  Resistive random-access memory (RRAM) is a promising candidate for
next-generation memory devices due to its high speed, low power consumption,
and excellent scalability. Metal oxides are commonly used as the oxide layer in
RRAM devices due to their high dielectric constant and stability. However, to
further improve the performance of RRAM devices, recent research has focused on
integrating artificial intelligence (AI). AI can be used to optimize the
performance of RRAM devices, while RRAM can also power AI as a hardware
accelerator and in neuromorphic computing. This review paper provides an
overview of the combination of metal oxides-based RRAM and AI, highlighting
recent advances in these two directions. We discuss the use of AI to improve
the performance of RRAM devices and the use of RRAM to power AI. Additionally,
we address key challenges in the field and provide insights into future
research directions
