AI GLOSSARY
Concept Drift
Concept drift occurs when the relationship between the input data and the target value changes in some way, potentially making the model inaccurate or unreliable. Concept drift can lead to a decline in the model’s accuracy because it was trained on data that no longer reflects the current patterns or relationships. Handling concept drift is essential in dynamic environments, such as financial markets or user behaviour prediction, where conditions evolve continuously.
5 min read
AI for Sensor Fusion: Sensing the Invisible
by Nick Sherman
In Defence and National Security, mission-critical data often emerges from a multitude of different sensor types. With AI, we can bring this...
5 min read
Insuring Against AI Risk: An interview with Mike Osborne
by Nick Sherman
When used by malicious actors or without considerations for transparency and responsibility, AI poses significant risks. Mind Foundry is working with...
Stay connected
News, announcements, and blogs about AI in high-stakes applications.