Deep machine learning has achieved remarkable success in various fields of artificial intelligence, but achieving both high interpretability and high efficiency simultaneously remains a critical ...

Dr. Adi Hod. Cofounder & CEO at Velotix. Driven by a passion for data and cybernetic AI. Entrepreneur, professor, leader & innovator. AI is fast becoming embedded in industries, economies and ...

Today, the trade-off between the accuracy and interpretability of predictive models has been broken (and maybe it never really existed). But, tools now exist to build accurate and sophisticated ...

The field of interpretability investigates what machine learning (ML) models are learning from training datasets, the causes and effects of changes within a model, and the justifications behind its ...

To that end, Dr Bhusan Chettri who earned his PhD in Machine Learning and AI for Voice Technology from QMUL, London described why there is a need for interpretability on today’s state-of-the-art ...

The goal of interpretability for machine learning is the opposite of this. It is to tell you if a system is not safe to use. It’s about revealing the truth. So “trust” isn’t the right word. So the ...

Machine-learning algorithms can make mistakes and be difficult to use, so scientists at the Massachusetts Institute of Technology created explanation methods to assist users in understanding when ...

Today, the trade-off between the accuracy and interpretability of predictive models has been broken (and maybe it never really existed). But, tools now exist to build accurate and sophisticated ...