A.I. Writes An Essay On Simulation

[Author’s input]

When it comes to simulating phenomena, there exists a class of phenomena that yields well to the realm of predictive dynamics, such as physics and chemistry, and another class that does not yield so well to prediction or re-creation, such as financial markets and disaster modelling. Why is this the case? Are we missing certain elements of understanding that are key to transfer knowledge from one class to another? Or is it knowledge that is completely independent of what we currently possess?

This essay is supported by Generatebg

[Artificial Intelligence responds]

Another way of putting this is that while physics is deterministic, we do not yet know whether it is predictable. Finance, on the other hand, is clearly unpredictable, but whether or not it is deterministic is still an open question. There are some simple reasons for why this may be the case. Systems like those that exist in physics are closed systems, where all variables are accounted for; there exists a perfect knowledge of the initial state of the system and its current state. On the other hand, in finance there are many unknown variables and unknown strategies that traders take advantage of. Furthermore, there is no globally agreed upon set of rules as to how pricing should work in financial markets; if there were, then they could be modeled and simulated with ease.

What we need to decide on is whether or not we believe that systems that are fundamentally unpredictable can yield to simulation; this can be done by studying the underlying cause of unpredictability in these systems and proving a link between these causes and what we currently know about simulation. If such a link does exist then this may lead us to devise new algorithms or procedures for simulating these systems which may be more realistic than what we currently possess.

So let us take a look at why these two classes of systems exist and how they differ with regards to predictive capabilities. So why are some systems more predictable than others? In physics, one can model a system using differential equations, which describe the behaviour of a system over time by taking into account the rate at which variables change rather than their absolute values at any one time (i.e., velocity as opposed to speed). This is because differential equations can simulate interactions between multiple factors without needing complete knowledge of each factor’s value at any given time; all that needs to be known is how quickly they change over time (i.e., velocity).

So if one were able to pinpoint exactly how quickly all factors changed over time then predictions could be made about how the system would behave in future states. This idea is represented by the following formula:

where S represents the state of a system at any given point in time (i.e., its velocity), t represents time, S represents its state at any given point in time (i.e., its speed), i represents time and j represents space.

The important thing to note here is that this equation describes change over time through a series of differentials; differential equations can model change over time by taking into account how quickly different elements within a system change over time rather than their values at any given point in time. Therefore, if we were able to perfectly model all interactions between variables within a system then we would also be able to predict future states and behaviours with perfect accuracy, as well as map out mathematically what will happen if certain changes occur within this system (i.e., calculating derivatives). This kind of modelling strategy was first developed by Isaac Newton with regards to Sir Isaac Newton’s laws of motion: “an object’s mass determines how strongly it will react when forces act upon it”, “an object will continue moving until an external force acts upon it”, etc.. It was also used by Leonhard Euler in his development of Euler’s equations: “the rate at which an object accelerates depends on both its mass and its velocity”, “if an object moves along an axis then it will move faster along that axis as its distance from that axis decreases”. To recap: differential equations attempt to understand change over time while ordinary differential equations attempt to understand change over distance through multiple differentials; whilst ordinary differential equations can only be used when dealing with closed systems (i.e., systems where

[The text ends abruptly because of the author’s-imposed token limitation]

A.I. Writes An Essay – Author’s Commentary:

After covering the basics of the AI in question close to a year back, I have now gained access (private beta) to the system’s API (only; no source-code access!). And this is the very first output that I was able to generate (after configuring the controlling parameters as per my requirements and preferences). I was initially spooked by the output, specifically by how it was able to continue the tone of the original writer.

Not only did it continue extrapolating language nuances (such as my spelling ‘behaviour’ instead of ‘behavior’), but it was also able to underrun my sensitivity threshold to coherence and pollution. In other words, it stayed on topic, and tried to address the core of the discussion. Up to a certain point, I had a feeling that it could have very well been me who had written the text (adding to the spook-factor) or it might have very well been a conversation between me and a colleague.

The initial results look promising, and its applications are much more. I will be continuing experimenting with potential applications in the near future. If you have interesting ideas, do get in touch. If the idea has merit, I’d be glad to work with you. However, please note that my token allocation is limited, and therefore, I am forced to prioritise ideas based on pure merit.

A.I. Writes An Essay – Proof of Work (original output, as is):

I hope you found this article interesting and useful. If you’d like to get notified when interesting content gets published here, consider subscribing.