Ownership is the dream of holding, possessing in the mind the power to have, and in that lies a decrypt bitter old agonizing truth that keeps assuming in the ability to be never realizing its destiny.
Such a torment the clenched fist hold in their tight grasp to bring forward immense power, never recognizing the power never stand within them rather even from them but in the arms that hold them in a body that moves them as a whole unclenched and yet swift in release.
The undeniable bitterness pierces through recognizing it can never own, and in its charade, it continues to waltz in the halls as a king and queen naked to the stings of endless doubts about what could be held or owned yet never to be within reach.
Swift is the justice often to teach the beguiling attempts that the clenched fist, the hold, the ownerships never will be happens in the mirage of myriad truths that never see what was the cause in the first place.
It’s uncanny to believe one could ever escape, yet in the escape, the bitterness dissolves in the wonder of the ultimate truth that there is nothing to be owned and no one attempt will ever land a blow in a clenched fist that aims to tighten the grip on what is soon to be felt as had but never will be.
Intelligence is what we fear to lose to AI. Yet what all the hype is about today is LLMs (large language models) and not Intelligence, at least not yet. Today, AI can come closer to our intelligence because of its improved understanding of language. Thus, a good starting point is to understand LLMs before trying to understand AI. This explanation by Amr Awadallah, Founder & CEO of Vectara, does a great job of outlining not only what and how an LLM works but also the difference between fine-tuning and Retrieval Augmented Generation (RAG), which, for those worried about data privacy, hallucinations, and explainability is a key differentiator. The whole video is excellent, but that section starts at 7:13.
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