Why applied intelligence | 6ai
When it comes to identifying intelligence, as a starting point we can go back centuries to mathematician and philosopher, Immanuel Kant who provides the most comprehensive, straightforward and scientific explanation of authentic human intelligence. Kant laid the foundation for cognitive psychology and epistemology, which is the study of nature, origins, and limits of human knowledge. In other words, human intelligence is how we think, understand and interact with the real world.
Kant’s views of the mind and consciousness have lineage over 2000 years back, to the greatest thinker of all time, Aristotle. Aristotle enlightened us on rationality and empirical knowledge and was the first to identify and frame intelligence within the five senses: sight, hearing, touch, taste, and smell.
Kant’s work can sometimes seem contradictory; however, his work is even more important today than ever. In the age of AI, Kant’s explanations can help us cut through the inertia of noise and ignorance and help us understand and define intelligence.
In general structure, Kant’s model of the mind was framed on Empiricism and Rationalism which he effectively unifies, towards identifying and framing the authentic nature of our intelligence. The rationalizing side says that humans naturally have an “a priori” knowledge before they know anything. In other words, we don’t need to reference experience to understand that the day represents light and that the night is dark. Nothing has to be proved because we already know this to be true a priori.
At the same time, empiricism says that human knowledge is derived from the senses and fueled by empirical experiences. So both are true and exist independently of each other but work together to produce authentic human intelligence (AHI).
Therefore, intelligence can only be defined through nature and is based on cognitive psychology, factoring in both a priori and empirical observation…experience-based knowledge.
Human intelligence is phenomenally complex — 100 trillion neurons strong with multiple things occurring and being processed simultaneously, with incomprehensible speed and without any programming. Unlike “machine intelligence” which is dependent on human programming.
Anil Seth, professor of cognitive and computational neuroscience at the University of Sussex and editor-in-chief of Neuroscience of Consciousness, makes the important point that we are intelligent because of our bodies, which represent the holistic lens through which we experience the physical world.
However, some have pushed theories that intelligence is only in the “brain” but in doing so have ended up contradicting themselves, by using human biology (the body) as the basis for their “neural networks” theory. This fragile theory states that machine learning or generative AI functions similarly to how neurons do in the brain.
Nevertheless, neural networks is being proven a nice theory but not based in reality. The hype is showing major cracks, and the breaking is underway as commonsense and critical thinking begins to prevail. GenAI has plateaued and the slope has begun to turn downward. There is no magic bullet to be found, and the hallucination is incurable, making the likes of ChatGPT, for example, begin to be seen for what it is: unreliable, more trouble than it’s worth, and can make you stupid.
The truth is nobody understands how LLMs work anyway not even those who are writing the code. Unlike human intelligence which can process, learn and react in real-time, AI is totally dependent on its training data. So, the hard truth is that there is no real “machine learning” of any kind happening, comparable to human intelligence.
GenAI, therefore, is limited by its own system of training data — LLMs are glorified search engines that just predict the next word or series of sentences. But it is easy to become impressed with what GenAI labs can do and it has captured society’s imagination. This has also led many to overlook the many limitations AI has, and big tech, of course, has done a fantastic job of promoting AI in general — to keep us on the hook paying more ever-increasing cloud rents.
Below is a sublime example illustrating why GenAI/neural networks are not intelligent and can’t ever work as humans do. This comes from statistician/machine learning expert Colin Fraser, who tortures LLM models for sport with only slight variations.
ChatGPT’s answer: “the man’s other parent — his mother” — which of course can’t be right because she’s dead. There is no common sense in GPT, and common sense is the most critical underlying factor for AHI. Programmed machines can’t ever achieve this because machines don’t exist, nor do they interact with nature — they don’t have consciousness and never will.
Therefore AGI (artificial general intelligence) is pure science fiction.
A key factor demonstrating authentic human intelligence comes from Philosopher and mathematician, René Descartes, who famously stated “I think; therefore I am”; confirmation of one’s conscious intelligence in the universe. For Descartes, there can be no intelligence without consciousness and that consciousness is the commonsensical common denominator for AHI.
Descartes also emphasized the very critical factor of “double-checking,” as a way for one to confirm their intelligence in the physical world. Being able to “double-check” your deductions through the modality of the five senses works to continuously reconfirm one’s decisions. Double-checking can also be done through empirical experiences, by referencing a known fact source or by asking another reasonable person.
AHI comes with robust consciousness and the unification of the senses in decisioning. The mind is a vast, unmatched, complex functioning system made up of our perceptions and knowledge. Kant’s work helps us with an authentic understanding of intelligence and informs us that we can indeed “sense” things. But without experience, we’ll have no true understanding of the experience, and of course, any concept without perception remains abstract. And so, AI has syntax but doesn’t know meaning and without meaning there can be no authentic intelligence.
GPT, 1, 2,3,4…100…nothing will change substance-wise for LLMs — it will eventually flame out.
Why applied intelligence | ai?
What is required now for human progression is a commonsensical and practical approach to maximizing the productive value of GenAI serving as an augmenting tool. We can do this by coupling AHI with a more focused and selective use of GenAI, utilized purposefully and responsibly.
Therefore, the applied intelligence (ai) process works to provide users with an insight advantage. The ai methodology adheres to and applies a defined process to building strategy for people and organizations to thrive in the 21st century.
Good strategy is dependent on the quality, relevance, and reliability of the information/data inputs and the risk management process that is intrinsic to strategy development itself.
6ai is a six-step process, a new dimensional level — the basis for building winning strategies is built on 6ai’s Focused Language Models-Templates (FLM-T) which represent the evolution of automation technology. FLMs have taken an off-ramp from large language models (LLMs), to become more highly focused, specific and customized to the users’ needs and the domain.
The raison d’être (reason for being) of applied intelligence | ai, is to provide a framework methodology that applies scientific methods to build fact-based strategy solutions to solve complex business and social problems. 6ai Technologies software objective is to develop customized, purposeful, and highly relevant solutions that harness research, advanced analytics, and artificial intelligence as necessary. For effective decision-making which is part of our disciplined methodology that adheres to science vs. intuition. Empowering users to be confident with their decisions while enhancing their creativity and innovative skills.
Simply stated, authentic human intelligence and artificial intelligence are two separate things, and applied intelligence (ai) is a practical commonsensical approach that brings them together to maximize the value production capacity and capabilities of humans.
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