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 | Conference PDF | Archive PDF | Plain Text | 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 <edwardhughes@google.com>, Michael Dennis <dennismi@google.com>. |
| 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. |