In September 2017, Unity Technologies released the first open beta of the Unity Machine Learning Agents Toolkit. With this toolkit, it is possible to train agents (e.g. through reinforcement learning) to solve a specific task in a simulated environment.
Letzte Woche waren wir stolze Gastgeber des „Women in Digital Health“ Meetups. Während des Meetups hat SCS-Projektleiterin Dr. sc. Susanne Suter die Diskussion der Nutzung von künstlicher Intelligenz für medizinische Softwareanwendungen eröffnet. Konkret präsentierte sie unsere Erkenntnisse über den Einsatz künstlicher Intelligenz, insbesondere von Machine Learning und Deep Learning, in medizinischen Anwendungen wie auf der Intensivstation, der Ophthalmologie oder der Fruchtbarkeitsmessung. Wir hatten einen inspirierenden Abend mit vielen bereichernden Diskussionen unter den Teilnehmenden.
The field of ML is growing up. In the past, questions like „which AI algorithm should I choose“ and „what is the best way to train it“ were the main focus of AI projects. Today, these topics are well explored. Startups and big cloud providers offer services to ease the first steps and usage of AI. In addition, we can use publicly available, pre-trained models and use transfer learning to get a sound predictive performance with less effort than building our own model from scratch.
Now, new questions get into focus: how can we bring our model actually to production in a reliable and reproducible way? How can we integrate data acquisition, training, and monitoring of our model into an automated system?
