# Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research

arXiv: https://arxiv.org/abs/1610.07432 · HTML: https://mlsanity.com/paper/1610.07432/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2016.

## Built on by (most cited first, 5 of 5)

- [Towards Socially Intelligent Agents with Mental State Transition and Human Utility](https://mlsanity.com/paper/2103.07011/) (2021)
- [LEBP -- Language Expectation & Binding Policy: A Two-Stream Framework for Embodied Vision-and-Language Interaction Task Learning Agents](https://mlsanity.com/paper/2203.04637/) (2022)
- [Building Human-like Communicative Intelligence: A Grounded Perspective](https://mlsanity.com/paper/2201.02734/) (2022)
- [Embodiment and Computational Creativity](https://mlsanity.com/paper/2107.00949/) (2021)
- [Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management](https://mlsanity.com/paper/2104.03349/) (2021)

## Abstract (arXiv)

Meaning has been called the "holy grail" of a variety of scientific disciplines, ranging from linguistics to philosophy, psychology and the neurosciences. The field of Artifical Intelligence (AI) is very much a part of that list: the development of sophisticated natural language semantics is a sine qua non for achieving a level of intelligence comparable to humans. Embodiment theories in cognitive science hold that human semantic representation depends on sensori-motor experience; the abundant evidence that human meaning representation is grounded in the perception of physical reality leads to the conclusion that meaning must depend on a fusion of multiple (perceptual) modalities. Despite this, AI research in general, and its subdisciplines such as computational linguistics and computer vision in particular, have focused primarily on tasks that involve a single modality. Here, we propose virtual embodiment as an alternative, long-term strategy for AI research that is multi-modal in nature and that allows for the kind of scalability required to develop the field coherently and incrementally, in an ethically responsible fashion.