Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence
Authors: Edward Hughes, Michael D Dennis, Jack Parker-Holder, Feryal Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this position paper, we argue that the ingredients are now in place to achieve openendedness in AI systems with respect to a human observer. |
| Researcher Affiliation | Industry | 1Google Deep Mind, London, UK. Correspondence to: Edward Hughes <EMAIL>, Michael Dennis <EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper is a position paper and does not describe a new method for which source code would be released. No statement about code availability is present. |
| Open Datasets | No | The paper is a position paper and does not conduct experiments that would involve training data. It does not provide access information for any dataset used in its own research. |
| Dataset Splits | No | The paper is a position paper and does not conduct experiments that would require specifying validation dataset splits. |
| Hardware Specification | No | The paper is a position paper and does not conduct experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is a position paper and does not conduct experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is a position paper and does not conduct experiments, thus no experimental setup details like hyperparameters or training settings are provided. |