Book Image

Creators of Intelligence

By : Dr. Alex Antic
Book Image

Creators of Intelligence

By: Dr. Alex Antic

Overview of this book

A Gartner prediction in 2018 led to numerous articles stating that "85% of AI and machine learning projects fail to deliver.” Although it's unclear whether a mass extinction event occurred for AI implementations at the end of 2022, the question remains: how can I ensure that my project delivers value and doesn't become a statistic? The demand for data scientists has only grown since 2015, when they were dubbed the new “rock stars” of business. But how can you become a data science rock star? As a new senior data leader, how can you build and manage a productive team? And what is the path to becoming a chief data officer? Creators of Intelligence is a collection of in-depth, one-on-one interviews where Dr. Alex Antic, a recognized data science leader, explores the answers to these questions and more with some of the world's leading data science leaders and CDOs. Interviews with: Cortnie Abercrombie, Edward Santow, Kshira Saagar, Charles Martin, Petar Veličković, Kathleen Maley, Kirk Borne, Nikolaj Van Omme, Jason Tamara Widjaja, Jon Whittle, Althea Davis, Igor Halperin, Christina Stathopoulos, Angshuman Ghosh, Maria Milosavljevic, Dr. Meri Rosich, Dat Tran, and Stephane Doyen.
Table of Contents (23 chapters)
1
Chapter 1: Introducing the Creators of Intelligence

Summary

I enjoyed discussing with Igor the uptake and increasing adoption of ML in finance and, in particular, the application of reinforcement learning. I think it’s worthwhile for all data scientists who work in the broader financial services field to investigate opportunities for the application of reinforcement learning. It was also great to discuss the importance of explainable AI in finance, and the challenges involved.

Igor talked about the value of seeing the world and specific problems from different perspectives. This principle operates on a number of levels – it can be achieved by building teams that include people with backgrounds in different disciplines, but it can also mean recreating a model using a different class of modelling – for instance applying a ML model to a problem generally represented by a linear model.

Regarding high failure rates of AI projects, Igor’s view is that as most ideas don’t end up being successful (as...