# "Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?

arXiv: https://arxiv.org/abs/2103.15294 · HTML: https://mlsanity.com/paper/2103.15294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [How to represent part-whole hierarchies in a neural network](https://mlsanity.com/paper/2102.12627/) (2021)
- [The Microsoft 2017 Conversational Speech Recognition System](https://mlsanity.com/paper/1708.06073/) (2017)
- [English Conversational Telephone Speech Recognition by Humans and Machines](https://mlsanity.com/paper/1703.02136/) (2017)
- [Artificial Intelligence is stupid and causal reasoning won't fix it](https://mlsanity.com/paper/2008.07371/) (2020)
- [Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces](https://mlsanity.com/paper/1911.00714/) (2019)
- [Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach](https://mlsanity.com/paper/1907.06377/) (2019)
- [Robust Particle Filter by Dynamic Averaging of Multiple Noise Models](https://mlsanity.com/paper/1609.01336/) (2016)
- [A Very Brief and Critical Discussion on AutoML](https://mlsanity.com/paper/1811.03822/) (2018)
- [Robust video object tracking via Bayesian model averaging based feature fusion](https://mlsanity.com/paper/1604.00475/) (2016)

## Abstract (arXiv)

AI has surpassed humans across a variety of tasks such as image classification, playing games (e.g., go, "Starcraft" and poker), and protein structure prediction. However, at the same time, AI is also bearing serious controversies. Many researchers argue that little substantial progress has been made for AI in recent decades. In this paper, the author (1) explains why controversies about AI exist; (2) discriminates two paradigms of AI research, termed "weak AI" and "strong AI" (a.k.a. artificial general intelligence); (3) clarifies how to judge which paradigm a research work should be classified into; (4) discusses what is the greatest value of "weak AI" if it has no chance to develop into "strong AI".