Recent advancements in Large Language Models (LLMs) have revealed that complex linguistic capabilities can emerge from machine learning processes without explicit programming of grammatical rules.

The fact that languages can be learned as an emergent property, by looking at large amounts of properly tagged information, is perhaps the most interesting finding of the whole LLM thing.

Because it basically challenges the traditional assumption that a machine (or even a human) needs to learn grammar in order to learn a new language.

All this was a kind of implicit knowledge for polyglot people. But the ability to speak does not imply sentience. While these models demonstrate impressive language skills, it is crucial to understand that language skills alone do not equate to general intelligence, sentience or consciousness.

Human perception of AI capabilities is heavily influenced by the prominence of language in human cognition. Many individuals think primarily in words, leading to an overemphasis on linguistic abilities when evaluating machine intelligence. This bias can result in the attribution of human-like qualities to AI systems based solely on their language outputs.

Experts in the field, such as Yann LeCun, emphasize that true intelligence encompasses much more than language skills. Human intelligence involves complex emotional processes, creativity, and forms of knowledge that are not primarily linguistic. Current LLMs, despite their linguistic prowess, lack these fundamental aspects of human cognition.

The emergence of language-like behavior in AI systems represents a significant breakthrough in machine learning techniques. However, it is essential to maintain a clear distinction between linguistic competence and the broader spectrum of cognitive abilities that constitute general intelligence.