Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation
Authors: Shiyang Lai, Yujin Potter, Junsol Kim, Richard Zhuang, Dawn Song, James Evans
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To explore AI collectives, we undertake a series of exploratory experiments, including the creation of free-formed collectives and exploration of their potential benefits and robustness to attack. |
| Researcher Affiliation | Academia | 1Department of Sociology & Knowledge Lab, University of Chicago 2Department of Electrical Engineering and Computer Sciences, UC Berkeley 3Santa Fe Institute. |
| Pseudocode | No | The paper describes experimental procedures and simulation steps in detail within the main text and appendices but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The experiments in this paper involve simulations with AI agents (Claude-2.1, GPT-4-Turbo, Gemini Pro) generating interactions and content, rather than using a pre-existing, externally defined public dataset for training or evaluation in the traditional sense. Therefore, no public dataset access information is provided. |
| Dataset Splits | No | The paper describes agent-based simulations and experiments rather than a traditional machine learning setup. Consequently, it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper specifies the use of language models like 'Claude-2.1', 'GPT-4-Turbo', and 'Gemini Pro' for simulations, but it does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used to run these experiments. |
| Software Dependencies | No | The paper mentions specific language models and embedding models like 'Claude-2.1', 'GPT-4-Turbo', 'Gemini Pro', and 'Open AI embedding model (text-embedding-3-large)', along with parameters like temperature and top-p. However, it does not list any specific software libraries or frameworks with version numbers (e.g., 'PyTorch 1.9', 'TensorFlow 2.x') that are typically required for replication. |
| Experiment Setup | Yes | We instantiate 10 agents driven by Claude-2.14 to participate in a cocktail party consisting of 30 rounds. In this simulation, agents can engage in bilateral interactions. Each round of communication consists of three steps... We used a temperature of 1 and a top-p of 0.7. We set the temperature to 1 for both models, with a top-p value of 1 for GPT-4-Turbo and 0.7 for Gemini Pro. |