AI and AGI

Artificial Intelligence v Artificial Intuition

THEE offers a form of «artificial intuition». It enables you to do the impossible: actively generate counter-intuitive intuitions. It is natural to speculate whether there could be a link to research in Artificial Intelligence (AI), perhaps particularly in relation to «singularity»concerns.

Artificial Intelligence started in the 1960s with high hopes and hype. Although AI achievements since then have been remarkable in many ways, even extraordinary, hype has recurred every decade or so. At the time of writing (2023), there is another phase of hype with the launch of Chat GPT4 and derivative software.

As amazing as current developments are, the visionary goal of building a flexible human-level intelligence is not noticeably closer. At the same time, expectations have increased rather than diminished. Hopes are currently fostered by extraordinary progress in software, robotics, nanotechnology and biotechnology.

AI scientists value knowledge, so «common sense» is viewed as a form of everyday knowledge (e.g. rooms have windows; white wine is a transparent pale yellow). By contrast, «common sense» in everyday life is about action (e.g. if you are in a hole stop digging; don't spend more than you earn; if it looks too good to be true it probably is so don't invest).

AI progress has capitalized on the ever-increasing processing power and storage capacity that allow computers to do what they do best. However, social situations that require commitment over long time-frames may not be amenable to simple logic, sophisticated rationality or any amount of number-crunching. They require engagement, perception, a sense of responsibility and embodied handling within the psychosocial world—which is the realm delineated by THEE.

It is perhaps worth emphasizing that so-called neural nets in AI, at the heart of the current wave of enthusiasm, have no relation at all to how the brain functions. "Neural" is simply a popular metaphor.

People don't fit the AI Model

The paradox is that, unlike AI researchers, the average person has a poor grasp of physical reality, mathematical probabilities and logic. We do not see intelligence in those terms. Instead, we use intelligence and common sense to make our way in life, get security, earn a living, have a family, and generally get ahead in a way that makes us happy. In other words, human intelligence is used to create and operate effectively within psychosocial reality. It is about thriving, not just completing tasks or producing things.

If this is the case, the taxonomic architecture must have evolved within human beings to address specific and recurrent situations in everyday life.
ClosedEveryday issues are tricky because they…►

Surprisingly, AI scientists label the conditions and concerns of everyday life: «bizarre systems». (A psychoanalyst like myself might think this was an example of projection.)
ClosedClick to see an AI diagram … courtesy of Monica Anderson.

MA writes: "In important problem domains [i.e. of everyday life], all of these 16 problem types typically occur together … [and] … resist logic-based approaches."—which is 100% correct cf. inner experience.

While most AI researchers seem to have only a vague grasp of the order inherent in the psychosocial realn, more recent attempts to develop AGI (Artificial General Intelligence) seem promising. THEE is a simplification and codification of psychosocial reality (or from the AI perspective, «the mind») that might be relevant for their research.

Being human is about continually facing situations that are orders of magnitude more complex and chaotic than the ordered, if complicated, situations addressed by AI scientists. Whereas AI experts have been rescued by computing power from the excessive complexity of ludicrously simple situations, the rest of us make decisions in far more complicated situations as a matter of course.

… not just for their expertise, but also to make and deliver on predictions embodied in their agreed plan. They must then interact with their own prediction and with others who pursue their own agendas independently, even including some who are supposedly employed to deliver the plan.

In some cases, the plan may require completion over a few days or weeks, in others over many months, and in yet others over several years. Often the plan must be subtlely or radically adjusted.

This is why THEE finds that intelligence-in-practice, i.e. work capability, is about mastery of complexity, and shows up as an ability to construct and predict reality, and to commit oneself fully to fulfilling that prediction by changing reality. Here is the basis for the distinction between IQ-type intelligence and the ability of achievers.

The Future

In despair at the failure of traditional AI, physicists and other quantitative natural scientists have generated chaos theory, fractal theory, complexity theory and more. The current fashion is to think that brain wiring will give the answer. Certainly, it will provide knowledge and give answers, and the Taxonomy must have an evolutionary relationship with the brain. But from a consciousness and psychosocial reality perspective, lab studies are currently too artificial.

If we continue along the current AI path then, at some point, the machines may indeed take over. And that is the goal of some, especially those currently working in AGI: artificial general intelligence. Machines are viewed as the next evolutionary stage for mankind, indeed for the universe. There are leading figures warning about the dangers. Perhaps a rapprochement might lead to a sensible way forward.

Originally posted: July 2009; Amended: 7-Oct-2016. Last updated: 23 Jun 2023.