Investigating Biological Assumptions through Radical Reimplementation

Lehman, Joel; Stanley, Kenneth O.

doi:10.1162/artl_a_00150

Cited by 8 publications

(6 citation statements)

References 76 publications

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“…One persistent misconception of digital evolution is that, because it is instantiated in a computational substrate, it lacks relevance to the study of biological evolution. Yet both philosophical arguments [16,21,34,17,40] and high-profile publications [41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56] suggest that digital evolution can be a useful tool to aid and complement the study of biological evolution. Indeed, these evolving systems can be seen as real instances of evolution, rather than mere simulation of evolution [57].…”

Section: Digital Evolutionmentioning

confidence: 99%

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

et al. 2020

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Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

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Section: Digital Evolutionmentioning

confidence: 99%

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

et al. 2020

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“…Because of its central importance, love has been intellectually explored across diverse areas of study, such as positive psychology [29], philosophy [60], and spiritual traditions [61,62]. Thus, in the spirit of fields such as machine intelligence and artificial life, which seek to understand the core of a phenomenon and implement it in ways fitting a new medium [63], the question asked here is if the rich cultural and philosophical studies of love can abstractly inform how to apply ML towards human flourishing? In other words, the hope is to abstract from previous conceptions of love a version that is both appropriate and useful for guiding machine action, i.e.…”

Section: Optimization Pressure For Engagement Undermines Flourishingmentioning

confidence: 99%

Machine Love

Lehman¹

2023

Preprint

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From social media to recommendation and search engines, the impact of machine learning (ML) upon our lives and society continues to grow. While ML generates much economic value, many of us have problematic relationships with social media and other ML-powered applications. One reason is that ML often optimizes for what we want in the moment, which is easy to quantify but at odds with what is known scientifically about human flourishing. Thus, through its impoverished models of us, ML currently falls far short of its exciting potential, which is for it to help us to reach ours. While there is no consensus on defining human flourishing, from diverse perspectives across psychology, philosophy, and spiritual traditions, love is understood to be one of its primary catalysts. Motivated by this view, this paper explores whether there is a useful conception of love fitting for machines to embody. While such machine love might seem like an error of category, historically it has been generative to explore whether some kernel of a nebulous concept, such as life or intelligence, can be thoughtfully abstracted and reimagined in a different medium, as in the fields of machine intelligence or artificial life. This paper forwards a candidate conception of machine love, inspired in particular by work in positive psychology and psychotherapy: to provide unconditional support enabling humans to autonomously pursue their own growth and development. While many alternate conceptions are possible, this one benefits from not requiring machines to simulate emotional affect or relationships, instead focusing on biasing an ML system's actions to support our growth. Through proof of concept experiments, this paper aims to highlight the need for richer models of human flourishing in ML, provide an example framework through which positive psychology can be combined with ML to realize a rough conception of machine love, and demonstrate that current language models begin to enable embodying qualitative humanistic principles. The conclusion is that though at present ML may often serve to addict, distract, or divide us, an alternative path may be opening up: We may align ML to support our growth, through it helping us to align ourselves towards our highest aspirations. * Much of this work was completed during a residency at Stochastic Labs.Preprint. Under review.

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“…Evolutionary computation has benefited from innovation inspired by insights in natural evolution since its inception. [1, 2] For evolutionary scientists, algorithms can function as in silico models, allowing specific aspects of evolution to be isolated and studied from a different perspective and with a level of control that is not possible in nature [3, 4].…”

Section: Introductionmentioning

confidence: 99%

Homology and linkage in crossover for linear genomes of variable length

2019

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The use of variable-length genomes in evolutionary computation has applications in optimisation when the size of the search space is unknown, and provides a unique environment to study the evolutionary dynamics of genome structure. Here, we revisit crossover for linear genomes of variable length, identifying two crucial attributes of successful recombination algorithms: the ability to retain homologous structure, and to reshuffle variant information. We introduce direct measures of these properties—homology score and linkage score—and use them to review existing crossover algorithms, as well as two novel ones. In addition, we measure the performance of these crossover methods on three different benchmark problems, and find that variable-length genomes out-perform fixed-length variants in all three cases. Our homology and linkage scores successfully explain the difference in performance between different crossover methods, providing a simple and insightful framework for crossover in a variable-length setting.

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Investigating Biological Assumptions through Radical Reimplementation

Cited by 8 publications

References 76 publications

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Machine Love

Homology and linkage in crossover for linear genomes of variable length

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