Data privacy - one of machine learning’s biggest challenges and how to tackle it

Why is it that despite countless papers demonstrating proof-of-concept and high performance on validation sets, AI adoption by the healthcare industry is relatively scarce?

Conversations about AI are difficult. This is partially due to people having different definitions, or not even consistently standing behind one definition, but rather forming their opinion based on some fluctuating intuition of what AI is.

It has been over half a year since I published the article Mythbusters: 7 common myths about AI. On my quest to demystify AI, I have held many workshops and talks. In this follow-up post, I have collected and debunked seven more frequently encountered myths.

Every problem worth solving needs great tools for support. Deep learning is no exception. If anything, it is a realm in which good tooling will become ever more important over the coming years.