# Review of the state of the art in autonomous artificial intelligence

arXiv: https://arxiv.org/abs/2210.10659 · HTML: https://mlsanity.com/paper/2210.10659/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Non-stochastic Best Arm Identification and Hyperparameter Optimization](https://mlsanity.com/paper/1502.07943/) (2015)
- [Feature Engineering for Predictive Modeling using Reinforcement Learning](https://mlsanity.com/paper/1709.07150/) (2017)
- [Probabilistic Matrix Factorization for Automated Machine Learning](https://mlsanity.com/paper/1705.05355/) (2017)
- [AlphaD3M: Machine Learning Pipeline Synthesis](https://mlsanity.com/paper/2111.02508/) (2021)
- [Automating biomedical data science through tree-based pipeline optimization](https://mlsanity.com/paper/1601.07925/) (2016)
- [Autostacker: A Compositional Evolutionary Learning System](https://mlsanity.com/paper/1803.00684/) (2018)
- [Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings](https://mlsanity.com/paper/1809.05250/) (2018)
- [Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees](https://mlsanity.com/paper/1908.10410/) (2019)
- [Urban flows prediction from spatial-temporal data using machine learning: A survey](https://mlsanity.com/paper/1908.10218/) (2019)
- [Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection](https://mlsanity.com/paper/1812.02855/) (2018)

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

- [ALPACA -- Adaptive Learning Pipeline for Comprehensive AI](https://mlsanity.com/paper/2412.10950/) (2024)
- [Impact of HPO on AutoML Forecasting Ensembles](https://mlsanity.com/paper/2311.04034/) (2023)
- [SALSA: Semi-Autonomous Literature Summarization Assistant](https://mlsanity.com/paper/2609.22210/) (2026)

## Abstract (arXiv)

This article presents a new design for autonomous artificial intelligence (AI), based on the state-of-the-art algorithms, and describes a new autonomous AI system called AutoAI. The methodology is used to assemble the design founded on self-improved algorithms that use new and emerging sources of data (NEFD). The objective of the article is to conceptualise the design of a novel AutoAI algorithm. The conceptual approach is used to advance into building new and improved algorithms. The article integrates and consolidates the findings from existing literature and advances the AutoAI design into (1) using new and emerging sources of data for teaching and training AI algorithms and (2) enabling AI algorithms to use automated tools for training new and improved algorithms. This approach is going beyond the state-of-the-art in AI algorithms and suggests a design that enables autonomous algorithms to self-optimise and self-adapt, and on a higher level, be capable to self-procreate.