From Generative AI to Innovative AI: An Evolutionary Roadmap

TL;DR

The paper addresses the gap between Generative AI's ability to produce high-quality content and the need for AI that can autonomously innovate. It proposes an evolutionary roadmap to InAI by integrating computational creativity, reinforcement learning, and multimodal reasoning, supported by theoretical and practical guidance. Key contributions include a clear articulation of GenAI limitations, a taxonomy of innovative AI capabilities, and concrete directions for autonomous problem formulation, cross-domain synthesis, and ethical governance. The work highlights the potential impact of InAI on science, technology, and the arts by enabling AI-driven ideation, problem solving, and design across domains, while stressing responsible development and human–AI collaboration.

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.

Figures (1)

• Figure 1: Conceptual roadmap from GenAI to InAI, illustrating the transition from systems that generate content through data recombination to those capable of autonomous, creative innovation.