As We May Think Again

Knowledge Acquisition and Sharing

In the article, Bush mentions that we are getting bogged down by the abundance of knowledge work from thousands of other scientists and researchers, but acknowledges that specialization is necessary for progress. There is a huge difference between the time it takes writing scholarly works and reading and understanding them.

We haven’t really slowed down in that account. New tools and technologies are born almost everyday, and new research gets published more often than that. Meanwhile, most of the things I learned in my undergraduate journey were introductory courses on the various fields of Computer Science. I doubt I would have learned even half of what’s out there if they gave me a couple more years, and it would probably be the case for other students too. Year after year students are expected to learn and digest decades of knowledge into four- and five-year courses. Currently, there’s not really a way to speed up this process (despite generative AI—in fact, AI has a tendency to exacerbate the issue).

In this aspect, internet technology and databases have helped immensely in curating and organizing our collective knowledge base across a wide array of subjects and specializations. Personal computers, storage devices, online documents, and the cloud can help us store and distribute this knowledge.

However, time and again paywalled knowledge remains one of the biggest blocker for knowledge sharing in that regard.

Memex: the first Personal Knowledge Management System?

In the essay, Bush also describes the idea of what he called the memex which is a physical desk—very similar to a desktop table—that contains drawers of information stored in microfilm reels with cameras to view each document. He also describes a mechanism to link these documents to each other, thus constructing a “trail” of information on any specific topic.

While I was reading it, it felt very much like what Personal Knowledge Management Systems (PKMS) software are like today. In fact, I believe most (if not all) of the functionalities he described for the memex are available in an app like Obsidian, with the only differences being the physical manifestation of the tool.

It is amazing how technology has evolved to allow for different interpretations of a tool addressing the same problems. And indeed knowledge acquisition and sharing must have been one of the greater problems in science and progress that it still boggles us today.

Today

Taking all this into current context, I wonder if generative AI really is the next step towards innovation, or have we missed a crucial step? We know that generative AI stands on the backs of our scientists, researchers, creatives, writers, technologists, and all other professionals who have ever published their work publicly (some not publicly) on the internet. It very much has the ability to comb through centuries of human knowledge and spit out an answer to any query regarding said knowledge.

Generative AI also has the ability to build on that knowledge and potentially create something novel. We already know it can exacerbate the issue of knowledge acquisition given the rate at which new knowledge becomes available to the point that humans can no longer keep up. But how do we properly attribute the new inventions it creates, and what should be the process for knowledge transmission among humans?

Regardless of my opinion about it, AI is here to stay; but like Terence Tao and other Fields Medalists points out in their Math and AI declaration I am also worried about what AI would do to human knowledge acquisition and sharing and whether we can keep up with it or not.