Seeking the essence of intelligence

artificial-intelligence
agi

#1

Hi, this thread’s category states that personal/ member projects can be listed here… this is a quick overview of my personal project and my progress so far.

Prologue

The age of man is coming to an end. Born not of our weak flesh but our unlimited imagination, our mecca progeny will go forth to discover new worlds; they will stand at the precipice of creation, a swan song to mankind’s fleeting genius, and weep at the shear beauty of it all.

Reverse engineering the human brain… how hard can it be?

Brief project overview

I’ve built a neuromorphic simulation of the human connectome on which I run my AGI.

The 3D volume connectome simulation comprises the main brain structures, lobes, white/ grey matter tracts, neuron types, electro chemical synapse, dendrites, neurotransmitters, cortical columns, etc. There are also algorithms that simulate myelination, neurogenesis, aging, plasticity, synaptic pruning, circadian rhythms, growth, self organisation, etc.

So the AGI runs on the wetware simulation not on the ‘Von Neumann’ based hardware.

Through research and experimentation I’ve narrowed my connectome design down to a very specific model. The same connectome can audibly recognise phonemes; visually recognise objects/ words/ faces/ etc it incorporates prediction, high dimensional (holographic) memory, unsupervised learning, damage and repair, etc as well as basic emotional traits.

My hardware comprises of several PC nodes in a cluster. I’ve wrote the neuromorphic CAD simulation from scratch to suit my own needs, it’s highly parallel and can run any kind of neural structure. Separate nodes run the audio/ visual/ tactile modules and I’ve built a simple bot head, with stereo HD cams, microphones, etc to aid in research, the whole system is a closed schema, it can only use its own senses to experience reality.

The system requires periodic sleep cycles and even dreams.

I’m slowly figuring out each piece, my current area of research is machine consciousness and how to control it.

Brief definition of the connectome

The beauty is that the emergent connectome defines both the structural hardware and the software. The brain is more like a clockwork watch or a Babbage engine than a modern computer. The design of a cog defines its functionality. Data is not passed around within a watch, there is no software; but complex calculations are still achieved. Each module does a specific job, and only when working as a whole can the full and correct function be realised. (Clockwork Intelligence: Korrelan 1998)

In my AGI model experiences and knowledge are broken down into their base constituent facets and stored in specific areas of cortex self organised by their properties. As the cortex learns and develops there is usually just one small area of cortex that will respond/ recognise one facet of the current experience frame. Areas of cortex arise covering complex concepts at various resolutions and eventually all elements of experiences are covered by specific areas, similar to the alphabet encoding all words with just 26 letters. It’s the recombining of these millions of areas that produce/ recognise an experience or knowledge.

Through experience areas arise that even encode/ include the temporal aspects of an experience, just because a temporal element was present in the experience as well as the order sequence the temporal elements where received in.

Low level low frequency circadian rhythm networks govern the overall activity (top down/ front to back) like the conductor of an orchestra. Mid range frequency networks supply attention points/ areas where common parts of patterns clash on the cortex surface. These attention areas are basically the culmination of the system recognising similar temporal sequences in the incoming/ internal data streams or in its frames of ‘thought’, at the simplest level they help guide the overall ‘mental’ pattern (sub conscious); at the highest level they force the machine to focus on a particular salient ‘thought’.

So everything coming into the system is mapped and learned by both the physical and temporal aspects of the experience. As you can imagine there is no limit to the possible number of combinations that can form from the areas representing learned facets.

The connectome is where experiences and knowledge are stored; the information is encoded into the physical structure of the connectome… Just like the physical wiring in your house encodes which light comes on from which switch.

The Global Thought Pattern (GTP)

This is the activation pattern that is produced by simulated electro-chemical activity within the connectome. When a virtual neuron fires action potentials travel down virtual axons, simulated electro-chemical gates regulate synaptic junctions, simulated Dopamine and other compounds are released that regulate the whole system/ GTP.

It’s a symbiotic relationship, within the system the GTP defines the connectome, and the connectome guides the GTP. This is a top down, bottom up schema; they both rely on each other.

Brief consciousness theory

To achieve machine consciousness my basic idea is to get the connectome to learn to recognise its own internal ‘thought’ processes. So as sections of cortex recognise external sensory streams, other sections will be learning the outputs/ patterns (ie frontal cortex) being produced by the input sections. The outputs from these sections go back into the connectome and influence the input sections… repeat. This allows the system to settle into a stable internal global minima of pattern activity that represents the input streams, what it ‘thinks’ about the streams, how it ‘thought’ about the streams and what should happen next etc.

It’s a complex feedback loop that allows the AGI to both recognise external sensory streams and also recognise how its own internal ‘thought’ processes achieved the recognition in the same terms as it’s learning from external experiences.

Consciousness seems so elusive because it is not an ‘intended’ product of the connectome directly recognising sensory patterns, consciousness is an extra layer. The interacting synaptic networks produce harmonics because each is using a specific frequency to communicate with its logical/ connected neighbours. The harmonic/ interference patterns travel through the synaptic network just like normal internal/ sensory patterns… it’s an interference bi-product that piggy-backs/ influences the global thought pattern.

Cortical regions learn the harmonics… our sub-conscious is just out of phase, or to be more precise, our consciousness is out of phase with the ‘logical’ intelligence of our connectome.

Our consciousness is like… just the surface froth, reading between the lines, or the summation of interacting logical pattern recognition processes.

Consciousness is just the sound of all the gears grinding…

Thank you for taking the time to read my overview.

Progress is slow; I don’t get much time to work on my project as I have to earn a living, etc… but I’ll get there.

A few videos of it in action…go to the AGI list…

or…

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