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

Discussing the high failure rates of AI projects

AA: You’ve been in the field for a while now; you've managed teams and done a lot of fantastic work that I’ve followed through LinkedIn. There are often reports of high failure rates for AI projects. What do you think are some of the common challenges that organizations face that could contribute to these failures?

DT: I’ve managed quite a few teams at different organizations, and they were pretty successful from my perspective. I know people for whom it’s been tougher, though.

The most common pattern I’ve seen is companies making the wrong investment or doing the wrong thing at the start of their AI or data science project. They start by hiring a lot of people because that is what is common. They want to build a new capability, so they hire a lot of people. They allocate a very big budget to that.

But what happens is that you have a lot of new people with no industry experience. A lot...