Brain-inspired Artificial Intelligence: A Comprehensive Review

• URL: http://arxiv.org/abs/2408.14811v1
• Date: Tue, 27 Aug 2024 06:49:50 GMT
• Title: Brain-inspired Artificial Intelligence: A Comprehensive Review
• Authors: Jing Ren, Feng Xia,
• Abstract summary: Review explores the diverse design inspirations that have shaped modern AI models, i.e., brain-inspired artificial intelligence (BIAI) We present a classification framework that categorizes BIAI approaches into physical structure-inspired and human behavior-inspired models. We examine the real-world applications where different BIAI models excel, highlighting their practical benefits and deployment challenges.
• Score: 15.964784631512414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current artificial intelligence (AI) models often focus on enhancing performance through meticulous parameter tuning and optimization techniques. However, the fundamental design principles behind these models receive comparatively less attention, which can limit our understanding of their potential and constraints. This comprehensive review explores the diverse design inspirations that have shaped modern AI models, i.e., brain-inspired artificial intelligence (BIAI). We present a classification framework that categorizes BIAI approaches into physical structure-inspired and human behavior-inspired models. We also examine the real-world applications where different BIAI models excel, highlighting their practical benefits and deployment challenges. By delving into these areas, we provide new insights and propose future research directions to drive innovation and address current gaps in the field. This review offers researchers and practitioners a comprehensive overview of the BIAI landscape, helping them harness its potential and expedite advancements in AI development.

Related papers

• Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report [12.204470166456561]
  Generative AI shows significant potential in health economics and outcomes research (HEOR) Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration.
  arXiv  Detail & Related papers  (2024-10-26T15:42:50Z)
• A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends [67.43992456058541]
  Image restoration (IR) refers to the process of improving visual quality of images while removing degradation, such as noise, blur, weather effects, and so on. Traditional IR methods typically target specific types of degradation, which limits their effectiveness in real-world scenarios with complex distortions. The all-in-one image restoration (AiOIR) paradigm has emerged, offering a unified framework that adeptly addresses multiple degradation types.
  arXiv  Detail & Related papers  (2024-10-19T11:11:09Z)
• Artificial intelligence inspired freeform optics design: a review [5.118772741438762]
  This article reviews the latest developments in AI applications within freeform optics design. It addresses the benefits of AI, such as improved accuracy and performance, alongside challenges like data requirements, model interpretability, and computational complexity. The future of AI in freeform optics design looks promising, with potential advancements in hybrid design methods, interpretable AI, AI-driven manufacturing, and targeted research for specific applications.
  arXiv  Detail & Related papers  (2024-09-18T00:53:27Z)
• Generative Artificial Intelligence: A Systematic Review and Applications [7.729155237285151]
  This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI. The major impact that generative AI has made to date, has been in language generation with the development of large language models. The paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.
  arXiv  Detail & Related papers  (2024-05-17T18:03:59Z)
• Generative AI Models for Different Steps in Architectural Design: A Literature Review [14.910709576423576]
  It is essential to comprehend the principles and advancements of generative AI models and analyze their relevance in architecture applications. This paper first provides an overview of generative AI technologies, with a focus on probabilistic diffusion models (DDPMs), 3D generative models, and foundation models. We subdivide the architectural design process into six steps and review related research projects in each step from 2020 to the present.
  arXiv  Detail & Related papers  (2024-03-30T13:25:11Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape [5.852005817069381]
  The study critically examined the current state and future trajectory of generative Artificial Intelligence (AI) It explored how innovations like Google's Gemini and the anticipated OpenAI Q* project are reshaping research priorities and applications across various domains. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare.
  arXiv  Detail & Related papers  (2023-12-18T01:11:39Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)

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.