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Fears of the artificial the news fills the mind: job loss, inequality, discrimination, misinformation or even a world-dominant superintelligence. Everyone thinks the only group is the business, but the data doesn’t seem to match. Among all the horrors, there have been U.S. businesses Slow to adopt the most advanced AI technologies, and there is little evidence that these technologies are significantly contributing productivity growth or job creation.
This disappointing performance is not only due to the relative maturity of AI technology. It also stems from a fundamental mismatch between the needs of businesses and the way many in the technology sector today are conducting AI, originating in the role of Alan Turing in the 1950s “imitation game” and the so-called Turing test. he proposed there.
Turing’s test defines the intelligence of machines by depicting a computer program that can so successfully mimic a human being in an open text conversation where one cannot know whether one is conversing with a machine or a person.
At best, this was the only way to articulate the intelligence of machines. Turing himself and other technology pioneers, such as Douglas Engelbart and Norbert Wiener, understood that computers would be most useful for business and society when they increased and complemented human capabilities, not when they competed directly with us. Search engines, spreadsheets, and databases are good examples of these complementary forms of information technology. Although their impact on the business has been tremendous, they aren’t usually referred to as “AI,” and the success story they’ve embodied in recent years has been immersed in a craving for something “smarter”. This craving is poorly defined, however, and with a few surprising attempts to develop an alternative vision, it increasingly means overcoming human performance in tasks like sight and speech, and in board games like chess and Go. This picture has become dominant in the public debate and in terms of capital investment around AI.
Economists and other social scientists emphasize that intelligence arises not only in individual human beings, but especially in groups such as companies, markets, education systems, and cultures. Technology can play two key roles in supporting collective intelligence. First, as highlighted by Douglas Engelbart’s pioneering research in the 1960s and the subsequent creation of the field of human-computer interaction, technology can improve the ability of individual human beings to participate in collectives by providing information, knowledge, and interactive tools. Second, technology can create new types of collectives. This last option offers the greatest transformative potential. It provides an alternative framework for AI that has major implications for economic productivity and human well-being.
Companies achieve this on a large scale when they distribute work well and bring different sets of skills to teams that work together to create new products and services. Markets are successful when they attract a large number of participants, facilitating specialization to improve overall productivity and social well-being. This is what Adam Smith understood more than two and a half centuries ago. In returning his message to the current debate, technology should focus on the game of complementarity, not the game of imitation.
We already have many examples of machines that improve productivity by performing the tasks that humans do. These include massive calculations based on the operation of everything from modern financial markets to logistics, the transmission of long-distance images over long distances, and the classification of a range of information to extract important elements.
It is new in today’s era that computers can do more than just run lines of code written by a human programmer. Computers are able to learn from data and can now interact, infer, and intervene in real-world problems, along with humans. Instead of seeing this progress as an opportunity to turn machines into silicon versions of humans, we should focus on how computers can use data and machine learning to create new types of markets, new services, and new ways to connect humans. economically rewarding ways.
An early example of this economically-aware machine learning is provided by recommendation systems, an innovative form of data analysis in consumer-oriented companies in the 1990s, such as Amazon (“You’ll Like It”) and Netflix (“Top”). choices for you “). Recommendation systems have become ubiquitous ever since, and have had a profound impact on productivity. They create value by using the collective wisdom of the crowd to connect people with products.
New examples of this new paradigm include the use of machine learning to create direct links musicians and listeners, writers and readers, and game creators and players. Among the first innovators in this space are Airbnb, Uber, YouTube and Shopify, and the phrase “the economy of the creator”Is being used as the trend picks up steam. The key aspect of these groups is that they are markets — the economic value of which is linked to the links between the participants. Research is needed on how to combine machine learning, economics and sociology so that these markets are healthy and have a sustainable income for participants.
Democratic institutions can also support and strengthen this innovative use of machine learning. Taiwan Digital Ministry has taken advantage of statistical analysis and network participation to scale deliberative interviews to lead to better decision-making in the best managed companies.
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