From Generative AI to Innovative AI: An Evolutionary Roadmap

• URL: http://arxiv.org/abs/2503.11419v1
• Date: Fri, 14 Mar 2025 14:03:28 GMT
• Title: From Generative AI to Innovative AI: An Evolutionary Roadmap
• Authors: Seyed Mahmoud Sajjadi Mohammadabadi,
• Abstract summary: This paper explores the transition from Generative Artificial Intelligence (GenAI) to Innovative Artificial Intelligence (InAI)<n>In this context, innovation is defined as the ability to generate novel and useful outputs that go beyond mere replication of learned data.<n>The paper proposes a roadmap for developing AI systems that can generate content and engage in autonomous problem-solving and creative ideation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the critical transition from Generative Artificial Intelligence (GenAI) to Innovative Artificial Intelligence (InAI). While recent advancements in GenAI have enabled systems to produce high-quality content across various domains, these models often lack the capacity for true innovation. In this context, innovation is defined as the ability to generate novel and useful outputs that go beyond mere replication of learned data. The paper examines this shift and proposes a roadmap for developing AI systems that can generate content and engage in autonomous problem-solving and creative ideation. The work provides both theoretical insights and practical strategies for advancing AI to a stage where it can genuinely innovate, contributing meaningfully to science, technology, and the arts.

Related papers

• Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research [1.6311895940869516]
  We consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts.<n>We explore the unique features of GenAI, which are rooted in the continued change from symbolism to connectionism.
  arXiv  Detail & Related papers  (2025-02-25T16:34:23Z)
• Artificial Intelligence in Creative Industries: Advances Prior to 2025 [4.732983123464898]
  The rapid advancements in artificial intelligence (AI) have profoundly impacted the creative industries.<n>This paper explores how these developments have expanded creative opportunities and efficiency.<n>Despite these innovations, challenges remain, particularly for the media industry, due to the demands on communication traffic from creative content.
  arXiv  Detail & Related papers  (2025-01-06T02:46:33Z)
• Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms [0.0]
  Deep generative models, an integration of generative and deep learning techniques, excel in creating new data beyond analyzing existing ones. By automating design, optimization, and innovation cycles, Generative AI is reshaping core industrial processes. In the financial sector, it is transforming risk assessment, trading strategies, and forecasting, demonstrating its profound impact.
  arXiv  Detail & Related papers  (2024-10-19T20:57:16Z)
• 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)
• Exploring the intersection of Generative AI and Software Development [0.0]
  The synergy between generative AI and Software Engineering emerges as a transformative frontier. This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development. It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
  arXiv  Detail & Related papers  (2023-12-21T19:23:23Z)
• 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)
• 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)
• Learning from learning machines: a new generation of AI technology to meet the needs of science [59.261050918992325]
  We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data.
  arXiv  Detail & Related papers  (2021-11-27T00:55:21Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Artificial Intelligence in the Creative Industries: A Review [2.657505380055164]
  This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. We categorise creative applications into five groups related to how AI technologies are used. We examine the successes and limitations of this rapidly advancing technology in each of these areas.
  arXiv  Detail & Related papers  (2020-07-24T07:29:52Z)

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.