The Philosophical Foundation And Interpretability Of The Research Paradigm Of Machine Learning

In the era of big data,human thinking cannot effectively reconstruct highly complex natural phenomena.Machine learning integrates the ideas of restoration through data mining,and then analyzes the information through inductive computational models,changing human understanding of the world.But in the development of machine learning,it is also accompanied by its philosophical thinking.Analyzing the research paradigms and interpretable issues of machine learning from the perspective of philosophical meaning can better guide human thinking on the development of artificial intelligence and computer science,which will make machine learning able to create greater value for humanity and society as well as promote the development of fields including philosophy,computer science,and cognitive science.On this basis,this paper has a preliminary understanding framework for machine learning,then starts from the case analysis and further explains the cognitive changes brought by machine learning,the development process of research paradigms,and interpretability under ethical guidelines,etc.This paper regards cognition as the concept of the pilot,on the one hand,focusing on the comparative development of the machine learning research paradigm,on the other hand,it is characterized by the modeled thinking on the issue of the ethical norms of technological phenomena.Based on the perspective of philosophy,the paper considers the changes brought by technological innovation and examines the realization of machine learning,explores different approaches on the process to the development prospects of machine learning.For this reason,this text carries the systematic exposition and the exploration on machine learning.The introduction begins from the research background of machine learning,summarizes the development application and classification of machine learning.The main body is divided into four parts.The first chapter mainly discusses the definition and development history of machine learning concepts,reveals the rise of machine learning by the method of time axis,and introduces the three categories of machine learning field.Chapter 2 revolves around the philosophical foundations of each of the five research paradigms in the process of machine learning development and the debates between them,exploring the links between the five paradigms of machine learning and their respective development approaches,and considering the future development of machine learning.Focusing on the interpretability of machine learning,the third chapter aims at the current lack of interpretability of machine learning and proposes ways to improve interpretability,including strengthening regulatory governance,establishing a legal accountability system,and increasing transparency.The European Union has taken the lead in the exploration of legislative practice for increasing interpretability.The conclusion part summarizes the paradigm basis of machine learning and looks forward to the future development trends and challenges of machine learning.