Machine learning
Machine learning gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).[1][2] It is a subfield of computer science.[3]
The idea came from work in artificial intelligence.[4] Machine learning explores the study and construction of algorithms which can learn and make predictions on data.[5] Such algorithms follow programmed instructions, but can also make predictions or decisions based on data.[6]: 2 They build a model from sample inputs.
Machine learning is done where designing and programming explicit algorithms cannot be done. Examples include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[7] optical character recognition (OCR),[8] search engines and computer vision.
Using machine learning has risks. Some algorithms create a final model which is a black box.[9] Models have been criticized for biases in hiring,[10] criminal justice,[11] and recognizing faces.[12]
References
- ↑ John McCarthy & Edward Feigenbaum 1990. In Memoriam Arthur Samuel: pioneer in machine learning. AI Magazine. AAAI. 11 (3).[1] Archived 2018-01-22 at the Wayback Machine
- ↑ Phil Simon (2013). Too big to ignore: the business case for big data. Wiley. p. 89. ISBN 978-1-118-63817-0.
- ↑ "Machine Learning | Data Basecamp". 2021-11-26. Retrieved 2022-08-14.
- ↑ "Machine learning | artificial intelligence | Britannica".
- ↑ Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274. doi:10.1023/A:1007411609915. S2CID 36227423.
- ↑ Christopher Bishop 1995. Neural networks for pattern recognition. Oxford University Press. ISBN 0-19-853864-2
- ↑ "TechCrunch".
- ↑ Wernick et al 2010. Machine learning in medical imaging, IEEE Signal Processing Society|IEEE Signal Processing Magazine. 27, 4, 25-38.
- ↑ "Government aims to make its 'black box' algorithms more transparent". Sky News. Retrieved 2021-12-02.
- ↑ "Amazon scraps secret AI recruiting tool that showed bias against women". Reuters. 2018-10-10. Retrieved 2021-12-02.
- ↑ Mattu, Jeff Larson,Julia Angwin,Lauren Kirchner,Surya. "How We Analyzed the COMPAS Recidivism Algorithm". ProPublica. Retrieved 2021-12-02.
{{cite web}}: CS1 maint: multiple names: authors list (link) - ↑ "The Problem of Bias in Facial Recognition". www.csis.org. Retrieved 2021-12-02.