Breaking black-box AI

Machine learning and artificial intelligence are becoming wide-spread and productionalised - you no longer need a mathematics PhD and months of software development time to implement and use a machine learning algorithm. You can just call an API and you get the answer! You can treat them completely as black boxes and use them directly in your applications! But beware - all the algorithms have some cases when they fail to deliver what you're expecting. This talk is packed with live demos that show failure cases of popular algorithms, from linear regression to cutting-edge deep learning. Evelina will look at practical examples, use standard algorithms as black boxes and observe when they fail and why. You will learn that although you can treat the algorithms as black boxes, they can fail silently and what to do about it.

OBJECTIVES

Audience will learn that AI algorithms all have data on which they produce unexpected results and how important it is to understand your data before doing any machine learning.

TARGET AUDIENCE

Anyone currently using or thinking about using AI in their projects.