General Intelligence via “Selves and Conversations all the Way Up”

Source: Deep Learning on Medium

General Intelligence via “Selves and Conversations all the Way Up”

The oddest thing about Artificial Neural Networks is that they actually work despite being based on a completely false model of a biological neuron. Why Artificial Neural Networks (ANN) work remains a mystery. Understanding that “Why” can inform us why real biological neurons might work. We can make progress by identifying universal characteristics between the biological and the synthetic.

One universality that we can be certain of is that both biological and artificial neurons are pattern-matching machines. The kind of pattern matching machine depends on the purpose of the agent. In general, we can think of human brains as having five self-models that each have a goal of maintaining themselves. The goal of selves is self-preservation. Different kinds of selves seek out different kinds of knowledge to preserve themselves.

Another universality in common between ANN and biological neurons is what Daniel Dennett calls ‘Inversion of Reasoning’ or ‘Competence without Comprehension’. This is a universal characteristic of many kinds of evolution. I define here evolution as a knowledge discovery process. Natural evolution has many species discovering fitness. Biological brains have neurons optimizing their inference to best fit with their niche. Technological evolution makes progress by finding and combining different technologies to form new more useful technologies.

What we have recently discovered is that biological neurons are individually unimaginably complex:

But how do individually complex neurons collaborate towards a coherent and purposeful whole? A ‘Theory of Intelligence’ requires a richer understanding of how competence is scaled in collective intelligence. Almost all models of intelligence are uninspiring because they fail to address the notion of nano-intentionality and its collective behavior. What I am trying to say here is that biological neurons have individually complex behavior. That is, each neuron has a sophisticated level of intentionality. The importance and pervasiveness of intentionality in biological systems were first proposed by Rosenbleuth, Wiener, and Bigelow in their 1942 paper “Behavior, Purpose and Teleology”.

One reason that Wiener’s Cybernetics approach was lost in history was that a competing narrative coined as ‘Artificial Intelligence’ focused instead on the new emerging paradigm introduced by digital computers. Computers essentially made possible the capability of ‘Artificial Logic’. This appealed to the prevalent Western bias for Descartes’ dualism. Thus the original idea of achieving artificial intentional machines was hijacked by an alternative narrative. This led to decades of favoritism for the GOFAI approach to intelligence

Biological systems are not like technological systems that are designed using an additive construction process. Rather, they take a very different process, that is, biology works by differentiation of existing nano-intentional components. Billions of years of evolution have created sophisticated cells that are able to differentiate into a multitude of capabilities that are relevant at different scales of competence.

Biological innovation is unlike technological innovation in that they primarily employ parallelized invention processes rather than sequential processes. The reason human invention tends to favor sequential processes is that our minds require chunking to understand complexity.

Scaling intelligence, however, requires coordinating parallel cognitive processes to drive faster innovation. This parallel engine of innovation generation is present in all kinds of evolutionary processes (i.e. natural evolution, brains and cultural). At its core, nano-intentional agents coordinate via complex conversations.

This is where we discover the limitations of the methods of physics. To understand emergent innovation from comprehension-free evolution one needs to understand the nature of generative modularity:

When you work yourself up from quarks to living organisms, you eventually arrive at the invention of the “self”. Nano-intentionality by definition requires the encapsulated self-preserving notion of the self. A self manages it’s interior and cooperates with its exterior environment.

Brains consist of multitudes of selves in conversation with each other bubbling all the way up into a manifestation of consciousness. This is why one cannot understand human cognition without including the notion of a self-model. Instead of “Turtles all the way down”, biological brains are “selves and conversations” all the way up.

Biology has invented “selves and conversations” billions of years before homo sapiens. The complexity of survival at the cellular level doesn’t require less cognitive ability than that of the scale of human cognition. It is simply on a different scale with different problems.

Multicellular creatures are not necessarily more robust than single-cell creatures. It is just that multicellular creatures employ a different strategy towards fitness. Wired brains with neurons are not necessarily more fit than liquid brains (i.e. bees and the immune system). It’s just that they are configured to solve different problems.

But both neurons and t-cells have the same cognitive machinery. They differ in that neurons have connectivity and are recruited for more narrow tasks. This perhaps how Artificial Neural Networks relate to biological neurons. The narrow complexity of ANN might be just a slice (or projection) of the overall complexity of a biological neuron. This is a way to reconcile the limited complexity of ANN with the massive complexity of real biological neurons.

What framework can we use to better understand “selves and conversations” all the way up model of the brain? One compelling approach is hinted by Chuck Pezeshki which he calls Structural Memetics.

Pezeshki ties together the social structure of human organizations with how knowledge is structured and how this drives the design process. He is inspired by Conway’s Law that states that:

organizations which design systems … are constrained to produce designs which are copies of the communication structures of these organizations.

In the same way, my approach will is that the organization of nano-intentional neurons will be constrained to produce cognitive behavior that reflects the conversational structure of these organizations. Think of it as Conway’s Law applied to the biological brains instead of human organizations.

Pezeshki adopts ideas from Spiral Dynamics to identify different social structures:

The brain is structured differently from social organizations. Rather, it is influenced by evolution. Paul Cisek argues that an alternative taxonomy that is derived from the evolutionary process is more informative than the conventional taxonomy of behavior (consisting of perception, cognition, and action). He proposes an alternative taxonomy that follows a historical development of skills:

But despite the difference in organization, we can seek out common patterns and elucidate the kinds of cognitive behavior that arises from the organization. The basic research approach here is that collective organizational behavior leads to emergent intelligence. There are many methods in organizational processes that we already know of that can be shown to scale intelligence. Of these methods or principles, are these methods also employed by the biological brain?

It does appear strange to use human organizational social structure to inspire an understanding of biological cognition. Unfortunately, there is a scarcity of models of cooperation between multiple agents. Furthermore, the assumption of nano-intentionality makes it reasonable that the effectiveness of human social structure can also translate to the social structure of nano-intentional neurons. Despite the strangeness of this approach, Pezeshki links up social organizational dynamics with different evolutionary stages of the human brain.

Spiral Dynamics has come up with term ‘v-Meme’ that represents the value sets associated with different kinds of social organizations. It’s analogous to the ‘intrinsic motivators’ for each self-models.

Further Reading