Our tech highlights focus on topics that have a large impact over different customer projects and across different AI domains
Our current tech highlight is 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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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 Bias and fairness in ML.
Decision making can either be automated or augmented with ML. In case these decisions impact people directly, it’s of utmost importance to make sure that our models act unbiased/fair. During this highlight we focused on methods to measure bias & ways to remove bias.
If you missed any of our last tech highlights, and you would like to get more information, you can contact us by filling in the small form.
