Singularities And The Principle Of Reinvestment Of Methods
by Matthew Turco
If a system can be quantified, then we would be able to measure it. Intelligence is a system.
Whether we consciously acknowledge it or not, we all employ thinking strategies. Some are more powerful than others. Some will show a quantifiable improvement in less time than others. And some will actually induce atrophy (addiction to Aaron Spelling TV shows comes to mind).
Whatever strategy we use, the law of diminishing returns will eventually take effect. For example, if we use a creativity technique and it doubles our output for every week we practice it, eventually the improvements will decrease and stop. Any one strategy alone cannot continue to increase output ad infinitum.
Once the diminishing begins, the only way to continue increasing output is to employ a new strategy. The strategy can be one that moves toward sensory perception (thus having high transferability), it can be one that is similar is scope to the previous strategy, or it can be one that is more focused in application (having lower transferability but more tangible implications).
The Law of Singularity states that if the system that we are working on improving actually contributes to further improvements of itself, then the advancements will not only sustain its own improvements, but it will accelerate-eventually reaching a point where the system improves itself on its own.
Ugh, what did I just say? Let's try a common example.
We double computing speed every two years of work. Work does not necessarily mean human work. As computing power increases, computers contribute more and more to their own advancement.
Two years after computers reach human equivalence, it takes only one year of real time to do two subjective years of work. One year later, it takes six months to double the amount of computing power. And so on.
Six months - three months - 1.5 months ... Singularity. Portable supercomputers on sale-two for a dollar with coupon!
This is only theory, of course. And it certainly is not without its flaws. Scientists love simple systems and linearly extrapolating numbers. But let's consider the implications.
Let's take intelligence. If I employ a method of improving intelligence (image steaming, for example) and my measurement doubles in 10 years, then I will be twice as smart (of course, this is contingent on how accurate my measurements are). If I employ the method again, will I double my intelligence in another 10 years?
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There are two answers to that question - no and no.
First of all, the law of diminishing returns will tell you that image streaming alone (however powerful and transferable a method) will run out of gas. Thus, it may take 15, 20, even 40 years to double your intelligence again.
On the other hand, the Law of Singularity states that since intelligence itself contributes to its own improvement (a self-reinforcing system), then it should take LESS than 10 years to double your intelligence again.
So where the hell am I going with this?
In order for your gains to accelerate and keep doing so, you must practice the principle of reinvestment of methods. In other words, you must consciously use your gains to improve how you seek further gains. Thus, image streaming must evolve into borrowed genius, alternate earth, advanced photoreading, etc.
There's another problem with intelligence. Intelligence is an internal system. You cannot cheat - you can't stand on someone else's shoulders like you can in an external systems like science and technology. You cannot spend four years in school and enter the game decades ahead of your predecessors like you can in most sciences.
Yes, perhaps as time rolls on the starting methods will improve, but only incrementally. Even other people's advanced methods will work up to a certain point. But wherever you start, the real gains occur from self-reinvestment.
Why? Eventually you will notice that other people's advanced methods aren't much use to you because the system itself can't be leveraged past a certain point. Internal systems are built upon different foundations. Even if we use the same methods and techniques, we won't use them in exactly the same way. The differences will become pronounced as we progress through the system. We will need to learn how to reinvest our gains on our own.
In an earlier chapter, I pointed out that while leverage is necessary to gain knowledge and perspective, nothing can replace direct experience. The sharing of knowledge and methods is fundamentally flawed because there are always little things that aren't picked up through higher order perceptions like knowledge. I could write a book thicker than the bible trying to articulate every nuance of image streaming, but the law of diminishing returns will rear its ugly head there too. Eventually, only direct experience will teach you more. Then perhaps a direct dialog with other image streamers would prove considerably valuable and the articulation would again reap rewards, but only AFTER AND DURING actual direct experience.
In other words, if I told you that I have a technique to memorize symphonies after one listen, it won't do you any good. You won't be able to just skip the years of image streaming, musical studies and everything else. And even if you could, there's no telling that my method will gel with your thought processes.
Lastly, let me mention another glaring flaw in the Law of Singularity. In order for it to work, the approach must be global AND the approach to reinvesting methods must be global.
What do I mean? We can't just tackle one skill like adding large numbers and take it to its end. Eventually, you must improve complementary and even seemingly unassociated skill sets in order to make further improvement in the first one.
This flaw also has tremendous implications for computing power and other anticipated singularities, but this isn't the time or the place.
And if that doesn't confuse you enough, there will also be a time where the approach to the principle of reinvestment itself must be self-rearticulated.
That's it, pass the Tylenol
©1997 Matthew Turco