AI Research Paradigm
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A AI Research Paradigm is a research paradigm that focuses on the strategies and methodologies used in artificial intelligence (AI) research.
- Context:
- It can typically involve a set of assumptions, values, and practices that dictate how AI research is conducted.
- It can (often) include the debate between leveraging human knowledge and relying on increasing computation power.
- ...
- Example(s):
- A Computation-based methods AI Research Paradigm.
- A Human-knowledge-based approaches in AI, as discussed in Richard S. Sutton's 2019 article, "The Bitter Lesson."
- ...
- Counter-Example(s):
- The Scientific Method as a more general research paradigm that applies across various fields of science, not specifically tailored to the nuances and challenges of AI research.
- See: AI Research History, Computation Power, Human Knowledge in AI, Moore's Law.
References
2019
- (Sutton, 2019) ⇒ Richard S. Sutton. (2019). "The Bitter Lesson." Blog post
- QUOTE: "The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin."
- QUOTE: "Most AI research has been conducted as if the computation available to the agent were constant ... but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available."
- QUOTE: "The only thing that matters in the long run is the leveraging of computation."
- QUOTE: "There were many examples of AI researchers' belated learning of this bitter lesson, and it is instructive to review some of the most prominent."