TECHNOLOGY

Bad Turing Test for Business


artificial fears Intelligence fills the news: job losses, inequality, discrimination, misinformation, or even superintelligence taking over the world. The only group everyone assumes would benefit is business, but the data seems to be inconsistent. Amid all the hype, so were corporate America Slow to adopt the most advanced AI techniquesThere is little evidence that these technologies contribute significantly to productivity growth or job creation.

This disappointing performance is not only due to the relative immaturity of AI technology. It also comes from a fundamental mismatch between business needs and the way AI is currently perceived by many in the tech sector – a mismatch that traces its origins to Alan Turing’s groundbreaking 1950 “imitation game” paper and the so-called Turing Test he proposed.

The Turing test determines machine intelligence by imagining a computer program that can successfully simulate a human in an open text conversation so that it is not possible to know if one is speaking with a machine or a person.

At best, this was only one way to demonstrate machine intelligence. Turing himself, and other technology pioneers such as Douglas Engelbart and Norbert Wiener, recognized that computers would be most beneficial to business and society when they augment and complement human capabilities, rather than when they compete with us directly. Search engines, spreadsheets, and databases are good examples of these complementary forms of information technology. While their impact on business has been enormous, they are not usually referred to as “artificial intelligence,” and in recent years the success story they embody has been flooded with a yearning for something more “smarter.” However, this craving is poorly defined, and with surprisingly little attempt to develop alternative vision, it has increasingly come to mean exceeding human performance in tasks such as vision and speech, and in parlor games such as chess and joe. This framing has become dominant both in the public discussion and in terms of capital investment surrounding AI.

Economists and other sociologists assert that intelligence arises not only, or even primarily, in individual individuals, but most of all in groupings such as firms, markets, educational systems, and cultures. Technology can play two major roles in supporting collective forms of intelligence. First, as emphasized in Douglas Engelbart’s pioneering research in the 1960s and the subsequent emergence of the field of human-computer interaction, technology can enhance the ability of individuals to participate in groups, by providing them with information, insights, and interactive tools. Second, technology can create new types of cooperatives. This last possibility offers the greatest transformative potential. It provides an alternative framing for artificial intelligence, which has significant implications for economic productivity and human well-being.

Companies succeed at scale when they manage to divide work internally and bring diverse skill sets into teams that work together to create new products and services. Markets succeed when they bring together diverse groups of participants, which facilitates specialization in order to enhance overall productivity and social well-being. This is exactly what Adam Smith understood more than two and a half centuries ago. Translating his message to the current debate, technology should focus on the game of integration, not the game of imitation.

We already have many examples of machines that enhance productivity by performing tasks that are complementary to those performed by humans. These include massive computations that support the functioning of everything from modern financial markets to logistics, transmitting high-resolution images over long distances in the blink of an eye, and sorting through packets of information to pull out relevant items.

What’s new in the current era is that computers can now do more than just execute 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 alongside humans. Instead of viewing this breakthrough as an opportunity to turn machines into silicon versions of humans, we should focus on how computers use data and machine learning to create new types of markets, new services, and new ways to connect humans to each other in economically rewarding ways.

An early example of such economy-aware machine learning is provided by recommendation systems, an innovative form of data analysis that came to prominence in the 1990s in consumer-facing companies such as Amazon (“You might like it too”) and Netflix (“the top picks for you”) Since then, recommendation systems have become ubiquitous, and have had a huge impact on productivity.They create value by exploiting the collective wisdom of the audience to connect people to products.

Emerging examples of this new paradigm include using machine learning to create direct connections between Musicians and listenersAnd Book and readers, And Game creators and players. Among the early innovators in this field are Airbnb, Uber, YouTube, Shopify and the phrase “creative economy“Where the trend is gaining traction. A key aspect of these groups is that they are, in fact, markets – economic value is linked to the connections between participants. Research is needed on how to blend machine learning, economics and sociology so that these markets are healthy and generate sustainable income for participants. .

Democratic institutions can also be supported and strengthened through this innovative use of machine learning. Digital Ministry of Taiwan sneered Statistical analysis and online engagement to expand the type of deliberative conversations that lead to effective group decision making in the best managed companies.



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