Tools and Weapons: The Promise and the Peril of the Digital Age

by Brad Smith

As the afternoon passed, I mused to John and Dominic, “Perhaps we’re part of a secret club that is so secret we don’t even know we’re a member.”

Use technology to improve what can be improved while respecting what works well already.

In addition, the North Koreans had added what security experts call “kill switches,” which made it possible to stop the malware from spreading further. One kill switch directed the malware to look for a specific web address that did not yet exist. As long as it wasn’t there, WannaCry would continue to spread. But once someone registered and activated the web address, which was a simple technical step, the code would stop replicating. Late on May 12 a security researcher in the United Kingdom analyzed the code and found this kill switch. For the modest price of $10.69, he registered and activated the URL, stopping WannaCry from spreading further.

If a city loses its electricity, telephones, gas lines, water system, and internet, it can be thrown back into something that can feel like the Stone Age. If it’s winter, people may freeze. If it’s summer, people may overheat. Those who rely on medical devices to survive could lose their lives. And in a future with autonomous vehicles, imagine a cyberattack that penetrates automobile control systems as cars barrel down the highway.

But I felt that we needed to partner where we could even if we needed to stand apart where we should.

Everyone was pointing a finger at Facebook, but no one was pointing a finger at the prime culprit. It was like yelling at the person who forgot to lock the door without talking about the thief who broke in.

Foreign interference with democratic processes can be met successfully only if the stakeholders in a republic set aside enough of their differences to work together to respond effectively.

As we evolved our cybersecurity efforts, we recognized the need to take—and talk about—three distinct strategies. The first and most obvious was to strengthen technical defenses.

The second approach, involving what we call operational security, was in some ways more of a priority at Microsoft than at some other tech companies. It includes the work of our threat intelligence teams to detect new threats, the focus of our Cyber Defense Operations Center to share this information with customers, and the work of the Digital Crimes Unit to disrupt and take action against cyberattacks.

When we combined these two strategies, they still seemed an insufficient response to broadening attacks. We needed to prop the cybersecurity stool with a third important leg: stronger international rules and coordinated diplomatic action to restrain cyberthreats and help galvanize the international community to pressure governments to stop indiscriminate cyberattacks.

As one Trump adviser challenged me on a trip to Washington, DC, “As an American company, why won’t you agree to help the US government spy on people in other countries?” I pointed out that Trump Hotels had just opened a new property in the Middle East as well as down the street on Pennsylvania Avenue. “Are these hotels going to spy on people from other countries who stay there? It doesn’t seem like it would be good for the family business.” He nodded.

Ironically and in our view unfortunately, the Paris Call garnered all this support without the backing of the United States government, which didn’t sign the declaration in Paris. Although we originally were hopeful that Washington would sign on, it became apparent a month before the Paris meetings that the U.S. government wasn’t ready to take a position one way or another. The political winds among some on the White House staff were not blowing in favor of multilateral initiatives, regardless of the issue. It put us in an unusual position, as we had our government affairs teams around the world asking other countries to support the effort.

The New Zealand government’s team worked pretty much around the clock, juggling feedback from government leaders and other stakeholders. On a late-night phone call that Satya and I had with Ardern, I mentioned how struck I was by the government’s speed. She replied, “When you’re small, you have to be nimble!”

Launched just a little more than six months apart, the Paris and Christchurch calls highlight the progress the world can make by advancing what Casper Klynge likes to call “techplomacy.” Instead of relying on governments alone, a new approach to multi-stakeholder diplomacy brings governments, civil society, and tech companies together.

Schrems proudly recalled how he helped his American peers circumvent the blocks the school had put on Google searches. “I showed them that there’s Google.it, which works perfectly fine because the school only blocked dot-com,” he said. “The exchange student introduced the school to international top-level domains!”

As the meeting progressed, Satya suddenly exclaimed with a bit of a chuckle, “Isn’t this great?” He continued, “For years it has been next to impossible to get all the engineers across the company to agree on a single privacy architecture. Now the regulators and lawyers have told us what to do. The job of creating a single architecture just got a whole lot easier.” It was an interesting observation. Engineering is a creative process and engineers are creative people. When two software engineering teams approached the same problem in different ways, it could be enormously difficult to persuade them to reconcile their differences and develop a common approach. Even if the differences didn’t go to the heart of a feature of fundamental importance, people tended to hold on to what they had created.

I believed the privacy issue would be quiet until the day it was not. A firestorm could break out with little of the political foundation in place for a more thoughtful approach. The public ambivalence toward privacy reminded me of the nuclear power industry’s experience decades earlier. Throughout the 1970s the nuclear power industry had failed to engage in an effective public discussion about the risks associated with its technological advances, leaving the public and politicians alike unprepared for the meltdown that occurred at the Three Mile Island Nuclear Generating Station in Pennsylvania in 1979. As a result of the calamity, and unlike in other countries, the political fallout from Three Mile Island stopped American nuclear power construction in its tracks. It would take thirty-four years before construction would start on another nuclear power plant in the United States.

The combination of the efforts of Max Schrems and Alastair Mactaggart reveals several important lessons for the future. First, it’s hard to believe privacy will ever die the quiet death that some in the tech sector predicted a decade or two ago. People have awakened to the fact that virtually every aspect of their lives leaves behind some type of digital footprint. Privacy needs to be protected, and stronger privacy laws have become indispensable. The day will come when the United States joins the European Union and other countries in applying a law like the GDPR.

This lack of broadband in rural communities isn’t a question of affordability—these people can’t buy the service if they want it. Many rely on dial-up technology to transmit data over copper lines, unable to access online services most of us take for granted at basic download and upload speeds.3 In other words, a significant portion of rural communities lack the internet speeds that were available in urban areas over a decade ago.

Today, rural areas that lack broadband are still living in the twentieth century. And it shows in almost every economic indicator. Our data science team confirmed what universities and research institutions around the world have been finding: The highest unemployment rates in the country are frequently located in the counties with the lowest availability of broadband, highlighting the strong link between broadband availability and economic growth.

The immigration issue has long been a tough one for the tech sector in the United States. At one level, it has been indispensable to the country’s technology leadership in the world. There is simply no way the United States would be the global leader in information technology if it had not attracted many of the best and brightest people in the world to come work at leading universities or live in technology centers around the country.

In 2016, I spent some time on the emergence of new AI issues with Microsoft’s Dave Heiner, who at the time was working with Eric Horvitz, who had long led much of our basic research in the field. When I pressed Dave, he provided me with what I still regard as one helpful way to think about AI: “AI is a computer system that can learn from experience by discerning patterns in data fed to it and thereby make decisions.” Eric uses a somewhat broader definition, suggesting that “AI is the study of computational mechanisms underlying thought and intelligent behavior.” While this often involves data, it can also be based on experiences such as playing games, understanding natural languages, and the like. The ability of a computer to learn from data and experience and make decisions—the essence of these definitions of AI—is based on two fundamental technological capabilities: human perception and human cognition.

Three recent technological advances have provided the launchpad that has let AI take flight. First, computing power finally advanced to the level needed to perform the massive number of calculations needed. Second, cloud computing made large amounts of this power and storage capacity available to people and organizations without the need to make large capital investments in massive amounts of hardware. And finally, the explosion of digital data made it possible to build massively larger data sets to train AI-based systems. Without these building blocks, it’s doubtful that AI would have accelerated so quickly. But it took a fourth foundational element, which has been critical in helping computer and data scientists to make artificial intelligence effective. This goes to the second and even more fundamental technological capability needed for AI, cognition—in other words, the ability of a computer to reason and learn.

In hindsight, a system based on such detailed rules was perhaps an approach that only lawyers could love. Since the 1980s, an alternative approach to AI has proved superior. This approach uses statistical methods for pattern recognition, prediction, and reasoning, in effect building systems through algorithms that learn from data.

The Pope then reminded me of what Einstein had said after the Second World War: “I know not with what weapons World War III will be fought, but World War IV will be fought with sticks and stones.”

“I think leading a tech company is becoming more like leading a university,” I said. “We have researchers with PhDs who are like the faculty. We have interns and young employees who sometimes have views similar to university students. Everyone wants to be heard, and some want us to boycott a government agency much like they want a university to boycott the purchase of stock in a company that’s doing something objectionable.”

As George Orwell described in his novel 1984, one vision of the future would require citizens to evade government surveillance by finding their way secretly to a blackened room to tap in code on each other’s arms—because otherwise cameras and microphones will capture and record their faces, voices, and every word. Orwell sketched that vision nearly seventy years ago. We worried that technology now makes that type of future possible.

After more than fifty years of service, the fire horse had lost its job. It was a story about changing technology and its impact on work. Fire horses themselves had previously replaced men in pulling fire engines. Volunteer teams of men and boys had originally pulled these fire engines, but in 1832, when the New York fire department’s force was depleted by the city’s cholera epidemic, horsepower came to the rescue. “Not enough men . . . could be mustered to drag the engine to the scene of the conflagration.” Necessity, being the mother of invention, forced the FDNY to spend a hefty sum of $864 to purchase a fleet of horses to replace the sick and dying firemen.4 It wasn’t until the 1860s that manpower was officially swapped out for horsepower in firehouses. But the transition wasn’t easy. One obstacle was the firefighters’ pride in their work as haulers. In 1887, Abraham Purdy, known at the time as one of the oldest living firemen, said the introduction of horses had created so many squabbles within the fire department that members resigned.5 But nothing could stem the tide of progress. Continuing advancements in equipment, including quick-hitch horse collars, eventually allowed horses to relieve volunteers of their duties hauling hoses by hand. By 1869, well-trained horses and men could exit a firehouse in less than a minute.6 In the twentieth century, however, horses met the same fate in pulling a fire wagon as people had the century before: They were replaced in their j...

we talked about two places we can look for insights. The first is to understand what AI can and cannot do well and appreciate how this will impact jobs and work. To state the obvious, AI will most readily replace jobs that involve functions that it can perform well. It makes sense to consider the recent advances that have enabled AI to understand human speech, recognize images, translate languages, and reach new conclusions based on an ability to discern patterns. If a large part of a job involves tasks that can be completed by AI—and faster—then that job is probably at risk of being replaced by a computer. If we had to predict a job that AI is likely to eliminate sooner than most, we’d nominate the position of taking orders from customers in the drive-through window at a fast-food restaurant. Today, a human being listens to what we say and enters our order into a computer. But with improved outdoor microphones, artificial intelligence can capture and understand the spoken word as well as a person, meaning that this task could soon be performed entirely by a machine. Before we know it, we’ll approach a drive-through and talk to a computer rather than a person. The computer may not be 100 percent accurate, but neither is a human being. That’s why there will be an opportunity to verify and correct our order.

Even an advanced degree or sophisticated set of skills won’t keep workers from being robbed of their jobs. AI will impact every level of the income scale. Take radiologists, who today earn an average of four hundred thousand dollars a year.9 They spend much of their day scanning CT and MRI scans looking for abnormalities. If you feed enough images into an AI-powered machine, it can be trained to identify normal and abnormal X-rays, whether they involve broken bones, hemorrhages, or cancerous tumors.

As one historian has commented, “Every family in the United States in 1870 was directly or indirectly dependent on the horse.”22 Across the country, there was one horse for every five people.23 Because a typical horse consumes ten times as many daily calories as a person,24 many farmers depended on growing food for horses even more than for people.

As Henry Kissinger has noted, China owes its millennial survival to “the community of values fostered among its population and its government of scholar-officials.”12 Kissinger has probably spent more time focused on China than any other American official of the past century. As he observes, the values that continue to guide official thinking in China today derive from teachings by Confucius, who died more than two centuries before the Qin dynasty was born. His teachings included a commitment to compassionate rule, devotion to learning, and a pursuit of harmony based on a hierarchical code of social conduct that included a fundamental duty to “Know thy place.”13 As Nisbett points out, the Greek philosophy that remains the foundation of Western political thought shared a strong sense of curiosity with Confucius’s devotion to learning, but was grounded in a different sense of personal agency—a sense that people “were in charge of their own lives and free to act as they chose.”14 As developed by Aristotle and Socrates, the very definition of happiness for the ancient Greeks “consisted of being able to exercise their powers in pursuit of excellence in a life free from constraints.”

“In China,” Professor He said, “we see everything moving in cycles. Like the signs of the zodiac, we believe that life is a circle and that everything will come back to its original point at some time in the future.” It led people in China to look backward as well as forward and to focus more on the whole picture rather than an individual piece.

AI depends upon cloud-based computing power, the development of algorithms, and mountains of data. All three are essential, but the most important of these is data—data about the physical world, the economy, and how we live our daily lives. As machine learning has evolved rapidly over the past decade, it has become apparent that there’s no such thing as too much data for an AI developer.

So how do we create a bigger opportunity for smaller players in a world where large quantities of data matter? One person who may have the answer is Matthew Trunnell. Trunnell is the chief data officer at the Fred Hutchinson Cancer Research Center, a leading cancer research center in Seattle named for a hometown hero who pitched ten seasons for the Detroit Tigers and managed three major league baseball teams. In 1961, Fred Hutchinson took the Cincinnati Reds to the World Series. Sadly, Fred’s successful baseball career and life were cut short when he died of cancer in 1964 at the age of forty-five.5 His brother, Bill Hutchinson, was a surgeon who treated Fred’s cancer. After his younger brother’s death, Bill founded the “Fred Hutch,” a research center devoted to curing cancer.

But as Trunnell recognized, “the divide between those producing the data and those building novel tools is a huge missed opportunity for making impactful, life-changing—and potentially lifesaving—discoveries using the massive amount of scientific, educational, and clinical trial data being generated every day.”

Technology innovation is not going to slow down. The work to manage it needs to speed up.