Review of the state of the art in autonomous artificial intelligence
Not on the map. Why?
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 50 references that are in the map. First posted on arXiv 17 Oct 2022.
Builds on
10 of 50 references.
References · 10
- 2015Non-stochastic Best Arm Identification and Hyperparameter Optimizationcited by 219
- 2017Feature Engineering for Predictive Modeling using Reinforcement Learningcited by 78
- 2017Probabilistic Matrix Factorization for Automated Machine Learningcited by 31
- 2021AlphaD3M: Machine Learning Pipeline Synthesiscited by 24
- 2016Automating biomedical data science through tree-based pipeline optimizationcited by 21
- 2018Autostacker: A Compositional Evolutionary Learning Systemcited by 18
- 2018Real-Time Nonparametric Anomaly Detection in High-Dimensional Settingscited by 10
- 2019Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Treescited by 10
- 2019Urban flows prediction from spatial-temporal data using machine learning: A surveycited by 7
- 2018Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selectioncited by 5
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.
3 papers.
- 2024ALPACA -- Adaptive Learning Pipeline for Comprehensive AIcited by 1
- 2023Impact of HPO on AutoML Forecasting Ensemblescited by 0
- 2026SALSA: Semi-Autonomous Literature Summarization Assistantcited by 0
- 2026SALSA: Semi-Autonomous Literature Summarization Assistant1 Sep 2026
- 2024ALPACA -- Adaptive Learning Pipeline for Comprehensive AI14 Dec 2024
- 2023Impact of HPO on AutoML Forecasting Ensembles7 Nov 2023