Creative AI Through Evolutionary Computation: Principles and Examples

• URL: http://arxiv.org/abs/2008.04212v3
• Date: Mon, 15 Feb 2021 02:10:27 GMT
• Title: Creative AI Through Evolutionary Computation: Principles and Examples
• Authors: Risto Miikkulainen
• Abstract summary: Population-based search techniques make it possible to find creative solutions to practical problems in the real world. evolutionary computation is the likely "next deep learning"
• Score: 16.8615211682877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional, and complex search spaces. Population-based search techniques, i.e. variants of evolutionary computation, are well suited to finding them. These techniques make it possible to find creative solutions to practical problems in the real world, making creative AI through evolutionary computation the likely "next deep learning."

Related papers

• Open-world Machine Learning: A Review and New Outlooks [83.6401132743407]
  This paper aims to provide a comprehensive introduction to the emerging open-world machine learning paradigm. It aims to help researchers build more powerful AI systems in their respective fields, and to promote the development of artificial general intelligence.
  arXiv  Detail & Related papers  (2024-03-04T06:25:26Z)
• Can AI Be as Creative as Humans? [84.43873277557852]
  We prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. The debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data.
  arXiv  Detail & Related papers  (2024-01-03T08:49:12Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• An Initial Look at Self-Reprogramming Artificial Intelligence [0.0]
  We develop and experimentally validate the first fully self-reprogramming AI system. Applying AI-based computer code generation to AI itself, we implement an algorithm with the ability to continuously modify and rewrite its own neural network source code.
  arXiv  Detail & Related papers  (2022-04-30T05:44:34Z)
• Co-evolutionary hybrid intelligence [0.3007949058551534]
  The current approach to the development of intelligent systems is data-centric. The article discusses an alternative approach to the development of artificial intelligence systems based on human-machine hybridization and their co-evolution.
  arXiv  Detail & Related papers  (2021-12-09T08:14:56Z)
• Qualities, challenges and future of genetic algorithms: a literature review [0.0]
  Genetic algorithms are computer programs that simulate natural evolution. They have been used to solve various optimisation problems from neural network architecture search to strategic games. Recent developments such as GPU, parallel and quantum computing, conception of powerful parameter control methods, and novel approaches in representation strategies may be keys to overcome their limitations.
  arXiv  Detail & Related papers  (2020-11-05T17:53:33Z)
• Exploring the Nuances of Designing (with/for) Artificial Intelligence [0.0]
  We explore the construct of infrastructure as a means to simultaneously address algorithmic and societal issues when designing AI. Neither algorithmic solutions, nor purely humanistic ones will be enough to fully undesirable outcomes in the narrow state of AI.
  arXiv  Detail & Related papers  (2020-10-22T20:34:35Z)
• Quality and Diversity in Evolutionary Modular Robotics [1.290382979353427]
  In Evolutionary Robotics a population of solutions is evolved to optimize robots that solve a given task. Quality Diversity algorithms try to overcome premature convergence by introducing additional measures that reward solutions for being different.
  arXiv  Detail & Related papers  (2020-08-05T13:08:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.