  In the burgeoning field of artificial intelligence (AI), the unprecedented
progress of large language models (LLMs) in natural language processing (NLP)
offers an opportunity to revisit the entire approach of traditional metrics of
machine intelligence, both in form and content. As the realm of machine
cognitive evaluation has already reached Imitation, the next step is an
efficient Language Acquisition and Understanding. Our paper proposes a paradigm
shift from the established Turing Test towards an all-embracing framework that
hinges on language acquisition, taking inspiration from the recent advancements
in LLMs. The present contribution is deeply tributary of the excellent work
from various disciplines, point out the need to keep interdisciplinary bridges
open, and delineates a more robust and sustainable approach.
