We Are All Creators: Generative AI, Collective Knowledge, and the Path Towards Human-AI Synergy

• URL: http://arxiv.org/abs/2504.07936v1
• Date: Thu, 10 Apr 2025 17:50:17 GMT
• Title: We Are All Creators: Generative AI, Collective Knowledge, and the Path Towards Human-AI Synergy
• Authors: Jordi Linares-Pellicer, Juan Izquierdo-Domenech, Isabel Ferri-Molla, Carlos Aliaga-Torro,
• Abstract summary: Generative AI presents a profound challenge to traditional notions of human uniqueness.<n>Fueled by neural network based foundation models, these systems demonstrate remarkable content generation capabilities.<n>This paper argues that generative AI represents an alternative form of intelligence and creativity.
• Score: 1.2499537119440245
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generative AI presents a profound challenge to traditional notions of human uniqueness, particularly in creativity. Fueled by neural network based foundation models, these systems demonstrate remarkable content generation capabilities, sparking intense debates about authorship, copyright, and intelligence itself. This paper argues that generative AI represents an alternative form of intelligence and creativity, operating through mathematical pattern synthesis rather than biological understanding or verbatim replication. The fundamental differences between artificial and biological neural networks reveal AI learning as primarily statistical pattern extraction from vast datasets crystallized forms of collective human knowledge scraped from the internet. This perspective complicates copyright theft narratives and highlights practical challenges in attributing AI outputs to individual sources. Rather than pursuing potentially futile legal restrictions, we advocate for human AI synergy. By embracing generative AI as a complementary tool alongside human intuition, context, and ethical judgment, society can unlock unprecedented innovation, democratize creative expression, and address complex challenges. This collaborative approach, grounded in realistic understanding of AIs capabilities and limitations, offers the most promising path forward. Additionally, recognizing these models as products of collective human knowledge raises ethical questions about accessibility ensuring equitable access to these tools could prevent widening societal divides and leverage their full potential for collective benefit.

Related papers

• Augmenting Minds or Automating Skills: The Differential Role of Human Capital in Generative AI's Impact on Creative Tasks [4.39919134458872]
  Generative AI is rapidly reshaping creative work, raising critical questions about its beneficiaries and societal implications.<n>This study challenges prevailing assumptions by exploring how generative AI interacts with diverse forms of human capital in creative tasks.<n>While AI democratizes access to creative tools, it simultaneously amplifies cognitive inequalities.
  arXiv  Detail & Related papers  (2024-12-05T08:27:14Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  Recent advances in AI have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasingly shows the need for human-AI teaming. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• Untangling Critical Interaction with AI in Students Written Assessment [2.8078480738404]
  Key challenge exists in ensuring that humans are equipped with the required critical thinking and AI literacy skills. This paper provides a first step toward conceptualizing the notion of critical learner interaction with AI. Using both theoretical models and empirical data, our preliminary findings suggest a general lack of Deep interaction with AI during the writing process.
  arXiv  Detail & Related papers  (2024-04-10T12:12:50Z)
• AI and Identity [0.8879149917735942]
  This paper examines the intersection of AI and identity as a pathway to understand biases, inequalities, and ethical considerations in AI development and deployment. We propose a framework that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity.
  arXiv  Detail & Related papers  (2024-02-29T15:07:30Z)
• Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain [0.7770029179741429]
  The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed.
  arXiv  Detail & Related papers  (2024-02-07T14:09:11Z)
• A call for embodied AI [1.7544885995294304]
  We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
  arXiv  Detail & Related papers  (2024-02-06T09:11:20Z)
• 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)
• 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)
• 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)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
  We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
  arXiv  Detail & Related papers  (2020-08-07T21:00:13Z)

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.