By Joseph S. Nye
CAMBRIDGE, Mass. ( Project Syndicate )—An elder statesman, a retired Big Tech CEO, and a computer scientist meet in a bar. What do they talk about? Artificial intelligence, of course, because everyone is talking about it—or to it, whether they call it Alexa, Siri, or something else. We need not wait for a science-fiction future; the age of AI is already upon us. Machine-learning, in particular, is having a powerful effect on our lives, and it will strongly affect our future, too.
That is the message of this fascinating new book by former U.S. Secretary of State Henry A. Kissinger, former Google /zigman2/quotes/205453964/composite GOOG +0.71% CEO Eric Schmidt, and MIT dean Daniel Huttenlocher. And it comes with a warning: AI will challenge the primacy of human reason that has existed since the dawn of the Enlightenment.
Can machines really think? Are they intelligent? And what do those terms mean? In 1950, the renowned British mathematician Alan Turing suggested that we avoid such deep philosophical conundrums by judging performance: If we cannot distinguish a machine’s performance from a human’s, we should label it “intelligent.” Most early computer programs produced rigid and static solutions that failed this “ Turing test ,” and the field of AI went on to languish throughout the 1980s.
But a breakthrough occurred in the 1990s with a new approach that allowed machines to learn on their own, instead of being guided solely by codes derived from human-distilled insights. Unlike classical algorithms, which consist of steps for producing precise results, machine-learning algorithms consist of steps for improving upon imprecise results. The modern field of machine-learning—of programs that learn through experience—was born.
The technique of layering machine-learning algorithms within neural networks (inspired by the structure of the human brain) was initially limited by a lack of computing power. But that has changed in recent years. In 2017, AlphaZero, an AI program developed by Google’s DeepMind, defeated Stockfish , the most powerful chess program in the world. What was remarkable was not that a computer program prevailed over another computer program, but that it taught itself to do so. Its creators supplied it with the rules of chess and instructed it to develop a winning strategy. After just four hours of learning by playing against itself, it emerged as the world’s chess champion, beating Stockfish 28 times without losing a match (there were 72 draws).
AlphaZero’s play is informed by its ability to recognize patterns across vast sets of possibilities that human minds cannot perceive, process, or employ. Similar machine-learning methods have since taken AI beyond beating human chess experts to discovering entirely new chess strategies. As the authors point out, this takes AI beyond the Turing test of performance indistinguishable from human intelligence to include performance that exceeds that of humans.
Generative neural networks also can create new images or texts. The authors cite OpenAI’s GPT-3 as one of the most noteworthy generative AIs today. In 2019, the company developed a language model that trains itself by consuming freely available texts from the internet. Given a few words, it can extrapolate new sentences and paragraphs by detecting patterns in sequential elements. It is able to compose new and original texts that meet Turing’s test of displaying intelligent behavior indistinguishable from that of a human being.
I know this from experience. After I inserted a few words, it scoured the internet and in less than a minute produced a plausible false news story about me. I knew it was spurious, but I do not matter that much. Suppose the story had been about a political leader during a major election? What happens to democracy when the average internet user can unleash generative AI bots to flood our political discourse in the final days before people cast their ballots?
Democracy is already suffering from political polarization, a problem exacerbated by social media algorithms that solicit “clicks” (and advertising) by serving users evermore extreme (“engaging”) views. False news is not a new problem, but its fast, cheap, and widespread amplification by AI algorithms most certainly is. There may be a right to free speech, but there is not a right to free amplification.
These fundamental issues, the authors argue, are coming to the fore as global network platforms such as Google, Twitter /zigman2/quotes/203180645/composite TWTR +1.16% , and Facebook /zigman2/quotes/205064656/composite FB +0.78% employ AI to aggregate and filter more information than their users ever could. But this filtration leads to segregation of users, creating social echo chambers that foment discord among groups. What one person assumes to be an accurate reflection of reality becomes quite different from the reality that other people or groups see, thus reinforcing and deepening polarization.
AI is increasingly deciding what is important and what is true, and the results are not encouraging for the health of democracy.
Cracking new codes
Of course, AI also has huge potential benefits for humanity. AI algorithms can read the results of a mammogram with greater reliability than human technicians can. (This raises an interesting problem for doctors who decide to override the machine’s recommendation: will they be sued for malpractice?)
The authors cite the case of halicin , a new antibiotic that was discovered in 2020 when MIT researchers tasked an AI with modeling millions of compounds in days—a computation far exceeding human capacity—to explore previously undiscovered and unexplained methods of killing bacteria. The researchers noted that without AI, halicin would have been prohibitively expensive or impossible to discover through traditional experimentation.
As the authors say, the promise of AI is profound: translating languages, detecting diseases, and modeling climate change are just a few examples of what the technology could do.