Turing Tests Are Bad For Business

Fear of Manufacturing wisdom fills the void: unemployment, disunity, prejudice, lies, or even the wisest ruler on a global scale. One group that everyone thinks will benefit the business, but the data seems to be contradictory. Amidst all the challenges, US businesses have remained late in adoption of the most advanced AI technologies, and there is limited evidence that such techniques are effective yield growth or making work.

This frustration is not simply the result of technological advances in AI. It also stems from the deep confusion between business needs and the way AI is currently being used by many in the technical field – a discrepancy that stemmed from Alan Turing’s Pathbreaking The 1950 edition of “imitation” paper and the so-called Turing experiment. He asked in it.

Turing tests refer to mechanical intelligence in comparison to a computer program that can effectively mimic a person in open speech so that it is impossible to determine if a person is talking to a machine or a person.

Well, this was just one way to explain mechanical intelligence. Turing himself, and other professional pioneers such as Douglas Engelbart and Norbert Wiener, understood that computers can be very useful for business and people as they increase and enhance human skills, not when they compete with us. Search engines, spreadsheets, and archives are good examples of similar types of information technology. Although their involvement in business has been considerable, they are often not referred to as “AI,” and in recent years the success story they have is diminished by a desire for something “intelligent”. This passion is not clearly articulated, however, and surprisingly little attempt to create other visions, has been meant to transcend human norms in professions such as vision and speech, and on-field games such as chess and Go. This planning has been extensive in public discussions as well as in terms of funding surrounding AI.

Economists and other sociologists emphasize that intelligence is not limited to the individual, but especially in sectors such as companies, markets, educational systems, and cultures. Technology has the potential to play two major roles in supporting all types of intelligence. First, as emphasized in Douglas Engelbart’s pioneer research in the 1960’s and the subsequent emergence of the human computer system, technology can enhance the ability of individuals to participate in groups, by providing information, notifications, and support tools. Second, technology can create new types of teams. This latter capability offers the greatest potential for change. It provides an alternative to AI, which has significant implications for economics and human health.

Businesses thrive when they divide the workforce internally and bring a variety of skills into teams that work together to create new products and services. Markets thrive when they combine different groups of participants, directing experts to improve overall productivity and quality of life. This is what Adam Smith understood more than two and a half years ago. To translate its message into modern dialogue, technology should focus on the role model, not the simulation game.

We already have many examples of machines that make work possible by doing tasks that are consistent with what people do. These include large numbers that support the functioning of everything from the modern markets to the management system management system management system management system management system management system management system management system management system the management of the administration of the public administration of the public administration.

What is new in modern times is that computers can do much more than just copy lines by a programmer. Computers can learn from data and are now able to interact, interact, and interact with real-world problems, along with people. Instead of viewing the future as an opportunity to transform machines into silicon forms of humanity, we should focus on how computers can use data and learn from machines to create new markets, new services, and new ways to connect with each other economically viable ways.

An early example of the study of financial analysts is provided by production systems, a new data analysis method that became popular in the 1990s in consumer-focused companies such as Amazon (“You may also like it”) and Netflix (“Above). . your choices “). Counseling has been widely known, and it has had a profound effect on yields. They make a profit by using organizational skills to connect people to things.

Upcoming examples of the new paradigm include the use of learning machines to create direct connections between singers and audience, writers and readers, and gamers and players. Top startups on the site include Airbnb, Uber, YouTube, and Shopify, with the words “creator of wealth”Is used as a vapor barrier method. An important feature of such groups is that, in essence, financial markets are linked by links between participants. Research is needed on how to integrate technical, economic, and social studies to make these markets healthier and more sustainable for participants.

Democratic institutions can also be supported and encouraged by making wise use of technology to learn. Digital service in Taiwan has caught statistical analysis and online participation to promote the types of discussions that lead to group decision making in the most successful companies.

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