# A Very Brief and Critical Discussion on AutoML

arXiv: https://arxiv.org/abs/1811.03822 · HTML: https://mlsanity.com/paper/1811.03822/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 3 of 3)

- ["Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?](https://mlsanity.com/paper/2103.15294/) (2021)
- [Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT](https://mlsanity.com/paper/2105.14625/) (2021)
- [Exploring the Link Between Bayesian Inference and Embodied Intelligence: Toward Open Physical-World Embodied AI Systems](https://mlsanity.com/paper/2507.21589/) (2025)

## Abstract (arXiv)

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.