Friday, June 19, 2015

Why Would an AI System Need Phenomenal Consciousness?

In my last post on Jesse Prinz, we learned about the distinction between immediate, phenomenal awareness in consciousness in contrast to our more deliberative consciousness that operates with the contents of short term and longer term memory.  From moment to moment in our experience, there are mental contents in our awareness.  Not all of those contents make it into the global workspace and become available to reflective, deliberative thought, memory, or other cognitive functions.  That is, there are contents in phenomenal awareness that are experienced, and then they are just lost.  They cease to be anything to you, or part of the continuous narrative of experience that you reconstruct in later moments because they never make it to the neural processes that would capture them and make them available to you at later times.  

We also know that these contents of phenomenal consciousness are also most closely associated with the qualitative feels from our sensory periphery.  That is, phenomenal awareness is filled with the smells, tastes, colors, feels, and sounds of our sensory inputs.  Phenomenal awareness is filled with what some philosophers call qualia. 

Let me add to this account and see what progress we can make on the question of building a conscious AI system. 

Daniel Kahneman and Amos Tversky got the Nobel Prize for their work uncovering what they call Dual Process Theory in the human mind.  We possess a set of quick, sloppy cognitive functions called System 1, and a more careful, slower more deliberative set of functions called System 2.  

System 1
System 2
Unconscious reasoning
Conscious reasoning
Judgments based on intuition
Judgments based on critical examination
Processes information quickly
Processes information slowly
Hypothetical reasoning
Logical reasoning
Large capacity
Small capacity
Prominent in animals and humans
Prominent only in humans
Unrelated to working memory
Related to working memory
Operates effortlessly and automatically
Operates with effort and control
Unintentional thinking
Intentional thinking
Influenced by experiences, emotions, and memories
Influenced by facts, logic, and evidence
Can be overridden by System 2
Used when System 1 fails to form a logical/acceptable conclusion
Prominent since human origins
Developed over time
Includes recognition, perception, orientation, etc.
Includes rule following, comparisons, weighing of options, etc.

In short, System 1 makes gains in speed for what it sacrifices in accuracy, and System 2 gives up speed for a reduction in errors. 

The evolutionary influences that led to this bifurcation are fairly widely agreed upon.  System 1 gets us out of difficulties when action has to be taken immediately so we don’t get crushed by a falling boulder, fall from the edge of a precipice, eaten by a charging predator, or smacked in the head by a flying object.  But when time and circumstance allows for rational deliberation, we can think things through, make longer term plans, strategize, problem solve, and so on. 

An AI system, depending on its purpose, need not be similarly constrained.  An AI system may not need to have both sets of functions.  And the medium of construction of an AI system may not require tradeoffs to such an extent.  Transmission time for conduction across neural cells is about 150 meters per second.  By the time the information about the baseball that is flying at you gets through your optic nerve, through the V1 visual cortex, and up to the pre-frontal lobe for serious contemplation, the ball has already hit you in the head.  Transmission time for silicon circuitry is effectively the speed of light.  We may not have to give up accuracy for speed to such an extent.  Evolution favored false positives over false negatives in the construction of many systems.  It’s better to mistake a boulder for a bear, as they say, than a bear for a boulder.  A better safe than sorry strategy is more favorable to your contribution to the gene pool for the species in many cases.  We need not give up accuracy for speed with AI systems, and we need not construct them to make the systematic errors we do. 

The neural processes that are monitoring the multitude of inputs from my sensory periphery are hidden from the view of my conscious awareness.  The motor neurons that fire, the sodium ions that traverse the cell membranes, the neurotransmitters that cross the synaptic gaps when I move my arm are not events that I can see, or detect in any fashion as neural events.  I experience them as the sensation of my arm moving.  From my perspective, moving my arm feels one way.  But the neural chemical events that are physically responsible are not available to me as neural chemical events.  A particular amalgam of neural-chemical events from my perspective tastes like sweetness, or hurts like a pin prick, or looks like magenta.  It would appear that evolution stumbled upon this sort of condensed, shorthand monitoring system to make fast work of categorizing certain classes of phenomenal experience for quick reference and response.  If the physical system in humans is capable of producing qualia that are experiencable from the subject’s point of view (It’s important to note that whether qualia are even real things is a hotly debated question  

then presumably a physical AI system could be built that generates them too.  Not even the fiercest epiphenomenalist, or modern property dualist denies mind/brain dependence.  But a legitimate question is, do we want or need to build an AI system with them?  What would be the purpose, aside from intellectual curiosity, of building qualia into an AI system?  If AI systems can be better designed than the systems that evolution built, and if AI systems need not be constrained by the tradeoffs, processing speed limitations, or other compromises that led to the particular character of human consciousness, then why put them in there? 

Monday, June 8, 2015

Artificial Intelligence and Conscious Attention--Jesse Prinz's AIR theory of Consciousness

Jesse Prinz has argued for that consciousness is best understood as mid-level attention.

Consciousness, Prinz argues, is best understood as mid-level attention.  

Low level representers in the brain are neurons that perform simple discrimination tasks such as edge or color detection.  They are activated early on in the process of stimuli from the sensory periphery. 

(a poorly taken, copyright violating picture from Michael Gazzaniga's Cognitive Neuroscience textbook.)

The activation of a horizontal edge detector, by itself, doesn’t constitute organized awareness of the object, or even the edge. 

Neuron complexes in human brains are also capable of very high level, abstract representation.  In a famous study, “Invariant visual representation by single neurons in the humanbrain,” Quiroga, Reddy, Kreiman, Kock, and Fried, they discovered the so-called Halle Berry neuron with some sensitive detectors inserted into different regions of the brains of some test subjects.  This neuron’s activity was correlated with activation patterns for a wide range of Halle Berry images. 

What’s really interesting here is that this neuron became active with quite varied photos and line drawings of Halle Berry, from different angles, in different lighting, in a Cat Woman costume, and even, remarkably, in response to the text “Halle Berry.”   That is, this neuron plays a role in the firing patterns for a highly abstract concept of Halle Berry. 

Prinz is interested in consciousness conceived as mid-level representational attention that lies somewhere between these two extremes.  “Consciousness is intermediate level representation.  Consciousness represents whole objects, rich with surface details, located in depth, and presented from a particular point of view.”  During the real time moments of phenomenal awareness, various representations come to take up our attention in the visual field.  Prinz argues that, “Consciousness arises when we attend, and attention makes information available to working memory. Consciousness does not depend on storage in working memory, and, indeed, the states we are conscious of cannot be adequately stored.”

When you look at a Necker cure, you can first be aware of the lower left square as the leading face.  Then you can switch your awareness to seeing the upper right square as the leading face.  So you attention has shifted from one representation to another. 

That is the level at which Prinz is located the mercurial notion of consciousness, and trying to develop a predictive theory based on the empirical evidence.  And Prinz goes to some lengths to argue that consciousness in this sense is not what’s moved into working memory, it’s not the contents necessarily that have become available to the global workspace such as when they are stored for later access.  These contents may or may not be accessible later for recall.  But at the moment they are the contents of mind, part of the flow and movement of attention. 

Here I’m not interested in the question of whether Prinz provides us with the best theory of human consciousness, but I am interested in what light his view can shed on the AI project.  I’m particularly interesting in Prinz here because it’s arguable that we already have artificial systems that are capable, more or less, of doing the low level and the high level representations described above.  Edge detection, color detection, simple feature detection in a “visual” field are relatively simple tasks for machines.  And processing at a high level of conceptual abstraction has been accomplished in some cases.  IBM’s Jeopardy playing system Watson successfully answered clues such as, “To push one of these paper products is to stretch established limits,” answer:  envelope.  “Tickets aren’t needed for this “event,” a black hole’s boundary from which matter can’t escape,” answer:  event horizon.  “A thief, or the bent part of an arm,” answer:  crook.  Even Google search algorithms do a remarkable job of divining the intentions behind our searches, excluding thousands of possible interpretations of our search strings that would be accurate to the letters, but have nothing to do with what we are interested in.

So think about this.  Simple feature detection isn’t a problem.  And we are on our way to some different kinds of high level conceptual abstraction.  Long term storage for further analysis also isn’t a problem for machines.  That’s one of the things that machines already do better than us.  But what Prinz has put his finger on is the ephemeral movement of attention from moment to moment in awareness.  During the course of writing this piece, I’ve been multi-tasking, which I shouldn’t have.  I’ve been answering emails, sorting out calendar scheduling, making plans to get kids from school, and so on.  And now I’m trying to recall what all I’ve been thinking about over the last hour.  Lots of it is available to me to now.  But there were, no doubt, a lot of mental contents, a lot of random thoughts, that came and went without leaving much of a trace.  I say, “no doubt,” because if they didn’t go into memory, if they didn’t become targets of substantial focus, then even though I had them then I won’t be able to bring them back now.  And I say, “no doubt,” because when I am attending to my conscious experience now, from moment to moment, and I’m really concentrating on just this point, I realize that I’m aware of the feeling of the clicking keyboard keys under my fingers, then I notice the music I’ve got playing in the background, then I glance at my email tab, and so on.  That is, my moments are filled with miscellaneous contents.  I’ve mode those particular ones into a bigger deal in my brain because I just wrote about them in a blog post.  But lots of our conscious lives, maybe most, those contents come and go, like hummingbirds flitting in and out of the scene.  And once they are gone, they are gone.

Now we can ask the questions:  Do we want an AI to have that?  Do we need an AI to have that?  Would it serve any purpose? 

Bottom Up Attention

That capacity in us served an evolutionary purpose.  At any given time, there are countless zombie agents, low level neuronal complexes, that are doing discriminatory work on information from the sensory periphery and from other neural structures.  The outputs of those discriminators may or may not end up being the subject of conscious attention.  In many cases, those contents become the focus of attention from the bottom up.  So lower level system deems the content important enough to call your attention to it, as it were.  So when your car doesn’t sound right when it’s starting up, or when a friend’s face reveals that he’s emotionally troubled it jumps to our attention.  Your brain is adept at scanning your environment for causes for alarm and then thrusting them into the spotlight of attention for action.  

Top Down Attention

But we are able to direct the spotlight as well.  We can focus our attention, sustain mental awareness on a task or some phenomena, to suss out details, make extended plans, anticipate problems, and model out possible future scenarios and so on.  You can go to work finding Waldo:

gives a more detailed account of the evolutionary functions of consciousness. 

Given what we saw above about the difference in Prinz between conscious attention and short and long term memory, we can see conscious attention can be seen as a sort of screening process.  A lot of ordinary phenomenal consciousness is the result of low level monitoring systems crossing a minimal threshold of concern.  This, right here is important enough to take a closer look at.  

Part of the reason that the window of our conscious attention is temporally brief and spatially finite is that resources are limited.  Resources were limited when evolution was building the system.  It’s kludged up from parts and systems that we re-adapted from other functions.  There was no long view, or deliberate planning on the process.  Just the slow pruning of mutation branches on the evolutionary tree.  And it modifies the gene pool according to the rates at which organisms, equipped as they are, manage to meet survival challenges. 

Kludge:  Consider to different ways to work on a car.  You could take it apart, analyze the systems, plan, make modifications, build new parts, and then reassemble the car.  While the car is taken apart and while you are building new parts, it doesn’t function.  It’s just a pile of parts on the shop floor. 
But imagine that the car is in a race, and there’s a bin of simple replacement parts on board, some only slightly different than the ones currently in the car, and modifications to the car must be made while the car is racing around the track with the other cars.  The car has to keep going at all times, or it’s out of the race for good.  Furthermore, no one gets to choose which parts get pulled out of the bin and put into the car.  That’s a kludge. 

Resources are also limited because evolution built a system that does triage.  The cognitive systems just have to be good enough to keep the organism alive long enough to bear its young, and possibly make a positive contribution toward their survival.  The monitoring systems that are keeping track of its environment just need to catch the deadly threats, and catch them only far enough in advance to save its ass.  It’s not allowed the luxury of long term, substantial contemplation of one topic or many to the exclusion of all others.  Furthermore, calories are limited.  Only so many can be scrounged up during the course of the day.  So only so many can be dedicated to the relatively costly expenditure of billions of active neural cells. 

The evolutionary functions of consciousness for us give us some insight into whether it might be useful or dangerous in an AI.  First, AIs can be better planned, better designed than evolution’s brains.  An AI need not be confined to triage functions, although we can imagine modeling human brains to some extent and using them to keep watch on bigger, more complex systems where more can go wrong than human operators could keep track of.  An AI might run an airport better, or a subway system, or a power grid, where hundreds or thousands or more subsystems need to be monitored for problems.  The success of self-driving Google cars already suggest what could be possible with wide spread implementation on the street and highway systems.  So bottom up indicated monitoring could clearly be useful in an AI system. 

Top down, executive directed control of the spotlight of attention, and the deliberate investment of processing resources into a representational complex with longer term planning and goal directed activity driving the attention could clearly be useful for an AI system too.  “Hal, we want you to find a cure for cancer.  Here are several hundred thousand journal articles.” 

The looming question, of course, is what about the dangers of building mid-level attention into an AI?  Bostrom’s Superintelligence has been looming in the back of my mind through this whole post.  It’s a big topic.  I’ll save that for a future post, or 3 or 10 or 25.  

Friday, June 5, 2015

Evil Demonology and Artificial Intelligence

Eliminativists eliminate.  In history, the concepts and theories that we build about the world form a scaffold for our inquiries.  As the investigation into some phenomena proceeds, we often find that the terms, the concepts, the equations, or even whole theories have gotten far enough out of synch with our observations to require consignment to the dustbin of history.  Demonology was once an active field of inquiry in our attempts to understand disease.  The humour theory of disease was another attempt to understand what was happening to Plague victims in the 14th century.  Medieval healers were trying to explain a bacterial infection with yersenia pestis 600 years before the microbe, the real cause, had even been identified.  Explanations of the disease symptoms in terms of imbalances of yellow bile, blood, black bile, and phlegm produced worthless and ineffective treatments.  So we eliminate humour theory of disease, demonology, the elan vital theory of life, God, Creationism, and so on as science marches on.  

Eliminativism has taken on the status of a dirty word among some philosophers, a bit like people who are quick to insist that they believe women are equal and all that, but they aren’t “feminists” because that’s too harsh or strident.  

But we can and should take an important lesson from EM, even if we don’t want to be card carrying members.  Theory changes can be ontologically conservative or ontologically radical depending on the extent to which they preserve the entities, concepts, or theoretical structures of the old account. 

That is, we can be conservative; we can hold onto the old terms, the old framework, the old theory, and revise the details in light of the new things we learn. 

Here’s how the Churchlands explains the process. 

We begin our inquiry into what appears to be several related phenomena, calling it “fire.”  Ultimately, when a robust scientific theory about the nature of the phenomena is in place, we learn that some of the things, like fireflies and comets, that we originally thought were related to burning wood, are actually fundamentally different.  And we learn that “fire” itself is not at all what we originally thought it was.  We have to start with some sort of conceptual scaffolding, but we rebuild it along the way, jettison some parts, and radically overhaul parts of it. 

My point then, is that we must take a vital lesson from the eliminativists about the AI project.  At the outset of our inquiry, it seems like terms such as “thinking,” “consciousness,” “self-awareness,” “thoughts,” “belief,” and so on identify real phenomena in the world.  These terms seem to break nature at the joints, as they say.  But we should be prepared, we should be eager even, to scrap the term, overhaul the definition, toss the theory, or otherwise regroup in the light of important new information.  We are rapidly moving into the golden age of brain science, cognitive science, and artificial intelligence research.  We should expect that to produce upheaval in the story we’ve been telling for the last several hundred years of thinking about thinking.  Let’s get ahead of the curve on that. 

With that in mind, I’ll use these terms in what follows with a great big asterisk:  * this is a sloppy term that is poorly defined and quite possibly misleading, but we’ve gotta start somewhere. 

Folk psychological terms that I’m prepared to kick to the curb:  belief, idea, concept, mind, consciousness, thought, will, desire, freedom, and so on.  That is, as we go about theorizing about and trying to build an AI, and someone raises a concern of the form, “But what about X?  Can it do X?  Oh, robots will never be able to do X….”  I am going to treat it as an open question whether X is even a real thing that needs to be taken into account. 

Imagine we time traveled a medieval healer from 14th century France to the Harvard school of medicine.  We show him around, we show him all the modern fancy tools we have for curing disease, we show him all the different departments where we address different kinds of disease, and we show him lots of cured patients.  He’s suitably impressed and takes it all in.  But then he says, “This is all very impressive and I am amazed by the sights and things going on here.  But you call yourselves healers? What you are doing here is interesting, but where are your demonologists?  In 700 years, have you not made any progress at all addressing the real source of human suffering which is demon possession?  Where is your department of demonology?  Those are the modern experts who I’d really like to talk to.”    

We don’t want to end up being that guy. 

We should expect, given the lessons of history, that some of the folk psychological terms that we’ve been using are going to turn out to not identify anything real, some of them will turn out to not be what we thought they’d be at all, and we’re going to end up filling in the details about minds in ways that we didn’t imagine at the outset.  Let’s not be curmudgeonly theorists, digging in our heels and refusing to innovate our conceptual structures.  But on the other hand, let’s also not be too ready to jump onto to every new theoretical bandwagon that comes along.   

Wednesday, June 3, 2015

Turing and Machine Minds

In 1950, mathematician Alan M. Turing proposed a test for machine consciousness.  If a human interrogator could not distinguish between the responses of a real human being and a machine built to hold conversations, then we would have no reason, other than prejudice, for not admitting that the machine was in fact conscious and thinking. 

I won’t debate the merits or sufficiency of the Turing Test here.  But I will use it to introduce some clarifications into the AI discussion.  Turing thought that if a machine could do some of the things we do, like have conversations, that would be an adequate indicator of the presence of a mind.  But we need to get clear on the goal in building an artificial intelligence.  Human minds are what we have to work with as a model, but not everything about them is worth replicating or modeling.  For example, we are highly prone to confirmation bias, we have loss aversion, and we can only hold about 7-10 digits (a phone number) in short term working memory.  Being able to participate in a conversation would be an impressive feat, give the subtleties and vagaries of natural language.  But it’s a rather organic, idiosyncratic, and anthropocentric task.  And we might invest substantial effort and resources into replicating contingent, philosophically pointless attributes of the human mind instead of fully exploring some of the possibilities of a new, artificial mind of a different sort.  Japanese researchers, for example, have invested enormous amounts of money and effort into replicating subtle human facial expressions on robots.  Interesting for parties maybe, but we shouldn’t get lost up side tributaries as we move up the river to the source of mind. 

One of the standard objections to Turing’s thesis is this:  But a Turing machine/Artificial intelligence system can’t/doesn’t have _______________, where we insert one of the following: 

a. make mistakes.    (Trivial to build in, but inessential and unimportant.)
b. have emotions  (Inessential, and philosophically and practically uninteresting.)
c. fall in love  (Yawn.)
d. care/want  (Maybe this is important.  Perhaps having goals is essential/interesting.  It remains to be seen if this cannot be built into such a system.  More on goals later.)
e. freedom  (Depends on what you mean by freedom.  Short answer:  there don’t appear to be any substantial reasons a priori why an artificial system cannot be built that has “freedom” in the sense that’s meaningful and interesting in humans.  See Hume on freewill.
f. produce original ideas.  ( What does original mean?   A new synthesis of old concepts, contents, forms, styles?  That’s easy.  Watson, IBM’s jeopardy dominating system is being used to make new recipes, and lots of innovate, original solutions to problems.)
g. creativity  (What does this mean?  produce original new ideas?  See above.  Complex systems, such as Watson, have emergent properties.  They are able to lots of new things that their creators/programmers did not foresee.)
h. do anything that it’s not programmed to do.  (“Programmed” is outdated talk here.  More later on connectionist systems.  Can sophisticated AI programs do unpredictable things now?  Yes.  Can they now do things that the designers didn’t anticipate?  Yes.  Will they do more in the future as the technology advances?  Yes.) 
i. feel pleasure or pain  (I’ll concede, for the moment, that building an artificial system that has this capacity is a ways off technologically.  And I’ll concede that it’s a very interesting philosophical question.  I won’t concede that building this capacity in is impossible in principle.  And we must also ask why is it important?  Why do we need an AI to have this capacity?)
j. intelligence
k. consciousness
l. understand
m. qualitative or phenomenal states  (See Tononi, Koch, and McDermott)

I think objections a-h miss the point entirely.  I take it that for a-h, the denial that a system can be built with the attribute is either simply false, will be proven false, or the attribute isn’t interesting or important enough to warrant the attention.  i through m, however, are interesting.  And there’s a lot more to be said about them.  For each, we will need more than a simple denial without argument.  We need an argument with substantial principled, non-prejudicial reasons for thinking that these capacities are beyond the reach of technology.  (In general, history should have taught us to be very skeptical of grumbling naysaying the form of “This new-fangled technology will never be able to X.”  But one of the things I’m going to be doing in the blog in the future is caching out in much more detail what the terms intelligence, consciousness, understand, and phenomenal states should be taken to mean in the AI project context, and working out the details of what we might be able to build.   

But more importantly, I think the list of typical objections to Turing’s thesis raises this question:  just what do we want one of these things to do?  Maybe someone wants to simulate a human mind to a high degree of precision.  I can imagine a number of interesting reasons to do that.  Maybe we want to model up the human neural system to understand how it works.  Maybe we want to ultimately be able to replicate or even transfer a human consciousness into a medium that doesn’t have such a short expiration date.  Maybe we want to build helper robots that are very much like us and that understand us well.  Maybe a very close approximation of a human mind, with some suitable tweaks, could serve as a good, tireless, optimally effective therapist.  (See the early AI experiments with a therapy program.) 

But the human brain is a kludge.  It’s a messy, organic amalgam of a lot of different models and functions that evolved under one set of circumstances that later got repurposed for doing other things.  The path that led from point A to point B, where B is the set of cognitive capacities we have is convoluted, circuitous, full of fits and starts, peppered with false starts, tradeoffs, unintended consequences, byproducts, and the like.

A partial list of endemic cognitive fuckups in humans from Kahneman and Tversky (and me):  Confirmation Bias, Sunk Cost Fallacy, Asch Effect, Availability Heuristic, Motivated Reasoning, Hyperactive Agency Detection, Supernaturalism, Promiscuous Teleology, Faulty Causal Theorizing, Representativeness Heuristic, Planning Fallacy, Loss Aversion, Ignoring Base Rates, Magical Thinking, and Anchoring Effect. 

So with all of that said, again, what do we want an AI to do?  I don’t want one to make any of the mistakes on the list just above.  And I think that we shouldn’t even be talking about mistakes, emotions, falling in love, caring or wanting, freedom, or feeling pleasure of pain.  What these things show incredible promise at doing is understanding complex, challenging problems and then devising remarkable and valuable solutions to them.   Watson, the Jeopardy dominating system built by IBM, has been put to use devising new recipes.  Chef Watson is able to interact with would be chefs, compile a list of preferred flavors, textures, or ingredients, and then create new recipes, some of which are creative, surprising, and quite good.  The tamarind-cabbage slaw with crispy onions is quite good, I hear.  But within this seemingly frivolous application of some extremely sophisticated technology, there is a more important suggestion.  Imagine that Watson’s ingenuity is put to work in a genetics lab, in a cancer research center, in an engineering firm building a new bridge, or at the National Oceanic and Atmospheric Administration predicting the formation and movement of hurricanes.  I submit that building a system that can grasp our biggest problems, fold in all of the essential variables, and create solutions is the most important goal we should have.  And we should be injecting huge amounts of our resources into that pursuit.  

Tuesday, June 2, 2015

Building Self-Aware Machines

The public mood toward the prospect of artificial intelligence is dark.  Increasingly, people fear the results of creating an intelligence whose abilities will far exceed our own, and who pursues goals that are not compatible with our own.  See Nick Bostrom’s Superintelligence:  Paths, Dangers, Strategies for a good summary of those arguments.  I think resistance is a mistake (and futile) and I think we should be actively striving toward the construction of artificial intelligence.

When we ask “Can a machine be conscious?,” I believe we often misses several important distinctions.  With regard to the AI project, we would be better off distinguishing at least between qualitative/phenomenal states, exterior self-modeling, interior self-modeling, information processing, attention, sentience, executive top-down control, self-awareness, and so on.  Once we make a number of these distinctions, it becomes clear that we have already created systems with some of these capacities, others are not far off, and still others present the biggest challenges to the project. Here I will focus just on two, following Drew McDermott:  interior and exterior self-modeling. 

A cognitive system has a self-model if it has the capacity to represent, acknowledge, or take account of itself as an object in the world with other objects.  Exterior self-modeling requires treating the self solely as a physical, spatial-temporal object among other objects.  So you can easily spatially locate yourself in the room, you have a representation of where you are in relation to your mother’s house, or perhaps to the Eiffel Tower.  You can also easily temporally locate yourself.  You represent Napoleon as am 18th century French Emperor, and you are aware that the segment of time that you occupy is after the segment of time that he occupied.  Children swinging from one bar to another on the playground are employing an exterior self-model, as is a ground squirrel running back to its burrow.  

Exterior self-modeling is relatively easy to build into an artificial system compared to many other tasks that face the AI project.  Your phone is technologically advanced enough to put itself in a location in space in relationship to other objects with its GPS system.  I built a CNC machine in my garage (Computer Numeric Controlled cutting system) that I ”zero” out when I start it up.  I designate a location in a three dimensional coordinate system as (0, 0, 0) for the X, Y, and Z axes, then the machine keeps track of where it is in relation to that point as it cuts.  When it’s finished, it returns to (0, 0, 0).  The system knows where it is in space, at least in the very small segment of space that it is capable of representing (About 36” x 24” x 5”). 

Interior self-modeling is the capacity to represent yourself as an information processing, epistemic, representational agent.  That is, a system has an interior self-model if it represents the state of its own informational, cognitive capacities.  Loosely, it is knowing what you know and knowing what you don’t know.  It is a system that is able to locate the state of its own information about the world within a range of possible states.  When you recognize that watching too much Fox News might be contributing to your being negative about President Obama, you are employing an interior self-model.  When you resolve to not make a decision about which car to buy until you’ve done some more research, or when you wait until after the debates to decide which candidate to vote for, you are exercising your interior self-model.  You have located yourself as a thinking, believing, judging agent within a range of possible information states.  Making decisions requires information.  Making good decisions requires being able to assess how much information you have, how good it is, and how much more (or less) you need or how much better you need it to be in order to decide within the tolerances of your margins of error. 

So in order to endow an artificial cognitive system with an interior self-model, we must build it to model itself as an information system similar to how we’d build it to model itself in space and time.  Hypothetically, a system can have no information, or it can have all of the information.  And the information it has can be poor quality, with a high likelihood of being false, or it can be high quality, with a high likelihood of being true.  Those two dimensions are like a spatial-temporal framework, and the system must be able to locate its own information state within that range of possibilities.  Then the system, if we want it to make good decisions, must be able to recognize the difference between the state it is in and the minimally acceptable information state it should be in.  Then, ideally, we’d build it with the tools to close that gap.  Imagine a doctor who is presented with a patient with an unfamiliar set of symptoms.  Recognizing that she doesn’t have enough information to diagnosis the problem, she does a literature search so that she can responsibly address it.  Now imagine an artificial system with reliable decisions heuristics that recognizes the adequacy or inadequacy of its information base, and then does a medical literature review that is far more comprehensive, consistent, and discerning than a human doctor is capable of.  At the first level, our AI system needs to be able to compile and process information that will produce a decision.  But at the second level, our AI system must be able to judge its own fitness for making that decision and rectify the information state short coming if there is one.  Representing itself as an epistemic agent in this fashion strikes me as one of the most important and interesting ways to flesh out the notion of being “self-aware” that is often brought up when we ask the question “Can a machine be conscious?” 

McDermott, Drew.  “Artificial Intelligence and Consciousness,”  The Cambridge Handbook of Consciousness, 117-150.  Zelazo, Moscovitch, and Thompson, eds.  2007.  Also here: