Author Topic: The last invention.  (Read 9864 times)

8pla.net

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Re: The last invention.
« Reply #30 on: July 02, 2016, 03:02:23 PM »
Don't look now, but USB 4.0 is on the horizon.

Maybe we should just hardwire our own interface

directly into the computer?   And, forget the operating

system, just bootstrap directly into our A.I. program?
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korrelan

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Re: The last invention.
« Reply #31 on: July 03, 2016, 08:49:19 PM »
I’ve been messing around with my system again.

Part of my theory is that the whole brain is laid out the way it is because it has no choice; its function and layout are a result of the DNA blueprint that initially builds it and the properties of the data streams that drive it.

I need to be able to cut huge holes in the cortex and it should rebuild based on the ‘qualities’ of the incoming data streams. The self organising structure should at least partly rebuild or repair damage (loss of vision/ stroke etc).



 
« Last Edit: July 03, 2016, 11:11:12 PM by korrelan »
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8pla.net

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Re: The last invention.
« Reply #32 on: July 04, 2016, 02:21:50 PM »
My theory is that there is a choice which takes tens of thousands of years to make.
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korrelan

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Re: The last invention.
« Reply #33 on: July 04, 2016, 02:34:31 PM »
Then we agree...

The DNA blueprint which guides our connectomes initial layout; is the result of genetic evolution over thousands of years.

Unfortunately I don't have that much time lol, so I'm startling from an initial 'best guess' base connectome and learning as I go along.

 :)
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korrelan

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Re: The last invention.
« Reply #34 on: September 02, 2016, 09:44:36 PM »
I found this old video (7 years) and thought I’d add it to my project thread.

The general consensus amongst neuroscientists is that the mammalian ocular system is based on orientation/ gradient/ etc cortex maps.

There are thousands links to scholarly research / findings regarding the maps.

https://scholar.google.co.uk/scholar?start=10&q=visual+orientation+maps&hl=en&as_sdt=0,5&as_vis=1

I was trying to figure out how the output of Approx 100 million rods and cones in the fovea and peripheral areas could be condensed by the 2 neuron layers and Ganglion cells in the retina, down into the approx 1 million axons in the optical nerve without loosing detail or resolution. I knew each nerve carried the left half of one retina combined with the right side of the other and that they exchanged in the optical chiasm before terminating in the LGN (The LGN is very important).

I knew from reading research papers that the main theory was that the mammalian ocular system recognised images from the assemblies of gradients and angled line fragments detected by the primary visual cortex after pre-processing by the retina and LGN. We never see or perceive a full view/ image of our world but build an internal representation from the sparse encoded ocular data streams.

Took me a few years but I think I eventually figured out the ideal neural connectome/ structure and synaptic signalling methodology to enable me to start coding some basic simulations.



As you can hopefully see, I started out with a sheet of untrained V1 neurons (4 layers) and after a very short training session of the system was able to recognise the four basic orientations (colours and orientation shown in center) in the image. This was a first low resolution test but I thought I’d show it for completeness.

This is the same system that later grew to be able to recognise objects/ faces/ words/ illusions/ etc.



I’ll post a general update to my AGI project soon.

 :)



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Freddy

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Re: The last invention.
« Reply #35 on: September 02, 2016, 10:00:46 PM »
Is this a bit like edge detection ? I'm only familiar with that kind of thing in my graphics work, there's a thing called Non Photorealistic Rendering, which is making a 3D renderer create things like line drawn comic art, or a close approximation. So one would build a shader to pick out edges and shadows etc. But I think I digress.

Are the light grey lines places where it hasn't been able to tell ? Would more passes fill in those gaps ? Sorry I'm not well versed in how the eyes work. I knew about rods and cones, but don't remember there being so many. I'd be interested in how it keeps it's resolution too.

100 million rods and cones is almost 47 HD screens. But it's probably not like for like.

korrelan

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Re: The last invention.
« Reply #36 on: September 02, 2016, 10:22:53 PM »
Yeah! I figured LGN does many jobs, one of which is to apply a kind of filter that picks out the edges based on contrast. A separate stream does the same with colour/ motion, but these takes different paths.

A full V1 orientation map detects all angles not just the basic four shown. The grey areas represent the angles that are not triggering a trained neurons receptive field.

Quote
I'd be interested in how it keeps it's resolution too.

Small groups of cones form 'centre surround on/ off' sensors; these groups are arranged in a hexagonal matrix across the retina concentrated at the fovea, becoming sparser to the periphery.

The spike trains produced by the rod/ cone group sensors are modulated by the ganglion cells, and a temporal shift in the spike frequencies carries the groups collective information down the optic nerve to the LGN.

This is how I have found it too work; it’s not a general consensus.

 :)
« Last Edit: September 04, 2016, 05:52:26 PM by korrelan »
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korrelan

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Re: The last invention.
« Reply #37 on: October 06, 2016, 11:03:50 PM »
Hi all…

I’ve been busy lately so I’ve not had much time to work on my AGI project. I have managed to make some progress integrating the sensory cortexes though.

As you know from my previous posts on this thread I have designed a single neuromorphic neural net that can learn un-supervised from its experiences.  The basic idea is to create an intelligent ‘alien’ connectome and then teach it to be human. My connectome design can recognise objects or audio phonemes equally as well, its self organising and self categorising.

When fed an ocular sensory stream for example, the connectome map becomes the equivalent of the visual cortex (V1-V4). Camera data is fed in, the system learns to recognise objects from their optical 3D properties, and it spits out a result as a unique set of firing neurons. A Zebra (lol) for example will always produce the approx same output, no matter what angle, scale or lighting conditions are used/ viewed once its been learned.  Audio, visual, tactile, joint torque, etc are all learned, categorised and encoded into a small set of output neurons; it’s these output neurons from the individual maps this example uses.

The next step was to integrate the various sensory cortex maps into one coherent system.

This is the basic sensory connectome layout (excuse crudity it was drawn a while ago).



I recorded the outputs of the input sensory maps whilst recognising various sensory streams relative to their functions, audio, visual, etc. I then chained them all together and fed them into a small/ young connectome model (9.6K neurons, 38K synapse, 1 core). This connectome is based on a tube of neuron sheet; the output of the five layers is passed across the connectome (myelinated white matter) to the inputs of the frontal cortex (left side).

As usual the injected pattern is shown on the right, the pattern number and the systems confidence in recognising the pattern is shown lower left.  The injected pattern is a composite of outputs from five sensory maps, audio, visual, etc. (40 patterns)



On the right just below the main input pattern of (5 X 50 inputs) you can see the sparse output of the frontal cortex; this represents the learned output of the combined sensory maps inputs. This gets injected (cool word) back into the connectome, it will eventually morph the overall ‘thought’ pattern to be a composite of input and output, so any part of the overall pattern from any sensory map will induce the same overall ‘thought’ pattern through out the whole connectome. This will enable the system to ‘dream’ or ‘imagine’ the mixed combinations of sensory streams; just the same as it can a single stream.

0:19 shows the 3D structure and the cortical columns formed on the walls of the tube, the periodic clearing of the model during the video shows only the neurons/ synapse/ dendrites involved in recognising that particular pattern.

Anyway… the purpose of the test was to show the system had confidence (lower left) in the incoming mixed sensory streams, and could recognise each mixed pattern combination.

Each pattern was recognised.

 :)
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kei10

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Re: The last invention.
« Reply #38 on: October 07, 2016, 01:18:47 AM »
That is astonishingly impressive! I'm too dumb to get it, though.

That's gonna beat me to it... Dayum!  ;D

Keep up the amazing work! :)
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korrelan

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Re: The last invention.
« Reply #39 on: October 07, 2016, 07:17:18 AM »
@Kei10

Quote
That's gonna beat me to it

Not necessarily, this is just my best attempt at an AGI and not the only one/ method. It’s still basically a glorified pattern matcher and I have many huge obstacles to overcome. I still might fail at the end of it all… it’s about the journey lol.

Plus I’m not getting any younger and have limited life left, though once the AGI is up and running it will soon sort my longevity out and I can live forever… muah! Lol.

Never give up… it’s not solved until someone can prove it’s solved.

 :)
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keghn

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Re: The last invention.
« Reply #40 on: October 07, 2016, 03:32:29 PM »
 Very nice work @Korrelan! 
 I am working on AGI too. But i will use any un human theory to make it work. You and Jeff Hawkins really try hard to match the human organic style of the human brain.

korrelan

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Re: The last invention.
« Reply #41 on: October 07, 2016, 04:58:25 PM »
@Keghn

Quote
I am working on AGI too.

Jeff Hawkins is a cleaver bloke; I wish I had his funding lol. I’ve read your AGI theory and it’s very interesting… I even made notes; kei10’s theories too… everyone’s… all ideas are relevant until proven otherwise lol.

Quote
You and Jeff Hawkins really try hard to match the human organic style of the human brain.

The computer you are using is running a program, the program consists of code, the code consists of sentences, sentences consist of words, the words consist of letters, the letters are made from pixels and eventually you get to the binary base language/ architecture. It’s impossible to figure out how a modern computer works by looking at the program. (Make sense?)

If I was to give you a large bag full of perfectly spherical balls and an empty bucket. Your task would be to calculate how many of the small balls would fit into the bucket. There are two ways of achieving this task, the first is to write a complex computer algorithm that utilizes 3D volumes etc to get the calculation or… you could just pour the balls into the small bucket till it’s full and then count them.

I finally chose this approach because I believe (Neo) that nature is giving something away for free, something that won’t be realised unless I build and test this kind of system. I’m 99% certain I’ve figured out what it is too.

We are the only highly intelligent species we currently know of… why try to reinvent the wheel, we are like we are… for a reason. (Cue spooky music).

 :)
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Freddy

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Re: The last invention.
« Reply #42 on: October 07, 2016, 05:35:24 PM »
Really impressive work there Korrelan, very impressed am I  8)

korrelan

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Re: The last invention.
« Reply #43 on: October 07, 2016, 06:08:02 PM »
@ Freddy

You were probably to busy thinking about if you could reply, to stop and think if you should… certainly wasn't a forced reply though.

  8)
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Freddy

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Re: The last invention.
« Reply #44 on: October 07, 2016, 08:08:46 PM »
Well I tried to think of something clever to say, but to do that I would have to be able to understand what you are doing more  ;)

I saw the copyright on the image was 2001 or something, so you've been working on it for a long time. No wonder I can't quite grasp it.

From a purely aesthetic perspective the video imagery was wonderful to see. I felt like saying something positive and so did. :)

 

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