Star Wars has a simple dichotomy between good and evil. But as the stories have evolved, the nuances between these distinct poles also has acquired a richness that is hard to dismiss.

In the latest trilogy, we discover a dysfunctional civilization even after the defeat of the empire. The argument that the remnants of the empire make is that the galaxy stills seeks a need for order. Order and chaos are at seperate poles where evil is aligned with order.

We know however from D&D alignment charts that good and evil, order and chaos can exist in many combinations:

It is also interesting that the Sith (the Evil sorcerers) only come in pairs as opposed to the Jedi that can be entire communities. Apparently, to sustain evil one has to restrict agency and individuality to just a few. A community of evil-doers very quickly turns on itself.

So at one extreme we have evil, order, individuality and at the other we have good, chaos, collective. Which brings up the question, what is the definition of evil and good? Something that we took for granted ever since the first Star Wars movie.

All Biological life on earth is possible because of a discrete digital mechanism of preservation and replication.

The mechanism of signaling between the superorganism we know of as bacteria and its ongoing war against viruses is through a digital mechanism.

The source of innovation in biology is in this milieu of microbiome and viromes that are ubiquitous in every complex multicellular organism on earth. Humans are walking ecologies and cannot survive stripped of these ecologies.

We can picture this as a cloud of digital interaction that surrounds all of us.

The cells of multicellular creatures however do not communicate in the same way as bacteria. The DNA of mammals are sequestered from daily activity so as to prevent wear and tear.

My attempt to map the research *doctrines* of various groups working on Artificial General Intelligence.

Although one might see names of individuals here as well as organizations, take it just as identification of the organization behind the person and a very narrow slice of their overall research portfolio.

Here is a @swardley map that captures the relationship of doctrine with other individual and organizational processes.

We can capture a party's doctrine by either an explicitly stated agenda or an aggregation of the kind of approach that is pursued. A doctrine is an invariant property of the overall behavior of a party.

In this map one can see the conflict between different parties (compare @garymarcus with the deep learning folks).

A children's book written in 1954 inspired by Wiener's Cybernetics that explains some common day analog systems:

Analog computation originally meant the construction of a simulation of another system by means of a mechanical or electrical system that represented the same dynamics as the physical system. That is an analog of what's being simulated.

Nobert Wiener generalized this idea in Cybernetics to describe control systems that through negative feedback promoted stability.

W.Ross Ashby argued that negative feedback was insufficient for maintaining the stability (homeostasis) of complex system. Ashby proposed the Law of Requisite Variety.

These machines that 'seem to think' are examples of what was known as analog computation. Cybernetics, control theory, biology are examples of analog computation. But what does this mean other than being the opposite of discrete/digital computation?

Abduction is that thought process where we renew our past, question our present, and reimagine our future.

It was C.S. Peirce, a century ago, that coined the use of this term to describe a thought process that would convey hypothesis making.

A little over 50 years ago, proponents of Artificial Intelligence, proposed that deduction would be the core feature of human intelligence. This idea was driven by the bias towards formal logical systems and the emergence of computers.

This approach was in stark contrast to cybernetics (introduced a decade early) that proposed that dynamical systems were a model for intelligence. The descendant of this approach was coined as artificial neural networks.

This dynamical systems approach was later understood as being similar to an induction based thought process. Deep Learning leads to what can be characterized as Artificial Intuition.

We wake up each day with the same attention budget as everyone else. This is the one thing all humans equally share.

Spending that attention budget by doing this is what gives meaning to our lives.

It's is only through doing that we learn. It is only through learning that we find meaning.

Yet many of us waste attention budget exclusively on consumption. Seeking the random dopamine hits that tickle our curiosity. Yet we seek only the convenient things to do.

Activities that are convenient, that takes little effort don't consume a lot of attention. But enough of these small distractions will. But unfortunately, there's little learning taking place.

Those trained as physicists strive to see the world in terms of symmetries and processes that break symmetries. It's a meta-principle that guides their questioning.

This is very different from how many are taught physics (I guess in high school). We come to know of physics as consisting of laws that describe the behavior of inanimate (or is it indifferent) objects? These laws are described as expressions of equalities (i.e. equations).

But laws like Newton's f=ma or Einstein's e=mc^2 are derived from a higher meta principal. These principles are known as conservation laws. Energy, momentum, angular momentum are all conserved.

Conservation laws however are also derived from a higher principle. A conservation law implies that something remains the same as the situation of study change. Energy conservation implies that it is that something that does not change in time. In short, a symmetry.

This idea that laws are derived from conservation principles that is derived from symmetries is at the core of the study of physics. The laws of physics are only authoritative as long as they don't violate a higher principle. Ultimately, that principle is all about symmetry.

I woke up realizing that there is something very wrong with thinking in categories and classifications. Many people accept the idea that this kind of thinking is at the core of abstract thought.

Douglas Hofstadter argues otherwise in a very thick (not dumb in the Brit sense) book of his ('Surface and Essences') where he argues that analogy is at the core of thinking. You'll have to read his book, specifically the last chapter to understand what he means.

The problem with categories is that they create artificial boundaries that are too crisp. But we know that when we master any subject that there is a distinct fluidity between concepts. The master seems wrong from a by-the-book novice because he keeps on breaking definitions.

Analogy making employs the tacit knowledge gained through experience to adroitly navigate between similar and competing concepts. A master has an intuitive feel of the vocabulary he employs to create expressions that have previously never been heard.

Biological brains feel their way through their realities. Something feels like red because our interactions with that thing feels the same as if it was red. We do not see the color red, we feel the color red. There is no red in this picture: