THE END OF ARTIFICIAL ARTIFICIAL INTELLIGENCE

Prepare for a Future of Machine Learning

Intelligence is the ability to adapt to change.

—Stephen Hawkings

In the early 2000s, Amazon released the Mechanical Turk, a tool advertised as “artificial artificial intelligence.” The Mechanical Turk was built as a platform to outsource small pieces of work, called human intelligence tasks (HITs) to people around the world who would do the typically small pieces of work. Since then, it has been used primarily for scaling the kind of work that is difficult for computers to accomplish, such as reviewing the quality of written content or images.

However, AI and machine learning are progressing at a rapid clip—exponentially, if Elon Musk is to be believed.¹ Regardless of its exact evolutionary rate, AI and machine learning can tackle an increasingly wide array of tasks, including those often performed by Mechanical Turk. The potential power and impact is such that Jeff Bezos included a special warning (or encouragement?) in his 2017 letter to shareholders, in which he advised his audience to “embrace external trends.” “We’re in the middle of an obvious one right now: machine learning and artificial intelligence,” he warned. When Bezos takes the time to deliver a specific warning, I’d recommend sitting up and paying attention.

IT’S CONNECTED

It’s not accidental that many of the ideas we’ve discussed in this book are building toward being able to take advantage of the machine-learning age. Collecting lots of data about your customer experiences, your processes, your environment? Vital. Defining your processes in a deliberate and granular manner, figuring out how to make them services, and “doing the math” and trying to create the rules and formulas for your work and decisions? Great building blocks. Understanding your principles, how you make decisions, and the patterns of your logic? Essential. These types of deliberate engineering and introspection are the bedrock of what algorithms need to automate a process.

Amazon is well situated to take full advantage of these capabilities because they’ve built these underlying blocks. And they recognize the need, so they start learning and experimenting. “In the early part of this decade, Amazon had yet to significantly tap these advances, but it recognized the need was urgent. This era’s most critical competition would be in AI—Google, Facebook, Apple, and Microsoft were betting their companies on it—and Amazon was falling behind. We went out to every [team] leader to basically say, ‘How can you use these techniques and embed them into your own businesses?’” said David Limp, Amazon’s VP of devices and services.²

PRINCIPLES

“Everything happens over and over again,” explained Ray Dalio, founder of Bridgewater Associates. “Principles are a way of looking at things so that everything is viewed as ‘another one of these,’ and when another one of those comes along, how do I deal with that successfully?”

Dalio built a decision-making system by writing down the criteria of every issue he encountered. This system allowed him to characterize issues, develop criteria, and easily identify the signal from the noise. In addition, he could synchronize with others and convert many of these issues to algorithms.³

In their 2018 Artificial Intelligence Innovation Report, the good people at Deloitte characterized the future of AI in executive decision-making as a “partnership,”—one in which humans define the issues and have a final say on the best answer for their business, while AI analyzes terabytes of data to provide a basis for the decision.⁴

Dalio likens the perfect relationship between human and machine to playing chess side-by-side with a computer. “So, you make the move, it makes the move,” he said. “You compare your moves, and you think about them, and then you refine them.”⁵ Needless to say, Dalio continued, it can be difficult to understand cause and effect in a complex, black-box model.

We can leverage his approach to take advantage of machine learning for our core management approaches. Specifically, we can adopt his clarity and meticulous attention to detail regarding thinking through the patterns in our businesses, creating rules to manage them, writing them down so others can use and improve on them, and making computer models from them.

What’s the minimum an executive or board should be doing with regard to machine learning? Actively learning, interviewing, and paying attention to how it is affecting the industry and functions your company is in. You should be constantly probing “how and when” to start and find ways to do small pilots. An organization needs to build experience and the capacity to experiment with innovations if it is going to be able to rely on those capabilities later on.

BE A PREPPER

“The outside world can push you into Day 2 if you won’t or can’t embrace powerful trends quickly,” Bezos wrote in Amazon’s 2016 letter to shareholders. “If you fight them, you’re probably fighting the future. Embrace them and you have a tailwind. These big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We’re in the middle of an obvious one right now: machine learning and artificial intelligence.”⁶

Looking back, it’s easy to identify the tidal waves of technological progress—the printing press, the electric light, the automobile, the transistor—that drove business and society into entirely new eras. All of these inventions had (mostly) positive impacts on society, but their wholesale social applications and adoptions were not without fear and lessons learned.

In his book Hit Refresh: The Quest to Rediscover Microsoft’s Soul and Imagine a Better Future for Everyone, Microsoft CEO Satya Nadella wrote, “Today we don’t think of aviation as ‘artificial flight’—it’s simply flight. In the same way, we shouldn’t think of technological intelligence as artificial, but rather as intelligence that serves to augment human capabilities and capacities.”⁷ In the same way as “e-commerce” is becoming just commerce, in the coming decade “artificial intelligence” will become just part of our management intelligence, integrated into everyday processes and everyday decisions.

Management must be deeply curious, not just “paying attention.” Figure out how to ride the wave instead of being crushed beneath it. As we will discuss in the next chapter, we must train ourselves and our teams to make good decisions.