The DOI matches its Crossref record.
The master algorithm: how the quest for the ultimate learning machine will remake our world
Abstract
Wonderfully erudite, humorous, and easy to read. --KDNuggets In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
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Citations
Cited by
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World
Parker, S. K., & Grote, G. (2022) · Applied Psychology · doi:10.1111/apps.12241 · Cited by 960
Srinivasan, R., & Swink, M. (2018) · Production and Operations Management · doi:10.1111/poms.12746 · Cited by 885
On Defining Artificial Intelligence
Wang, P. (2019) · Journal of Artificial General Intelligence · doi:10.2478/jagi-2019-0002 · Cited by 740
Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022) · Psychology & Marketing · doi:10.1002/mar.21619 · Cited by 631
Metadata
- Venue
- Choice Reviews Online
- Publisher
- American Library Association
- Published
- 2016-02-18
- Volume
- 53
- Issue
- 07
- Pages
- 53-3100-53-3100
- ISSN
- 0009-4978 (ISSN-L)
- 1523-8253
- OpenAlex
- W2275841169
- Cited by
- 805
- References
- 0
- Type
- book review
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
