Creators of Intelligence by Dr. Alex Antic
Summary
Eighteen relatively deep interviews of folks who have deployed AI in unique ways to drive value for their companies.
Foreword
- John K. Thompson, author of Data for All, Building Analytics Teams, and Analytics: How to Win with Intelligence.
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p.4 – “I have always believed that data has incredible intrinsic value, and analytics is how we discover, unlock, refine, revise, and leverage that value.”
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p.4 – “We are still in the early days of data and analytics.”
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p.4 – “The foundations of the data and analytics field were established in the 1930s and 1940s. The field attracted serious attention and began to grow in the 1950s and beyond.”
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p.4 – “… we are just scratching the surface of how we can organize, clean, integrate, analyze, and exploit data and analytics.”
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Table of Contents
- Preface
- Introducing the Creators of Intelligence
- Cortnie Abercrombie Wants the Truth (C-Suite)
- Edward Santow vs. Unethical AI (Legal)
- Kshira Saagar Tells a Story (Senior Leadership)
- Consulting Insights with Charles Martin (Consulting)
- Petar Velickovic and His Deep Network (Research, Neural Networks)
- Kathleen Maley Analyzes the Industry (Senior Leadership)
- Kirk Borne Sees the Stars (Influence)
- Nikolaj Van Omme Can Solve Your Problems (Consulting, Operations)
- Jason Tamara Widjaja and the AI People (Senior Leadership)
- Jon Whittle Turns Research into Action (Research)
- Building the Dream Team with Althea Davis (C-Suite)
- Igor Halperin Watches the Markets (Practice)
- Christina Stathopoulus Exerts Her Influence (Senior Leadership)
- Angshuman Ghosh Leads the Way (Senior Leadership)
- Maria Milsavljevic Assesses the Risks (C-Suite)
- Stephane Doyen Follows the Science (Practice)
- Intelligent Leadership with Meri Rosic (C-Suite)
- Teaming Up with Dat Tran (Senior Leadership)
- Collective Intelligence
Common themes that are jumping off the page:
- Building a data-driven team
- Setting yourself up for success in the data and analytics field
- Influencing your business to be more data driven
- Setting your AI project up for success
- Culture surrounding data
- The future of AI: hopes and dangers
Thoughts, by the pages
Note from the blogger: For Essentialism it felt like theft to transcribe Greg McKeown’s words here into my own blog. I’ve grown out of that discomfort. Words as they appear in the original text will be encapsulated in quotation marks and paired with a page number from my PDF version of the book. Words that aren’t in quotations are my own. I don’t yet have a convention for distinguishing thoughts from questions or trains of thought. I know other tools would let me tag quotes. I know the way I’m going about this isn’t the most effective. I’m hoping the friction I encounter will show me what I need in an effective content management tool.
Preface
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p.xv – “Amidst all the hype surrounding AI, how do you build a successful career in this rapidly evolving field, and develop the necessary skills and knowledge to lead impact and change for your organization?”
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p.xv – “… those aspiring to reach these positions will find value in the advice and insights shared on how to transition to senior levels, and the fundamental skills necessary for these roles.”
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p.xv – “… those looking to reskill or upskill… will be given invaluable advice and recommendations on the necessary skills and knowledge needed – and important insights on what employers look for when interviewing candidates.”
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p.xv – “The book makes no assumptions about the technical background in data, analytics, AI, or technology of the reader…”
Chapter 1: Introducing the Creators of Intelligence
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p.1 – “I subsequently launched my consulting business, and again, there were common themes in the challenges faced by my clients in their journey to unlock the value of their data, and in maturing their analytics capability.”
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p.1 – “That was my inspiration – what if I could bring together the collective wisdom and knowledge of some of the leading data leaders from around the world and share it, in a way that make you feel that you’re a part of the conversation?”
- For what it’s worth, this is how I feel about this entire Letters To My Friends project. I want to pull together all the bits of the world that make sense to me and hold them up.
- The concept of this book also reminds me very much of The Tim Ferriss Show and Tim’s book called Tools of Titans. It’s interesting that other people also feel compelled to comb through the experience of others in the search for… truth(?).
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p.1 – “I also focused on individuals with a proven ability to build and lead a successful data and analytics capability from the ground up.”
- Exciting!
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p.3 – Definitions of key terms:
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Data Science: “intersection of math/stats, programming, business knowledge; science of change enabling an evidence-based data-informed culture; science–change–culture; people are the focus; often implies the create of machine learning models so AI <–> data science”
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Machine Learning (ML): “computer programs that can learn from data, rather than explicitly needing to be programmed to perform a specific task; trained on large representative samples of data”
- Natural Language Processing – summarizing text
- Computer Vision – identifying objects/people in images
- Fraud Detection – identifying unexpected results based on preceding behavior
- Calls into question my principle that computers are stupid and will only do exactly what they’re asked to do.
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Artificial Intelligence (AI): frequently is misused because the speaker means ML; “refers to computer programs that can perform tasks that typically require human intelligence, such as reasoning and abstraction”
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Chapter 2: Cortnie Abercrombie Wants the Truth
- Cortnie’s credentials:
- Former CDO of IBM
- Founder of AI Truth
- Founding editorial board member of Springer Nature’s AI and Ethics journal
- Author of What You Don’t Know: AI’s Unseen Influence on Your Life and How to Take Back Control
Getting into the business
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p.5 – “But my boss said, ‘No, we’ve got to rethink this whole entire function so that it is strategic and customer-driven, and I want to use databases and data to do that.’”
- Yes! I want to do this for engineering ops and delivery… but it has to solve a real problem. Here’s the problem… price tags on apps is going up, but value isn’t flowing through to paying users.
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p.6 – “The IT department and the marketing department did not get along at all. The IT department did not consider marketing to be of strategic importance in that era, which meant that of all the project backlogs they had, we were the lowest priority… They did not take it seriously when I would ask for their help in getting more data to understand how we could grow our share of the wallet with customers or increase revenue by targeting specific customer segments.”
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p.6 – “I took a bunch of classes there,… My thinking was that if the IT people could learn it, then I could learn it better. Then I befriended and bought pizzas [for admins to configure data pipelines for me].”
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p.6 – She knew her analysis was good because people wanted it. End of story.
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p.7 – “My boss’ persistence and insistence made me believe, at a very early point in my career, that you can learn anything – just get over there and learn it!… The way I see data science as a profession is that it’s all about asking the right questions and looking across the business at the business needs in the same way that I did when I was sitting there as a 20-something-year-old, trying to figure out what kind of analytics to put forward to an executive team who wanted to know everything about our customers. I needed to think about what we were trying to do as a company. I still take that approach to this day.”
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p.7 – “I think they forget that what they have to do is stick to the strategic plans of the business. It’s been a struggle, with everybody I’ve worked with or that I bring on, to try and convince them to have that tenacious, persistent personality. I can teach a person any skill. Tech skills change with the times.”
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p.7 – “The way that you think about solving problems and home in on the things that matter: that’s going to determine the longevity and relevance you have in your career.”
Discussing diversity and leadership
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pp.7-8 – “But today, in a lot of data-driven use cases, we’re talking about data that we are trying to use to emotionally drive people to do things. We’re trying to understand emotions, even in cars.”
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p.8 – “…men tend to be very strong linearly and chronologically with thinking through problems. Meanwhile, women can comfortably think about patterns all over the place, all at the same time.
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p.8 – “It takes a lot of tolerance for each other’s styles, and it takes a lot of giving each other grace and credit.”
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p.8 – “In this case, they could benefit by having someone from a divergent socioeconomic background question this assumption and also inform the data science team of the personal impact on a person who is declared an irresponsible driver…”
- Yes! This! This is just software testing and risk management. What’s at stake and for who? Players; actions; what could go wrong. That simple analysis can unravel a lot of waste and keep teams returning to the train of thought that will most likely yield profits for the company. After all, that is The Goal.
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p.9 – “We need to make sure that our norms, processes, and tools allow for the time, space, and ability to push back. It’s one thing to have a diverse team fully able to bring many perspectives to the table, but people need to be able to speak for themselves.”
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p.10 – “The first thing I always ask is, ‘What is the strategy?’ It’s the same question I asked myself in the role I was telling you about: how can you be relevant to the company? How can you be the most relevant? How can you be essential?
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p.10 – “You don’t start with, ‘I’m going to be data-driven.’ You start with, ‘I’m going to achieve this strategic goal, and this is how I’m going to use data to do it.’”
—- pick up right after this quote.