Evelina Gabasova
Evelina is a machine learning and data science researcher. She works as Principal Research Data Scientist at The Alan Turing Institute, the UK's national institute for data science and artificial intelligence. She is a member of the research engineering team where she is connecting academic research with real-world applications. Her passion is to make data science understandable and accessible to everyone. When not wrangling data or training machine learning models, she is an active member of the F# community, Microsoft MVP and a technical speaker.
Breaking Black-Box AI
Machine learning and artificial intelligence are becoming wide-spread and productionalized - 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. I 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.
Evelina is a machine learning and data science researcher. She works as Principal Research Data Scientist at The Alan Turing Institute, the UK's national institute for data science and artificial intelligence. She is a member of the research engineering team where she is connecting academic research with real-world applications. Her passion is to make data science understandable and accessible to everyone. When not wrangling data or training machine learning models, she is an active member of the F# community, Microsoft MVP and a technical speaker.
Breaking Black-Box AI
Machine learning and artificial intelligence are becoming wide-spread and productionalized - 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. I 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.