It's All Analytics!

The Foundations of Al, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government

Scott Burk, Gary D. Miner

• Publisher: Productivity Press
• Year: 2020
• eBook ISBN: 9781000067224
• Topic: Medicine
• Subtopic: Business General

It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690)

Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, "analytics," is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology?

This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.

Chapter 1

You Need This Book

Keywords: Bias, Media, Hype, Landscape, AI (Artificial Intelligence), ML (Machine Learning), Analytics, Big Data

Preamble

The authors have heard that we need to be data-driven for more than 20 years. We hear the same thing today! What happened!! Have we ignored the message?? Have we done it wrong? Probably the latter… Thus this book. Why do you need this book? No other book has a comprehensive view of the landscape. Yes, you can get a lot of information from the Internet, but it is not curated or validated. Is it someone’s blog post who is trying to promote themselves as an expert? Anyone can write a blog and there are many analytics websites which are platforms for marketecture, which means form without substance.

This book is Part 1 of a three-part series. This series will attempt to set the record straight and make it easy for the practitioner or the executive decision-maker to understand what they really need in their setting. In Chapter 1 we look at why you need an unbiased understanding of the subject in order to make the right decisions – how and when you need to be data-driven.

The Hip, the Hype, the Fears, the Intrigue, and the Reality:

Hype, Fear, and Intrigue No 1:

The worrying question today for many is whether democracy can survive artificial intelligence (AI). The reality is that disruptive technologies like AI cannot be put back in the box. AI is becoming a mainstream technology that will need to be effectively integrated into 21st-century decision-making across all institutions – especially government. In the wake of this digital revolution, some functions of government will necessarily be eliminated.

(Araya, Forbes, 2019)

Reality

As long as elected officials are in office, they will not cede control to AI or anything else but themselves. Anyway, most AI is augmentation of human intelligence, not replacement of it. As far as we can see, the democratic governments of the West will not be replaced anytime soon. It is interesting that the same article mentions impacts to China and we find these predictions much more likely. With AI, China could definitely entrench its authority into a digital technocracy. Unlike the West, in which there is a major push in pulling back the covers of AI and adopting policies such as GDPR (General Data Protection Regulation), communist countries could use AI to strengthen their hand and control over the masses.

Hype, Fear, and Intrigue No 2:

As Jonathan Zittrain commented in a panel discussion sponsored by Harvard Medical School, Precision Medicine June 2019:

I think of machine learning (artificial intelligence) kind of like asbestos… It turns out that it’s all over the place, even though at no point did you explicitly install it, and it has possibly some latent bad effects that you might regret later, after it’s already too hard to get it all out.

Reality

How easy it is to turn such comments into an article purporting that AI and machine learning (ML) are the new cancer-causing agents that will make millions sick and law firms rich. However, you really need to take all the comments and the related contexts of these comments into consideration. Many of the statements made were accurate and we will review and analyze these in later sections. However, the reader has seen remarks like the ones mentioned above turned into a gloom and doom sensational article. We do not think this was Professor Zittrain’s intent; many of the comments he made were accurate and we will review and analyze these in later sections as well as a related article by Casey Ross.

Hype, Fear, and Intrigue No 3:

Headline – “Will robots take your job? Humans ignore the coming AI (Artificial Intelligence) revolution at their peril” (NBCNEWS.COM, November 2018). Robots are taking over industrial manufacturing and they will soon take over food production and restaurant kitchens. In just a few years they will take over trucking, ride services, the financial industry, accounting, healthcare, and citizen services. Humans will be able to pursue their desires and do whatever they wish without needing to work.

Reality

The AI revolution started back in the 1950s, but you would never guess that based on the headlines today. It is sensationalized like it is brand-new, nascent technology. Even special programming languages like Lisp were adopted to support AI research in the late 1950s. Over the years since then, AI has been resurrected a few times: for example, Texas Instruments created a special computer called the TI Explorer specifically to do very fast (for the time) computing and was used in for AI scheduling of the Hubble Space Telescope even in the 1970s and 1980s and continuing forward (Miller, 1989; and Johnston and Miller, 1992). It is true that this new era of AI this time in the 2nd and 3rd decades of the 21st century is more substantial than ever, but robots will not displace humans. We see AI and other technologies enhancing human existence. It will have positive impacts on everything from personal transportation to augmented citizen services and government (see Mehr, 2017). We must be careful; it benefits most people to have a basic understanding of what AI is, what it is not and how it will impact them. Every technological revolution has displaced workers but also created new opportunities. Those workers that were willing to learn new technology and adapt succeeded and those that did not were left to wither.

Prevalence of Analytics, AI and Data Science in Everyday Life

Example of the HYPE about AI and ML– we see it almost EVERY DAY now in the news and social media; here is an example pulled from “One moment in time” on a day in early July 2019:

AI and ML dominate the news including LinkedIn – 7/6/19 (Saturday) pulled from LinkedIn – They do a daily notification on their “DAILY RUNDOWN” newsfeed. The following is just an example, but indicative of how prevalent this subject is in professional news cycles. There are 5 headlines and 4 of the 5 are related to the topics in this book. (www.linkedin.com/pulse/breakthrough-hiv-research-tech-takes-beatles-more-top-rundown-us-/)

Scientists claim they have cured nine mice of HIV for the first time in history, according to a new study (Dash, PK; Kaminski, R; Gendelman et al., 2019). The researchers from Temple University and the University of Nebraska Medical Center used a combination of CRISPR gene-editing technology and an antiretroviral therapy called LASER ART to kill the virus. One of the doctors who coauthored the report called it a “major breakthrough.” About 1.1 million people in the U.S. live with HIV (Turner, 2019), which is a virus that attacks the body’s immune system and if left untreated, can turn into AIDS.

Breaking down the Beatles: Paul McCartney and John Lennon wrote some of the most famous songs in history, yet for the songs where they are listed as coauthors; it is not clear who wrote what. With the help of a machine, a team of researchers from Harvard and Canada’s Dalhousie University developed a system where they were able to correctly differentiate Lennon’s tracks from McCartney’s with 76% accuracy. They then applied this model to eight songs or fragments of songs whose authorship is a bit murky.

Fewer American teens are getting jobs. Over the past nearly two decades, teenage labor force participation has dropped significantly – 15.9% between 2000 and 2018, according to research compiled by the Brookings Institute (Glickman, Brown, and Song, 2019). Fewer teens (16- to 19-year-olds) are seeking summer employment, and they’re also becoming less likely to double up on school and work during the academic year. “The intensity of high school may have increased in ways that have helped crowd out non-school activities,” per Brookings.

No Amazon? No problem for Long Island City. The Queens neighborhood, according to real estate data acquired by Vox (Molla, 2019), leased more commercial property in the first half of 2019 than it has in any year since such data started to be recorded. Last November, Amazon tapped Long Island City to be the location of its second headquarters (HQ2). And although protests forced the e-commerce giant to scrub its plans, the activity still “functioned as a giant advertisement” (Molla, 2019) for the New York neighborhood.

AI is poised to play a bigger role in healthcare, from speeding up drug development to helping to diagnose disease. But to do so, it needs data (and lots of it). According to the World Health Organization (Wall, 2019), AI can currently only access 20% of global medical data. And while making more available could spur innovation and help overcome resource shortages, it also requires trust – that patient data “won’t be used for commercial reasons without their consent or to discriminate against people,” WHO chief scientist Soumya Swaminathan tells the BBC (Wall, M., 2019).

And you see it all over the Internet. Credible analyst firms and premier management consulting firms are supporting the new wave, a tsunami into AI, data science and analytics. According to a Forrester Research report from 2017 published in Forbes (see Press, 2016). “AI-driven enterprises will steal 1.2 trillion from competitors by 2020.” That is a pretty bold claim, but they are not the only research firm making these kinds of predictions. In a 2018 report titled Notes from the AI frontier: modeling the impact of AI on the world economy (see Bughin et al., 2018), McKinsey and Co. analyzed 400 use cases in 19 different industries to understand the broad use and significant economic potential of deploying advanced deep learning AI techniques. It found that companies with faster AI adoption and absorption (Front Runners) are creating big economic gains.

Professionals Need This Book

Introduction

Gartner is an independent analysis firm that reports on the technology sector. They phrased one need for this book in several comments on their website including the need for members of an organization to speak the same language. Kasey...

Table of contents

• Cover
• Half-Title
• Title
• Copyright
• Contents
• Foreword Number One
• Foreword Number Two
• Foreword Number Three
• Preface
• Endorsements
• Authors
• 1 You Need This Book
• 2 Building a Successful Program
• 3 Some Fundamentals – Process, Data, and Models
• 4 It’s All Analytics!
• 5 What Are Business Intelligence (BI) and Visual BI?
• 6 What Are Machine Learning and Data Mining?
• 7 AI (Artificial Intelligence) and How It Differs from Machine Learning
• 8 What Is Data Science?
• 9 Big Data and Bigger Data, Little Data, Cloud, and Other Data
• 10 Statistics, Causation, and Prescriptive Analytics
• 11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More)
• 12 Looking Ahead
• Index