The Importance of Open-Endedness (for the Sake of Open-Endedness)

• URL: http://arxiv.org/abs/2006.03079v1
• Date: Thu, 4 Jun 2020 18:02:31 GMT
• Title: The Importance of Open-Endedness (for the Sake of Open-Endedness)
• Authors: Tim Taylor
• Abstract summary: A paper in the recent Artificial Life journal special issue on open-ended evolution (OEE) presents a simple evolving computational system. I emphatically reject the suggestion that OEE is not a worthy research topic in its own right. I demonstrate the importance of studying open-endedness for the sake of open-endedness.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A paper in the recent Artificial Life journal special issue on open-ended evolution (OEE) presents a simple evolving computational system that, it is claimed, satisfies all proposed requirements for OEE (Hintze, 2019). Analysis and discussion of the system are used to support the further claims that complexity and diversity are the crucial features of open-endedness, and that we should concentrate on providing proper definitions for those terms rather than engaging in "the quest for open-endedness for the sake of open-endedness" (Hintze, 2019, p. 205). While I wholeheartedly support the pursuit of precise definitions of complexity and diversity in relation to OEE research, I emphatically reject the suggestion that OEE is not a worthy research topic in its own right. In the same issue of the journal, I presented a "high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems" (Taylor, 2019). In the current brief contribution I apply my framework to Hinzte's model to understand its limitations. In so doing, I demonstrate the importance of studying open-endedness for the sake of open-endedness.

Related papers

• An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms [62.878616839799776]
  We propose SynthRAG, an innovative framework designed to enhance Question Answering (QA) performance. SynthRAG improves on conventional models by employing adaptive outlines for dynamic content structuring. An online deployment on the Zhihu platform revealed that SynthRAG's answers achieved notable user engagement.
  arXiv  Detail & Related papers  (2024-10-23T09:14:57Z)
• A Comprehensive Survey on Evidential Deep Learning and Its Applications [64.83473301188138]
  Evidential Deep Learning (EDL) provides reliable uncertainty estimation with minimal additional computation in a single forward pass. We first delve into the theoretical foundation of EDL, the subjective logic theory, and discuss its distinctions from other uncertainty estimation frameworks. We elaborate on its extensive applications across various machine learning paradigms and downstream tasks.
  arXiv  Detail & Related papers  (2024-09-07T05:55:06Z)
• On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
  Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in unseen domains. This paper presents a broad review of recent advances in element-wise ZSIR. We first attempt to integrate the three basic ZSIR tasks of object recognition, compositional recognition, and foundation model-based open-world recognition into a unified element-wise perspective.
  arXiv  Detail & Related papers  (2024-08-09T05:49:21Z)
• Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence [18.130525337375985]
  This paper presents a framework for grappling with openness across the AI stack. It summarizes previous work on this topic, analyzes the various potential reasons to pursue openness. It outlines how openness varies in different parts of the AI stack, both at the model and at the system level.
  arXiv  Detail & Related papers  (2024-05-17T20:35:39Z)
• Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents [80.5213198675411]
  Large language models (LLMs) have dramatically enhanced the field of language intelligence. LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer. Recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents.
  arXiv  Detail & Related papers  (2023-11-20T14:30:55Z)
• Interpretability is not Explainability: New Quantitative XAI Approach with a focus on Recommender Systems in Education [0.0]
  We propose a novel taxonomy that provides a clear and unambiguous understanding of the key concepts and relationships in XAI. Our approach is rooted in a systematic analysis of existing definitions and frameworks. This comprehensive taxonomy aims to establish a shared vocabulary for future research.
  arXiv  Detail & Related papers  (2023-09-18T11:59:02Z)
• Admissibility in Strength-based Argumentation: Complexity and Algorithms (Extended Version with Proofs) [1.5828697880068698]
  We study the adaptation of admissibility-based semantics to Strength-based Argumentation Frameworks (StrAFs) Especially, we show that the strong admissibility defined in the literature does not satisfy a desirable property, namely Dung's fundamental lemma. We propose a translation in pseudo-Boolean constraints for computing (strong and weak) extensions.
  arXiv  Detail & Related papers  (2022-07-05T18:42:04Z)
• A Survey on Neural Open Information Extraction: Current Status and Future Directions [87.30702606041407]
  Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora. We provide an overview of the-state-of-the-art neural OpenIE models, their key design decisions, strengths and weakness.
  arXiv  Detail & Related papers  (2022-05-24T02:24:55Z)
• The Need for Standardized Explainability [0.4588028371034407]
  This paper offers a perspective on the current state of the area of explainability, and to provide novel definitions for Explainability and Interpretability. We provide an overview of the literature on explainability, and of the existing methods that are already implemented. We offer a tentative taxonomy of the different explainability methods, opening the door to future research.
  arXiv  Detail & Related papers  (2020-10-20T08:40:20Z)
• Towards an Open Science definition as a political and legal framework: on the sharing and dissemination of research outputs [0.0]
  It is widely recognised nowadays that there is no single, accepted, unified definition of Open Science. It motivates our proposal of an Open Science definition as a political and legal framework where research outputs are shared and disseminated. We elaborate this proposal through a detailed analysis of some selected EC policies and laws as well as of the function of research evaluation practices.
  arXiv  Detail & Related papers  (2020-10-09T11:44:37Z)
• Generalization in Deep Learning [103.91623583928852]
  This paper provides theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima. We also discuss approaches to provide non-vacuous generalization guarantees for deep learning.
  arXiv  Detail & Related papers  (2017-10-16T02:21:24Z)

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.