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

My conversation with Meri was both insightful and thought-provoking. With a wealth of experience and expertise, she is well placed to provide advice on how to develop a successful, sustainable, and scalable data science capability.

When discussing the oft-quoted high failure rates in AI projects, Meri was aligned with the common consensus throughout this book that data science and AI projects are scientific in nature, and as a result of their experimental nature, failure is part of the process. It’s important to understand that if we define failure as the project not delivering the expected business benefits, then there are a multitude of non-technical reasons for “failure” in this context. To help mitigate failures due to scalability issues, her recommendation is to start with the problem – and not the data – to build solutions that are truly scalable and aren’t limited to the dataset used for a POC or small-scale solution.

I...