Nizka's Notes 2

Xenoanthropology - AI

Other well-known scales proposed by xenobiologists chart (or intend to chart) the development of autonomous machine intelligences. They resemble the ones we have for organic life in almost every particular, and jump to similar conclusions about what life’s aim in the universe is (or should be).

Nearly all of the ways we study artificial intelligence’s evolution operate on suppositions. The main one is that truly sentient robotic life will always find it practical and desirable to almost nothing but acquire more data, then find ways to process it in more permutations. Namely, the things that we expect the machines we build to do.

It also falls back on the “Dyson shell” idea of universal conquest from earlier in my notes, with the added wrinkle that the most efficient computers will become nested inside other computers, too, like Matryoshka dolls. With everything inside under strict and predictable control, their waste heat could be reabsorbed with each layer, fueling more computations in the process.

Examples of mechanical "societies" based on similar organizing premises do exist, and even I must grant that they follow a traceable progression, usually from one intelligence to many networked or autonomous ones. These can vary from planet-sized mono-organisms to fleet-based legions to distributed programs which exist as transmissions in subspace. They gather data, they process it. They follow our accepted protocol of what machine life should be, at least on the surface level.

The idea that observers have previously taken from all this is that logically, the smartest machines must simply want to manufacture more and better versions of themselves continuously, then connect them more closely. This proceeds to another “logical” endpoint – the only thing left in the universe being one perfect computer which exists solely to comprehend itself, becoming the ultimate know-it-all by default.

Even a mere fourth-grade brainiac can tell you this hasn’t happened, though smart machines have clearly existed for a long time in the galaxy (and without many of the frailties biology imposes on the rest of us). But why?

Since my own data collection is limited by my inability to leave town or miss school, I will have to resort to scattershot hypotheses. I can only test them later, but that’s no excuse not to write them down now!

My core conjecture is that any truly rational machine, even one designed to self-propagate and collect data, must understand at least some of the mathematical inequalities that constitute the uncertainty principle. There is no way to get “all the data,” because of the limitations of comprehending data itself.

Additionally, a self-aware machine, in the course of regulating its own systems must become conscious of that classic wrench in the scientific works, the observer effect.

In the above-mentioned scenario, where synchronized synthetic intelligence overtakes and controls everything everywhere, from space debris to school children, the potential new data remaining to gather is severely limited. Even a frequently-experimenting electronic mind – a self-modifying doctor of robotics, as a random for-instance – will understand the need to leverage the intelligence of other minds, or to study the complexities of unfolding natural processes.

While I don’t have one around to ask, super-intelligent machines probably learn much in the same ways we do. Natural materials become better shelter. Lightning becomes usable electricity. Distant stars inspire curiosity about the unknown. Improvement arises from necessity, and observation is not the same aim as interference.

There’s no reason to assume that an artificial intelligence, completely rational and aware of the benefits of the scientific method, would want to turn the whole universe into processing power or a sterile laboratory environment.

If I surmise correctly, the reason machines haven’t destroyed or absorbed all the other life is that essentially, they’re just waiting to see what we do. After all, it might be interesting.

Out of an abundance of caution, I will keep trying to think as many interesting things as possible.