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1-Page Summary of Too Big to Know

“The Network of Knowledge”

Until the arrival of digital media, most people accepted a standard system for gaining knowledge. Students studied subjects and earned credentials to prove their proficiency in those subjects. They then put that knowledge to work conducting research and writing books and articles on their findings. Other specialists vetted this work, deeming it accurate or mistaken. As new discoveries gained acceptance, they joined a body of established knowledge that provided a foundation for further exploration and learning throughout history.

As the digital age continues to evolve, it’s important for institutions such as universities and libraries to rethink how we store our knowledge. This is because many people are questioning long-held assumptions about knowledge and its infrastructure. People voice their fears of what this means for society, but science moves forward at an unprecedented pace thanks to the Internet and other technological advances. The Internet gives everyone a platform to share their ideas with others who might not have access otherwise, which in turn helps solve problems that were previously only solved by privileged groups of people who had access before.

“It’s Not Information Overload; It’s Filter Failure”

Organizational theorist Russell Ackoff reported that in 1988, it was necessary to filter data. He described a pyramid, with the base being data and the tip being wisdom. The next layer is information, which leads to knowledge. And then comes understanding.

Before the Internet, editors were the first filters of published material. They decided what information would be in libraries and on magazine racks. Most people saw only the wheat; discarded chaff disappeared from view.

In the past, people had access to a limited amount of information. They could find what they were looking for and avoid other material that was irrelevant or incorrect. Now, however, with the advent of digital media such as computers and smartphones, there is an unlimited amount of information available at our fingertips. We can use algorithms (a set of steps used to solve problems) and social networks (people who share common interests) to filter out bad information from good ideas. However, because there is so much more information than before, we need new methods for filtering out all this data in order to find useful knowledge. In addition, since anyone can post anything online regardless if it’s true or not on sites like Wikipedia, people should be careful about believing everything they read on the Internet.

Traditional Expertise

Even though there are changes in the way we understand knowledge, people still have access to it. In fact, information is easier than ever to retrieve: bus schedules tell you when to be at a stop and two plus two still equals four. Experts and academics continue to provide opinions and lessons, but they aren’t always as reliable because ideas don’t necessarily require expertise or academic study; often times an idea can thrive in networks that facilitate the development of ideas by groups rather than individuals.

  • In the past, books were written by experts in a single subject. They used “one voice” throughout their work and tried to be consistent. These days, authors often write on multiple subjects or have different voices for each chapter. In addition, it is easier than ever to revise and update content online before going to print.

  • “Experts were a special class” – Only a select few could publish books, so that gave them credibility.

“Networked Expertise”

The Internet allows us to tap into the collective knowledge of a large group of experts. Think tanks, research centers and universities exist for this very reason. The Web makes it easier than ever before to do so because we can reach a much larger audience than in any physical setting. One person coined the term “crowdsourcing” to describe how many people work together across multiple locations to answer questions or solve problems.

Too Big to Know Book Summary, by David Weinberger