Learn about the approach and impact of the model we built to predict skin tone from face images and videos using a unique combination of computer vision and machine learning to help organizations monitor for racial biases in large datasets.
Here is what we see as the most valuable high-impact trends in AI/ML for life sciences and healthcare
Five challenges and solutions critical to the success of Data Science in Healthcare and Life Sciences
“Mercury Data Science has taken a tool it originally developed for COVID-19 research and applied it into new areas of research and innovation.”
Most data is raw and needs to be shaped into usable features that highlight signal and minimize noise. “Feature engineering” is the most important part of real-world data science, paving the path from raw data to valuable insights.
Why companies need to think ahead about infrastructure for data science.
Domain and technical knowledge aren’t sufficient criteria when looking for your new data science hire. We have found that a better characterization of a data scientist’s innate potential is the way they balance creativity and skepticism.
How to review and gain insights from the mountain of medical literature without reading it.
Accelerate hypothesis development with AI-driven insights from scientific literature.
We are constantly sending signals that communicate emotion, intent, and even underlying medical conditions.