# Bridging the Gap between Artificial Intelligence and Artificial General Intelligence: A Ten Commandment Framework for Human-Like Intelligence

arXiv: https://arxiv.org/abs/2210.09366 · HTML: https://mlsanity.com/paper/2210.09366/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 87 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Deep learning for universal linear embeddings of nonlinear dynamics](https://mlsanity.com/paper/1712.09707/) (2017)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Risks from Learned Optimization in Advanced Machine Learning Systems](https://mlsanity.com/paper/1906.01820/) (2019)
- [Mastering Atari Games with Limited Data](https://mlsanity.com/paper/2111.00210/) (2021)
- [The Consciousness Prior](https://mlsanity.com/paper/1709.08568/) (2017)
- [Building machines that adapt and compute like brains](https://mlsanity.com/paper/1711.04203/) (2017)
- [How Can We Be So Dense? The Benefits of Using Highly Sparse Representations](https://mlsanity.com/paper/1903.11257/) (2019)
- [Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data](https://mlsanity.com/paper/1711.03121/) (2017)
- [DeepAbstract: Neural Network Abstraction for Accelerating Verification](https://mlsanity.com/paper/2006.13735/) (2020)
- [Enabling real-time multi-messenger astrophysics discoveries with deep learning](https://mlsanity.com/paper/1911.11779/) (2019)
- [Supervision via Competition: Robot Adversaries for Learning Tasks](https://mlsanity.com/paper/1610.01685/) (2016)
- [Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks](https://mlsanity.com/paper/2112.13896/) (2021)
- [How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites](https://mlsanity.com/paper/1601.00720/) (2016)
- [Batch Exploration with Examples for Scalable Robotic Reinforcement Learning](https://mlsanity.com/paper/2010.11917/) (2020)
- [Deep learning and face recognition: the state of the art](https://mlsanity.com/paper/1902.03524/) (2019)
- [Neural Mechanisms of Human Decision-Making](https://mlsanity.com/paper/1912.07660/) (2019)
- [Complementary Structure-Learning Neural Networks for Relational Reasoning](https://mlsanity.com/paper/2105.08944/) (2021)
- [State-Denoised Recurrent Neural Networks](https://mlsanity.com/paper/1805.08394/) (2018)
- [A Mathematical Approach to Constraining Neural Abstraction and the Mechanisms Needed to Scale to Higher-Order Cognition](https://mlsanity.com/paper/2108.05494/) (2021)

## Built on by (most cited first, 2 of 2)

- [From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape](https://mlsanity.com/paper/2312.10868/) (2023)
- [BDIQA: A New Dataset for Video Question Answering to Explore Cognitive Reasoning through Theory of Mind](https://mlsanity.com/paper/2402.07402/) (2024)

## Abstract (arXiv)

The field of artificial intelligence has seen explosive growth and exponential success. The last phase of development showcased deep learnings ability to solve a variety of difficult problems across a multitude of domains. Many of these networks met and exceeded human benchmarks by becoming experts in the domains in which they are trained. Though the successes of artificial intelligence have begun to overshadow its failures, there is still much that separates current artificial intelligence tools from becoming the exceptional general learners that humans are. In this paper, we identify the ten commandments upon which human intelligence is systematically and hierarchically built. We believe these commandments work collectively to serve as the essential ingredients that lead to the emergence of higher-order cognition and intelligence. This paper discusses a computational framework that could house these ten commandments and suggests new architectural modifications that could lead to the development of smarter, more explainable, and generalizable artificial systems inspired by a neuromorphic approach.