Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 33,608 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,815 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |