A Declarative Systematic Approach to Machine Learning

In the last 20 years, artificial intelligence (AI) (ML) has naturally evolved from a research project to an innovation that is now used in almost every element of computing. Today, ML-based components are integrated into every aspect of our modern life, from making suggestions about what to look at...

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Bibliographic Details
Published in2022 International Conference on Smart and Sustainable Technologies in Energy and Power Sectors (SSTEPS) pp. 95 - 99
Main Authors Babu, G. Ramesh, Phaneendra Varma, Ch, Sree, Pokkuluri Kiran, Sai Chaitanya Kumar, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2022
Subjects
Complexity theory
Declarative Ml
Humanities
Machine learning
Predictive models
Programming
Systematics
Technological innovation
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Summary:In the last 20 years, artificial intelligence (AI) (ML) has naturally evolved from a research project to an innovation that is now used in almost every element of computing. Today, ML-based components are integrated into every aspect of our modern life, from making suggestions about what to look at to predicting our research aim to assisting low- end participants in risky and purchasing situations. Additionally, as machine learning continues to advance in the intrinsic sciences, it is now clear that ML may be utilised to solve some of the most challenging real-world issues currently facing humanity. For these reasons, ML has evolved into the foundation of technological businesses' methodologies and has received more attention from the academic community than ever before. The majority of engineers who create and use machine learning models now often have advanced degrees and work for large organisations, but the incoming flood of ML frameworks could open the door to many more users-possibly even those with little programming experience-playing. similar errands are run. These new ML frameworks will give clients a more dynamic connection point that isn't both a request and instead more recognised, rather than expecting them to completely understand every nuance of how models are created and utilised for forecasting (a huge barrier to transfer). The clear points of interaction are ideal for achieving this goal because they hide complexity and promote interest differentiation, which leads to increased efficiency.
DOI:10.1109/SSTEPS57475.2022.00034