Creative AI Through Evolutionary Computation

Miikkulainen, Risto

doi:10.1007/978-3-030-39831-6_19

Cited by 8 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Evolutionary approaches have been suggested as alternative methods for problems in which the "search space" is high-dimensional or not "wellbehaved", as they can explore multiple parallel objectives to avoid getting stuck or being "deceived" by local optima (Miikkulainen 2020). However, the externally imposed and pre-defined objective function remains, now cast as an evolutionary fitness function.…”

Section: The Problem Of Objective Functionsmentioning

confidence: 99%

Creative Artificial Intelligence within the Artificial Life Installation “Infranet”

Wakefield

2021

Electronic Workshops in Computing

View full text Add to dashboard Cite

The authors explore how current mainstream data-driven AI approaches can be questioned critically from a perspective of computational creativity and ecosystemic art. This centres on a critique of the future as being over-determined by the past; both from the data used, and in the questions or objectives assumed by training. The main contributions of this paper are to apply alternative creative approaches to nature-inspired artificial intelligence, and to detail some of these through their embodiment in the authors' artwork "Infranet". Infranet is a neuro-evolutionary art installation that exhibited at three international locations over 2018-2019. It uses geospatial data of the host city not as a training material but as a habitat for artificial life. In contrast to training-based AI systems, in Infranet there is no objective or fitness function and very little evolutionary pressure or competition. Moreover, it eschews the trend of a large and pre-specified neural network structure in favour of a population of thousands of small interacting neural networks, each with distinct structure, in a "liquid" process of continuous reorganization; resonating with some contemporary theories and models of non-conscious cognition in biological and ecological systems.

show abstract

Section: The Problem Of Objective Functionsmentioning

confidence: 99%

Creative Artificial Intelligence within the Artificial Life Installation “Infranet”

Wakefield

2021

Electronic Workshops in Computing

View full text Add to dashboard Cite

show abstract

“…More specifically, AI in the future is not limited to prediction, but it can also prescribe what decisions need to be made to achieve given objectives [7]. But only humans can define those objectives-we cannot delegate them to AI agents.…”

Section: Phase 4: Foundationalizationmentioning

confidence: 99%

Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential

Miikkulainen¹,

Greenstein²,

Hodjat³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Artificial Intelligence (AI) technology is rapidly changing many areas of society. While there is tremendous potential in this transition, there are several pitfalls as well. Using the history of computing and the world-wide web as a guide, in this article we identify those pitfalls and actions that lead AI development to its full potential. If done right, AI will be instrumental in achieving the goals we set for economy, society, and the world in general.

show abstract

“…This extension is indeed underway in AI through several approaches, such as reinforcement learning, Bayesian parameter optimization, gradient-based approaches, and evolutionary computation [9]- [11]. The approach taken in this article is based on evolutionary surrogate-assisted prescription (ESP; [12]), a technique that combines evolutionary search with surrogate modeling (Fig.…”

mentioning

confidence: 99%

From Prediction to Prescription: Evolutionary Optimization of Nonpharmaceutical Interventions in the COVID-19 Pandemic

Miikkulainen

Francon

Meyerson

et al. 2021

IEEE Trans. Evol. Computat.

Self Cite

View full text Add to dashboard Cite

Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with nonpharmaceutical interventions, such as social distancing restrictions and school and business closures. This article demonstrates how evolutionary AI can be used to facilitate the next step, i.e., determining most effective intervention strategies automatically. Through evolutionary surrogate-assisted prescription, it is possible to generate a large number of candidate strategies and evaluate them with predictive models. In principle, strategies can be customized for different countries and locales, and balance the need to contain the pandemic and the need to minimize their economic impact. Early experiments suggest that workplace and school restrictions are the most important and need to be designed carefully. They also demonstrate that results of lifting restrictions can be unreliable, and suggest creative ways in which restrictions can be implemented softly, e.g., by alternating them over time. As more data becomes available, the approach can be increasingly useful in dealing with COVID-19 as well as possible future pandemics.

show abstract

Creative AI Through Evolutionary Computation

Cited by 8 publications

References 29 publications

Creative Artificial Intelligence within the Artificial Life Installation “Infranet”

Creative Artificial Intelligence within the Artificial Life Installation “Infranet”

Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential

From Prediction to Prescription: Evolutionary Optimization of Nonpharmaceutical Interventions in the COVID-19 Pandemic

Contact Info

Product

Resources

About