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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.19054 |
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| _version_ | 1866916303879536640 |
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| author | Sonntag, Daniel Barz, Michael Gouvêa, Thiago |
| author_facet | Sonntag, Daniel Barz, Michael Gouvêa, Thiago |
| contents | This DFKI technical report presents the anatomy of the No-IDLE prototype system (funded by the German Federal Ministry of Education and Research) that provides not only basic and fundamental research in interactive machine learning, but also reveals deeper insights into users' behaviours, needs, and goals. Machine learning and deep learning should become accessible to millions of end users. No-IDLE's goals and scienfific challenges centre around the desire to increase the reach of interactive deep learning solutions for non-experts in machine learning. One of the key innovations described in this technical report is a methodology for interactive machine learning combined with multimodal interaction which will become central when we start interacting with semi-intelligent machines in the upcoming area of neural networks and large language models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19054 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE) Sonntag, Daniel Barz, Michael Gouvêa, Thiago Machine Learning Artificial Intelligence Human-Computer Interaction This DFKI technical report presents the anatomy of the No-IDLE prototype system (funded by the German Federal Ministry of Education and Research) that provides not only basic and fundamental research in interactive machine learning, but also reveals deeper insights into users' behaviours, needs, and goals. Machine learning and deep learning should become accessible to millions of end users. No-IDLE's goals and scienfific challenges centre around the desire to increase the reach of interactive deep learning solutions for non-experts in machine learning. One of the key innovations described in this technical report is a methodology for interactive machine learning combined with multimodal interaction which will become central when we start interacting with semi-intelligent machines in the upcoming area of neural networks and large language models. |
| title | A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE) |
| topic | Machine Learning Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.19054 |