Author Topic: The last invention.  (Read 9717 times)

LOCKSUIT

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Re: The last invention.
« Reply #150 on: February 10, 2017, 02:49:06 PM »
You have to define what "is" danger.

Pattern recognizers will link to according actions. You may not turn your eyes over there.

Just because you see some rustle in the leafs, it doesn't mean your eyes move to it.

It makes you get more "score" there. And you can see there. Without turning the eye center at it.

korrelan

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Re: The last invention.
« Reply #151 on: April 12, 2017, 09:44:23 AM »
This is just a clearer culmination of three rushed posts describing my theories on human memory.

I thought I’d place it here on my project thread to save reposting on the original thread.

Memory Theory

I think that long/ medium/ short term memories/ experiences and knowledge all use exactly the same architecture; what differentiates long/ short memories/ knowledge is that when memories are first created they are weak (short term) and as they become consolidated over time they form stronger (long term) memories/ knowledge.

Short term memories are initially formed from the current state ‘global thought pattern’ (GTP); or pattern of activation within the synapses/ neurons at any given moment. Associations are formed through weak synaptic links carved by our current thought pattern.  The weak memory engram exists within the structure of the strong long term learning.

Long term memories are stored in the synapse patterns that connect the groups of neurons.  Remembered items are composed of sparsely distributed neuron groups that represent a particular facet of the item.

Our consciousness is the pattern of activation running within this memory/ knowledge structure.



I’ve shown this vid before but it’s a good example of how complex the GTP is. Each pixel represents a neuron in the AGI’s frontal cortex.  Linked to the back of each pixel are connections (white matter tracts) to other areas of the overall cortex. The machine is sleeping and what you are looking at are patterns formed from memories/ experiences blending together; each memory is triggering other memories; the pattern constantly flows from one state of ‘thought’ to the next. At 6 seconds into the vid a blue (high activity) region appears (lower middle)… this was me talking. The machine was still listening even though it was sleeping… that activity influenced the whole pattern as it tried to make sense of the sensory input; a ‘thought’ pattern is very fluid and complex.

Forming New Memories

We build our knowledge representations in a hierarchical manner; new knowledge is based/ built on our understanding of old knowledge. 

Our understanding of our current experience is created from the GTP which is comprised of the parts of memories/ knowledge relevant to this moment of time… it’s the state of this pattern that new memories are formed from.  When we initially form a memory we are linking together existing knowledge/ understanding/ experiences with weak synaptic connections/ associations.

If you were to learn a new property of water for example; your brain doesn't have to update all your individual knowledge/ memories regarding water and its uses/ properties… it simply has to include the new property into the GTP representing water when ever you think of it.

The brain tends to record only novel experiences; a new memory is formed from novel differences the brain has not encountered before.  This happens at the synaptic level and so is very difficult to relate to the consciousness level.

So any new memory is the brain recording our current state of consciousness; to understand this moment in time we are using hundreds of ‘long term’ memories and knowledge.  To remember a list of words or numbers for example, you have to be able to recognise and understand the items; you can’t remember what you don’t recognise/ understand.

The brain doesn’t record the incoming experience… it records its current understanding of it though its previous experiences/ knowledge.

Look at the letter ‘C’; that has just included a pattern into your GTP that represents (too you) that letter… now look at ‘A’; now that pattern is included. The two separate patterns have created a merged pattern that represents ‘CA’ and already your brain/ cortex is firing other patterns that relate to ‘CA’. Now look at ‘T’… bingo. The combined pattern of the three letters was instantly recognised by areas of your cortex that then ‘fired’ the pattern for ‘CAT’ back into you GTP intern firing patterns that represent the general concept of ‘CAT’. At the same time there where patterns running that covered this topic, your body posture, how comfortable you feel, etc. Reading this paragraph and your thoughts/ opinions on it have altered your GTP; you don’t need to remember the letters ‘CAT’ or even the basic method of explanation I’ve used; they are already well engrained… it’s the new/ different bits of the pattern that get etched into your synapses.

If you look at this sum 5+5= you don’t have to mentally count or add the numbers on your fingers; the visual pattern of seeing the sum fires a globally understood pattern that represents 10.

Memory Structure

Different brain structures contribute certain facets to the memory.  The limbic network adds a very strong emotional facet to a memories overall pattern; other areas add temporal and episodic order to our memories. 

This might explain why a scientist/ neuroscientist might think different memories are stored in separate brain regions. Different brain regions supply different facets of a memory.  The Hippocampus (could be revised) for example provides an index/ temporal date stamp to a memory as its being stored (not read); if they where viewing frmi results on memory consolidation they would be measuring the difference between existing knowledge and new learning. The new learning would require a recent time/ stamp that would activate the Hippocampus (episodic), blood would flow to this region highlighting it.

This is a diagram showing a rough map of where the various categories within the circle where detected upon the human cortex surface.



This is a short video showing the same/ similar category organisation within my AGI’s cortex.  As usual the forty test patterns (phonemes, images, etc) are shown on the right; the confidence in recognition (height of the bar) is shown on the bottom left. Notice the regular modulated input pattern below the pattern input on the right. The cortex section has very high confidence in its recognition of the patterns until I click ‘A’ in the lower right to turn this regular injected pattern off. Then the cortex sections confidence drops/ stops… I have removed a facet of the overall pattern that the system was using to recognise the patterns. This is a kin to disconnecting the hippocampus or limbic system… it makes a big difference.



A Memory is never moved around inside the brain; it’s never moved from short to long term storage.  ‘Memories’ are never searched either (searching is a serial schema); because of the parallel architecture the brain has no need to search. The ‘thought pattern’ flows from one pattern of depolarized neurons to the next pattern through the axons/ synapses/ connectome; it’s the structure that stores the long term memories. 

Accessing Memories

We access our memories in a holographic manner; any part of a memory will trigger the rest.

When we use a piece of knowledge or a skill we also have access to when and where we learned it; sometimes it will effect our use of the knowledge (bedside manner for a doctor); how the use of the memory/ knowledge effects us is governed by the global thought pattern and what the main situation/ topic/ goal/ task is.  It’s our focus/ attention (also part of the global pattern) that dictates the effect of the memory/ knowledge on our current consciousness and what sections of the memory/ knowledge are relevant to the current task.

When a particular pattern is recognised the resulting output pattern is added to the overall GTP; this changes/ morphs/ phases the GTP which is in turn recognised by other areas… repeat.

A young child has more synapse than you or I but could never grasp the concepts of this conversation because they have no prior experience/ knowledge to create their memories on/ from. 

Memory Problems

If any of the original facets that the memory was comprised of are compromised in some way it can make retrieval difficult from that aspect, time, location, etc… we all use the tactic of thinking of related/ similar memories when trying to recall a weak memory, you’re just trying to produce a global pattern conducive to triggering the required memory; trying to fill in the missing blanks in the pattern that will trigger retrieval.

To remember what I had for breakfast I have to use strong/ long term memories. To understand what the items were, what they were called, even the concept of ‘breakfast’ requires a lot of strong/ long term understanding/ knowledge/ memories.  The weak/ short term bit of the memory is what links all the various items together along with a location/ timestamp/ index/ etc that I would recall as ‘earlier today’.  For the unfortunate people who have difficulty retrieving today’s (short term) memories I would wager they have a problem with a brain region responsible for temporal tagging/ indexing the memory as ‘today/ recent’

My AGI is based on the mammalian connectome and exhibits both/ all these memory traits; this is why I believe I am correct in my assumptions.

 :)
« Last Edit: April 12, 2017, 10:25:41 AM by korrelan »
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kei10

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Re: The last invention.
« Reply #152 on: April 12, 2017, 10:33:28 AM »
This is absolutely mesmerizing to read, thank you for sharing!
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korrelan

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Re: The last invention.
« Reply #153 on: April 12, 2017, 01:56:23 PM »
Ageing Memory

What changes as we get older?  Personal thoughts and observations from my project.

One of the main mechanisms our brain uses to consolidate learning is the strengthening of the synaptic connections between neurons.  A set of neurons with strong synaptic connections can impose a strong influence on the global thought pattern (GTP).  The more we experience a set of facets that a moment in time is constructed from; the easier we can both recognise and infer similar situations/ experiences.

So an older person is ‘wiser’ because they can quickly recognise the similar well experienced traits/ situations or knowledge and infer their learning into a new problem space… been there… done that.

Younger minds ‘think’ more flexibly because there are no really strong overriding patterns that have been forged through long term exposure/ use; thus more patterns are fired and each has to vie for attention.  Loads of ideas… not much sense lol.

Both the young and old versions of the human memory schema have their advantages… but obviously the ideal combination is wisdom with flexibility… so how can we achieve this?

The Problem

The main problem is that the old adage ‘use it or lose it’ also applies to your neural skill set.

We are the sum of our experiences and knowledge.  Everything we learn and experience affects ‘who’ we are and the ‘way’ we think.  All the skills we acquire through our lives and the problems we solve are mapped into our connectome and ALL this information is accessed and brought to bare when required.  The fact that I can repair clocks/ engines and the techniques I have used are also used when I’m solving neuroscience related problems.  Every good theory/ thought I have is comprised from all the facets of the many varied topics mixed together in my brain.  When I consider a possible relationship between disparate subjects I’m actually applying a general rule that is comprised of all the different types of relationships I have ever encountered.

As we get older our connectome fine tunes more and more to become expert in our chosen vocational field. We can recognise the common relationship problems a younger person is experiencing because we have seen it so many times before… the price for this wisdom is loss of plasticity/ flexibility; just the process of becoming proficient at life harms our imaginative thinking powers.

Besides learning new skills/ topics it’s very important from a mental perspective to exercise previously learned skills or knowledge frequently.  The general purpose rules we constantly apply have been built up through a hierarchical learning process and depend on all the various facets of the skills and knowledge that were present when they where originally consolidated. If enough of the underlying skills/ knowledge is lost/ forgotten then although the general purpose rule still exists it can’t be applied as flexibly as before. 

This is where an elderly person can loose mental flexibility; they are wise enough to know the correct answer through experience; but because the original skill set has been lost they lack a deep understanding of how they arrived at the answer… and without this information they can’t consider new/ fresh avenues of thought. 

The Solution

Don’t just learn new topics/ skills… frequently refresh old learning/ knowledge and skills.

I’m off now to break my neck on my sons skateboard lol.

 :)
« Last Edit: April 18, 2017, 11:47:29 PM by korrelan »
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korrelan

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Re: The last invention.
« Reply #154 on: May 13, 2017, 11:27:51 AM »
Time for a project update; I’ve been busy doing other projects/ work lately but I have still managed to find time to bring my AGI along. First a quick Re-cap…

Connectome

I have already perfected a neural network/ sheet that is capable of learning any type of sensory or internal pattern schema.  It’s a six layer equivalent to the human cortex and is based on/ uses biological principals.  Complex sensory patterns are decoded and converted/ reduced to a sparse output that fully represents the detail of the original input; only simplified and detail recoded through a temporal shift.  Long to short term memory and prediction etc are all tested and inherent in the design.

Consciousness Theory

To achieve machine consciousness the 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. I envisage that eventually as it learns our language/ world physics/ etc it will ‘think and understand’ in these learned terms… as we do.

A Working Model

Now this is where things get extremely complex and I must admit it threw me a curved ball and slowed my overall progress down; until I wrote the tools to cope/ understand exactly what was happening in the connectome/ system.



This vid shows the top of a large neural tube; it’s a starting connectome and will grow in both size and complexity as the AGI soaks up information and experiences. The right side represents the sensory inputs and the left is the precursor to what will develop into the frontal cortex.

I’ve trained the model to recognise forty audio phonemes and you can see its confidence in the height of the bars lower left < 0:20 into the vid. I then turn off the phoneme patterns <0:40 and inject a random pattern to show the connectome has no recognition. At 0:50 the phonemes are turned back on and recognition re-commences. The system is stable; on the right you can see the input patterns at the top of the window and the sparse frontal cortex activation patterns just below them.  I then add the random element/ pattern to the phoneme pattern.

At 1:09 it reaches the point where the feed back from the frontal cortex starts to influence the input to the sensory areas and a feedback cascade begins… this is what I’m after.

The frontal cortex has learned the outputs form the sensory areas and begin adding their own patterns to the mix which in turn begins to influence the sensory input areas.

I have to be careful of my choice of words but at this point the connectome has become ‘aware’ of its own internal processes expressed in terms it’s learned from the external world.

I then turn off the external sensory stimulus and the global ‘thought’ pattern slowly fades because there are no circadian rhythms being injected to support it.

One of the problems I’m facing besides the shear complexity of the patterns is that once the feed back ‘spark’ begins that connectomes existence in time is forged, the complex global pattern relies on millions of pattern facets timed to the millisecond… once its stopped it can’t be re-created exactly the same.

So a bit more done…

 :)
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Art

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Re: The last invention.
« Reply #155 on: May 14, 2017, 11:34:12 AM »
It would be quite interesting to be able to record or see if it could be able to think about that which it has experienced or learned, which in a sense would be something the equivalent of dreaming (or daydreaming).

It was interesting to see the cascading episode as it began growing.

Is it possible to note the areas where certain items or "things" are being stored or recognized by the system and are they the same every time?


Quite a nice experiment! O0

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LOCKSUIT

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Re: The last invention.
« Reply #156 on: May 14, 2017, 02:05:10 PM »
"One of the problems I’m facing besides the shear complexity of the patterns is that once the feed back ‘spark’ begins that connectomes existence in time is forged, the complex global pattern relies on millions of pattern facets timed to the millisecond… once its stopped it can’t be re-created exactly the same. "

If you mean, the result of the hierarchy is lost after it's turned back on, then try either giving all senses and links a strengthening and weakening process so they last plus erase and/or a threshold to self-ignite and fire so they not only take action on their own but also will work together at the right times.

korrelan

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Re: The last invention.
« Reply #157 on: May 15, 2017, 01:09:16 PM »
@Art

Quote
It would be quite interesting to be able to record or see if it could be able to think about that which it has experienced or learned, which in a sense would be something the equivalent of dreaming (or daydreaming).

It definitely does re-use the learned facets of its knowledge/ experiences.  That’s where the feedbacks coming from. The ‘frontal cortex’ learns the order/ sequences of the sensory cortex’s outputs and attention areas form that learn the similarities in the input/ feedback streams. So yes… it does dream/ daydream about its experiences.

Quote
Is it possible to note the areas where certain items or "things" are being stored or recognized by the system and are they the same every time?

I can highlight and examine any area of cortex to see what has been learned in that area but initially the cortex areas are very fluid; they tend to move as more information is soaked up because the cortex is self organising. They will eventually stabilize and become fixed as the synapse strengthen through experience and lock them into position.

@Lock

Quote
If you mean, the result of the hierarchy is lost after it's turned back on, then try either giving all senses and links a strengthening and weakening process so they last plus erase and/or a threshold to self-ignite and fire so they not only take action on their own but also will work together at the right times.

I can save the connectome at any time recording all of its current states and then re-commence from where it left off… the problem is that once a global thought pattern has been allowed to fade/ die it can never be restarted exactly the same.  The momentum/ complexity/ inertia of the pattern has to be sustained whilst the system is running. 

I had a problem with ‘epilepsy’ in the connectome; very local cortex feedback triggered by a certain frequency of visual input would start a very fast local feedback cascade that would cease/ crash the global pattern… I had to re-build the pattern from scratch.

Hopefully this will be less of a problem once the connectome ages and becomes robust to sudden changes in the various streams/ patterns.

I intend to keep it running 24/7 anyway.

 :)
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LOCKSUIT

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Re: The last invention.
« Reply #158 on: May 15, 2017, 01:20:19 PM »
If a GTP can fade/die, and never restarted the same, well, how is it created anyhow? For example is it by neuro-muscles that stay existing OR like RAM i.e."tourists" that are new folks and so when turned off they didn't like you know, save.

> Therefore, shouldn't a GTP be strengthened into neurons and not water piped around like RAM? Knowledge is saved. It's simple...

WriterOfMinds

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Re: The last invention.
« Reply #159 on: May 15, 2017, 02:14:03 PM »
Re: your most recent post -- nice animation.  I was interested in how exactly the patterns coming out of the cortex influence the input once feedback starts.  How do you blend the sparse patterns from the cortex with the noisier or more complex input data?  And what's the overall effect -- does it intensify the input patterns?

I don't have much neural network background, so apologies if I am asking dumb questions.

keghn

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Re: The last invention.
« Reply #160 on: May 15, 2017, 03:24:43 PM »
 Is the sequences memory clocked by a daisy chain of nerves or a up counter/down counter, or random access in a sequence?

korrelan

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Re: The last invention.
« Reply #161 on: May 16, 2017, 08:40:53 AM »
@Lock

Quote
If a GTP can fade/die, and never restarted the same, well, how is it created anyhow?

Very gradually lol.  The connectome starts with a random configuration.  It then alters its self over time and learns/ adapts to its environment/ inputs.

This is the GTP from a simple connectome seed. Every point means something; a whole memory and all its complexities can be encoded/ recalled by just one of these synaptic pulses.  Information is encoded both spatially and temporally; so if one facet is out of phase by an ms or missing it means something totally different to the system.  It’s this constant ever-changing pattern that can’t be reproduced because it has been built from experiences over time. The pattern has been produced by a constantly changing connectome that has been learning over time. I can stop and save it… and continue but if it ever fades out through lack of neural stimulation/ feedback… it’s gone.

The pattern is the program running on the connectome/ neuromorphic processor.



@WoM

It’s a hybrid schema designed from scratch using my own interpretation of a neuron, synapse, axon, etc. It’s sensitive to both patterns and pattern facet frequencies.  The design is comprised of the best bits of the many different common types of artificial neural net, convolution, pooling, spiking, liquid, reservoir, recurring, etc with other mechanisms that mimic biological principles to bring them together into a cohesive system. 

Quote
How do you blend the sparse patterns from the cortex with the noisier or more complex input data?

The cortex sheet is self organising and learns to break the complex sensory stream down into regular, sparse recognised facets; which exit through the equivalent of pyramid neurons into the connectome.  So the complex audio sensory data for example is handled by the audio cortex and the rest of the connectome only receives the sparse data re-encoded into the connectomes native schema. Because the cortex sheet has several inputs it can also learn to combine data streams coming from different cortex regions.  Initially the audio cortex just learns the incoming audio sensory stream but once other cortex regions learn to recognise its sparse outputs their interpretation of the data is bounced back.  The audio cortex then starts to learn both the audio stream and the streams coming from other cortex areas.  Some of these streams are predictive or represent the next ‘item’ that should occur based on past experience.

In engineering terms I suppose the input layer of the cortex sheet could be viewed as a set of self adapting/ self wiring logic gates that are sensitive to both logic and temporal phase/ signals. So with excitation/ inhibition/ neurotransmitters/ etc it adapts over time to the incoming signals and learns to filter and re-code recognised elements.

Base data representation within the system is not at the level of say ‘cat’ or even whole English words; it’s at a much lower abstract representation.

@Keghn

Quote
Is the sequences memory clocked by a daisy chain of nerves or a up counter/down counter, or random access in a sequence?

All of the above… it adapts to use the best/ most efficient encoding schema for its problem space.  Hard to explain…it learns to encode episodic memories just because there is episodic information encoded in the input streams.  The streams have a temporal element, they have a fourth dimension, the system encodes time just the same as it encodes everything else… So areas of cortex learn that information/ memories come in sequences. 

I’ve designed it to be able to adapt and learn anything and everything.

Analogy: Think of all the wonderful things modern computers can do with just 0 and 1’s.  A modern computer just runs at one frequency; but imagine if every frequency band could run a different program through the same processor logic; parallelism by frequency band separation not architecture.  All the programs can interact at every level, they all share the same logic/ holographic memory which in turn can be modified by any program… this is one of the base ideas of my design along with self organisation/ learning etc.

It’s still a work in progress.

 :)
« Last Edit: May 16, 2017, 09:13:09 AM by korrelan »
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LOCKSUIT

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Re: The last invention.
« Reply #162 on: May 16, 2017, 01:11:09 PM »
But think hard korrelan, you're saying your program is literally the GTP, and it vanishes !

All we learn in life MUST be strengthened, and stored forever.

I know you say it forms from a whole lot of stuff, and you're probably sad you can't find how to save it.

Keep this in mind. We store senses sequentially. Some become strong enough to not shrink.

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Re: The last invention.
« Reply #163 on: May 16, 2017, 04:08:16 PM »
@Lock

Analogy: Imagine you own a business that delivers top secret correspondence/ letters.  In your office you have a wall full of reinforced pigeon holes with combination locks; one for each of your clients.  You have ten members of staff who have worked for you since the business began; their daily routine is to remove letters from the IN bin and place them into the correct clients pigeon hole ready for delivery. Over time each member of staff as claimed certain pigeon holes for each of their clients and for security reasons only they know the combination to the lock; and there are no names on the pigeon holes.  No employee is allowed to handle anyone else’s correspondence but the system works perfectly.  Every day hundreds of letters arrive and are allocated/ sorted by your team… it’s a well oiled machine… every one knows what they are doing.

They all die… (Sh*t happens lol) and you employ a new team of ten.

How does the new team take over seamlessly from the old team? All the information is still there… the letters are still in their respective pigeon holes… there is no loss of actual physical data… it’s the mechanism/ system that stored the data that has gone.

Information is stored in the ‘physical’ connectome structure and is very robust so long as it’s accessed fairly regularly.  The GTP that placed/ sequenced/ encoded the information into the connectome is the only one that can access it.

The only reason the GTP ever fails/ stalls is through design errors made by me.  It will continue indefinitely once I have all the bugs ironed out… in the mean time when ever it does fail… I have to rebuild/ restart it from scratch.

 :)
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Re: The last invention.
« Reply #164 on: May 16, 2017, 07:03:40 PM »
So, this is your business, and it's called GTP.

...

Quote
How does the new team take over seamlessly from the old team? All the information is still there… the letters are still in their respective pigeon holes… there is no loss of actual physical data… it’s the mechanism/ system that stored the data that has gone.

If all employees die, and the new team can't do the same, then there IS a loss of information - the memories in the deceased employees's brains. SAVE THEM FOR LORDS SAKE. "SAVE"

Isn't that like saying "my circuit board or gear disappeared"? Don't let your AI circuit/metal mechanism vanish! That's the most simplest thing ever...

Save memories. And strengthen them to resist erasing.

Again, save save save. If you need your employees, save em before shutdown time.

I know this is a save problem. I should've realized that as soon as you said "I lose the GTP on shutdown".

 

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