Artificial Intelligence—The Revolution Hasn’t Happened Yet
By Michael I. Jordan. 2019
256 citations recorded by the source corpus.
We praise Jordan for bringing much needed clarity about the current status of Artificial Intelligence (AI)—what it currently is and what it is not—as well as explaining the current challenges lying ahead and outlining what is missing and remains to be done. Jordan makes several claims supported by a list of talking points that we hope will reach a wide audience; ideally, that audience will include academic, university, and governmental leaders, at a time where significant resources are being allocated to AI for research and education.
Subfields: Artificial Intelligence, History and Philosophy of Science, Industrial and Manufacturing Engineering, Philosophy
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Martin Jaggi
- Eric P. Xing
- Percy Liang
- Michael I. Jordan
- Jakub Konecný
- Peter Richtárik
- Quoc V. Le
- Ameet Talwalkar
In History and Philosophy of Science
In Industrial and Manufacturing Engineering
In Philosophy
- Dawn Song
- Eric P. Xing
- Andrew Gordon Wilson
- Charles Peter Elkan
- Christopher R'e
- Afshin Rostamizadeh
- Alex Smola
Citations in the corpus
- SysML: The New Frontier of Machine Learning Systems (2019, background): “Finally, we see the systems machine learning community as an ideal jumping-off point for even larger-scale and broader questions, beyond how to interface with, train, execute, or evaluate single models [4].”
- MLSys: The New Frontier of Machine Learning Systems (2019, background): “Finally, we see the systems machine learning community as an ideal jumping-off point for even larger-scale and broader questions, beyond how to interface with, train, execute, or evaluate single models [4].”
- On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games (2019, unknown): “…actual adversaries (Xu et al., 2009) and with regard to mismatch between assumptions and data-generating mechanisms (Yang, 2011; Giordano et al., 2018), and an increasing awareness that real-world machine-learning systems are often embedded in larger economic systems or networks (Jordan, 2018).”
- On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems (2019, background): “Moreover, there is increasing awareness that machine-learning systems are embedded in real-world settings involving scarcity or competition that impose game-theoretic constraints [Jordan, 2018].”