Artificial Intelligence -
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? LMAO
I've been a member for while and have posted some videos and theories on other peeps threads; I thought it was about time I start my own project thread to get some feedback on my work, and log my progress towards the end. I think most of you have seen some of my work but I thought I’d give a quick rundown of my progress over the last ten years or so, for continuity sake.
I never properly introduced my self when I joined this forum so first a bit about me. I’m fifty and a family man. I’ve had a fairly varied career so far, yacht/ cabinet builder, vehicle mechanic, electronics design engineer, precision machine/ design engineer, Web designer, IT teacher and lecturer, bespoke corporate software designer, etc. So I basically have a machine/ software technical background and now spend most of my time running my own businesses to fund my AGI research, which I work on in my spare time.
I’ve been banging my head against the AGI problem for the past thirty odd years. I want the full Monty, a self aware intelligent machine that at least rivals us, preferably surpassing our intellect, eventually more intelligent than the culmination of all humans that have ever lived… the last invention as it were (Yeah I'm slightly nutts!).
I first started with heuristics/ databases, recurrent neural nets, liquid/ echo state machines, etc but soon realised that each approach I tried only partly solved one aspect of the human intelligence problem… there had to be a better way.
Ants, Slime Mould, Birds, Octopuses, etc all exhibit a certain level of intelligence. They manage to solve some very complex tasks with seemingly very little processing power. How? There has to be some process/ mechanism or trick that they all have in common across their very different neural structures. I needed to find the ‘trick’ or the essence of intelligence. I think I’ve found it.
I also needed a new approach; and decided to literally back engineer the human brain. If I could figure out how the structure, connectome, neurons, synapse, action potentials etc would ‘have’ to function in order to produce similar results to what we were producing on binary/ digital machines; it would be a start.
I have designed and wrote a 3D CAD suite, on which I can easily build and edit the 3D neural structures I’m testing. My AGI is based on biological systems, the AGI is not running on the digital computers per se (the brain is definitely not digital) it’s running on the emulation/ wetware/ middle ware. The AGI is a closed system; it can only experience its world/ environment through its own senses, stereo cameras, microphones etc.
I have all the bits figured out and working individually, just started to combine them into a coherent system… also building a sensory/ motorised torso (In my other spare time lol) for it to reside in, and experience the world as it understands it.
I chose the visual cortex as a starting point, jump in at the deep end and sink or swim. I knew that most of the human cortex comprises of repeated cortical columns, very similar in appearance so if I could figure out the visual cortex I’d have a good starting point for the rest.
The required result and actual mammal visual cortex map.
This is real time development of a mammal like visual cortex map generated from a random neuron sheet using my neuron/ connectome design.
Over the years I have refined my connectome design, I know have one single system that can recognise verbal/ written speech, recognise objects/ faces and learn at extremely accelerated rates (compared to us anyway).
Recognising written words, notice the system can still read the words even when jumbled. This is because its recognising the individual letters as well as the whole word.
Same network recognising objects.
And automatically mapping speech phonemes from the audio data streams, the overlaid colours show areas sensitive to each frequency.
The system is self learning and automatically categorizes data depending on its physical properties. These are attention columns, naturally forming from the information coming from several other cortex areas; they represent similarity in the data streams.
I’ve done some work on emotions but this is still very much work in progress and extremely unpredictable.
Most of the above vids show small areas of cortex doing specific jobs, this is a view of whole ‘brain’. This is a ‘young’ starting connectome. Through experience, neurogenesis and sleep neurons and synapse are added to areas requiring higher densities for better pattern matching, etc.
Resting frontal cortex - The machine is ‘sleeping’ but the high level networks driven by circadian rhythms are generating patterns throughout the whole cortex. These patterns consist of fragments of knowledge and experiences as remembered by the system through its own senses. Each pixel = one neuron.
And just for kicks a fly through of a connectome. The editor allows me to move through the system to trace and edit neuron/ synapse properties in real time... and its fun.
Phew! Ok that gives a very rough history of progress. There are a few more vids on my Youtube pages.
Edit: Oh yeah my definition of consciousness.
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) 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.
I have a schema for prediction in place so the system recognises ‘thought’ frames and then predicts which frame should come next according to what it’s experienced in the past.
I think consciousness is the overall ‘thought’ pattern phasing from one state of situation awareness to the next, guided by both the overall internal ‘personality’ pattern or ‘state of mind’ and the incoming sensory streams.
I’ll use this thread to post new videos and progress reports as I slowly bring the system together.