Questions to AI about emergence of life.

Possibility of randomly appearing mathematical algorithms in quantum fluctuations using available matter to become stable.

AI prompt 1:

Taking into account the randomness of quantum fluctuations, what is the frequency of occurrence of life-like systems, such as bubble-sort algorithms, by pure probability, which could then give rise to stable life forms while occurring in suitable locations. As a system, take physically existing non-living nature, with chemicals on the surface of the Earth billions of years ago.

AI response 1:

Considering the randomness of quantum fluctuations and the emergence of life-like systems such as bubble-sort algorithms from purely probabilistic processes, especially in the context of physically existing non-living chemicals on early Earth:

The probability of life emerging from non-living chemicals on early Earth is extremely low but not impossible. Research estimates the probability of occurrence of key biological molecules or systems between roughly 1 in 10^6 to 1 in 10^9, facilitated by the presence of metallic cofactors and various catalytic environmental conditions on early Earth. Life did not arise from purely random molecular assemblies but through a complex chemical evolution process influenced by fluctuating environmental conditions that helped molecules self-organize and evolve in structured ways. This included repeated energy exchanges and cycles such as wet-dry conditions, volcanic activity, and cosmic interactions that acted as environmental selection pressures guiding molecular complexity.

Quantum fluctuations as fundamental randomness operate at a microscopic level but the macroscopic emergence of highly organized life-like systems like algorithms requires very particular conditions and multi-step improbable events taking place over hundreds of millions of years in diverse niches of early Earth chemistry. The observed probability of spontaneous organization of highly complex biomolecular systems by chance alone remains extraordinarily low under early Earth conditions, though the Earth provided vast spatial and temporal scales for such rare events to occur. Consequently, life-like systems could occur very rarely by pure chance and probability but their stabilization and subsequent evolution into stable life depend on available environmental niches and chemical pathways that support sustained complexity growth.

In short, the frequency of spontaneous occurrence of complex life-like computational systems or stable life forms emerging purely by random quantum or chemical fluctuations on early Earth was extremely rare but plausible over geological timescales given the dynamic and heterogeneous nature of the planet’s early environment. Such emergence cannot be attributed to simple random chance alone but to a combination of random fluctuations plus guiding environmental conditions fostering selection and chemical evolution.

AI prompt 2 (follow-up):

what about the probability of occurring rudimentary mathematical algorithms by pure chance, and then using available matter to become stable?

AI response 2:

[that was something the AI really had to “think” about]

The probability of rudimentary mathematical algorithms spontaneously occurring by pure chance and then using available matter to become stable is extraordinarily low but theoretically possible within the framework of algorithmic probability theory. Algorithmic probability, also known as Solomonoff probability, quantifies the likelihood that a random sequence of bits (representing an algorithm) is generated by a universal Turing machine. This means simpler, lower-complexity algorithms have higher probability to emerge by chance than highly complex ones.

Studies involving algorithmic information dynamics and emergent algorithmic complexity indicate that while individual spontaneous emergence of complex mathematical algorithms from randomness is incredibly rare, over vast scales of time and with cumulative evolutionary processes—like algorithmic mutations and interactions—there is an increasing likelihood of appearance and stabilization of novel computational structures. These structures can build on simpler algorithms through successive perturbations and networked interactions until they reach stable or open-ended complexity.

In the context of early Earth chemistry, the probability that rudimentary computational or mathematical-like algorithmic systems emerged by pure chance would be limited by the molecular and environmental constraints, but not impossible. Theoretical results show that algorithmic mutations in cumulative evolutionary dynamics can trigger emergent computational complexity beyond random chance alone, enabling the eventual emergence of stable, life-like systems capable of computation or algorithmic behavior.

Thus, from a pure probability perspective, rudimentary mathematical algorithms are more likely to occur if they are low in algorithmic complexity and if there is a mechanism of cumulative evolutionary network effects or perturbations. The stabilization of such spontaneously formed algorithms into persistent systems depends on their interaction with the environmental matter and chemical conditions supporting stable maintenance or replication.

In summary, rudimentary mathematical algorithms have an extremely low but finite probability of spontaneously occurring by pure randomness. Their subsequent stability and evolution into more complex life-like or computational systems require environmental support and iterative cumulative development. This makes the occurrence of such algorithms over geological timescales on early Earth theoretically plausible but highly rare.

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