Artificial Swarm Intelligence, a Human-in-the-Loop Approach to A.I.
Authors: Louis Rosenberg
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Early testing suggests that human swarming has significant potential for harnessing the Collective Intelligence (CI) of online groups, often exceeding the natural abilities of individual participants. and Pilot tests of artificial human swarms have demonstrated accurate predictions and estimations, outperforming votes and polls and other traditional methods of harnessing the collective intelligence of groups (Rosenberg 2015). |
| Researcher Affiliation | Industry | Unanimous A.I, San Francisco, California, USA Louis@Unanimous AI.com |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | To support the ongoing study of human swarms, the UNU platform is available to researchers who wish to run their own experiments. For access, visit: unanimous.ai |
| Open Datasets | No | The paper describes experiments involving human users on the UNU platform, but it does not mention the use of any specific publicly available datasets for training, nor does it provide access information (link, DOI, citation) for any data used in its evaluations. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or methodology for training, validation, or test sets) needed to reproduce data partitioning. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment were provided in the paper. |
| Experiment Setup | No | The paper describes the general system mechanics (e.g., puck physics, user input method) but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for reproducibility. |