# Meaning Versus Information, Prediction Versus Memory, and Question Versus Answer

arXiv: https://arxiv.org/abs/2107.13393 · HTML: https://mlsanity.com/paper/2107.13393/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep Visual Foresight for Planning Robot Motion](https://mlsanity.com/paper/1610.00696/) (2016)
- [Automatic Goal Generation for Reinforcement Learning Agents](https://mlsanity.com/paper/1705.06366/) (2017)
- [Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions](https://mlsanity.com/paper/1901.01753/) (2019)
- [Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration](https://mlsanity.com/paper/2002.09253/) (2020)
- [Word meaning in minds and machines](https://mlsanity.com/paper/2008.01766/) (2020)
- [PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World](https://mlsanity.com/paper/2106.00188/) (2021)
- [Why AI is Harder Than We Think](https://mlsanity.com/paper/2104.12871/) (2021)
- [Learning by Asking Questions](https://mlsanity.com/paper/1712.01238/) (2017)
- [iVQA: Inverse Visual Question Answering](https://mlsanity.com/paper/1710.03370/) (2017)
- [Independently Controllable Features](https://mlsanity.com/paper/1703.07718/) (2017)
- [A Philosophical Understanding of Representation for Neuroscience](https://mlsanity.com/paper/2102.06592/) (2021)
- [Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience](https://mlsanity.com/paper/1806.02739/) (2018)

## Abstract (arXiv)

Brain science and artificial intelligence have made great progress toward the understanding and engineering of the human mind. The progress has accelerated significantly since the turn of the century thanks to new methods for probing the brain (both structure and function), and rapid development in deep learning research. However, despite these new developments, there are still many open questions, such as how to understand the brain at the system level, and various robustness issues and limitations of deep learning. In this informal essay, I will talk about some of the concepts that are central to brain science and artificial intelligence, such as information and memory, and discuss how a different view on these concepts can help us move forward, beyond current limits of our understanding in these fields.