THE SACRED COMPUTER - СВЕЩЕНИЯТ СМЕТАЧ, a.k.a. ARTIFICIAL MIND: Thinking Machines, Creativity and Human Development. A pioneering virtual Research Institute for Artificial General Intelligence (AGI), Cosmism and Transhumanism, AI, Software, Interdisciplinary General Research, Creativity, Versatility, Universal Men ~ Twenkids etc. Created in 2000. (...)
Selection of recommended talks on AGI 2010 conference:
Marcus Hutter - Universal Artificial Intelligence, AGI 2010
Tutorial on Mini-Column Hypothesis in the Context of Neural Mechanisms of Reinforcement Learning - by Randal A. Koene, AGI 2010
Related to M. Hutter - Jurgen Schmidhuber notices that reinforcement learning needs many steps, many decision points, and marks compression progress as an abstract form of reward for cognitive processes:
Jurgen Schmidhuber-Artificial Scientists Artists Based on the Formal Theory of Creativity, AGI 2010
The following one is mostly to get familiar about the state of the art of Virtual Worlds simulations for AGI - it seems pretty primitive... Taking into account also the demos with reinforcement learning agents in other talks, where the agents play simple games such as Pacman, "Pocman" (partially-observable environment), tic-tac-toe or tank games, or the dogs in Ben Goertzel's demo which learn to carry the object back to their master (coordinates of a bounding rectangle)...
I've been speculating about different virtual world systems for AGI seed AI and other intelligent agents experiments myself, but it was some 6 years ago; wished to start experimenting in formalizing so called my "Teenage Theory of Mind and Universe" and testing it in such worlds, but it was for a short time, and it has left as just ideas because of all the other stuff I had to deal with. However I am back, and certainly there's a lot of work to be done in this field.
Ben Goertzel - Using Virtual Agents and Physical Robots for AGI Research
This is a direction I realized last year during a discussion on Boris' knols and mentioned there, but later I shortened the comment there, because it wasn't the appropriate place for the details
The idea is about designing cognitive algorithm achieving properties that Boris proposes, however grounding it and deriving it on a supposedly simpler and easier to understand cognitive algorithm that has existed before in lower species and was slightly modified by evolution.
- Embryogenesis is selective segmentation and differentiation
In general, organisms are deveolped by selective segmentation (separation) and differentiation of cells, a sequence of activation of appropriate genes.
- Small quantity of germ cells divide to form bulky tissues/regions - initial complexity is much lower than final and there are interdependencies. Simple mathematical example is fractals .
One reason neocortex may have relatively similar columns all over might be because they might be building block of the cognitive algorithm. However another reason, in another POV is that DNA has just not enough capacity to code complex explicit circuitry to make them all specialized by directed growing. Even if it had the capacity in theory, it's questionable whether biological "technology" would be capable to connect it with the required precision, because organism parts "grow like branches of a tree" ("The Man and The Thinking Machine", T.A. 2001) .
Bottom line: there are "leaves" of the tree, and the complexity of the leaves is limited.
- Evolution steps in phylogeny are supposed to be very small, and genome development is chaotic in mathematical sense - a small difference in the initial state (DNA) may lead to (apparently) vast difference in the final state - fully developed body.
Apparently big differences in structure may be caused by very small and elegant, functionally purposeful changes inside.
-Some of the operations that a mutation may cause could be, besides formation of a new protein: be or result in something like the following:
- Copy a segment (a block) once more, i.e. initiating division once more cycle
- Connect to another segmentation module (especially in brains)
- Amphibian's and Reptilian's forebrain, their most evolved part - archicortex/neopalium - has 3 layers. In comparison, general mammalian and human's most evolved (the external) part - the neocortex has 6 layers*
- Evolution, especially in brain, is mostly building "add-ons" , "patches" and slight modification and then multiplication of components(?)
Triune theory of brain, the new is a layer above, the old is preserved. The new modules are connected back to the old ones and have to coordinate their operation, and new modules receive projection from the previous. I think this implies also, that the higher layer should be "smarter" (more complex/higher capacity memory/processing power) than the lower, allowing more complex behavior/adaptation - otherwise it would just copy the lower layer results.
Amphibian's and reptilian's brains had cortex lacking 6-layer columnar structure of mammals, it's 3-layer (I don't know a lot about its cytoarchitecture yet). I couldn't accept that archicortex lacks some sort of a modular design, somewhat similar to the columns; it makes no sense for the archicortex to have been a random jelly of neurons, because even basic behaviors such as finding lair and running for cover require integration of multimodal information and memory. I don't believe also that mini-columns had appeared from scratch in the higher mammals.
Recently a little support on this speculation appeared; regarding birds, though, a parallel line of evolution:
"...A new study, however, by researchers at the University of California, San Diego School of Medicine finds that a comparable region in the brains of chickens concerned with analyzing auditory inputs is constructed similarly to that of mammals.
(...)
But this kind of thinking presented a serious problem for neurobiologists trying to figure out the evolutionary origins of the mammalian cortex, he said. Namely, where did all of that complex circuitry come from and when did it first evolve?
Karten's research supplies the beginnings of an answer: From an ancestor common to both mammals and birds that dates back at least 300 million years.
The new research has contemporary, practical import as well, said Karten. The similarity between mammalian and avian cortices adds support to the utility of birds as suitable animal models in diverse brain studies.
"Studies indicate that the computational microcircuits underlying complex behaviors are common to many vertebrates," Karten said. "This work supports the growing recognition of the stability of circuits during evolution and the role of the genome in producing stable patterns. The question may now shift from the origins of the mammalian cortex to asking about the changes that occur in the final patterning of the cortex during development.
- The function of the Archicortex (hippocampus) in mammals is declarative memory and navigation.
See some of my speculations on: April 24, 2010 - Learned or Innate? Nature or Nurture? Speculations of how a mind can grasp on its own: animate/inanimate objects, face recognition, language...
Hippocampus
- formation of long term memory - navigation - head direction cells - spatial view cells - place cells
At least several or even all of these can be generalized. Places and navigation go together. Places are long-term memories of static immovable inanimate objects (the agent has not experiences that these entities move).
Navigation, head-direction, spatial-view, place-cells - they all are a set of correlations found between motor and sensory information, and long-term memories, which are invoked by the ongoing motor and sensory patterns.
The static immovable inanimate objects (places) change - they translate/rotate etc. - most rapidly in a correlation with head direction (position) and head movements.
Navigation and spatial view are derived from all.
Boris Kazachenko's comment:
(...) Regarding hippocampus, it controls formation of all declarative (not long-term) memories, not just about places. Declarative means the ones that got transfered high enough into association cortices to be consciously accessible. My personal guess is that hippocampus facilitates such transfer by associating memories with important locations [mapping] . You'll pay a lot more attention to something that happened in your bedroom then to the same thing that happened on the dark side of the moon. I call it "conditioning by spatial association". (...)
// There's a whole topic about hippocampus functions and its competition with neocortex, for now I plan to put it alone and link to this.
Reptiles don't have association cortices, though, yet pretty impressive behavior of lizzards could be seen, such as this curious iguana looking behind the mirror to see where the other one is, and eventually hitting the mirror - see at 5:33.
My guess about archicortex' contribution is discovery of means to:
- Archicortex maybe records exact memories and correlations between memories/compare for match between sequences of sensory patterns
There should be limitations of the length of sequences, part of it might be caused by size constraints - animals with archicortex only, lacking the higher layers* just have very small brains. (*Cingulate cortex and neocortex for mammals)
I'm not an expert in vertebrate embryology yet, but I guess a simple reason why fish and reptiles with big body keep very small brains - like 3,6 m white shark with 35 g of brains should be that:
- Germ cells that give birth of brain tissue of fish and reptiles divide less, or/and these species lack some hormonal growing mechanisms that species with bigger brains have
Both are a sort of "scaling issues".
Too small a brain has insufficient cognitive resources. On the other hand, these brains maybe don't scale also, because they wouldn't/didn't work better if they are/were bigger.
- Assuming general intelligence is a capability for ever higher generalization, expressed in a cognitive hierarchy (see J.Hawkins, B. Kazachenko, T. Arnaudov) and mini-column is assumed to be the building block of this process in neocortex, there should be a plausible explanation of why and how this module was formed and why this function gets successful
My functional explanation is the following:
- There already existed templates of circuits for exact recording, but they didn't scale - The simplest form of generalization is recording at lower resolution than the input, and fuzzy comparison. It's partially inherited by the imprecise biology. - Updated form of these circuits maybe added more divisions and cascade connections (and this may have started in cingulate cortex or higher reptiles, as well) which allowed for hierarchical scaling. Neocortex is assumed to have 6 layers, archicortex has 3. I'm not an expert in cytoarthitecture, and should check out cingulate cortex, but if there are no inter-stages between 3 and 6, this sounds suspicious for simple doubling somewhere during division and specialization. Or it could be several doubling operations. - These new cascade connections allow for deeper hierarchy, scaling and multi-stage generalization. (Recording "exactly" alone is "generalization" and lossy, but without hierarchy which is deep enough this cannot go far - just for coping with basic noise.) - There are mice with less than 1 g of brain which of course are much smarter than sharks (not to mention smart birds); however the advantage in micro-structure (mini-column) doesn't deny that mammalian brain scales in size and there is a correlation between brain size (cognitive reources) and intelligence, even though it's not a straight line. Spindle neurons, connecting directly distant regions in the neocortex are one of my guesses about why pure size might be not enough; another one is the area of the primary cortices, especially somatosensory (elephants, dolphins and whales have bigger brains than humans). See Boris' article about Spindle neurons and generalization: http://knol.google.com/k/cognitive-focus-generalist-vs-specialist-bias
- Neocortex does scale, but it's not surprising that it has constructive limitations as well as archicortex did.
- Classical and Operant conditioning, dopamine and temporal difference learning
It's quite a global feature of entire brain, maybe; but it has to be considered - classical evolving to Operant requires predictive processing.
Conclusion
1. Design a basic cognitive algorithm/module, scalable by biologically-like mechanisms, which allows reaching for, say, reptilian behavior. 2. Tune this basic module, multiply and connect intentionally to form a mechanism that "stacks" on a hierarchy, generalizes and scale the global cognitive capacity.
Как работи мозъкът. Хипотезата за миниколоните в неокортекса като градивен елемент на разума. Съзнание. Еволюция на човека. Трансхуманизъм. Класическо кондициониране и други интересни материали.
Some cited from Boris Kazachenko's knols, pointed by himself or commenters, some suggested by me, browsing around.
На български, бърз курс за работата на мозъка и допълнителни връки в моята лекция от курса по Универсален изкуствен разум (Brain Architecture):
Как работят наркотиците, как наркотиците въздействат на невротрансмитерите и невроните в мозъка (анимирано) How drugs work, how drugs affect neurons (animated): How drugs affect neurotransmitters:
See also, though: Confusing cortical columns, Pasko Rakic* :
http://www.pnas.org/content/105/34/12099.full
Функционални различия между лявото и дясното полукълбо на мозъка: Left and Right hemisphere functional differences of the brain: The Split Brain - Revisited, By Michael S. Gazzaniga About 35 years ago in Scientific American, I wrote about dramatic new studies of the brain.
Human neurophysiology: a student text By Oliver Holmes
http://books.google.com/books?id=ql4VAAAAIAAJ&pg=PA197&lpg=PA197&dq=neocortex+myelination+age+sequence&source=bl&ots=csg_LPPfSY&sig=ruo4Acyh2GZBrVDvC-I5daA-xyU&hl=en&ei=LGESSoTIPJiu8QSH7aSQBA&sa=X&oi=book_result&ct=result&resnum=8#v=onepage&q=neocortex%20myelination%20age%20sequence&f=false Other materials
Accelerating Future: Transhumanism, AI, nanotechnology, the Singularity, and extinction risk:
Classical Conditioning experiment with a baby, taught to be afraid of rats, eventually any furry objects. Displays also stimulus generalization. Immoral, but interesting:
Little Albert experiment: http://en.wikipedia.org/wiki/Little_Albert_experiment
Human Evolution:
http://www.evolution-of-man.info/
Obstetrical Dilemma:
http://en.wikipedia.org/wiki/Obstetrical_Dilemma
--> bigger the brain/head of proto-human baby --> harder birth and dependent on help from others (obstetrician) --> shorter the pregnancy in order the baby to get ouf of the mother's pelvis --> more immature newborn baby --> more underdeveloped brain of the baby (20-25% fully developed, vs 45-50% in chimps) --> baby is more dependent on mother's care for a longer time --> mother is dependent on others' carе --> family, division of labour, society
"Акушерската дилема" в еволюцията на човека
--> по-голям мозък/глава на бебетата на еволюционните предци на човека --> по-тежки раждания и зависимост от чужда помощ (акушерки) --> по-кратка бременност, за да може бебето да се промуши през тазобедрената става на майка си --> по-недоразвито новородено --> по-недоразвит мозък (20-25% напълно развит при човека, докато 45-50% при шимпанзетата) --> бебето е по-зависимо от грижите на майка си за по-дълъг период --> майката е по-зависима от грижите на другите --> семейство, разделение на труда, общество
Emotional facial expressions are innate, I remember a many decades old research on understanding facial expressions around the world, which shows that we're compatible at emotions expression level, no matter of culture and the level of society development. I think this was proven thousands of times by Cinema, and it matches with the hypothesis that emotions are driven by older parts of the brain: Cingulate Cortex --> Fornix --> Thalamus --> Hippocampus --> Neocortex.
The Neocortex eventually learns to control face as well, and good actors do it well, however, being an actor myself, I would say that good acting involves feeling the emotions of the character, "living a part", which goes below neocortex. Indeed, I believe this is easier, because it happens partially subconsciously and automatically.
Perhaps neocortex "calls" or better "recalls" and "reruns" complex functions from the lower parts - memories of already felt emotions or better - situations when emotions were felt. On the other hand, bad actors probably do not feel, because cannot recall emotional memories so well. Bad actors are trying consciously /mechanically to pull-up/pull-down facial muscles which makes their faces to look unrealistic and unconvincing (like if their characters felt nothing or something inappropriate). Perhaps, because conscious cannot do control precisely in parallel so many muscles, and maybe because these muscles are too strongly linked to their thalamic nuclei.
I would suggest here: it's better not to smile if you're not happy, rather than do a "false smile".
Facial Hardware
Our face has a dedicated cranial nerve like the other sensory and motor head-"interfaces", and like the rest, except olfaction, but including optic nerve, facial nerve passes through a thalamic nuclei in the thalamus:
Going up, Thalamus has projections to the neocortex and back, so eventually it can turn into "magic neurons" up there.
BK: "I think there have been minor genetic changes in humans that produced a major increase in intelligence, mostly through the growth & folding of neocortex"
I believe that the face might be one of the important aspects of human evolution, as well.
If brains and intelligence have evolved to cope with more complex social interactions in the high level of processing, face should have taken a part of the evolution of the "Physical Level Interface" and the thalamic nuclei are a middle layer, doing primary decoding of physical layer signals, sorting, redirecting. I suspect they might/should be correlated at DNA level, because, growing one thing requires growing a counter-part, all muscles or receptors etc. need to have appropriate nerves that eventually reach the Central Nervous System, and I guess that evolving parts in pathways inside the CNS may also involve all subregions, if they develop together prenatally. (I'm not yet that much deep into embryology, though.)
Flexible physical layer interface allows telling others clearly and visually how do you feel, so they can react appropriately and/or learn using it as a sign/conditioned stimulus. I guess this should be related also to the grow of the importance of vision in human senses.
Basic Emotions and Mammals
All mammals have the basic emotions, lower animals also should have at least part of theirs (such as: Fear, Panic, Lust, Search; even the Octopus seems to have an elaborated brain and "Play" system on its own). There's a funny research showing that rats laugh when being tickled...
However, most mammals, e.g. cats or dogs display just a portion of their emotions using face. Expression of Rage (human also growls and clenches its teeth) and Fear/Panic? (eyes wide open) seem to be similar, and I think they both are amongst the evolutionary older emotions.
When we do say that a cat or a dog has "a sad face", though I suspect it's rather a blind visual similarity we spot (like finding faces in a fish or insect), a form of anthropomorphizing, than real. Cats and dogs faces are not that flexible and expressive as ours - as Alice says in Wonderland: "Cats can't smile". Well, I don't know. :)
Whitney was a sad little kitty, starring in a photo story of mine. Photos - (C) Todor Arnaudov, 2006 - "The Ghost" photo story
Big Apes
I haven't studied well great apes facial expressions to speak seriously. Of course they do use faces for expressing emotions, but I suspect their face is not as versatile as human's, the visual contrast between features and background is worse and they lack eyebrows, which are clues for human emotions.
However mirror neurons and facial expressions imitation were found as early as rhesus monkeys, so this line may started long ago.
Conclusion
Overall, I guess that mirror neurons might be projections or related to projections from facial and optic thalamic nuclei, maybe related to a primary integration of both. I suspect that part of the integration between face and optic, dealing with imitation of facial expressions, can be done as early as the thalamus itself, by sort of low resolution processing; I haven't studied internal thalamus anatomy yet, though (is it studied/understood?).
I would conclude, that if one day I could do study myself how exactly a baby imitates faces, I would try to find spatial/contrast etc. resolution thresholds when these reactions first appear. E.g. a baby boy may put his tongue out if he sees a quick-enough change in contrast in a wide spot of the visual field, not only in a mouth - it might be a high-contrast line moving quickly vertically anywhere etc. I don't know if such tests had been done already.
Appendix 1: Talents I think there's no doubt that there are innate predispositions/talents for arts - drawing, music, dancing, also acting. So far I thought that it's related to details about personal cortical architecture, maybe differences in the speed of learning - ease/speed/durability of synaptogenesis/neuroplasticity. Now I have an additional guess: Thalamic Nuclei may also play a significant part in talents. E.g. people who can't feel the rhythm can barely learn dancing, while the others start dancing even without being taught. At a low level dancing seems to be related to basic prediction of sound patterns, linked with syncrhonized motions. Cingulate Cortex, related to emotions, has projections into motor-pathway as well, which may add to explanation why dancing is an emotional activity. I suspect that acting talent may have some of its roots in Cingulate Cortex as well.
Finally, good dancers and any talented people may have an advantage in that lower-level preprocessing part. The cerebellum of course should be also a "suspect" whenever there is fine motor-coordination, and it has much more neurons than the neocortex and supposingly vast computing power. I can't speak about cerebellum with supporting data yet, but my first guess is that some people have faster "processors" - cerebellum has longer-lasting and faster neuroplasticy capabilities, maybe wider range connections. Appendix 2: Lower parts of the brain and prediction - prediction mechanisms below neocortex
Many if not all AGI researchers would agree that prediction and compression are amongst the main basic keywords when defining the substance of intelligence. I assume that classical conditioning, which seems to exist even in fish*, can be assumed as a primitive form of prediction of one stimuli, associated with another. The emotions or the basic behavioral drives are also forms of prediction, predicted behavioral patterns, appropriate for particular situations. This is there in all animals, in insects as well - genetically predicted/precomputed patterns.
I think a nice example is the ants "conditioning" to care for their "farms" of greenflies, which ants keep to milk juice. Ants are not eating the greenflies, but are just milking them, and even further - they are protecting their animals from enemies such as lady-birds...
It is known that thalamus does make primary decisions about what sensory information should pass up to neocortex and what should be processed faster through lower pathways.
I suspect (it may be a well known fact), that Thalamus and Cingulate Cortex do have capabilities to predict or drive complex motor patterns like the Neocortex, but of course their models are much vague, short and specific - emotional patterns, face expressions, body language(?).
I suspect that it may be connected with part of the body-language as well, which seems to be uniform in many cultures. (Not all gestures, though - e.g. the middle finger evidently passes through a visual analogy... ;) )
