The oddest thing about Artificial Neural Networks is that they actually work despite being based on a completely false model of a biological neuron. #ai #deeplearning
Why Artificial Neural Networks work remains a mystery. Understanding that "Why" can inform us why real biological neurons might work. This is what's exciting, we got something synthetic that does work!!
Dennett uses the term 'Inversion of Reasoning' or 'Competence without Comprehension' to describe a universal characteristic of many kinds of evolution. Artificial Neural Networks share this common characteristic with biological neurons.
But where can we discover these 'Universalities' so that we can better reason about complex systems like ANN and biological brains? medium.com/intuitionmachi…
Biological neurons certainly are *individually* unimaginably complex medium.com/intuitionmachi… Is complexity reduced as neurons act in a collective manner?
Is this analogous with asking if civilizations more intelligent than individual intelligence? Intuitively, in terms of generating innovation they are. In terms of agency, civilizations seem to be unable to make progress with purpose.
How the complexity of the individual translates to the complexity of the collective isn't well understood. I propose though that a 'Theory of Intelligence' requires a rich understanding of how competence is scaled in collective intelligence.
Almost all models of intelligence (and consciousness) are uninspiring when considering the notion of nano-intentionality and collective behavior. Many mathematical models are afraid to tackle nano-intentionality. medium.com/intuitionmachi…
In the odd twists of human history, it was Norbert Wiener who realized the importance of intentionality in complex systems: courses.media.mit.edu/2004spring/mas…
The reason why Wiener's approach was lost in history was that a competing narrative coined as 'Artificial Intelligence' focused instead on the new paradigm introduced by digital computers.
When 'Artificial Logic' was invented via computers, Western bias for Descartes' dualism hijacked the narrative. This led to decades of favoritism for the GOFAI approach to intelligence
Biological systems are not like technological systems that are designed using additive construction methods. Rather, they work 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 innovation are sequential processes is that our minds require chunking to understand complexity.
Scaling intelligence 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).
Co-evolution is the word that I should have used. Neverthelesss, at its core, nano-intentional agents coordinate via complex conversations. It is therefore in the development of conversational cognition that we arrive at AGI. medium.com/intuitionmachi…
This is where we discover the limitations of the methods of physics. To understand innovation that emerges out of evolution one needs understand the nature of generative modularity: medium.com/intuitionmachi…
When you work yourself up from quarks to living organisms, you eventually arrive at the invention of the "self". Nano-intentionality assumes the encapsulated self-preserving notion of the self.
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 cognition without including the notion of a self-model: medium.com/intuitionmachi… .
Instead of "Turtles all the way down" en.wikipedia.org/wiki/Turtles_a…, 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 don'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.
@threadreaderapp unroll
