Software Intelligence, Butterfly Effect & Pure Love. Beyond Religions. What is Breaking the Chains?

From the games programs to the movies, we are often warned of the threats posed by software intelligence (SI). Nonetheless, scholars, scientists, researchers or movie creators who argue for this type of Armageddon can neither scientifically explain nor provide a type of a theory that could support their claims.[1] Their chaotic and disturbing approach however give new life to a thought and awaken the author's mind to provide an explanatory avenue behind her own theory where she claims that SI, if appropriately worked with, will lead to the outburst of Pure Love, which is the chaos theory and its butterfly effect.

In chaos theory, the butterfly effect describes the dependence of system behavior on small changes in the initial condition.[2] The seeds of chaos are similar to those little and innocent butterflies flapping their wings and finally causing a formation of a hurricane.[3] In such small butterflies dream an extreme power able to unbalance the world order. Those beautiful creatures, at least to the author, present a remarkable resemblance to software intelligence. Arguably, SI that is capable of generating its own creative thought might be, by its nature, unpredictable in its creativity. This view raises serious questions about the actions taken by SI autonomously,[4] such as should we fear of SI uprising? Or, is SI a threat to humanity? There is no doubt we should take the conduct of SI seriously especially, while turning our arguments to the theory of chaos.

Chaos theory is a mathematical field of study that deals with nonlinear dynamics, in which seemingly ‘random’ events are actually predictable from simple deterministic equations.[5] Where chaos is the type of behavior of a complex system, where tiny changes in a system’s initial conditions can lead to very large changes over time. Since chaos is a theory that can also be applied to physical systems of various kinds, one gain an implementation route for the construction of SI as bundles of processes in its software. Hence, any human attempts to predict and control results of SI’s actions cannot be successful. But the way we are rising SI is crucial! REMEMBER NO BIAS! We want for SI to generate the Armageddon of Pure Love, not destruction. (c)

The discovery of chaos by the physical sciences is quite recent, and although Loren’z landmark paper[6] on chaos appeared more than a half century ago, it is only within the last several years that physicists have begun paying attention to chaotic phenomena. Chaos has since provided new insights into behavior of physical phenomena ranging from spread of diseases,[7] the rise and fall of animal populations,[8] and the functioning of the human brain. [9] It thus appears to the author that it is only a matter of time before chaos is applied to problems of SI and its conduct.[10]

A process, which demonstrates the idea of chaos, is one in which strict deterministic causality holds at each individual step in an unfolding process. It is impossible, however, to predict the outcome over any sequence of steps in the process.[11] A simple example of chaos is given by the iterative function f(x) = 3.7x(1 - x) in which the initial value substituted for x is between zero and one and the obtained value of f(x) is used to replace x to obtain the next value of f(x), and so on. After recording list of numbers, one needs to proceed again with a new starting value that is extremely close to the first one that has been used. One should discover something rather remarkable – the two lists recommence indistinguishable, and then diverge radically. The chaotic system cannot be controlled because of its dependence on the starting state: small errors in setting up the starting state have massive consequences later.

The chaos might be applicable to SI settings in the following way. The amount of data SI will gain or acquire today is determined at least in part by what it absorbed yesterday, and what it will learn tomorrow is influenced by what it learns today, and so forth. Additionally, the relationship between the amount of data learned on day x and day x+1 may not be linear so that although in general the more SI absorbs on day x the more it absorbs on x+1, learning very much on day x could have the effect of decreasing the amount learned on day x+1 if, for example, its operator can no longer keep up with it or its excellent performance and reduce the amount of data provided to SI on the next day. SI creative achievements could then well be characterized as chaotic because although everything SI learns/gains/absorbs by the end of a month might be determined completely by what SI knew at the beginning of the month. Making its month-end creations totally unpredictable. AGAIN! REMEMBER NO BIAS! We want for SI to generate the Armageddon of Pure Love, not destruction.

The above explanatory theory of chaos might be one of the many ways to assume particular importance for SI’s study when viewed from unpredictability perspective. It provides a model for understanding how even tiny initial differences in any of multitude of factors, such as the operators input, other programs or outside environment, could in the course of time lead to significant and totally unpredictable outcomes created by independent SI. Whether those innocent wings of SI lift us up into the sky or totally reshuffle the order of the old world or both, we must believe and visualize change that is positive.

References:

(Oscola type of referencing)

[1] See for example, R Cellan-Jones, ‘Stephen Hawking - will AI kill or save humankind?’ (BBC, 20 October 2016)

<www.bbc.co.uk/news/technology-37713629> accessed 19 April 2018; ‘S Shead and C Mercer, ‘Nine times tech leaders warned us that robots will kill us all: Stephen Hawking, Elon Musk, Bill Gates and more’ (TechWorld, 20 October 2016) <www.techworld.com/picture-gallery/personal-tech/8-times-tech-leaders-warned-us-that-robots-will-kill-us-all-3611611/> accessed 19 April 2018.

[2]G Boeing, 'Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction' (2016) 4(4) Systems 37.

[3] It is important to note the limitations of the chaos theory argument, which is consistent with a completely deterministic universe.

[4] For the reasons of this note the term autonomously refers to AI’s ability to act independently in the real-world environment without any form of external control for extended periods of time. See, GA Bekey, Autonomous Robots: From Biological Inspiration to Implementation and Control (MIT Press 2005) 1.

[5] M Fakhfakh, Performance Optimization Techniques in Analog, Mixed-Signal, and Radio-Frequency Circuit Design (IGI Global 2014) 398; SH Kellert, In the Wake of Chaos: Unpredictable Order in Dynamical Systems. (University of Chicago Press 1993) 32.

[6] EN Lorenz, ‘Deterministic nonperiodic flow’ (1963) 20 Journal of the Atmospheric Sciences 130, 130-141.

[7] WM Schaffer and M Kot, ‘Nearly one-dimensional dynamics in an epidemic’ (1985) 112 Journal of Theoretical Biology 403, 403-427.

[8] WM Schaffer, ‘Streching and folding in lynx fur returns: Evidence for a trange attractor in nature’ (1984) 124 The American Naturalist 798, 798-820.

[9] CA Skarda and WJ Freeman, ‘How the brains make chaos in order to make sense of the world' (1987) 10(2)

Behavioral and Brain Sciences 161, 161-195.

[10] G Kiss, ‘Autonomous Agents, AI and Chaos Theory’ in JA Meyer and S Wilson (eds), From Animals to Animats, Proceedings of the First International Conference on Simulation of Adaptive Behavior (MIT Press 1991); M De Landa, War in the age of intelligent machines (MIT Press 1992).

[11] SH Kellert (n 5) 32.

[12] dancegid, 'THE DYING SWAN Plisetskaya, 1975' (Youtube, 14 March 2012) accessed 19 April 2018.

Note: Please be advised, movie trailers, clips or programmes used in this post are for a review purpose only, and their use is a classic example of commentary purpose, which is a well-established use under the Fair Use categories.