Artificial fear Intelligence fills the news: job loss, inequality, inequality, misinformation, even a super intelligence dominating the world. One group assumes business will benefit everyone, but the data seems to disagree. In all hype, the US has been in business Slow in adopting the most advanced AI technology, And there is very little evidence that such technology is contributing significantly Increase productivity Or Job creation.
This disappointing performance is not only due to the relative immaturity of AI technology. It also stems from a fundamental discrepancy between business demand and the way AI is now perceived by many in the technology sector — a discrepancy that originated in Alan Turing’s Pathbreaking 1950 “Imitation Game” paper and the so-called Turing test. He proposed there.
The Turing test defines machine intelligence by imagining a computer program that can mimic a person so successfully in an open text conversation that it is impossible to tell if someone is having a conversation with a machine or a person.
After all, it was the only way to express machine intelligence. Turing himself, and other technology pioneers such as Douglas Engelbert and Norbert Weiner, realized that computers would be most useful for business and society when they would enhance and complement human capabilities, when they would not compete directly with us. Search engines, spreadsheets and databases are good examples of such complementary forms of information technology. While their impact on business is immense, they are not commonly referred to as “AI” and the success story they embody in recent years has been overshadowed by the desire for something more “intelligent”. This aspiration is poorly defined, and with surprisingly little effort to develop an alternative approach, it is increasingly meant to surpass human performance, such as vision and speech, and parlor games such as chess and go-to. This framing has become influential both in public discourse and in the investment of capital around AI.
Economists and other sociologists emphasize that intelligence emerges not only in individuals, but also collectively, as in companies, markets, education systems, and cultures. Technology can play two key roles in supporting collective intelligence. First, as Douglas Engelbert’s pioneering research in the 1960s and the subsequent rise of the field of human-computer interaction emphasized, technology can enhance the ability of individuals to participate collectively by providing information, insights, and interactive tools. Second, technology can create new types of aggregates. This next possibility offers the greatest transformative potential. It provides an alternative framing for AI, which has a major impact for economic productivity and human welfare.
Businesses are successful on scale when they successfully divide labor internally and bring together different skill sets that work together to create new products and services. Markets are successful when they bring together different types of participants, giving the benefit of specialization to increase overall productivity and social welfare. That is exactly what Adam Smith understood more than two and a half centuries ago. By translating his message into the current debate, the focus should be on complementary games, not games of technology imitation.
We already have many examples of machines that increase productivity by complementing the tasks performed by humans. This includes huge calculations based on the effectiveness of everything from modern financial markets to logistics, transitioning high-fidelity images across long distances in the blink of an eye and sorting through the rim of information to extract relevant items.
What is new in the present age is that computers can now do more than just run lines of code written by a human programmer. Computers are capable of learning from data and they can now interact, guess and intervene with humans as well as in real-world problems. Instead of seeing this advancement as an opportunity to turn machines into human silicon versions, we should focus on how computers can create new types of markets, new services, and new ways to connect people using data and machine learning. Economically viable way.
An early example of this type of economy-conscious machine learning is provided by the recommendation system, an innovative form of data analysis that became popular in the 1990s at consumer-oriented companies such as Amazon (“You Can Choose”) and Netflix (“Top”). Pick for “). Recommendation systems have become ubiquitous since then, and have had a significant impact on productivity. They create value by utilizing the collective knowledge of the crowd to connect individuals with the product.
Emerging examples of this new paradigm include the use of machine learning for direct communication. Musicians and listeners, Writer and reader, And Game makers and players. Early inventors of this space include Airbnb, Uber, YouTube, and Shopify, and “Creator economyThe trend is being used as steam collection. A key aspect of such aggregates is that they are actually linked to links between market-economic value participants. Research is needed on how machine learning, economics and sociology can be combined to keep these markets healthy and sustainable for participants.
Democratic institutions can also be supported and strengthened by this innovative use of machine learning. Digital Ministry of Taiwan Used Increases the type of intentional conversation for statistical analysis and online participation that leads to effective team decision making in the best managed companies.