What factor has significantly contributed to the improvements and adoption of AI?

Increased computational power, particularly with the development and sophistication of modern GPUs, has played a pivotal role in the advancements and widespread adoption of AI technologies. The evolution of GPU architecture has enabled the parallel processing of complex calculations, which is essential for training large-scale machine learning models efficiently.

As datasets have grown in size and complexity, traditional CPUs often struggle to manage the computational demands of training deep learning models. GPUs, designed to handle multiple tasks simultaneously, have significantly reduced the time needed for training these models, leading to faster development cycles and the ability to experiment with more complex algorithms. This accessibility to powerful computing resources has empowered researchers and developers to innovate and bring new AI solutions to various industries at an unprecedented rate.

While other factors like the invention of new programming languages, ethical considerations, and social media have had their roles, the leap in computational capabilities stands out as the fundamental driver that has enabled more advanced AI applications to become feasible and widely implemented.