6: Platform Is the Key
In a business, AI should be a component of the technology platform. That platform involves both automated and human elements. Humans create, manage, and use tools such as data management systems, processors, sensors, and actuators. Semi- and fully automated systems in the platform need feedback loops to measure the effectiveness, satisfaction, and safety of the human user and those in the environment.
The technology platform for your organization must be well integrated. You have to be able to aggregate and analyze the data for the feedback loops so that you can assess how well your technology systems are doing what they do. The system applications have to complement each other.
And you have to maintain cost controls all throughout. If the technology platform costs more money than it’s worth, you have to change it, or you will harm your business. Never adopt a new tool just because it’s cool and shiny. Your AI system has to fit your technology platform, and vice versa.
Design principles in AI are the same as they are for all good products and businesses. Empower your people, your customers and employees. Humanize your business analysts, engineers, and data scientists. You stay in charge, as you are the responsible one.
In an autonomous car, you do the driving, whether you have your hands on the wheel—if there is one—or not. You decide to use it. You tell it where to go, and when. The vehicle’s guidance system, if well designed and maintained, enables people to go to their desired destination with less effort and greater safety. It’s just the tool, not the driver.
Rule 7: Abide by the Law and Act Ethically
You probably dislike lawyers—almost everyone does—but don’t hate the law. Many people in business view lawyers and legal controls as a hindrance, a drag on their profitability, or, as they say in Silicon Valley, “our creativity,” but the law is your friend. It’s what keeps us free.
If you don’t believe us, go start a subsidiary in Russia. There you will find corruption everywhere, an unfree judiciary, and crime as the norm. Law enforcement is to be feared and avoided at all costs. Choose your mafia partner wisely.
Legal control supports business control. Early, sound legal controls help to prevent technology train wrecks, including those caused by AI. And the sad truth is that those wipeouts lead to yet more regulations, many of them difficult and ineffectual. Businesses end up burdening themselves through not following the rules in the first place. It’s a vicious cycle, but you can avoid it with compliance built in from the get-go.
If you comply with the law as you adopt your AI system, you will have a kind of insurance policy against a wide variety of internal disasters and crippling lawsuits. (You will learn more about this from Joshua in chapters 5 and 6.)
The afterword in this book is a special chapter on “AI ethics.” Attention to ethical considerations can keep lawyers away and you out of all kinds of trouble. Ethics and the law go hand in hand. These days, public and private institutions have commissions, committees, boards, task forces, point people, and so on, all working on ethics for AI. And they should! While ethics lack the law’s teeth and are not in a position to command compliance, ethical discussions about what is right and wrong, what is appropriate and inappropriate, good and bad, should inform legislators in their work to order the state, society, and economy. Ethics should inform every worker in every organization, as well. You need to consider human rights, privacy, and stakeholder interests.
Putting people at the forefront of AI adoption, not the algorithms, helps your business succeed. People in business and government need to evolve and adapt their thought to meet the challenge of AI’s data-processing capabilities. Reject the myths, hold fast to the rules, compete to survive and succeed, and stay human. We need to start with healthy skepticism, maintain humility, and consider our neighbor throughout. This mind-set will produce better outcomes. Humanize your AI.
1.
J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon, “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence,” August 31, 1955. Last modified April 3, 1996. http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html.
2.
According to his best-selling, autobiographical book about AI and great power politics: Kai-Fu Lee, AI Superpowers: China, Silicon Valley, and the New World Order (Boston: Houghton Mifflin Harcourt, 2018), ch. 7.
3.
See Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford, England: Oxford University Press, 2014).
4.
5.
Mariya Yao, “Beyond Backpropagation: Can We Go Deeper than Deep Learning?” Topbots, November 9, 2017. https://www.topbots.com/deeper-than-deep-learning-beyond-backpropagation-geoffrey-hinton/.
6.
Matthew Herper, “MD Anderson Benches IBM Watson in Setback for Artificial Intelligence in Medicine,” Forbes.com, February 19, 2017. https://www.forbes.com/sites/matthewherper/2017/02/19/md-anderson-benches-ibm-watson-in-setback-for-artificial-intelligence-in-medicine/#3175068f3774.
Chapter 2
AI in Plain English
AI is cutting-edge, proliferating technology, devised by people, so it can be understood and used. There is no magic and no mystery involved. It cannot work wonders, but it can be applied to many, many tasks that we find in workplaces across the globe. As in other technological “revolutions,” research and development are leading the way.
In 2017, the number of AI-related patent applications worldwide rose to more than 55,000, up from 19,000 in 2013. Since 2013, as many patents have been awarded as in the preceding sixty years. IBM and Microsoft are leading the pack with applications, followed closely by Japanese and Korean tech companies. The 167 universities and research institutes that apply for patents are mostly in China, the United States, and South Korea.[1] In 2019, the United States awarded double the number of AI patents over the year before.
The patent explosion is following a similar trend in the publication of scientific papers. In 2016, there were three times as many scientific papers as commercial patents, down from eight times in 2010. Patents are filed for applications in the telecom, transportation, life and medical sciences, personal devices, computers, banking, entertainment, security, manufacturing, and agricultural industries.
AI tech has left the lab for the world market in goods and services, ready to be used, bought, and sold, not just by corporate giants, but millions of small to medium-sized businesses and organizations like yours.
The most commonly patented AI technology is machine learning, which frequently relies on “deep learning,” “neural network” algorithms. The number of machine-learning patents has lately grown 175 percent per
