Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence
Authors: Ece Kamar
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations of the Crowd Synth effort on Galaxy Zoo demonstrate that significant gains can be achieved from the optimization of access to human intelligence. The experiments show that Crowd Synth can achieve the maximum accuracy of the original system by hiring only 47% of the workers who participated in the open world run of the system. |
| Researcher Affiliation | Industry | Ece Kamar Microsoft Research eckamar@microsoft.com |
| Pseudocode | No | The paper describes algorithms like 'MC-VOI' but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodologies described. |
| Open Datasets | Yes | It uses Galaxy Zoo, a citizen science project that seeks volunteers input to classify images of millions of celestial objects, as a testbed for studies. |
| Dataset Splits | No | The paper mentions the datasets used (e.g., Galaxy Zoo, Planet Hunters) but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, libraries, or solver names with version numbers. |
| Experiment Setup | No | The paper discusses concepts like 'decision-making problem as a Partially Observable Markov Decision Process' and 'Monte-Carlo planning algorithm', but it does not include specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |