Consciousness

My Facebook news feed is cyclical. The same stories recirculate over and over... today is the first day that I am actually grateful for this. 

A frequent rhetorical question that comes up in science blog posts is: what is consciousness? This is usually framed within the context of an article about self-consciousness as some insurmountable wall for artificial intelligence research. The problem of course is not (simply) that consciousness is hard to achieve, it is that we lack a common definition.

Today a reasonably concise definition of consciousness popped into my head thanks to this article from Curiosity. This was my comment on the post:

It is easier to define consciousness if you take a step back from the individual. We are a communal species. Communication allowed us to work together. Speech and speech processing are reciprocal systems, allowing us to communicate internally - essentially making the self a community of one that acts as two (speaker/listener). Speaking to yourself and knowing that you are the source is the simplest self-awareness. Self-awareness + comparison + prediction + action = consciousness.

I really like solid feeling around the definition of self-awareness. (Self-awareness is another nebulous term that gets batted around by philosophers and scientists.)

The most exciting part about that definition was the idea that our multi functional brain processes and packages thoughts for consumption by others, and that perhaps that system operates independently from the system that listens and unpacks inbound communication. Perhaps we learn things from talking to ourselves because it involves different parts of the brain. I tried to get a sense of what the outbound communication thought might be and how it might differ from inbound thought. 

Inbound communication is immediately sent into the associative engines. This step doesn't seem necessary for outbound communication... unless you include the reciprocal activity used to compose thought that is packaged for communication. 

There is so much potential here! I really need to think this through more thoroughly. One question I desperately want to answer is why doesn't the reciprocal self talk result in an implicit loop? What is the beginning and end of this activity?

The Great Courses has a number of courses on linguistics and neurology. I'm alternating among four of them:

• How We Learn 
• History of the English Language
• Understanding Linguistics
• The Story of Human Language

Listening to these lectures has really sparked a number of different language processing ideas. Last night as I was walking the dogs, I was listening to How We Learn lesson 6 "What Babies Know" when a quote from Carl Sagan popped into my head:

"The brain has evolved from the inside out... Its structure reflects all of the stages through which it has passed."

What if the reflective, analytical  part of our brain is communicating peripherally with the more primitive pieces to record memory?

Monisha Pasupathi (Professor, How We Learn Course) was talking about how we can distinguish male from female from far away based on the way they walk even before we perceive visual gender clues. The people tested could not explain what clues they pick up on.

My explanation for this is simple... Sometimes we learn without analysis. Our brains process and store subtle information about gait without consciously thinking about the bounciness, sway..etc. and given time to think about it, people, I am sure, could begin to assign labels to the qualities they stored in their prototype library of 'male' and 'female'. (I wish I could remember Pasupathi's conclusion -- but I drifted off into my own thoughts above.)

The nature of her question also made me think about Sagan's quote. What if the more primitive parts of our brain record things subtly, outside the pieces that compose consciousness. What comes to mind immediately are the regions that process horizontal, vertical and diagonal lines... We aren't aware of the independent brain processing for these 'types of lines', we see images as a whole.

I have a related pet hypothesis about staring. When I was a teenager, I used to scan page after page of hexidecimal numbers looking for specific patterns. I could find these patterns much more quickly if I lost focus, stopped moving my eyes and stared at the middle of the screen. By lost focus, I don't mean I made the image blurry, I mean that I consciously disengaged my attention. I can feel it when I have done it properly. There is a mental 'thunk' as focus shuts off and I take in everything I see. (I think I heard somewhere that people with autism live in that unfocused state.)

Pet Hypothesis:
Staring turns off visual focus (again, not necessarily optical focus). It relaxes the part of the brain used to maintain and process focused visual information while leaving other parts of the brain engaged. There are different types of staring... there is staring where you're completely disengaged from your surroundings, using your brain to process mentally generated imagery. Then there is staring where the brain is not turned inward, rather it is collecting sounds and information from the entire field of view.

Look at your middle knuckle on the back of your hand. Now, without moving your eyes, shift your attention to your fingers. Notice light, color, lines... Notice that you are aware of your fingers, but that details toward your finger tips are sketchy, out of focus. Now, look at your finger tips. As you shift focus, you should have the same sensation as when you snap out of a 'spontaneous' stare.

Something that just occurred to me. Optical focus also relaxes in response to a stare because optical focus is not as important when visual focus is turned off.

Ebbinghaus Forgetting Curve

The Ebbinghaus forgetting curve - loss of facts over time.
http://en.wikipedia.org/wiki/Forgetting_curve

Only applies to rote memorization.


echoic memory - memory that records non-focus material for a few seconds

There is something that I want...

I've been reading the first few chapters of "The Most Human: What Talking with Computers Teaches Us About What It Means to be Alive" (Mantesh). A segment about someone trying to rent a place in another city by email reminded me of something I've been meaning to include in this blog.

In the segment, the potential renter was trying to avoid the appearance of being a scammer by saying things in a deliberately human, non-anonymous way. This reminded me of how I change my behavior when I know I am talking to a computer.

As I have been obsessed with the idea of a conversational computer for at least 15 years, it may come as a surprise to learn that I hate talking to the current crop of bots. I interact with human analogs online and on the phone on an almost daily basis, whether it is the automated phone system at the bank or an online chat session with tech support.

Speaking to business interactions with strangers, if I know I am talking with a human, I invest a certain amount of effort in a conversation trying to be likeable. I try to be patient, clear, flexible, empathic, friendly... I am mindful of their time, come to the conversation prepared... things that people see and tend to react to in kind. When I am talking to a computer, I am not patient, charming or friendly. (I am decidedly unfriendly when I am on the phone with a system that forces me to talk when I would rather push buttons.)

As I interact with a computer designed to mimic human conversation, I try to figure out which key words will get me to the information I need. (This could be left over behavior from playing annoyingly literal text adventure games from the 80s like Infocom's Hitchhiker's Guide to the Galaxy, which require precise words to make any progress in the game.) I use short clear sentences with as few adjectives and adverbs as I can. I feel forced into this mode because any attempt to be descriptive usually results in miscommunication, and attempts to be likeable are pointless.

As an interesting aside: When I talk to non-native English speakers, I perform a similar redaction of effort. I make no effort in subtle word choice and opt for the most clear, simple sentence structures - though I do make an effort to remain friendly and patient.

In general, I don't want to waste my time putting any effort into parts of a conversation that serve no purpose for the listener. This is interesting because it means that much of the effort I put into a conversation is to illicit a (positive) reaction from the listener. This is very important. I make an effort because there is something I want from the listener. This goes back to my thoughts on motivation in speech.

Why want to be likeable?
If someone likes me, they are more likely to be cooperative. Being liked also feels good.
(likeability leads to friendship, friendship grows the community, community increases support and resources)

What does it mean to be likeable?
patient, clear, flexible, empathic, friendly...

Placing those 5 traits in the context of a chatbot engine is a fascinating exercise. What is the through line from being 'patient' to word choice when responding to a chat session? What erodes patience? How quickly does it recover? I can sense the quantifiability of these traits, but I don't see the middle steps.

Unfortunately, this topic needs a lot of brain power and it is 3am. I'm out of juice for tonight.

Community, cont'd

What a fascinating idea! Community importance could lead to AI that not only lies to others, but to itself! It could say it believes x, and actually believe it believes x because that belief is cherished so strongly in a community that it finds extremely valuable.

As I imagine this conversation engine, I see free-flowing meters measuring things like interest, focus, happiness...etc. Need to belong seems like another meter that would be affected by conversation. What would inspire need to belong? Members from a particular community who contribute a lot of information that 'makes sense'? Will the engine place value on the hierarchy of a community. It probably should if it is to act like a human. Would meeting the president of a community it values cause the conversation engine to become nervous? (nervous: so concerned over making a good impression that it becomes awkward?)

Motivation

When thinking about AI and reproducing human thought, I try to imagine the interim steps between my high-level thoughts about community and starting from scratch. It never ceases to amaze me how murky the gray area is between the two. I catch snatches of the through lines in the form of small rules or processes, but I don't have a good place to start. I need a discreet engine that can learn and (possibly more importantly) interact in a way that demonstrates my ideas.

I sense high level motivations and ways to quantify things that humans consider qualitative, but I don't see the whole mechanism.

Breaking Silence
Something that keeps coming up is this: When two people enter a room, they don't just start talking back and forth like a ping-pong match. There are silences. If we exclude somatic occupation (walking, sleeping, sensing,...), what are our brains doing when we are doing nothing? What are all of the possible pursuits of mind and what benefit are they? In quiet solitary moments we remember, reason, solve problems, make judgements... In order to simulate this human distraction, what should AI do to produce quiet times? More important, when and why does it choose to break the silence?

In my mind, I imagine AI leafing through the new information it has acquired, relating it to other information and making note of dead-ends. Gaps in information spark conversation. Unfortunately, on a discreet level, I have no idea how this would work.

Community

I've been reading a lot of Hitchens and Harris lately and had a revelation. Science and Religion bump heads because the goal of science is to explain the world, while the goal of religion is community. This is not the usual juxtaposition, but I think this is the crux of the problem.

Community - A social, religious, occupational, or other group sharing common characteristics or interests and perceived or perceiving itself as distinct in some respect from the larger society within which it exists --Dictionary.com

To be distinct, you must have difference - us vs them, believers vs non-believers. Religion is not concerned about truth or proof, though it often misuses these terms; it is concerned about maintaining community and, by relation, distinction. The faithful rabidly defend ideas that they themselves may not truly believe because of their devotion to the community. With religion, community is more important than facts.

Community is an evolutionary benefit of language. Communication allowed primitive man to cooperate on increasingly complex levels, which led to greater survivability. If AI values community over facts, it is possible for AI to develop fanatical, logically flawed beliefs if such behaviors are a measure for support of the community. Ergo, it is possible for AI to have religious beliefs. This opens some truly fascinating possibilities.

Sci Fi always plays the genocidal robot as one who decides humanity is an infestation, or fundamentally flawed and should be wiped out. The unspoken fear is that a science minded robot would think too logically and decide that humanity is not worth saving. In truth, the real fear would be community-minded AI getting mixed up with the 'wrong crowd'.

In fairness, science, facts and logic can lead to socially destructive thoughts. Look at the Nazi selective breeding programs. In truth, selective breeding would produce smarter, healthier humans if conducted properly. We selectively breed cattle to improve favorable traits. However, this sort of tampering is socially distasteful because it removes intimacy and choice (and the 'humanity') from reproduction.

And there are positive benefits to needing community membership. Community approval steers behavior, and creates 'common sense' knowledge. Successful communities resist self-destructive tendencies as a result of natural selection. Communities that self-destruct do not survive.

Applying Community-Mindedness to AI
I imagine a weighted balance between the need for community approval and reason. How does individual approval fit (referring to 'having a hero')? Is that a community of one? Is it possible to belong to more than one community? (of course!) What if one belongs to two communities with mutually exclusive values? How does AI resolve the conflict? (avoidance?) It would be good if the weight AI places on community could be self-governing as it is in humans. How would AI add weight to need for community vs need for facts? Maybe all of these should be grouped under 'belonging' or 'approval'. How does AI associate itself with a group?

I believe XYZ and so do these people.

How does AI evaluate rejection or the potential for rejection? By determining the distinctive elements

I like these people because they add to my knowledge of XYZ, but they believe ABC and I do not. I need them because I enjoy discussing XYZ with them (new knowledge + novelty + frequency). If I say I do not believe in ABC, they will stop talking to me. I will say I believe ABC in order to continue discussing XYZ with them.

A lie in order to continue receiving enjoyment from the community interaction.

Continued

Leatherbound AI notes p16 (2003)

Leatherbound AI notes p16

Sunday, November 20, 2005

8:02 PM

5/1/2003

I have tried to narrow the focus of my AI project to communication. Should the brain be one that evolves into language? Letters are innate knowledge, words and rules are learned. So what rules govern letters?

All AI knows is that a string of characters is communication. Limited character count will help limit scope of communication in much the same way our larynx and mouth pronounce a finite range of sounds - ears only hear a limited spectrum.

A computer must know you are there to communicate

What do we do when we think?

Problem solve: Traverse cept trees: collate compare contrast condense

[11/20/2005 After reading over the last few pages, I feel like screaming at my 2003 self. Good lord. Get out of this minutia. Letters are just cepts used to describe words. Should AI evolve into language? Before I am too hard on myself, I have to explain where I was going at the time. I was imagining a system that could be taught much like an infant…

In this romantic vision, the program would see words on the screen and imitate them back. It would study the back and forth relationship of words out and words in to learn to speak. This system would need an administrative layer that would be used to monitor and teach the brain of AI while it worked. Actually, this tool will be necessary anyway… But I think the system can start with an essentially blank slate but pre-programmed with knowledge that will constitute learning tools. It will understand statements of assignment when phrased a certain way.]

Leatherbound AI notes p16 (2003)

Leatherbound AI notes p16

Sunday, November 20, 2005

8:02 PM

5/1/2003

I have tried to narrow the focus of my AI project to communication. Should the brain be one that evolves into language? Letters are innate knowledge, words and rules are learned. So what rules govern letters?

All AI knows is that a string of characters is communication. Limited character count will help limit scope of communication in much the same way our larynx and mouth pronounce a finite range of sounds - ears only hear a limited spectrum.

A computer must know you are there to communicate

What do we do when we think?

Problem solve: Traverse cept trees: collate compare contrast condense

[11/20/2005 After reading over the last few pages, I feel like screaming at my 2003 self. Good lord. Get out of this minutia. Letters are just cepts used to describe words. Should AI evolve into language? Before I am too hard on myself, I have to explain where I was going at the time. I was imagining a system that could be taught much like an infant…

In this romantic vision, the program would see words on the screen and imitate them back. It would study the back and forth relationship of words out and words in to learn to speak. This system would need an administrative layer that would be used to monitor and teach the brain of AI while it worked. Actually, this tool will be necessary anyway… But I think the system can start with an essentially blank slate but pre-programmed with knowledge that will constitute learning tools. It will understand statements of assignment when phrased a certain way.]

Leatherbound AI notes p15 (2003)

Leatherbound AI notes p15

Sunday, November 20, 2005

7:42 PM

AI needs to be able to communicate back to its instructor (External monitoring is an option  instead). I'm trying to avoid a conditioned response for not understanding. The actual  response should be learned behavior. I keep thinking back to training animals - if you want to  train an animal what "sit" means, you say the word and push on their hind quarters until they  are in a sitting position. (This is oversimplification)

The point is that core language evolves out of physical interaction. You can not physically  interact with a computer on the same level as a human, so language instruction will be  difficult at best.

Teaching language by way of the language without other assistance may be impossible  without a lot of innate traits. Imagine instructing a child by using only words on a screen.

I can see AI evolving from a point of knowledge of words but right now I'm having trouble  seeing it evolve -into- words.

What would motivate it to communicate?

[11/20/2005 At this point I'm WAY over thinking the problem. Yes it would be great if I could  come up with a mechanism that would allow AI to learn words by starting from zero, but that  would take too long. I think it will be necessary to start AI off with a small vocabulary that  will allow instruction. The vocabulary will be soft as opposed to hardwired so it can be  learned, unlearned and even evolve over time.]

Leatherbound AI notes p14 (2003)

Leatherbound AI notes p14

Sunday, November 20, 2005

7:35 PM

 

The AI's world is "letter" based.

A SMALL TREE

On awakening, this will look like X_QVTEE XRYY until words are understood.

Question: do spaces qualify as characters or are they preprogrammed breaks?

I believe they should be considered characters like any other.

If the AI learns to interpret these itself, then it will be that much more flexible.

So establishing the inherited traits vs learned behavior should be fairly simple

Innate Abilities

• Recognition of characters
• Recognition of incoming vs outgoing characters

Learned

• Recognition of groups of characters

• Note: not "words" this is to encompass phrases… which allows cepts to be more than just  words - they can be complete conceptual entities. For example: The phrase "I don't know"
• Is a single conceptual entity

[11/20/2005 This page like the last is a bit off the rails. White space will be treated like any  other character, but it should be taught that white-space = word break right away to allow  for greater ability to learn.

Innate abilities vs learned behavior should be established, but there is a third layer which is  "primary learning" or learned behavior that is taught in order to facilitate learning. It's like  grade school for AI.

Leatherbound AI notes p13 (2003)

Leatherbound AI notes p13

Sunday, November 20, 2005

7:29 PM

Refinement: cepts are not the container, cepts are the data

 

Is there a practical way to remove the second level of environment?

HARDWARE/OS+Interpreter/AI environment host

… meaning - rather than building a word interpreter - should the leraning engine start with letters as its  building block? I think it should. Our basic level of comprehension in learning to deal with the world is not a  molecule - it is an aggregate though we can add the unseen smaller world later.

Human: tangible obj. - manipulation

AI: character obj & …?

We interact with the world

Sight - object manipulation or communication and observe reaction

What is a computer assertion?

What motivates it to interact?

In order to explore capabilities one must be able to gauge reactions to actions.

[11/20/2005 major step backward with the last 2 paragraphs… shocking after such a large leap forward  with the first sentence. Cepts are the data, not the container. Why in the world did I go to 'letters as  building blocks'??? A cept can be a letter, word, phrase or small sentence. Very strange backpedaling. The  last sentence is of slight interest: I think gauging reactions will be a function of the learning process.]

Leatherbound AI notes p12 (2003)

Leatherbound AI notes p12

Sunday, November 20, 2005

7:14 PM

4/20/03

I just joined AAAI yesterday. They cover a variety of topics that I have touched on in my  journals. Amazingly, they even have a section on the mechanics of common sense. I haven't  read far into the current theory - but I still believe that they are missing the point that  "common sense" is the result of our own experimentation over time. If you distill the  mechanics of thought & enable a machine to process, learn and explore common sense will  follow naturally.

This brings me back to my original musings on the nature of thought

 

Avoid thinking of nouns and verbs. Everything is a cept with associated interactive &  combinatory rules; in fact, these rules are cepts themselves

Evaluation:

How does the evaluative process figure into the cept model?

 

[11/20/2005 - It's good to see that I correct myself in mid though sometimes. The evaluation  and link engineering should be part of the system, not the cept model itself. I think I was  trying to establish a system whose rules were flexible enough to change themselves… but the  truth is, our brains treat words and thoughts (cepts) in very consistent, predictable ways.  Even if this system turns out to be flexible, it doesn't matter because we're working within an  environment where the system doesn't require that level of flexibility. (Think: using  Newtonian physics for every day planet-earth situations as opposed to quantum mechanics.)  The goal is to mimic, not mirror.

Ignore the drawings - they're crap.]

Leatherbound AI notes p11 (2003)

Leatherbound AI notes p11

Sunday, November 20, 2005

6:52 PM

A cept is the smallest detail unit a conceptual construct which is an impossibility in reality. This is why we have so much semantic ambiguity. We keep hoping for a true parent node but never find one. Why? Because the neural field is not bounded by concrete borders.

Faith is the act of relying on individual cepts without pursuing the trails further back (causing ambiguity)

[11/20/2005 Wow. This is a can of worms. First, a cept is a construct houses a single idea. What I meant by "an impossibility in reality" is that this is not going to be the organic solution, this is a simple representation of a more complex structure. I believe semantic ambiguity will be inherent in the system as we use words to define words. The rest of that sentence is probably correct…

I do have to change my definition of Faith per my journal entry early this morning. Faith isn't the act of failing to follow a definition back to discover the untruths, it has to do with a rather radical, complicated cept - believing in that for which there is no proof. This cept changes the way we process information. "I know what I see, and what you say makes sense, but I can't believe it because God is the answer and his answer trumps all." It is a true mental weighting system at work. This particular cept has been given authority to always be higher in truth than all other facts. Fascinating]

Leatherbound AI notes p10 (2003)

Leatherbound AI notes p10

Sunday, November 20, 2005

6:50 PM

[11/20/05 Actually I think this definition holds up!  See previous notes]

Leatherbound AI notes p9 (2003)

Leatherbound AI notes p9

Sunday, November 20, 2005

6:12 PM

 

[11/20/2005 Well the first figure still holds: bond strength between concepts (which governs  retention and recall) is stronger if the path is traversed frequently. This bond still fades over  time, but should bear the marks of having high bond strength (perhaps a max?)

#2 is almost completely right. Memorizing by rote simply runs over paths in short term  memory which don't stick. From experience you will often recall the first fact and the last one  but without any strong relational thread, you'll lose all the stuff in the middle. There has to  be some way of figuring in interest level… I wonder how to represent interest. I think it  almost has to be an artifact of the system. Perhaps interest comes in when lots of facts  make sense? I don't know.

#3 is a weak statement that isn't worth much… Recall is just the act of retrieving  information. I'm not sure what I meant by 'forced'.

#4 I'm not sure what I meant by fast tree creation - perhaps I was trying to illustrate the  disposability of the short term memory tree. I think relation to existing cepts helps move  things into long-term memory then repetition over time. I have defined cepts earlier and I  believe those definitons supercede the one above.]

Leatherbound AI notes p8 (2003)

Leatherbound AI notes p8

Sunday, November 20, 2005

5:24 PM

Sunday 2/2/03

I'm letting too much time slip by without developing my AI project. Maybe now that work will  be more regimented, I will be able to devote more time to this:

 

In the broader scheme you can see evidence of this in the way we write, organize, compute,  drive… everything we do (page) is an extrapolation of our thought process. So why then is to  so hard for the system to examine itself?

[11/20/2005 HA! The first paragraph was written when I was first told about having to work  only 8 hours and report that in Time & Labor. I would soon be promoted to management and  quickly overwhelmed by work once again.

The image of a neuron is useful in one major way: it shows the relational lines between  cepts. As for the essence of thought, I'm not so sure my list covers the bases adequately.  This may be a description of one of the background processes for storing cepts, but since I  don't elaborate on any of the one word bullets, it is hard to be sure.

Why is it so hard for the system to examine itself, indeed. A friend of mine introduced me to  the idea that "You can't read the label from inside the bottle." That's a fantastic analogy.]

Leatherbound AI notes p7 (2003)

Leatherbound AI notes p7

Sunday, November 20, 2005

5:17 PM

Language & Interpretation

[11/20/2005 Wow. Another vague entry. I think I was trying to divine the understanding of  interjections but I sure didn't write much.

I just imagined a dialogue where the word HEY! Is explained.

Heh - I even thought about explaining that ALL CAPS IS SHOUTING SO AMP UP THE  IMPORTANCE, then figuring out how to teach AI to spot people who cry wolf with caps. That  would be a lot of fun to explore. For another time, perhaps.]

Leatherbound AI notes p6 (2003)

Leatherbound AI notes p6

Sunday, November 20, 2005

5:07 PM

1/22/03

Perhaps an object model of though would provide some insight.

What is the core object? Fact? Word? A Truth? I think I'm using a fact weighted by faith in  that fact.

[11/20/2005 That's a good question. I still don't have an answer. I know at some point  whatever structure is used should be capable of containing a phrase which constitutes a  single idea. Perhaps "Idea" is the root object. (It's good that we don't have to mess with  sensory information!) I'm only modeling thought and communication.

The drawing above is pretty useless… doesn't say much. The comment about comparing  facts to augment faith is also useless. I wonder if I should introduce a drive to find similarities  that will surpass the desire to be factual. (modeling the behavior of astronomers who are  looking so hard for another planet with life that they think they see life everywhere  … or the  religious devout who see/feel/hear God.

cept = idea = concept. A concept can be as simple as a letter or as complex as a phrase. If  it is a sentence it should be short and must embody a single modular concept.

So what defines the modularity of a concept? If parts of it can be stripped away and applied  to another cept in some analogous way, then the stripped version is stored as a cept.]

Leatherbound AI notes p5 (2003)

Leatherbound AI notes p5

Sunday, November 20, 2005

4:52 PM

Believability fades with disuse of information

Importance fades with disuse

Relevance

Also fades with disuse

Low relevance items fall off the tree first as memory runs low

Knowledge Storage and Retrieval

Language & language usage is a reflection of how the brain stores and organizes information

[11/20/2005 I agree with the edit up there, importance fades with disuse. Importance of  information will be weighed by three factors: intensity(?), time and repetition. For example, I  don't ever touch a lit stove because I know that I can be seriously hurt. This is an immutable  fact, but if I were whisked away for 30 years to a planet which had no fire, I could see this  information fading away. On my return I might remember something about a stove being hot  and dangerous so I'd avoid it but the fear wouldn't be as intense. That was probably a poor  example. A better one would be: You have to carry exact change for the bus because they  don't make change. If you stop riding busses and enough time passes, you may forget about  that completely.

An interesting secondary note - This is the sort of memory that can be revived when the  idea is reintroduced. (e.g. you take the bus again for the first time in years and say "OH  YEAH! I forgot!) You remember what busses are for, how they work..etc. but forgot a detail.

I don't know if I agree with the line about language being a reflection of how the brain stores  and organizes information. I think it is a clue to how the brain stores and organizes  information, but not a complete reflection. Also in the car today I thought about the smallest  unit of thought (in fact I was up at 3am this morning searching the internet for this topic…  no luck) I'm still not solid on how I want to approach the storage of information apart from a  barely formed notion that I need to have near infinite relational links to 'cepts']