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A Very Brief and Critical Discussion on AutoML

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arxiv 1811.03822 v1 pith:SDPDGWT2 submitted 2018-11-09 cs.AI

classification cs.AI
keywords automldiscussionnarrowbriefcriticalgeneralizedstrongvery
verification ladder T0 review T1 audit T2 compute T3 formal
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This contribution presents a very brief and critical discussion on automated machine learning (AutoML), which is categorized here into two classes, referred to as narrow AutoML and generalized AutoML, respectively. The conclusions yielded from this discussion can be summarized as follows: (1) most existent research on AutoML belongs to the class of narrow AutoML; (2) advances in narrow AutoML are mainly motivated by commercial needs, while any possible benefit obtained is definitely at a cost of increase in computing burdens; (3)the concept of generalized AutoML has a strong tie in spirit with artificial general intelligence (AGI), also called "strong AI", for which obstacles abound for obtaining pivotal progresses.

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    A position paper arguing that Bayesian inference could become a key design principle for embodied AI in open physical worlds, using Sutton's search-and-learning lens to explain its current absence.

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