don’t need to be afraid of AI—it’s just a technology like any other—but you do need to watch carefully and learn how to use it. And you need to share this knowledge with your coworkers, superiors, employees, engineers, data scientists, suppliers, vendors, neighbors, and so on.
To outsmart AI, you do not need to be able to code all kinds of complicated algorithms. You do not need to be able to explain how GPUs help CPUs with data processing. But you do need to understand the differences among checklists, decision trees, and neural networks, so that you can make sure that AI tools are giving you the right information you require. You will need to maximize the quality of your data and make sure that your AI system does not churn out garbage or lead you to break the law.
We will help you. We don’t claim to be engineers or computer scientists. You don’t have to be either. We have had real-world success in designing, deploying, and selling AI and other advanced technologies. We have been teaching, writing, and helping people to work better for twenty years. Let’s ignore the hype, cut to the chase, and get to work.
How to Read This Book
Chapter 1 tears down seven widely repeated myths about AI. If we keep our heads, then we will master AI as we have all technologies that have emerged in human history. We are fools if we let it master us. In the same chapter, we present seven clear, key ideas about AI that will keep us straight on the path toward profitable AI implementation and proper AI governance.
Chapter 2 explains the science behind AI in plain English. I will show you what it can—and cannot—do for your organization.
Chapter 3 will show you how to acquire AI technology at a reasonable cost and how it can be used to attain higher profits. Here you will see how AI can benefit pretty much every sector of the economy.
In chapter 4 you will learn how to estimate and control the costs of implementing AI. There is absolutely no point in adopting a new technology, in investing in it, unless it will make your organization more profitable and/or cost-effective. Every change entails risk, just like sitting there and doing nothing! Finance and tech have to work together to achieve AI success. Government organizations may not feel pressure to generate profit, but they sure can benefit the taxpayers by providing more—and better—for less.
In chapter 5 Joshua will address the platform you need to govern your data in an AI system. Your organization’s data is one of your most valuable assets! You will learn the EDEN method to keep your garden green and growing. We’ll say it again: AI without good data is a waste of time and money.
Chapter 6 lays out case studies about AI and legal controls. The law is your friend, not your enemy. Law is messy and imprecise, but in democracies it is the best way to curb abuses and guarantee personal freedoms. This chapter gives directions on how best to govern AI systems across the economy for the good of the state and society.
The afterword provides you with a simple, clear, workable ethical framework that can be applied to almost any organization where people work with data and automated systems.
So, if you want to wait for a future of perfect freedom, no restraints, and universal prosperity, you could waste your time and money on singularity science fiction, but we sincerely advise against it. AI is a very real technological advancement, and once achieved, these never go away. The future of your business and of our shared democratic, capitalist system requires your full attention to AI right now.
1.
https://www.humanbrainproject.eu/en/.
Chapter 1
7 AI Myths, 7 AI Rules
The term artificial intelligence is an old fund-raising gimmick. John McCarthy coined the term in 1955 to apply for Rockefeller money to pay for a conference at Dartmouth about “automatic computers.” The goal of the conference was “to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”[1] Computers are as far as away from that as ever, but in some applications they can at least appear to come close.
Today more than one thousand vendor companies use the term “AI” to sell their services or to raise money from investors to cover their expenses. Whether they actually use AI algorithms is another matter. Many exaggerate what AI can do, and there’s nothing new in that. In the past sixty years, we have gone through cycles called “AI winters” when soaring promises returned failed deliverables, underwhelmed investors, and sank research funding. Depending on how you count, an AI winter happens roughly once per decade.
We find ourselves in another hype cycle once again. Claims about AI capabilities have spun out of control in media and advertising, leaving the tight discipline of computer science firmly on the ground. Joshua and I do not, however, predict another AI winter because of the real strides being made in computer-processing speed and storage, and the explosive growth of available data. There will be a major shakedown among vendors, but the tech is only getting better. (I will explain how it works in chapter 2.)
The first step in your successful, profitable adoption of AI tech in your organization is to clear away the myths that cloud the real picture. Below are seven that are repeated all too often. Let’s make short work of them.
Myth 1: AI Is a Robot
AI and robots are not the same. AI is a family of data analytics procedures or algorithms performed by software run on hardware. Robots are contraptions equipped with sensors that produce digitized data, a central processing unit for that data, mechanical parts to complete tasks, and a power supply to run on. The robot’s data processor may or may not use AI algorithms.
The confusion is understandable. In journalism, stories about AI usually feature a picture of a robot or a digitized, humanoid face, probably because images of software, such as computer code or lots of 1s and 0s, are dead boring for most people.
Robots have caught people’s attention for millennia. From ancient Egyptian records, there are stories of statues of gods that gave advice or moved. In ancient Greece, there was a tale about a man made of bronze named Talos who guarded the island of Crete. Statues that come to life by one means or another are almost stock characters in fantasy literature. Little children everywhere love to imagine that their dolls, stuffed animals, and action figures are alive and interact with them. Big people don’t seem to want to give that one up too easily.
AI is not bound to any certain device. Many AI applications, perhaps most, process data with no input or output from any moving mechanism. AI in sales and marketing, HR, finance, customer support, education, legal, and government usually involves no robots at all. (Chatbots are another matter.) AI software, however, can be used to control virtually any hardware component that relies on data processing—from autonomous cars to automated welding arms to swarms of flying weapons, or whatever.
Robots can use AI in order to react to stimuli detected by their sensors and to adjust their motions or behavior accordingly. But AI doesn’t need a robot to make it worth your investment.
Even if AI is used to determine the actions of some robots, it does not attain anything close to human intelligence inside them, no matter how lifelike they may look or act. When you see a robot like “Eric,” “Kuri,” or “Jibo,” think “technologically animated plastic doll,” not “human replacement.” Saudi Arabia’s granting citizenship to a robot named “Sophia”
