like how a child learns, the algorithm slowly begins to acquire an understanding of its environment and begins to optimize actions to achieve particular outcomes. For instance, an algorithm may be optimized by playing successive games of chess, which allow it to learn from its past success and failures playing each game. Supervised machine learning is often used to create machine learning models used for prediction and classification purposes. Experiment at scale to deploy optimized learning models within IBM Watson Studio. Classical, or “non-deep”, machine learning is more dependent on human intervention to learn.
A comparison of machine learning algorithms in predicting COVID-19 prognostics
Thus, selecting a proper learning algorithm that is suitable for the target application in a particular domain is challenging. The reason is that the purpose of different learning algorithms is different, even the outcome of different learning algorithms in a similar category may vary depending on the data characteristics . In this paper, we have conducted a comprehensive overview of machine learning algorithms for intelligent data analysis and applications.