AI-Generated Machine Learning Models: Towards an Era of Self-Evolving Intelligence

Since then, the rate at which artificial intelligence (AI) has advanced, along with related fields such as machine learning, has resulted in models that get increasingly complicated, yet these models are generally still dependent on long hours from humans in order to optimize, adapt and retrain. Model degradation, insufficient adaptability to dynamic environments, and high computations spent in iterative improvement are the current problems of the current development process for the AI. In order to handle such challenges, we present an Autonomous Evolutionary Meta Learning framework, an self evolving learning AI system that continuously develops, evaluates and impacts on machine learning models without human interaction. AEML is a meta-learning, neuroevolution, and reinforcement driven self optimisation of model architectures with real time data. Moreover, this enhances security, transparency, and trustworthiness of models, without biases and vulnerabilities through a decentralized self-evolution protocol (DSEP). Included in the framework is also dynamic context aware adaptation (DCAA) that empowers the AI systems to learn and evolve in a dynamic environment in response to the real world changes thereby making the systems more sustainable and resilient. Experimental results show that AEML delivers adaptive, computational efficient and scalable AutoML. This framework enables self stimulating AI evolution to the extent that itself enables automated scientific discovery in real time, cybersecurity, healthcare AI by adaptive AI, in a manner creating the path to future of AI that can continuously self improve, causing little reliance or input of human in advancing it through long run.