by Kai-Fu Lee
But this is not a new Cold War. AI today has numerous potential military applications, but its true value lies not in destruction but in creation. If understood and harnessed properly, it can truly help all of us generate economic value and prosperity on a scale never before seen in human history.
In this sense, our current AI boom shares far more with the dawn of the Industrial Revolution or the invention of electricity than with the Cold War arms race. Yes, Chinese and American companies will compete with each other to better leverage this technology for productivity gains. But they are not seeking the conquest of the other nation. When Google promotes its TensorFlow technology abroad, or Alibaba implements its City Brain in Kuala Lumpur, these actions are more akin to the early export of steam engines and lightbulbs than as an opening volley in a new global arms race.
A clear-eyed look at the technology’s long-term impact has revealed a sobering truth: in the coming decades, AI’s greatest potential to disrupt and destroy lies not in international military contests but in what it will do to our labor markets and social systems. Appreciating the momentous social and economic turbulence that is on our horizon should humble us. It should also turn our competitive instincts into a search for cooperative solutions to the common challenges that we all face as human beings, people whose fates are inextricably intertwined across all economic classes and national borders.
GLOBAL WISDOM FOR THE AI AGE
As both the creative and disruptive force of AI is felt across the world, we need to look to each other for support and inspiration. The United States and China will lead the way in economically productive applications of AI, but other countries and cultures will certainly continue to make invaluable contributions to our broader social evolution. No single country will have all the answers to the tangled web of issues we face, but if we draw on diverse sources of wisdom, I believe there is no problem that we can’t tackle together. This wisdom will include pragmatic reforms to our education systems, subtle nuances in cultural values, and deep shifts in how we conceive of development, privacy, and governance.
In revamping our education systems, we can learn much from South Korea’s embrace of gifted and talented education. These programs seek to identify and realize the potential of the country’s top technical minds, an approach suited to creating the material prosperity that can then be broadly shared across society. Schools around the globe can also draw lessons from American experiments in social and emotional education, fostering skills that will prove invaluable to the human-centric workforce of the future.
For adaptations in how we approach work, we would be wise to look to the culture of craftsmanship in Switzerland and Japan, places where the pursuit of perfection has elevated routine work activities into the realm of human expression and artistry. Meanwhile, vibrant and meaningful cultures of volunteering in countries like Canada and the Netherlands should inspire us to diversify our traditional notions of “work.” Chinese culture can also be a source of wisdom when it comes to caring for elders and in fostering intergenerational households. As public policy and personal values blend, we should really take the time to study new experiments in defining and measuring progress, such as Bhutan’s decision to pursue “Gross National Happiness” as a key development indicator.
Finally, our governments will need to consistently look to one another in evaluating thorny new tradeoffs in data privacy, digital monopolies, online security, and algorithmic bias. In tackling these issues, we can learn much from comparing the different approaches taken by regulators in Europe, the United States, and China. While Europe has opted for a more heavy-handed approach (fining Google, for example, for antitrust and trying to wrest control over data away from the technology companies), China and the United States have given these companies greater leeway, letting technology and markets develop before intervening on the margins.
All these approaches present tradeoffs, with some favoring privacy over technological progress, and others doing the reverse. Leveraging technology to build the kind of societies we desire will mean following the real-world impact of these policies across geographies and remaining open-minded about different approaches to AI governance.
WRITING OUR AI STORY
But accessing and embracing these diverse sources of insight first requires we maintain a sense of agency in relation to this quickly accelerating technology. With the daily barrage of headlines about AI, it’s easy to feel as if human beings are losing control over our own destiny. Prophecies of both robot overlords and a “useless class” of unemployed workers tend to blend in our minds, conjuring up an overwhelming sense of human helplessness in the face of all-powerful technologies. Both of these doomsday scenarios contain a kernel of truth about AI’s potential, but the feelings of helplessness they engender obscure the key point: when it comes to shaping the future of artificial intelligence, the single most important factor will be the actions of human beings.
We are not passive spectators in the story of AI—we are the authors of it. That means the values underpinning our visions of an AI future could well become self-fulfilling prophecies. If we tell ourselves that the value of human beings lies solely in their economic contribution, then we will act accordingly. Machines will displace humans in the workplace, and we may end up in a twisted world like the one Hao Jingfang imagined in Folding Beijing, a caste-based society that divides and separates the so-called useful people from the “useless” masses.
But this is in no way a foregone conclusion. The ideology underlying this dystopian vision—of human beings as nothing more than the sum of their economically productive parts—reveals just how far we’ve led ourselves astray. We were not put on Earth to merely grind away at repetitive tasks. We don’t need to spend our lives busily accumulating wealth just so that we can die and pass it on to our children—the latest “iteration” of the human algorithm—who will refine and repeat that process.
If we believe that life has meaning beyond this material rat race, then AI just might be the tool that can help us uncover that deeper meaning.
HEARTS AND MINDS
When I launched my AI career in 1983, I did so by waxing philosophic in my application to the Ph.D. program at Carnegie Mellon. I described AI as “the quantification of the human thinking process, the explication of human behavior,” and our “final step” to understanding ourselves. It was a succinct distillation of the romantic notions in the field at that time and one that inspired me as I pushed the bounds of AI capabilities and human knowledge.
Today, thirty-five years older and hopefully a bit wiser, I see things differently. The AI programs that we’ve created have proven capable of mimicking and surpassing human brains at many tasks. As a researcher and scientist, I’m proud of these accomplishments. But if the original goal was to truly understand myself and other human beings, then these decades of “progress” got me nowhere. In effect, I got my sense of anatomy mixed up. Instead of seeking to outperform the human brain, I should have sought to understand the human heart.
It’s a lesson that it took me far too long to learn. I have spent much of my adult life obsessively working to optimize my impact, to turn my brain into a finely tuned algorithm for maximizing my own influence. I bounced between countries and worked across time zones for that purpose, never realizing that something far more meaningful and far more human lay in the hearts of the family members, friends, and loved ones who surrounded me. It took a cancer diagnosis and the unselfish love of my family for me to finally connect all these dots into a clearer picture of what separates us from the machines we build.
That process changed my life, and in a roundabout way has led me back to my original goal of using AI to reveal our nature as human beings. If AI ever allows us to truly understand ourselves, it will not be because these algorithms captured the mechanical essence of the human mind. It will be because they liberated us to forget about optimizations and to instead focus on what truly makes us human: loving and being loved.
Reac
hing that point will require hard work and conscious choices by all of us. Luckily, as human beings, we possess the free will to choose our own goals that AI still lacks. We can choose to come together, working across class boundaries and national borders to write our own ending to the AI story.
Let us choose to let machines be machines, and let humans be humans. Let us choose to simply use our machines, and more importantly, to love one another.
Acknowledgments
First and foremost, I want to thank my collaborator, Matt Sheehan, who did a tremendous amount of work on this book under a very tight deadline. If you feel this book is fun and easy to read, or maybe find it rich in information, Matt deserves much of the credit. I was lucky to find a collaborator like Matt, someone with a deep understanding of China, the United States, technology, and writing.
I was talked into doing this book by my friend and agent John Brockman and his team. His belief in the urgency of the subject and my ability to uniquely contribute to the conversation first persuaded me to consider taking on this project. In hindsight, I think he was absolutely right.
I’d like to thank Rick Wolff, who decided to bet on an unproven topic based on my own conviction. He is an outstanding editor and worked wonders in bringing this book to market. It was tremendous fun working with Rick—and pushing each other to be the best we could be.
I also want to thank Erik Brynjolfsson, James Manyika, Jonathan Woetzel, Paul Triolo, Shaolan Hsueh, Chen Xu, Ma Xiaohong, Lin Qi-ling, Wu Zhuohao, Michael Chui, Yuan Li, Cathy Yang, Anita Huang, Maggie Tsai, and Laurie Erlam for helping to read early drafts and giving me valuable feedback.
Final thanks go to my family, who have tolerated my inattentiveness during the past six months. I cannot wait to return to their embrace, an embrace that sustains me and has taught me so much. This should be my last book for a while. Then again, I’ve told them that seven times before—hopefully they’ll still buy it.
Notes
1. CHINA’S SPUTNIK MOMENT
atoms in the known universe: “Go and Mathematics,” in Wikipedia, s.v., “Legal Positions,” https://en.wikipedia.org/wiki/Go_and_mathematics#Legal_positions.
280 million Chinese viewers: Cade Metz, “What the AI Behind AlphaGo Can Teach Us About Being Human,” Wired, May 19, 2016, https://www.wired.com/2016/05/google-alpha-go-ai/.
issued an ambitious plan: Paul Mozur, “Beijing Wants A.I. to Be Made in China by 2030,” New York Times, July 20, 2017, https://www.nytimes.com/2017/07/20/business/china-artificial-intelligence.html.
making up 48 percent: James Vincent, “China Overtakes US in AI Startup Funding with a Focus on Facial Recognition and Chips,” The Verge, February 2, 2018, https://www.theverge.com/2018/2/22/17039696/china-us-ai-funding-startup-comparison.
first software program: Kai-Fu Lee and Sanjoy Mahajan, “The Development of a World Class Othello Program,” Artificial Intelligence 43, no. 1 (April 1990): 21–36.
to create Sphinx: Kai-Fu Lee, “On Large-Vocabulary Speaker-Independent Continuous Speech Recognition,” Speech Communication 7, no. 4 (December 1988): 375–379.
profile in the New York Times: John Markoff, “Talking to Machines: Progress Is Speeded,” New York Times, July 6, 1988, https://www.nytimes.com/1988/07/06/business/business-technology-talking-to-machines-progress-is-speeded.html?mcubz=1.
demolished the competition: ImageNet Large Scale Visual Recognition Challenge 2012, Full Results, http://image-net.org/challenges/LSVRC/2012/results.html.
for over $500 million: Catherine Shu, “Google Acquires Artificial Intelligence Startup for Over $500 Million,” TechCrunch, January 26, 2014, https://techcrunch.com/2014/01/26/google-deepmind/.
harnessing of electricity: Shana Lynch, “Andrew Ng: Why AI is the New Electricity,” The Dish (blog), Stanford News, March 14, 2017, https://news.stanford.edu/thedish/2017/03/14/andrew-ng-why-ai-is-the-new-electricity/.
add $15.7 trillion: Dr. Anand S. Rao and Gerard Verweij, “Sizing the Prize,” PwC, June 27, 2017, https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf.
2. COPYCATS IN THE COLISEUM
The Cloner: Gady Epstein, “The Cloner,” Forbes, April 28, 2011, https://www.forbes.com/global/2011/0509/companies-wang-xing-china-groupon-friendster-cloner.html#1272f84055a6.
“A Mark Zuckerberg Production”: 孙进, 李静颖 孙进,and 刘佳, “社交媒体冲向互联网巅峰,” 第一财经日报, April 21, 2011, http://www.yicai.com/news/739256.html.
“let some people get rich first”: “To Each According to His Abilities,” Economist, May 31, 2001, https://www.economist.com/node/639652.
“come to Shijingshan!”: Gabrielle H. Sanchez, “China’s Counterfeit Disneyland Is Actually Super Creepy,” BuzzFeed, December 11, 2014, https://www.buzzfeed.com/gabrielsanchez/chinas-eerie-counterfeit-disneyland.
0.2 percent of the Chinese population: Xueping Du, “Internet Adoption and Usage in China,” 27th Annual Telecommunications Policy and Research Conference, Alexandria, VA, September 25–27, 1999, https://pdfs.semanticscholar.org/4881/088c67ad919da32487c567341f8a0af7e47e.pdf.
“free is not a business model”: “Ebay Lectures Taobao That Free Is Not a Business Model,” South China Morning Post, October 21, 2005, http://www.scmp.com/node/521384.
his autobiography, Disruptor: 周鸿祎, “颠覆者” (北京: 北京联合出版公司, 2017).
Sinovation event in Menlo Park: Dr. Andrew Ng, Dr. Sebastian Thrun, and Dr. Kai-Fu Lee, “The Future of AI,” moderated by John Markoff, Sinovation Ventures, Menlo Park, CA, June 10, 2017, http://us.sinovationventures.com/blog/the-future-of-ai.
book The Lean Startup: Eric Ries, The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses (New York: Crown Business, 2011).
3. CHINA’S ALTERNATE INTERNET UNIVERSE
the Next Web: Francis Tan, “Tencent Launches Kik-Like Messaging App,” The Next Web, January 21, 2011, https://thenextweb.com/asia/2011/01/21/tencent-launches-kik-like-messaging-app-in-china/.
“remote control for life”: Connie Chan, “A Whirlwind Tour Through China Tech Trends,” Andreesen Horowitz (blog), February 6, 2017, https://a16z.com/2017/02/06/china-trends-2016-2017/.
“Pearl Harbor attack”: Josh Horwitz, “Chinese WeChat Users Sent out 20 Million Cash-Filled Red Envelopes to Friends and Family Within Two Days,” TechinAsia, February 4, 2014, https://www.techinasia.com/wechats-money-gifting-scheme-lures-5-million-chinese-users-alibabas-jack-ma-calls-pearl-harbor-attack-company.
“mass entrepreneurship and mass innovation”: “Premier Li’s Speech at Summer Davos Opening Ceremony,” Xinhua, September 10, 2014, http://english.gov.cn/premier/speeches/2014/09/22/content_281474988575784.htm.
nearly quadrupling: Zero2IPO Research, “清科观察：《2016政府引导基金报告》发布，管理办法支持四大领域、明确负面清单,” 清科研究中心, March 30, 2016, http://free.pedata.cn/1440998436840710.html.
quadrupled to $12 billion: “Venture Pulse Q4 2017,” KPMG Enterprise, January 16, 2018, https://assets.kpmg.com/content/dam/kpmg/xx/pdf/2018/01/venture-pulse-report-q4-17.pdf.
ten times the total: Thomas Laffont and Daniel Senft, “East Meets West 2017 Keynote,” East Meets West 2017 Conference, Pebble Beach, CA, June 26–29, 2017.
“do what we do best”: Joshua Brustein, “GrubHub Buys Yelp’s Eat24 for $288 Million,” Bloomberg, August 3, 2017, https://www.bloomberg.com/news/articles/2017-08-03/grubhub-buys-yelp-s-eat24-for-288-million.
study by McKinsey and Company: Kevin Wei Wang, Alan Lau, and Fang Gong, “How Savvy, Social Shoppers Are Transforming Chinese E-Commerce,” McKinsey and Company, April 2017, https://www.mckinsey.com/industries/retail/our-insights/how-savvy-social-shoppers-are-transforming-chinese-e-commerce.
753 million smartphone users: 第41次 “中国互联网络发展状况统计报告,” 中国互联网络信息中心, January 18, 2018, http://www.cac.gov.cn/2018=01/31/c_1122346138.htm.
“no cash
left in Hangzhou?”: “你的城市还用现金吗？杭州的劫匪已经抢不到钱了,” 吴晓波频道, April 3, 2017, http://www.sohu.com/a/131836799_565426.
iResearch estimated in 2017: “China’s Third-Party Mobile Payments Report,” iResearch, June 28, 2017, http://www.iresearchchina.com/content/details8_34116.html.
surpassed $17 trillion: Analysis 易观, “中国第三方支付移动支付市场季度监测报告2017年第4季度,” http://www.analysis.cn/analysis/trade/detail/1001257/.
for $2.7 billion: Cate Cadell, “China's Meituan Dianping Acquires Bike-Sharing Firm Mobike for $2.7 Billion,” Reuters, April 3, 2018, https://www.reuters.com/article/us-mobike-m-a-meituan/chinas-meituan-dianping-acquires-bike-sharing-firm-mobike-for-2-7-billion-idUSKCN1HB0DU.
three hundred to one: Laffont and Senft, “East Meets West 2017 Keynote.”
4. A TALE OF TWO COUNTRIES
“put AAAI on Christmas day”: Sarah Zhang, “China’s Artificial Intelligence Boom,” Atlantic, February 16, 2017, https://www.theatlantic.com/technology/archive/2017/02/china-artificial-intelligence/516615/.
23.2 percent to 42.8: Dr. Kai-Fu Lee and Paul Triolo, “China Embraces AI: A Close Look and a Long View,” presentation at Eurasia Group, December 6, 2017, https://www.eurasiagroup.net/live-post/ai-in-china-cutting-through-the-hype.
one hundred most-cited research institutions: Shigenori Arai, “China’s AI Ambitions Revealed by List of Most Cited Research Papers,” Nikkei Asian Review, November 2, 2017, https://asia.nikkei.com/Tech-Science/Tech/China-s-AI-ambitions-revealed-by-list-of-most-cited-research-papers.
“these Chinese people are good”: Same Shead, “Eric Schmidt on AI: ‘Trust Me, These Chinese People Are Good,’” Business Insider, November 1, 2017, http://www.businessinsider.com/eric-schmidt-on-artificial-intelligence-china-2017-11.
