A Review on Hidden Debts in Machine Learning Systems
| Autor: | Dev Kumar Chaudhary, Vikas Kumar, Sandeep Srivastava |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject 02 engineering and technology Machine learning computer.software_genre Authentication (law) Faith 03 medical and health sciences 0302 clinical medicine Software 020204 information systems Arrears Debt 0202 electrical engineering electronic engineering information engineering Systems design 030212 general & internal medicine Artificial intelligence business computer media_common |
| Zdroj: | 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT). |
| DOI: | 10.1109/icgciot.2018.8753081 |
| Popis: | In the present situation, Machine Learning provides a very sturdy toolkit for building applicable and useful complex prediction systems by providing new methods for creating high-show software. Unlike traditional software engineering, the collection of training examples is performed, and then the algorithms are applied. It is the hidden agenda behind artificial intelligence. Despite this fact, it is dangerous to think that this fast triumph comes for no cost. Glaring restrictions still exists in the measurements utilized and how much outcomes are imparted back to their radiating areas. We are evident of several ML-specific unpredictable factors to account for in the system design. These include algorithmic inclinations, technical arrears, data dependencies, configuration issues, the problem of authentication, granting-approval, along with faith, and human bias. We mean to motivate the continuous exchange and concentrate on coordinating the machine learning research. |
| Databáze: | OpenAIRE |
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