{
  "id": "2004.04686",
  "version": "v1",
  "published": "2020-03-27T19:09:57.000Z",
  "updated": "2020-03-27T19:09:57.000Z",
  "title": "Machine Learning in Artificial Intelligence: Towards a Common Understanding",
  "authors": [
    "Niklas Kühl",
    "Marc Goutier",
    "Robin Hirt",
    "Gerhard Satzger"
  ],
  "comment": "Hawaii International Conference on System Sciences (HICSS-52) 2019",
  "categories": [
    "cs.LG",
    "cs.AI"
  ],
  "abstract": "The application of \"machine learning\" and \"artificial intelligence\" has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we aim to clarify the relationship between these terms and, in particular, to specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents. Hence, we seek to provide more terminological clarity and a starting point for (interdisciplinary) discussions and future research.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-03-27T19:09:57.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "artificial intelligence",
      "machine learning",
      "common understanding",
      "review relevant literature",
      "conceptual framework"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}