by Martin Ford
Table of Contents
Architects of Intelligence
Introduction
1. MARTIN FORD
A Brief Introduction to the Vocabulary of AI
How AI Systems Learn
2. YOSHUA BENGIO
3. STUART J. RUSSELL
4. GEOFFREY HINTON
5. NICK BOSTROM
6. YANN LECUN
7. FEI-FEI LI
8. DEMIS HASSABIS
9. ANDREW NG
10. RANA EL KALIOUBY
11. RAY KURZWEIL
12. DANIELA RUS
13. JAMES MANYIKA
14. GARY MARCUS
15. BARBARA J. GROSZ
16. JUDEA PEARL
17. JEFFREY DEAN
18. DAPHNE KOLLER
19. DAVID FERRUCCI
20. RODNEY BROOKS
21. CYNTHIA BREAZEAL
22. JOSHUA TENENBAUM
23. OREN ETZIONI
24. BRYAN JOHNSON
25. When Will Human-Level AI be Achieved? Survey Results
26. Acknowledgments
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Introduction
Chapter 1. MARTIN FORD
AUTHOR, FUTURIST
Artificial intelligence is rapidly transitioning from the realm of science fiction to the reality of our daily lives. Our devices understand what we say, speak to us, and translate between languages with ever-increasing fluency. AI-powered visual recognition algorithms are outperforming people and beginning to find applications in everything from self-driving cars to systems that diagnose cancer in medical images. Major media organizations increasingly rely on automated journalism to turn raw data into coherent news stories that are virtually indistinguishable from those written by human journalists.
The list goes on and on, and it is becoming evident that AI is poised to become one of the most important forces shaping our world. Unlike more specialized innovations, artificial intelligence is becoming a true general-purpose technology. In other words, it is evolving into a utility—not unlike electricity—that is likely to ultimately scale across every industry, every sector of our economy, and nearly every aspect of science, society and culture.
The demonstrated power of artificial intelligence has, in the last few years, led to massive media exposure and commentary. Countless news articles, books, documentary films and television programs breathlessly enumerate AI’s accomplishments and herald the dawn of a new era. The result has been a sometimes incomprehensible mixture of careful, evidence-based analysis, together with hype, speculation and what might be characterized as outright fear-mongering. We are told that fully autonomous self-driving cars will be sharing our roads in just a few years—and that millions of jobs for truck, taxi and Uber drivers are on the verge of vaporizing. Evidence of racial and gender bias has been detected in certain machine learning algorithms, and concerns about how AI-powered technologies such as facial recognition will impact privacy seem well-founded. Warnings that robots will soon be weaponized, or that truly intelligent (or superintelligent) machines might someday represent an existential threat to humanity, are regularly reported in the media. A number of very prominent public figures—none of whom are actual AI experts—have weighed in. Elon Musk has used especially extreme rhetoric, declaring that AI research is “summoning the demon” and that “AI is more dangerous than nuclear weapons.” Even less volatile individuals, including Henry Kissinger and the late Stephen Hawking, have issued dire warnings.
The purpose of this book is to illuminate the field of artificial intelligence—as well as the opportunities and risks associated with it—by having a series of deep, wide-ranging conversations with some of the world’s most prominent AI research scientists and entrepreneurs. Many of these people have made seminal contributions that directly underlie the transformations we see all around us; others have founded companies that are pushing the frontiers of AI, robotics and machine learning.
Selecting a list of the most prominent and influential people working in a field is, of course, a subjective exercise, and without doubt there are many other people who have made, or are making, critical contributions to the advancement of AI. Nonetheless, I am confident that if you were to ask nearly anyone with a deep knowledge of the field to compose a list of the most important minds who have shaped contemporary research in artificial intelligence, you would receive a list of names that substantially overlaps with the individuals interviewed in this book. The men and women I have included here are truly the architects of machine intelligence—and, by extension, of the revolution it will soon unleash.
The conversations recorded here are generally open-ended, but are designed to address some of the most pressing questions that face us as artificial intelligence continues to advance: What specific AI approaches and technologies are most promising, and what kind of breakthroughs might we see in the coming years? Are true thinking machines—or human-level AI—a real possibility and how soon might such a breakthrough occur? What risks, or threats, associated with artificial intelligence should we be genuinely concerned about? And how should we address those concerns? Is there a role for government regulation? Will AI unleash massive economic and job market disruption, or are these concerns overhyped? Could superintelligent machines someday break free of our control and pose a genuine threat? Should we worry about an AI “arms race,” or that other countries with authoritarian political systems, particularly China, may eventually take the lead?
It goes without saying that no one really knows the answers to these questions. No one can predict the future. However, the AI experts I’ve spoken to here do know more about the current state of the technology, as well as the innovations on the horizon, than virtually anyone else. They often have decades of experience and have been instrumental in creating the revolution that is now beginning to unfold. Therefore, their thoughts and opinions deserve to be given significant weight. In addition to my questions about the field of artificial inte
lligence and its future, I have also delved into the backgrounds, career trajectories and current research interests of each of these individuals, and I believe their diverse origins and varied paths to prominence will make for fascinating and inspiring reading.
Artificial intelligence is a broad field of study with a number of subdisciplines, and many of the researchers interviewed here have worked in multiple areas. Some also have deep experience in other fields, such as the study of human cognition. Nonetheless, what follows is a brief attempt to create a very rough road map showing how the individuals interviewed here relate to the most important recent innovations in AI research and to the challenges that lie ahead. More background information about each person is available in his or her biography, which is located immediately after the interview.
The vast majority of the dramatic advances we’ve seen over the past decade or so—everything from image and facial recognition, to language translation, to AlphaGo’s conquest of the ancient game of Go—are powered by a technology known as deep learning, or deep neural networks. Artificial neural networks, in which software roughly emulates the structure and interaction of biological neurons in the brain, date back at least to the 1950s. Simple versions of these networks are able to perform rudimentary pattern recognition tasks, and in the early days generated significant enthusiasm among researchers. By the 1960s, however—at least in part as the direct result of criticism of the technology by Marvin Minsky, one of the early pioneers of AI—neural networks fell out of favor and were almost entirely dismissed as researchers embraced other approaches.
Over a roughly 20-year period beginning in the 1980s, a very small group of research scientists continued to believe in and advance the technology of neural networks. Foremost among these were Geoffrey Hinton, Yoshua Bengio and Yann LeCun. These three men not only made seminal contributions to the mathematical theory underlying deep learning, they also served as the technology’s primary evangelists. Together they refined ways to construct much more sophisticated—or “deep”—networks with many layers of artificial neurons. A bit like the medieval monks who preserved and copied classical texts, Hinton, Bengio and LeCun ushered neural networks through their own dark age—until the decades-long exponential advance of computing power, together with a nearly incomprehensible increase in the amount of data available, eventually enabled a “deep learning renaissance.” That progress became an outright revolution in 2012, when a team of Hinton’s graduate students from the University of Toronto entered a major image recognition contest and decimated the competition using deep learning.
In the ensuing years, deep learning has become ubiquitous. Every major technology company—Google, Facebook, Microsoft, Amazon, Apple, as well as leading Chinese firms like Baidu and Tencent—have made huge investments in the technology and leveraged it across their businesses. The companies that design microprocessor and graphics (or GPU) chips, such as NVIDIA and Intel, have also seen their businesses transformed as they rush to build hardware optimized for neural networks. Deep learning—at least so far—is the primary technology that has powered the AI revolution.
This book includes conversations with the three deep learning pioneers, Hinton, LeCun and Bengio, as well as with several other very prominent researchers at the forefront of the technology. Andrew Ng, Fei-Fei Li, Jeff Dean and Demis Hassabis have all advanced neural networks in areas like web search, computer vision, self-driving cars and more general intelligence. They are also recognized leaders in teaching, managing research organizations, and entrepreneurship centered on deep learning technology.
The remaining conversations in this book are generally with people who might be characterized as deep learning agnostics, or perhaps even critics. All would acknowledge the remarkable achievements of deep neural networks over the past decade, but they would likely argue that deep learning is just “one tool in the toolbox” and that continued progress will require integrating ideas from other spheres of artificial intelligence. Some of these, including Barbara Grosz and David Ferrucci, have focused heavily on the problem of understanding natural language. Gary Marcus and Josh Tenenbaum have devoted large portions of their careers to studying human cognition. Others, including Oren Etzioni, Stuart Russell and Daphne Koller, are AI generalists or have focused on using probabilistic techniques. Especially distinguished among this last group is Judea Pearl, who in 2012 won the Turing Award—essentially the Nobel Prize of computer science—in large part for his work on probabilistic (or Bayesian) approaches in AI and machine learning.
Beyond this very rough division defined by their attitude toward deep learning, several of the researchers I spoke to have focused on more specific areas. Rodney Brooks, Daniela Rus and Cynthia Breazeal are all recognized leaders in robotics. Breazeal along with Rana El Kaliouby are pioneers in building systems that understand and respond to emotion, and therefore have the ability to interact socially with people. Bryan Johnson has founded a startup company, Kernel, which hopes to eventually use technology to enhance human cognition.
There are three general areas that I judged to be of such high interest that I delved into them in every conversation. The first of these concerns the potential impact of AI and robotics on the job market and the economy. My own view is that as artificial intelligence gradually proves capable of automating nearly any routine, predictable task—regardless of whether it is blue or white collar in nature—we will inevitably see rising inequality and quite possibly outright unemployment, at least among certain groups of workers. I laid out this argument in my 2015 book, Rise of the Robots: Technology and the Threat of a Jobless Future.
The individuals I spoke to offered a variety of viewpoints about this potential economic disruption and the type of policy solutions that might address it. In order to dive deeper into this topic, I turned to James Manyika, the Chairman of the McKinsey Global Institute. Manyika offers a unique perspective as an experienced AI and robotics researcher who has lately turned his efforts toward understanding the impact of these technologies on organizations and workplaces. The McKinsey Global Institute is a leader in conducting research into this area, and this conversation includes many important insights into the nature of the unfolding workplace disruption.
The second question I directed at everyone concerns the path toward human-level AI, or what is typically called Artificial General Intelligence (AGI). From the very beginning, AGI has been the holy grail of the field of artificial intelligence. I wanted to know what each person thought about the prospect for a true thinking machine, the hurdles that would need to be surmounted and the timeframe for when it might be achieved. Everyone had important insights, but I found three conversations to be especially interesting: Demis Hassabis discussed efforts underway at DeepMind, which is the largest and best funded initiative geared specifically toward AGI. David Ferrucci, who led the team that created IBM Watson, is now the CEO of Elemental Cognition, a startup that hopes to achieve more general intelligence by leveraging an understanding of language. Ray Kurzweil, who now directs a natural language-oriented project at Google, also had important ideas on this topic (as well as many others). Kurzweil is best known for his 2005 book, The Singularity is Near. In 2012, he published a book on machine intelligence, How to Create a Mind, which caught the attention of Larry Page and led to his employment at Google.
As part of these discussions, I saw an opportunity to ask this group of extraordinarily accomplished AI researchers to give me a guess for just when AGI might be realized. The question I asked was, “What year do you think human-level AI might be achieved, with a 50 percent probability?” Most of the participants preferred to provide their guesses anonymously. I have summarized the results of this very informal survey in a section at the end of this book. Two people were willing to guess on the record, and these will give you a preview of the wide range of opinions. Ray Kurzweil believes, as he has stated many times previously, that human-level AI will be achieved around 2029—or just eleven years from the time of this writing. Rodney
Brooks, on the other hand, guessed the year 2200, or more than 180 years in the future. Suffice it to say that one of the most fascinating aspects of the conversations reported here is the starkly differing views on a wide range of important topics.
The third area of discussion involves the varied risks that will accompany progress in artificial intelligence in both the immediate future and over much longer time horizons. One threat that is already becoming evident is the vulnerability of interconnected, autonomous systems to cyber attack or hacking. As AI becomes ever more integrated into our economy and society, solving this problem will be one of the most critical challenges we face. Another immediate concern is the susceptibility of machine learning algorithms to bias, in some cases on the basis of race or gender. Many of the individuals I spoke with emphasized the importance of addressing this issue and told of research currently underway in this area. Several also sounded an optimistic note—suggesting that AI may someday prove to be a powerful tool to help combat systemic bias or discrimination.
A danger that many researchers are passionate about is the specter of fully autonomous weapons. Many people in the artificial intelligence community believe that AI-enabled robots or drones with the capability to kill, without a human “in the loop” to authorize any lethal action, could eventually be as dangerous and destabilizing as biological or chemical weapons. In July 2018, over 160 AI companies and 2,400 individual researchers from across the globe—including a number of the people interviewed here—signed an open pledge promising to never develop such weapons. (https://futureoflife.org/lethal-autonomous-weapons-pledge/) Several of the conversations in this book delve into the dangers presented by weaponized AI.
A much more futuristic and speculative danger is the so-called “AI alignment problem.” This is the concern that a truly intelligent, or perhaps superintelligent, machine might escape our control, or make decisions that might have adverse consequences for humanity. This is the fear that elicits seemingly over-the-top statements from people like Elon Musk. Nearly everyone I spoke to weighed in on this issue. To ensure that I gave this concern adequate and balanced coverage, I spoke with Nick Bostrom of the Future of Humanity Institute at the University of Oxford. Bostrom is the author of the bestselling book Superintelligence: Paths, Dangers, Strategies, which makes a careful argument regarding the potential risks associated with machines that might be far smarter than any human being.