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.

All Terms
A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z

Continue learning...

View Resources

6 min read
The Intelligent Application of Machine Learning in Defence
The rapid and dislocating advances in large language models (LLMs) and foundation models over the past three years has dominated the AI and machine...
5 min read
AI-enabled Acoustic Intelligence for Anti-Submarine Warfare
From detecting hidden threats to defending critical underwater infrastructure, Anti-Submarine Warfare (ASW) is a cornerstone of national security. AI...
5 min read
AI Assurance Explained: Trust, Safety, and Operational Impact
The UK-USA Technology Prosperity Deal sees overseas organisations pledging £31 billion of investment into UK AI infrastructure. As AI investment...

Stay connected

News, announcements, and blogs about AI in high-stakes applications.