A Survey of Current Practice and Teaching of AI
Authors: Michael Wollowski, Robert Selkowitz, Laura Brown, Ashok Goel, George Luger, Jim Marshall, Andrew Neel, Todd Neller, Peter Norvig
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Driven by a desire to expose our students to relevant and modern materials, we conducted two surveys, one of AI instructors and one of AI practitioners. The surveys were aimed at gathering information about the current state of the art of introducing AI as well as gathering input from practitioners in the field on techniques used in practice. In this paper, we present and briefly discuss the responses to those two surveys. |
| Researcher Affiliation | Collaboration | 1) Rose-Hulman Institute of Technology, wollowski@rose-hulman.edu, 2) Canisius College, selkowir@canisius.edu, 3) Michigan Technological Institute, lebrown@mtu.edu, 4) Georgia Institute of Technology, ashok.goel@cc.gatech.edu, 5) University of New Mexico, luger@cs.unm.edu, 6) Sarah Lawrence College, jmarshall@sarahlawrence.edu, 7) Discover Cards, ajneel@acm.org, 8) Gettysburg College, tneller@gettysburg.edu, 9) Google, pnorvig@google.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the methodology described, nor does it include links to a code repository. |
| Open Datasets | No | The paper conducts surveys and analyzes responses, which are not typical datasets in the context of machine learning. It does not provide concrete access information (link, DOI, repository, or formal citation) for publicly available or open datasets for training. |
| Dataset Splits | No | The paper describes survey analysis and does not involve typical machine learning dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes the analysis of survey responses and does not mention any specific hardware (e.g., GPU, CPU models, or cloud resources) used for experiments. |
| Software Dependencies | No | The paper describes the analysis of survey responses and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper details the methodology for conducting surveys and analyzing responses but does not include experimental setup details such as hyperparameters, training configurations, or system-level settings, as it is not a machine learning experimental paper. |