The Hype Cycle of Emerging Technologies

General hype cycle for technology

The hype cycle is a branded graphical presentation to represent the maturity, adoption and social application of specific technologies. The hype cycle claims to provide a graphical and conceptual presentation of the maturity of emerging technologies through five phases.

Simply put it looks like this:

An Example of Hype Cycle in Recent History

Hype is common in many emerging technologies, such as the railway mania or the dot-com bubble. The Artificial intelligence (AI) winter from 1984 through 2012 was the result of such hype, due to over-inflated promises by developers, unnaturally high expectations from end-users, insufficient computing capacity, and extensive promotion in the media. In 2005, Ray Kurzweil stated that: “Many observers still think that the AI winter was the end of the story and that nothing since has come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry.”

However, during the Uranus-Pluto Square Alignments between 2007 – 2020, interest in artificial intelligence (and especially the sub-field of machine learning) from the research and corporate communities led to a dramatic increase in funding, investment, and development. At present A.I. has transitioned from the “Trough of Dissilusimont” in the Hype Cycle the “Slope of Enlightenment” in a world running on Artificial Narrow Intelligence (ANI). ANI is a form of artificial intelligence that specializes in one area. For example:

  • Cars: Google’s self-driving car, contains robust ANI systems that allow it to perceive and react to the world around it.
  • Mobile Phone: The app’s on mobile phones uses ANI extensively for map navigation, to receive tailored music recommendations from Pandora, check tomorrow’s weather, talk to Siri, or dozens of other everyday activities.
  • Your email spam filter is a classic type of ANI—it starts off loaded with intelligence about how to figure out what’s spam and what’s not, and then it learns and tailors its intelligence to you as it gets experience with your particular preferences. The Nest Thermostat does the same thing as it starts to figure out your typical routine and act accordingly.
  • Online Shopping: When you search for a product on Amazon and then you see that as a “recommended for you” product on a different site, or when Facebook somehow knows who it makes sense for you to add as a friend? That’s a network of ANI systems, working together to inform each other about who you are and what you like and then using that information to decide what to show you. The ANI system gathers information from the behavior of millions of customers and synthesizes that information to intelligently and shrewdly upsell you so you’ll buy more things.
  • Language Translation: Google Translate is another classic ANI system—impressively good at one narrow task. Voice recognition is another, and there are a bunch of apps that use those two ANIs as a tag team, allowing you to speak a sentence in one language and have the phone spit out the same sentence in another.
  • The world’s best Checkers, Chess, Scrabble, Backgammon, and Othello players are now all ANI systems.
  • Google Search is one large ANI brain with incredibly sophisticated methods for ranking pages and figuring out what to show you in particular. The same goes for Facebook’s Newsfeed.
  • Finance: Sophisticated ANI systems are widely used in sectors and industries like military, manufacturing, and finance (algorithmic high-frequency AI traders account for more than half of equity shares traded on US markets.
  • Medicine: AI expert systems like those that help doctors make diagnoses. most famously, IBM’s Watson is another example of ANI systems.

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