The Test of Turing Evil For Business


Artificial Fear Intelligence fills the news: unemployment, inequality, discrimination, misinformation, or even a superintelligence that rules the world. One group that everyone believes would benefit is business, but the data doesn’t seem to agree. Amidst all the hype, U.S. businesses did slow to adopt the most advanced AI technologies, and there is little evidence that such technologies have contributed significantly to productivity growth or do work.

This disappointing performance is not only due to the relative immaturity of AI technology. It also stems from a fundamental mismatch between business needs and the way AI is now conceived in much of the technology sector — a mismatch that has its origins in Alan Turing’s 1950s “game imitation” and the so -called Turing test. he suggests it.

The Turing test defines intelligent intelligence by imagining a computer program that can successfully mimic a person in an open text conversation without it being possible to tell if someone is talking to a machine or a person.

At best, it’s just a way of expressing machine intelligence. Turing himself, and other technology pioneers such as Douglas Engelbart and Norbert Wiener, understood that computers could be most beneficial to business and society if they added and augmented human capabilities, not if they directly competed with us. Search engines, spreadsheets, and databases are good examples of such complementary forms of information technology. Even if their impact on the business is huge, they’re not often referred to as “AI,” and in recent years the success story they’ve compiled has been filled with a desire for something more “intelligent.” This desire is poorly defined, however, and with surprisingly little attempt to create an alternative vision, it increasingly means that the person is more accomplished in tasks such as vision and speech, and in parlor games like chess and Go. This framing has become dominant in public discussion and in terms of the capital investment surrounding AI.

Economists and other social scientists emphasize that intelligence is not only, or even primarily, of individual people, but above all of collectives such as companies, markets, education systems, and cultures. Technology can play two important roles in supporting collective forms of intelligence. First, as pioneered by Douglas Engelbart’s pioneering research in the 1960s and the subsequent emergence of the field of human-computer interaction, technology can improve the ability of individual people to participate in collectives, by giving them information, insights, and interactive tools. Second, technology can create new forms of collective. This latter possibility offers the greatest potential for change. It provides an alternative framing for AI, one that has major implications for economic productivity and human well-being.

Businesses will be successful in size if they successfully divide work internally and bring different skill sets to teams that work together to create new products and services. Markets are successful when they bring together different classes of participants, which facilitates specialization to improve overall productivity and social well-being. This is exactly what Adam Smith understood more than two and a half centuries ago. Translating his message into the current debate, technology should focus on the complementarity game, not the imitation game.

We already have many examples of machines that improve productivity by doing tasks that complement what people do. It includes numerous calculations that promote the movement of everything from modern financial markets to logistics, the transmission of images with high fidelity over long distances in the blink of an eye, and the multiplication. -has reams of information to get the relevant stuff.

What is new nowadays is that computers can now and more to do with lines of code written by a human programmer. Computers can learn from data and they can now interact, anticipate, and engage in real-world problems, with people. Instead of looking at this development as an opportunity to make machines silicon versions of humans, we need to focus on how computers can use data and machine learning to create new ones. class markets, new services, and new ways to connect people with each other in rewarding economic ways.

An early example of such a well-known economic machine learning is provided through recommendation systems, a new form of data analysis that became popular in the 1990s among companies dealing with consumers like Amazon (“You might like it too”) and Netflix (“Top. chosen for you”). Recommendation systems have since become ubiquitous, and have had a huge impact on productivity. They create value by leveraging the collective wisdom of the crowd to connect individuals to products.

Emerging examples of this new paradigm include the use of machine learning to make a direct connection between musician and listener, writer and reader, ug game creators and players. The first innovators in this space included Airbnb, Uber, YouTube, and Shopify, and the phrase “producer economy“used as fashion gathers steam. An important aspect of such collectives is that they are, in fact, markets — economic value is linked to participants’ connections. Research is needed on how to mix machine learning , economics, and sociology so that these markets are healthy and provide sustainable income for participants.

Democratic institutions can also be supported and strengthened through this new use of machine learning. The digital ministry in Taiwan naka harness statistical analysis and online participation to maximize the class of deliberative conversation that will lead to effective team decision-making in the best management companies.



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