Our tech highlights focus on topics that have a large impact over different customer projects and across different AI domains
Machine learning opened up new ways of solving technical challenges by training models on data instead of directly implementing rules & logic. This offers a lot of new opportunities for solving difficult problems. However, sometimes it can also be useful to combine these machine learning models with (expert) rules, to get the best possible outcome and leverage the benefits of both expert knowledge as well as machine learning models.
Hybrid AI is the name of this field, and focuses on combining non-symbolic AI (eg. machine learning), with symbolic AI (eg. expert rules).
Our previous tech highlight was Explainable AI.
This term is widely used as a single technical solution for a number of complex human-ML interaction problems sprouted from the “black box” nature of many ML models. We focus specifically on the impact of explainability on adoption & how it’s important to see it as part of a project context & not just a model context.
Our previous tech highlight was Data Centric AI.
While the importance of data for building performant ML solutions is undeniable, we see in practice that the main focus is often put on the AI model. Some have been calling to move away from this model-centric approach, and to focus on systematically changing data to improve performance of our solutions. In other words, taking a data-centric approach to AI.
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