# Transfer Learning Could Democratize Personalized Machine Intelligence by Peter Lee > "There is also exceptionally high potential for applications of transfer learning. So much of the practical value of machine learning, for example in search and information retrieval, has traditionally focused on systems that learn from the massive datasets and people available on the World-Wide Web. But what can web-trained systems learn about smaller communities, organizations, or even individuals? Can we foresee a future where intelligent machines are able to learn useful tasks that are highly specialized to a specific individual or small organization? Transfer learning opens the possibility that all the intelligence of the web can form the foundation of machine-learned systems, from which more individualized intelligence is learned, through transfer learning. Achieving this would amount to another step towards the democratization of machine intelligence." > **— Peter Lee**, *2017, Edge Annual Question, “What Scientific Term or Concept Ought to Be More Widely Known?”* ## Sources and Context - **Direct Edge response and surrounding essay:** [Transfer Learning](https://www.edge.org/response-detail/27125) — Edge published this passage in its 2017 Annual Question collection, *What Scientific Term or Concept Ought to Be More Widely Known?*. The full response preserves the qualifications and argument surrounding this exact extract. - **Complete local collection index:** [[research/Edge 2017 Full Quotation Curation|Edge 2017 Full Quotation Curation]] — retained source-first archive record for the complete yearly response cohort. ## Related Articles and Collections - **Collection:** [[collections/Edge|Edge]] - **Collection:** [[collections/Machine Succession|Machine Succession]] - **Collection:** [[collections/MARS|MARS]] - **Article:** [[articles/Modern Artificial Intelligence in the 1970s|AI in the 1970s]] - **Article:** [[articles/The Last Migration Will Not Be Human|The Last Migration]] - **Article:** [[articles/We Were Never Going to Make It|Never Going to Make It!]] - **Wiki map:** [[wiki/Machine Succession|Machine Succession]] ## Related Topics - [[wiki/Machine Learning|Machine Learning]] - [[wiki/Artificial General Intelligence|Artificial General Intelligence]] - [[wiki/Cognitive Architecture|Cognitive Architecture]] - [[wiki/Coordination Technology|Coordination Technology]] ## Share on Social Media ``` "There is also exceptionally high potential for applications of transfer learning. So much of the practical value of machine learning, for example in search and information retrieval, has traditionally focused on systems that learn from the massive datasets and people available on the World-Wide Web. But what can web-trained systems learn about smaller communities, organizations, or even individuals? Can we foresee a future where intelligent machines are able to learn useful tasks that are highly specialized to a specific individual or small organization? Transfer learning opens the possibility that all the intelligence of the web can form the foundation of machine-learned systems, from which more individualized intelligence is learned, through transfer learning. Achieving this would amount to another step towards the democratization of machine intelligence." — Peter Lee, 2017, Edge Annual Question, “What Scientific Term or Concept Ought to Be More Widely Known?” https://bryantmcgill.com/simple-reminders-peter-lee-transfer-learning-personalized-intelligence ```