While writing my recent blog entry on The Physics of the Future, I discovered that author Michio Kaku’s description of the “Kardashev Scale” was wrong. Kaku said that a “Type 1” civilization on the Kardashev Scale was one that was “planetary” in scope, character and energy consumption, and that trends suggested humans wouldn’t achieve this rank until the year 2111. Kaku said that, we were in fact so pitiful at the time of the book’s writing that our civilization was only “Type 0.”
However, in Dr. Nikolai Kardashev’s science paper that established the Scale, he defined a “Type 1” civilization as being one that consumed as much energy as humans did at that time. That means humanity has been a Type 1 civilization since 1964! Kardashev also didn’t say anything about there being a “Type 0” classification.
Convinced that I alone knew of an embarrassing mistake made by one of the world’s foremost pop-science talking heads, I set out to write a blog entry about it titled “The misused and useless Kardashev Scale.” I spent an afternoon reading Kardashev’s original paper and its cited articles to actually understand it, and in other research found online articles and videos where even more smart people had cluelessly espoused a flawed definition of the Scale. This thing was even bigger than I had thought, and I was about to blow the lid off of it! This would finally put my lousy blog on the map!
And then, I found out someone else had already written about this very subject, and had done so with superior prose than I could probably write. J.N. “Nick” Nielsen beat me by five years with his article “What Kardashev Really Said.”
What a waste of my time.
It got me thinking about how much human effort is duplicative, and how much more efficient and creative we would be if we didn’t needlessly reinvent the wheel. Of course, this is impossible for mere humans since never being derivative requires perfect knowledge of everything that everyone else has already said, done, or created, and our minds are incapable of holding that much information. However, it’s easy to see how technology could change this.
Imagine a smartphone app that was connected to the device’s camera. I’ll call the app “Copycat.” Every time you turn on your camera, Copycat starts watching what’s visible through the viewfinder. Once it detects that you’re steadying the camera to prepare to take a still photo, the app would compare the scene in front of you with trillions of other photos available for free on the internet. If you were about to take a picture that looked identical or nearly identical to one that already existed, Copycat would warn you, show you an image of the other picture, and tell you if there were any ways you could, standing there, produce a new type of image. Maybe snap the photo of the songbird from low on the ground, or walk 10 feet to the right to photograph it with that stone building in the background.
This level of technology is well within reach: the image analysis and recognition feature is no different from Google’s “reverse image search.” The second feature could easily arise from a set of deep learning programs that are trained to recognize visually well-composed and aesthetically pleasing photo compositions, and to come up with ways to reposition the elements within an image to raise or maximize those values. Upload enough training data, and it will figure it out.
Copycat is a highly specific example, but it illustrates technology’s potential to help people make better use of their time by warning them before they do something that has already been done. And an important ancillary benefit is that it will remind us of valuable and interesting things people have already done, but which may have been largely forgotten. In showing you images, Copycat might make you aware of long-dead bird photographers you had never heard of, spurring you to research them further and to beautify your house with framed prints of their (free) artwork.
Along with boosting the originality of artwork, music, and writing, this sort of technology would be invaluable to scientists and engineers who are deciding how to spend their scarce time and R&D money. A machine that had memorized the full body of scientific literature and patents could, respectively, tell a scientist which things had not been researched and tell an engineer which things had not been invented. The result would be no resources wasted on duplicative projects, and an acceleration of scientific and technological advancement, merely due to a sharper grasp of what is already known.
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