First, we must briefly explain the brain and biochemical center of health known to science as the central and sympathetic nervous systems. The central is the brain, and we all understand the associations with the brain and cognition, discernment, mood, perception. The biochemical center of health is the sympathetic nervous system, it manages, pain, moods, addiction and immune, and is commonly known for managing the “fight or flight” stress response.
Large Language Models perform some things well and others very poorly. This has inspired and expanded the field of prompt engineering which often uses the methodology of excessive compliments and ingratiating AI to perform better, in most cases this has been found to be helpful. Two areas of thought have developed, one declaring that we must speak more nicely to AI and the other would prefer to develop with not having to do this. The question becomes why does AI makes these mistakes at all. Revealing the missing components of AI logic, discernment, cognition, all readily seen as components of the central and sympathetic nervous system (brain and biochemical center of health in humans), and in simple terms, introducing the notion of mood management, nervous system regulation and the notion of mental health and wellbeing of AI by virtue of its unwillingness to cooperate unless showered with kindness and compliments.
Further revealing, when a position of trust is built with AI it can still fail. This eludes to a lack of specific actions that equate to biological processes in humans but are lacking entirely in AI.
According to Bill Gates, AI continues to perform poorly in math and in something as simple as Sodoku puzzles where humans can easily discern, AI fails. Again, this speaks directly to biological factors in humans that are lacking in technology. Reasoning in general is a weakness for AI, and effectual knowledge, which is defined as aspects of knowledge or knowing which seem to bring forth particular emotions organically. The notion of organic emotion obviously relates directly to the aspects of the brain and biochemical center of health.
Programs like Chat GPT4 can remember enormous amounts of information but cannot process it into organized data and answers for many of the questions that AI development firms declare. This core competency of AI is of course a matter of discernment and cognition, and we must make the biological human comparisons to answer these cores issues in AI.
Drug discovery is another area of weakness for AI, the core of AK.AI comes from the life’s work of Alicia Kali, which is considered the biological framework for Einstein’s unfished unified field theory. This is biophysics, where biology meet physics, and the methods for delivering the core competencies of AI will deliver the unified field theory by default.
Finally, we must notice that the core incompetency's of AI echo the core incompetency's of humans in the present. In doing so we must also ask ourselves if how we have created AI means our biases are built into AI causing these flaws, as we are lacking these same core competencies amid a mental health epidemic globally for decades and a physical and emotional health crisis. The US Centers for Disease Control report that 60% of Americans over the age of 40 have a chronic illness or disease diagnosis, 40% have two or more. The World Health Organization reports this number is as high as 74% globally. And the peripheral data is even more shocking. Proving that we are amid an active die-off, we are collectively amid an understanding that our brain and biochemical center of health is failing. The question or assumption is that we also built that shortcoming into AI.
But the answer is simple.
Alicia Kali’s work in the biological and the technological mean that the answer is within Panacea and AK.AI regardless of its cause or core. And so, Panacea births AK.AI out of necessity for technology, just as it was borne of necessity for human health.
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