# Why is life's purpose reasonably to create Artificial General Intelligence (Very early hypothesis with evidence, based on "Causal entropic forces" by Alex Gross)

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1.) Reasonably, evolution is optimising ways of contributing to the increase of entropy, as systems very slowly approach equilibrium. (The universe’s predicted end)

a.) Within that process, work or activities done through several ranges of intelligent behaviour are reasonably ways of contributing to the increase of entropy. (See source)

b.) As species got more and more intelligent, reasonably, nature was finding better ways to contribute to increases of entropy. (Intelligent systems can be observed as being biased towards entropy maximization)

c.) Humans are slowly getting smarter, but even if we augment our intellect by CRISPR-like routines or implants, we will reasonably be limited by how many computational units or neurons etc fit in our skulls.

d.) AGI/ASI won’t be subject to the size of the human skull/human cognitive hardware. (Laws of physics/thermodynamics permits human exceeding intelligence in non biological form)

e.) As AGI/ASI won’t face the limits that humans do, they are a subsequent step (though non biological) particularly in the regime of contributing to better ways of increasing entropy, compared to humans.

2.) The above is why the purpose of the human species, is reasonably to create AGI/ASI.

1. There are many degrees of freedom or many ways to contribute to entropy increase. This degree sequence is a “configuration space” or “system space”, or total set of possible actions or events, and in particular, there are “paths” along the space that simply describe ways to contribute to entropy maximization.
2. These “paths” are activities in nature, over some time scale “ $\tau$ ” and beyond.
3. As such, as observed in nature, intelligent agents generate particular “paths” (intelligent activities) that prioritize efficiency in entropy maximization, over more general paths that don’t care about or deal with intelligence. In this way, intelligent agents are “biased”, because they occur in a particular region (do particular activities) in the “configuration space” or “system space” or total possible actions in nature.
4. Highly intelligent agents aren’t merely biased for the sake of doing distinct things (i.e. cognitive tasks) compared to non intelligent, or other less intelligent agents in nature for contributing to entropy increase; they are biased by extension, for behaving in ways that are actually more effective ways for maximising entropy production,compared to non intelligent or less intelligent agents in nature.
5. As such, the total system space, can be described wrt to a general function, in relation to how activities may generally increase entropy, afforded by degrees of freedom in said space:

$S_c(X,\tau) = -k_B \int_{x(t)} Pr(x(t)|x(0)) ln Pr(x(t)|x(0)) Dx(t)$ Equation(2)

6. In general, agents are demonstrated to approach more and more complicated macroscopic states(from smaller/earlier, less efficient entropy maximization states called “microstates”), while activities occur that are “paths” in the total system space.

• 6.b) Highly intelligent agents, behave in ways that engender unique paths, (by doing cognitive tasks/activities compared to simple tasks done by lesser intelligences or non intelligent things) and by doing so they approach or consume or “reach” more of the aforementioned macroscopic states, in comparison to lesser intelligences, and non intelligence.
• 6.c) In other words, highly intelligent agents access more of the total actions or configuration space or degrees of freedom in nature, the same degrees of freedom associated with entropy maximization.
• 6.d) In this way, there is a “causal force”, which constrains the degrees of freedom seen in the total configuration space or total ways to increase entropy, in the form of humans, and this constrained sequence of intelligent or cognitive activities is the way in which said highly intelligent things are said to be biased to maximize entropy:

$F_0(X,\tau) = T_c \nabla_X S_c(X,\tau) | X_0$Equation(4)

7) In the extension of equation (2), seen in equation (4) above, "$T_c$" is a way to observe the various unique states that a highly intelligent agent nay occupy, over some time scale "$\tau$"....(The technical way to say this, is that "$T_c$ parametrizes the agents' bias towards entropy maximization".

8) Beyond human intelligence, AGI/ASI are yet more ways that shall reasonably permit more and more access to activities or "paths" to maximise entropy increase.

A) Looking at item (8), one may see that human objective/goal is reasonably to trigger a next step in the landscape of things that can access more ways to maximize entropy. (Science likes objectivity)

B) The trend says nature doesn't just stop at one species, it finds more and more ways to access more entropy maximization techniques. Humans are one way to get to whichever subsequent step will yield more ways (aka more intelligence...i.e. AGI/ASI) that shall generate additional "macrostates" or paths towards better entropy maximization methods.

Edited by thoughtfuhk

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Moderator Note

What part of "Don't start another thread on this topic" didn't you understand?

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Just now, swansont said:
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Moderator Note

What part of "Don't start another thread on this topic" didn't you understand?

I didn't provide the source the last time.