Journalist; editor, Nova 24, Il Sole 24 Ore
On Monday, October 19, 1987, a wave of sales in stock exchanges originated in Hong Kong, crossed Europe, and hit New York, causing the Dow Jones to drop by 22 percent. Black Monday was one of the biggest crashes in the history of financial markets, and there was something special about it. For the first time, according to most experts, computers were to blame: Algorithms were deciding when and how much to buy and sell on the stock exchange. Computers were supposed to help traders to minimize risks, but they were in fact moving all in the same direction, enhancing risks instead. There was a lot of discussion about stopping automated trading, but that didn’t happen.
On the contrary. Since the dot-com crisis of March 2000, machines have been used increasingly to make sophisticated decisions in the financial market. Machines are now calculating all kinds of correlations between incredible amounts of data. They analyze emotions people express on the Internet by understanding the meaning of their words; they recognize patterns and forecast behaviors; they’re allowed to autonomously choose trades; they create new machines—software called “derivatives”—that no reasonable human being could possibly understand.
An artificial intelligence is coordinating the efforts of a sort of collective intelligence, operating thousands of times faster than human brains, with many consequences for human life. The first signs of the latest crisis occurred in the United States in August 2007 and has had a terrible effect on the lives of people in Europe and elsewhere. Real people suffered immensely because of those decisions. Andrew Ross Sorkin, in his book Too Big to Fail, shows how even the most powerful bankers had no power in the midst of the crisis. No human brain seemed able to control the course of events and prevent the crash.
Can this example teach us how to think about machines that think?
Such machines are actually autonomous in understanding their context and making decisions. And they control vast dimensions of human life. Is this the beginning of a posthuman era? No: These machines are very much human, made by designers, programmers, mathematicians, economists, managers. But are they just another tool we can use, for good or for bad? No: In fact, we have little choice; we make the machines without thinking of the consequences; we are just serving a narrative. Those machines are shaped by a narrative that has been challenged by very few people.
According to that narrative, the market is the best way to allocate resources, no political decision can improve the situation, risk can be controlled as profits grow without limits, and banks should be allowed to do whatever they want. There’s only one goal and one measure of success: profit.
Machines didn’t invent the financial crisis, as the 1929 stock market crash reminds us. Without machines, nobody could deal with the complexity of modern financial markets. The best artificial intelligences are those that are made thanks to the biggest investments and by the best minds. They’re not controlled by any one individual. They’re not designed by any one person. They’re shaped by the narrative and make the narrative more effective. And this particular narrative is very narrow-minded.
If only profit counts, then externalities don’t count: Cultural, social, and environmental externalities are not the concern of financial institutions. Artificial intelligences shaped by this narrative will create a context in which people feel no responsibility. An emerging risk: Those machines are so powerful, and fit the narrative so well, that they discourage the questioning of the big picture, make us less likely to look at things from a different angle. That is, until the next crisis.
This story easily applies to other matters. Medicine, e-commerce, policy, advertising, national and international security, even dating and sharing are territories in which the same genre of artificial intelligence systems are starting to work. They’re shaped according to a focused narrative; they tend to reduce human responsibility and overlook externalities. What will medical artificial intelligence do? Will it be shaped by a narrative that wants to save lives or save money?
What do we learn from this? We learn that artificial intelligence is human, not posthuman, and that humans can ruin themselves and their planet in many ways, artificial intelligence being not the most perverse.
Machines that think are shaped by the way humans think and by what humans don’t think about deeply enough. All narratives illuminate some things and ignore others. Machines react and find answers in a context, reinforcing the frame. But asking fundamental questions is still a human function. And humans never stop asking questions, even ones not congruent with the prevailing narrative.
Machines that think are probably indispensable in a world of growing complexity. But there will always be a plurality of narratives to shape them. In natural ecosystems a monoculture is a fragile though efficient solution; similarly, in cultural ecosystems a single line of thought will generate efficient but fragile relations between humans and their environment, no matter which artificial intelligences they build. Diversity in ecosystems, and plurality in the dimensions in human history, are sources of different problems and questions that generate rich outcomes.
To think about machines that think means to think about the narrative shaping them. If new narratives emerge from an open, ecological approach, if they can grow in a neutral network, they will shape the next generation of artificial intelligences in a plural, diverse way, helping humans understand externalities. Artificial intelligence won’t challenge humans as a species, it will challenge their civilizations.