Evolving AI from Research to Real Life – Some Challenges and Suggestions
Authors: Sandya Mannarswamy, Shourya Roy
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this position paper, we argue that there are certain challenges AI still needs to overcome in its evolution from research to real life. We outline some of these challenges and our suggestions to address them. We provide pointers to similar issues and their resolutions in disciplines such as psychology and medicine from which AI community can leverage the learning. More importantly, this paper is intended to focus the attention of AI research community on translating AI research efforts into real world deployments. |
| Researcher Affiliation | Industry | Sandya Mannarswamy1 and Shourya Roy2 1 Conduent Labs India 2 American Express Big Data Labs |
| Pseudocode | No | The paper is a conceptual position paper and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses the importance of sharing code in research but does not provide or link to any open-source code for its own content or arguments. |
| Open Datasets | No | The paper is a conceptual position paper that discusses challenges in AI. It mentions various datasets as examples in the context of other research, but it does not use a dataset for its own analysis or provide access information for any dataset it utilized. |
| Dataset Splits | No | This paper is a conceptual position paper and does not involve empirical validation or dataset splits. |
| Hardware Specification | No | This paper is a conceptual position paper and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | This paper is a conceptual position paper and does not list any software dependencies with version numbers for its own work. |
| Experiment Setup | No | This paper is a conceptual position paper and does not describe any experimental setup details such as hyperparameters or training configurations. |