Description:
Neural networks are all the rage - input data, output magic results! In between is a black box of magic. In many use cases - from finance to healthcare - this black box is actually a challenge preventing AI adoption. Enter Explainability! Using one extra layer, we can also have our AI explain how it arrived at the decision.
We'll take a look at how to implement this layer of explainability using a Python-based toolbox and a Google Tensorflow AI model.
About Naomi Freeman:
Naomi is a software engineer and entrepreneur. Formerly CTO/co-founder of an AI company. Two-time nominated Woman of Influence for Royal Bank of Canada's Women Entrepreneur award. Currently a 2020/21 Women Who Code Fellow (Data Science & Blockchain). http://www.naomifreeman.com
SCHEDULE (*subject to change)
18:30 - 18:35 Event Starts / Settle down with cup of tea/coffee
18:35 - 18:45 Welcome & Announcements by Vicky
18:45 - 19:15 Talk: "AI: Intro to Explainability"
19:15 - 19:30 Q&A
19:30 Event ends