Once again, I'm still typing from a mobile, so please pardon my spelling and grammatical errors. The tedtalk involving the phantom arm and phantom pain is pretty wild (not just because it was on House) but because it opens up even more questions about the nature of the brain, the mind, and the relation between the two. To speak quite colloquially, it seems like the brain is doing its thing completly independent of the mind. Yes, the brain does work in involuntary ways, but not in such an impressive way as that of the tedtalk. The brain is inducing discomfort and stress to the mind via a body part that does not exist. The brain does this when I stub my toe or fall off my bike, but the phantom limb situation would be like feeling the pain of falling off my bike when there is no bike. I get this odd feeling of an even more seperation of mind and brain, but they are interelated---so how do we account for so. Also, for whomever knows the sense datum argument, think about that in conjunction to the phantom limb talk. Do this and you get a feel of the brain being in a vat where all apperances, sounds, etc. Are just epistemically in your head/brain being sorted out by the mind. Imagine a tv that would think (but no other could hear, see, etc. The TVs subjective self). Ok that sucked but you get the point. It's all in the head ( yes attack on you putnam).

The old saying that a picture is worth a thousand words kept cyclically returning itself to my mind as I watched Ghost in the Shell. That is not to say, I find the written word is any less appealing and grand, it is merely a comment on a vivid vision detailing what may soon come to pass. It frames the world in a way, that only few have successful in grasping. Such a story jumpstarts your mind, leaving questions to simultaneously acts as cause and effect. Questions like these begin to flood my world like a hurricane, tantalizing questions, questions that before this point never existed to me, and past observations from my time on this planet begin to untangle in their complexities like speaker wire. One question I find most fascinating is called singularity theory. Singularity theory (in mathematics) deals with the study of points and sets, and how the unfold and behave over time. When applied to the context of artificial and human intelligence, there is a tested theory that projects that at some point in the near future human intelligence will be surpassed by artificial intelligence to create a new being more intelligent than human can comprehend. In turn this point will allow machines to create new forms of intelligence by their own design that surpass (by a huge margin) the constructs and ideas of human engineering. In early October of this year, a conference on this subject was held in NY City. This conference was called the Singularity Summit and brought together in one place many of the brightest minds on this subject, and so the dialogue continues. The way I interoperate the singularity is as follows, at this point in time (i.e. the singularity) machines will have the power and knowledge create machine superior to themselves, laying spark to a cycle of extreme intelligence; and portrayals such as Ghost in the Shell not only interpretations but forecasts of things to come.

Once again, I will talk about the tedtalk, because I find them entertaining and amazing. The idea that a phantom arm can be cured with mirror suggests that the conscience of a person's physical sense of his/her self is beyond the actual physical self and has a lot to do with the mental image of how a person views him/her self. In other words, a person does not view him/her self just as the physical "shell" of a body, but there is a sense of self awareness of what one believes themselves to be. For example, in my self awareness I believe myself to have two arms. Which is also true for most people, even those who have had their arm cut off. Having the physical arm cut off, cuts it from the physical body, however does not cut it off from the sense of self--which is apart of the "existence" of a phantom limb. Therefore people are more then just a physical body and the mind encompasses more then just abstract entities.

I agree with what Matt said above me. With our current technology, there really is no way to map the brain. And even is we could, we don't know enough about what is going on to make much sense of it. Taking an MRI of the brain and seeing that part of it lights up given some input is akin to saying the hard drive in a computer makes noise when you open a file. All we can do today is describe what we are seeing and make interpretations from that. I'm a computer science major and I happen to be taking a class in A.I. One of the topics we discuss are neural networks. They were also mentioned in Mindware a bit. I have heard it said that if we have enough computing power we could mimic the human brain in a computer. While this is true, they miss the point that we also would need a complete understanding of the brain to mimic it. Unfortunately, we are not close to either of these goals. Our fastest computers get bogged down with even the simplest and smallest brain and neuron simulation. And these simulations are for the most part, poor, simplified representations of the real thing. I feel like both humanity and technology have a long way to go before we are able to comprehend ourselves in that scope.

Yo man, so here’s the deal, if we are aging mentally but not physically can we call that aging at all? Is this one of those cases that we learned about in logic called the "if and only if", were we are not aging if we are not doing so both mentally and physically. In the book that we just read there are two ways it seems to speed up mental aging with little or no sacrifice to physical aging. Through the use of E-therapy, in which the stimulation of some gland forces evolution within minutes instead of centuries. Or, through the use of the drug Chew-Z, were your mind evolves in a world created by you for as long as you would like while your body is trapped in “reality” not moving forward through time. So if I gave a baby Chew-Z the baby could theoretically have the intellectual capacity of a 50 yr old within a second still maintaining the body of a baby. WTF mate? In any case would this be a proper way to feed a societies drive for intelligence? I think it would but under the strict regulation of the government which would of course be abused. So maybe It shouldn’t, maybe it should just be available to me, leaving the rest of you to scurry about with the lack of intelligence that is found in a 21st century human.

Good point Mike. But, would the baby develop normally, without the influence of others? Or would it speak some weird language, have odd thought processes, or what? (throws hands down, palms facing the ground and shrugs shoulders). I think there is a question of nature and nurture with that question. Furthermore, I do not think it is aging to learn. Aging in my opinion is a purely physical phenomenon. Old age doesn't ruin brainpower in itself, it is diseases of the brain material that interfere with thought in old age. Alzheimers, dementia, senility, all of these things seem to me to be fleshly; not degeneration of the mind as it is separate of the brain. I think Chew-z is quite awesome. For now.

Accodmidation is a concept that one takes in information and molds it to his or her personal self. Now with a neural network we can see that it learns after many trials by strenghting the "neural" connections (as percentages). We can also assume that if a preworked neural network was eastablished it would be able to accomidate new knowledge. But is the artificial neural network accomidation the same as human accomidation? Animals accomidate and their accomidation differs from that of humans. Also accomidation differs between individuals. So, how does one draw the line? Or must we concede that accomidation has two different meanings per system? That human actions do not paste perfectly onto another system. Could we not have an avenue where neural network accomidation is defined as Z and human accomidation defined as G. They may have the same undelying foundation but differ on the surface. Also, how do we incorporate the postmodern idea of agency into A.I? Dunno, just talk.

To think of humans a a complex neural network like that of the fish who are constantly becoming more efficent eaters, we must first discover what it is that humans are becoming better at. In the example of the fish the top half of the population survived to create the next generation, whereas in humans though the ammount of resources stays the same or even lessens, the population continues to grow. What we seem to become consistantly better at is sustained life, an increase in numbers, and an increase in consumption which seem to be a self defeting neural network.

I think that perceptual adaptation is very interesting. Clark cites studies where participants wear lenses that flip the world upside-down, and after they get used to the lenses, the world appears to be normal again. This is wild. The findings of the studies seem to say that, if we can get used to the world being upside-down, we can get used to any changes whatsoever. However, he goes on to describe other studies where participants wore lenses that shifted the scene off-center, and the participants had to throw darts or throw a baseball at a target. The only hand that adapted to the perceptual shift was the dominant hand, and that only in over-arm throwing. The non-dominant hand did not adapt to the shift, and neither did the dominant hand adapt while under-arm throwing. What accounts for this? If we are right-handed, does a different part of our brain develop than if we were left-handed? And why does over-arm throwing adapt while under-arm throwing does not?

At the beginning of chapter six, Clark describes how crickets hear and how termites nest. Crickets do not have ears like we do; they have two ears, one on the left foreleg and one on the right foreleg. The ears are joined internally to two openings, called spiracles, on the top of its body. So, sound reaches it directly and indirectly. Clark says that the robot cricket emulates the real cricket well. The robot cricket does not have the complex internal system of a real cricket, but it is able to recognize the sound of its own species and move toward it. When termites are constructing their nests, they roll up mud balls and insert their scent into it. They deposit the balls wherever their scent is strongest, thus making a nest. Clark says that robot termites are also capable of doing the same thing. Clark concludes that complex systems found in real animals are not needed to simulate the same activity in robots. This is interesting. Does this mean that the robot cricket or termite is better than the real cricket or termite, because it is simpler?

I think that the most interesting part of chapter seven is where Clark talks about learning to walk. He says that from birth to two months of age, babies make stepping motions if held in the air. However, from two to eight months, that phenomenon goes away, unless the baby is being held in warm water, or being held on a treadmill. The phenomenon reappears again at eight-ten months of age, and the baby starts being able to walk on its own at around one year of age. One explanation for the disappearance of the phenomenon may be that the leg weighs too much for the baby to pick up. An explanation for the treadmill initiating stepping is that the stretched-out leg acts as a spring, and the treadmill helps it to recoil. This suggests that the right mix of factors have to be present for a baby to learn to walk.

In chapter 4 a biological criticism of first wave connectionism is discussed, and I thought it was a good point that the input and output representations of the experiments are very reliant on what people already know and expect to happen. It could be argued that this is a problem not just with AI but with the scientific field in general; every experiment is subject to the individual state of mind observing it and the scientific paradigm of the time. However, I think that this issue is especially problematic when attempting to prove that an artificial process is learning in the same way that we do. When setting up input data for the process to work through, we are (inevitably) going to draw from data which we have already had to work through- proof to ourselves that we are intelligent entities. We will then compare the output data to what our natural reactions have been. The problem is that this overlooks the long biological process which has led to our current state of intelligence, not just in our individual lives but in the life of our species. It seems naïve to think that the artificial processes will be able to acquire a comparable intelligence to ours simply by working through pre-conceived problems.

Alright, chapter 6. I really sort of appreciated Clark's treatment of the apparent different kinds of problems and different methods of solving them. It seems to be an interesting feature of biology, as he presents it, that would employ these very computationally different systems across the spectrum of life depending on, it seems to me, the amount of information naturally present in the situation. So while it would make sense for something like a termite to take advantage of a lot of external world-present information, it wouldn't rule out the fact that perhaps humans need a completely different system to solve problems where we don't have such an abundance of information at hand. The proposal that rationality is a kind of coping mechanism for a real lack of physical data is an interesting possibility to me that seems very likely.

Chapter 5 begins with giving the reader a fairly decent strategy for approaching the notion of any sort of artificial intelligence. The model given begins with a analysis of the task, then moves to creating a naming of potential variable associated with the task and a list of the mechanical steps for doing the initial task, and finally actually proceeding to carry out the given task. This seems to be fairly detached and abstract from the way it seems our brain works. As Carl Sagan said, "the brain has its own language for testing the structure and consistency of the world." I am not convinced that this sort of task management even comes close to spanning the gap between computational intelligence and whatever type of intelligence is associated with us humans.

In chapter 5 an “interactive vision” is discussed which discredits the simple “sense-think-act” process thought to be sufficient for visual interaction with the world. I particularly liked the second and third claims of the interactive vision account; that motor routines can be called upon to make better sense of visual input, and that real-world actions play an important role in the computational process. For instance, when I see, say, a stage with a curtain drawn across it, I can comprehend what’s in front of me based on my past experiences. A process which merely takes in visual input would register only the 3D data; that of a raised floor and what seems to be a wall of fabric. Given my complex history of experience, I know not only what the raised floor is for but also how the wall of fabric (the curtain) is constructed and what is likely behind it. It could be argued that these are mere guesses on my part fueled by likelihood, but these guesses give me a leg up on a simple processor, because in most instances they will pan out as correct. To give a robot the same understanding of visual input as us, it must be able to reasonably draw from its past experiences.

Chapter 5 begins by focusing on the history and idea of artificial intelligence. David Marr, the main focus of the history of artificial intelligence in the book, explains in three tasks how receiving and understanding input/information into the brain works. The first task is a “general analysis”, information is transferred (in a 2 dimensional setting) into the brain (where it altered into a 3 dimensional setting). Task one is the key stage for input. Next, in task two, is where the information is “represented” of what task it will perform and the way it should go about performing the task. In task three, the information/input and what task it should perform is understood, at some level. The next step into making artificial intelligence is to find a way in which one can build a machine that would be able to work through these three tasks on its own. The book then brings up the importance of the biological brain. Back in the 1980’s the biological brain was hardly focused on because everything seems to focus on “the computational and information-processing stages of the brain”. However nowadays, it seems as though the physical brain plays a huge part in the equation. “Biological brains are the product of biological evolution”. The way in which the brain has evolved biologically holds great importance, especially when trying to create artificial life. To further explain, it is easier to start where evolution left us instead of starting from nothing. In order to create an artificial brain, it is important to understand how the real brain works. Clark continues describing other ways in which input works, such as the “sense-think-act” cycle. He then leaves the reader with a feeling that there is still no clear way to see how input works in the biological way combined with cognitive organization.

If there's one sentence that best sums up the implications of the content of chapter 5, I think it's, "The brain is revealed not as primarily an engine of reason or quiet deliberation but as an organ of environmentally situated control." (p. 95) For philosophers, the ever-present temptation is to think that we can figure it all out with reason or quiet deliberation, but reason is just a small, necessary but not sufficient part of who we are. On another note, I don't feel the question posed on p. 100, "How might large scale coherent behavior arise from the operation of such an internally fragmented system?" is as menacing as Clark makes it out to be. I understand the motivation for the question, but besides the promising answers Clark gives, I think it's also worth considering an existentialist approach that denies that our actions are really coherent in the sense we want to believe them to be.

David Marr's idea of a three level computation system seems to me to be far to abstract to be a valid representation, even in a general sense, of how biological brains work. The idea that kept coming to me while reading chapter 5 was that of neural networks. We went over them a little during lecture, but the basic premise is that there are nodes, and edges connecting them. Each edge has a weight and these weight are modified until the network produces the correct result. The startling thing is that even for simple networks, these final weights can seem random. Our brain seems to work in a similar way. Computers are very deterministic, in that even at the very small scale, each transistor, each circuit, has a well defined purpose. With a neural network, whether artificial or biological, we have no understand of the connections between nodes. We cant look at a network and say "This node computes this value". We just know what the final result is. In the same sense, we cant look at our brain and know exactly what neurons control what functions. Clark talks about this on page 96, saying that how the circuits work is "mere implementation detail". Unfortunately, if we wanted to create an artificial representation much of this implementation would have to be know. And again, the final resulting network would, to us, look no different than a bunch of random weight that happen to do the right thing.

Chapter 6 is presented as an exciting alternative take on understanding our minds by looking at some interesting advances in robotics that depart from any representationalist picture. As Dennett posed the question, "Why Not the Whole Iguana?" meaning why stress isolated aspects of advanced cognition when taking a broader view might get us answers to those higher level questions we sought, plus a whole lot more? The robot examples seem to be making a lot of progress, and Clark talks about "a complex interaction among brain, body, and world, with no single component bearing the brunt of the problem-solving burden." (p.106) But in the sobering discussion, Clark brings us back down to earth by pointing out that these advances in robotics are nowhere near the advanced cognition we originally departed from. It was a good idea to depart from it and see where we could get, but it is obviously still important to us, and so if we haven't moved back in that direction the departure into robotics hasn't yielded what we wanted. Clark does a good job getting us (or me) excited about the what robotics have taught us about nature, but then reminds us of the reality that we are still far from the kind of artificial intelligence we originally wanted.

The most interesting claim in chapter 7 is the Radical Embodied Cognition Thesis, which rejects computational and representational explanations of cogntion. Thelen and Smith, some of the strongest supporters for this radical theory, insist that dynamic system explanations cannot be reduced to representational computations. Clark does a good job mediating between the old way and this radical new way. He points out that connectionism modified traditional computationalism (the progress we made through chapter 4) in a good way, and that dynamic systems may continue this progress. He summarizes this position in the paragraph about "dynamic computationalism" on p. 135. He returns to what was the big question in chapter 6: how do we get dynamical systems to solve more abstract or higher level problems? The Radical Embodied Cognition people are committed to the position that "you do indeed get full-blown, human cognition by gradually adding "bells and whistles" to basic (embodied, embedded) strategies of relating to the present at hand." This can work, Clark says, but only if you undestand it in a way that allows for the "vision for action versus vision for perception" distinction Clark explains. But this renders the REC (or "cognitive incrementalism" as Clark says) "insufficiently precise and empirically insecure." Like the robotics in chapter 6, cognitive incrementalism has a lot of explaining to do. This is not a reason to reject it off the bat, but I like Clark's strong caution towards embracing it.

I forgot to post this earlier so I figured, why waste the thought?
I find that one of the biggest philosophical questions is not only how do we obtain knowledge, but what exactly is this knowledge? How do we understand what makes up knowledge and how do we obtain it. Many have tried to answer this by simply saying that we have innate knowledge that we are born with that we build upon in our life. I, personally, am more of a fan of the idea of adventitious knowledge in which case we learn from the society that we have been born into. With that said, were I’m going with this is that through the neurons within our brains maybe it’s a mixture of both. Maybe instead of innate knowledge it’s more of innate “laws” by which our brains learn to function as a kid leaning which neurons to strengthen and which neurons to let go. If this is the case then the question that I proposed last week of; if we gave a baby Chew-Z could it have the intellectual capacity of a 50 yr old, would be a simple no. This is the case if neurons continue to strengthen and weaken as or brains learn new things making our brains subject to physical maturity in order to gain knowledge.

From the chapters that we had to read I got a feel of a desire within cognitive science, robotics, etc. to move away from a framework that is modeled on how the mind, cognition, and processing works to one that is centered on how does the physical interact with the enviornment. We can see this in our talks of modeling robots and such off of evolutionary processes: for before sentience it was not cognition interacting with the enviornment, but the physical. Take the cricket example in the book; the cricket is not doing anything special (don't think it can really, not higher processing brain). It is simply materially interacting with the material world. Male cricket makes sound, female crickets auditory reciports pick up on it and the neurons adjust to find the male. Physical interacting with the physical.

This does seem like a very good way try and build a robot, one that is more about the material world and its interaction. Like Herbert, the robot that picked up can but scanning the enviornment and interacting with object. For if we can model HOW things work and interact with the enviornment, than we may be able to replicate it. But that seems like a shallow statement, we don't want to replicate a cricket looking to mate, but we want to examine how a creature does an activity and try to impose that onto a practical active robot than can do things accurately and to one's desire. But I could not seem to put my finger on the point in the text where the mind of the human fits in with all of this. OK, we can see the material world of the cricket interacting, we can examine how the body walks using physics, etc. but where if the human mind. From the book and class we can chop down certain activities that we believed were actually being done by the mind (lets say where the brain induces phantom pain, not the mind, or how the leg's locomotion may be a product of physics) but what about comprehension? or thought? or language? or the application of the three? It still seems like, to me, that a purely mechanism in motion, like that of the cricket or legs won't really allow one to model the mind--perhaps the brain, but I'm being liberal by stating that.

The section on using the bonobo studies to measure higher order judgment seemed awkward to me. Most of these kinds of studies do because one is expanding upon the animal's capacity to do such an activity that is outside of their natural enviornment. Yes, this may be irrelevant, but one should not be mislead into thinking that because the bonobo can match shapes, etc. means that is what the brain of the bonobo was meant to do. It is more like coopting--where one can take a knife and use it to screw in the doorknob. One thing serves many different functions. This point, of co-opting, I feel is of much importance to robotic building because we see this all the time in nature. A good example of the human hand, was the human hand naturally selected for after the split with other higher primates for grasping, tool making, or for sexually stimulating a partner? The answer is that it could be all three---or neither. These could just be (well accept for grasping i guess) just co-opted features of the hand. But studying this it seems like one could produce, at least for robots, robots that are multifunctional in their limited resources of interacting with the material world. Example, the cricket. The legs are not JUST used as devices that permit the ability to hear potential mates--the legs also allow the cricket to proceede to the mate. yes, this does seem like a complete tauatology, but the point is that by examining co-opting one can get a different array of how an artifical life can exploit its enviornment in more precise and efficient ways.

I was pondering today's lecture about the cricket and found it fascinating that nature could solve such a complicated problem with such a simple solution with only two neurons. As a programmer, it is very easy to try to think of solutions to problems from a high level perspective and this was a humbling example of how one can learn to solve solutions in a simpler way by examining how a naturalistic framework would solve it. That is, how the universe, having no intelligence, comes to an evolutionary solution to a very complicated problem. I wonder if this is how we should look at cognition? Are we trying to solve the problem of consciousness, and other complex cognitive problems, the same way we tried to solve the "cricket" problem? By adding over complicated explanations and systems?

I found chapter 5 to be very interesting. The interactions between cognition, perception and motion are intricate and unintuitive. This makes me think that a haphazard assembly of different systems that perform specific functions may yield more interesting information about potential robotic capabilities than constructing a robot to perform a specific function. It also causes me to consider the possibility of combining genetic algorithms with crude robotic systems to essentially evolve a robot, like how that one robot learned to walk. We could start by training the robot to move using a GA and then from there see what the robot could be trained to do using a set of basic functional systems. Such an experiment would be very exciting to me because it could yield results that were not even imagined when the robot was constructed. It also curious in that the robot learns from interacting with the physical world and that the experimenter learns from the robot's achievements which are outside of the intentions of the experimenter and in this sense come from the real world and not one of the experimental laboratory setting. The only challenge would be creating a general enough reward system for the GA to engender unanticipated, but advantageous capabilities in the robot. Now that i think of it a GA with self programing reward systems, based on learned behaviors and physical environmental rewards, could be an suitable model for the soul and the evolution of our neural networks (i think i want to write my paper on this).

between the lectures and readings i has been really intrigued by bio-mimicry. It seems like such a great way to engineer or look for solutions. Most of the work has been done and rigorously tested for thousands of years. This process of thinking can potentially extend to many fields. I'm a biology major and recently at my work we were discussing bio-fuels and how to best break down plant material to better access the abundant cellulose. After reading about the current processes which require heavily on chemical and heat a coworker asked how does nature do it? Such an obvious question to ask. We had been trying to reinvent the wheel or reverse engineer the solution. When we looked at how nature accomplishes the analogous task we find an amazing array of enzymes and microorganisms that sinergize to accomplish an integral task in nature.

What i got from chapter 6 was that although it is impressive how much can be achieved with simple sensors and interaction with the environment we need to appreciate representations and models for how they allow you to in a sense interact with what is not present. Planning and anticipation are integral cognitive abilities, and have been proven to influence our perceptions of the world. Our memory is a perfect example of how we use abstract representation to in a sense experience a situation without the real world creating it at that moment. It seems that to make artificial life we would need it to be able to create internal representations. How these representations would be created and understood would be a challenge. For example think of the mix of sensory, emotional, conceptual/ historical information that combine to form a memory of an event. I think that a connectionist approach would best serve such an odd combination of information types because the weighted system offers a way to generalize the influence of these different information types.

This is really the first chapter in Clark that has caught my interest, or perhaps made the most sense to me. Reading in Chapter 5 about genetic algorithms was a new idea to me. These bit strings encode solutions and then are chosen by their performance to either be “bred” or let die. This idea allows nature to decide which bit strings will evolve with the highest functioning, while the others are weeded out and become extinct. Allowing a machine or robot to evolve itself based on its surroundings follows a human evolutionary pattern. It now seems obvious how a program could learn to adapt to nature in a way more efficient than we have. It is not necessary to rely on intelligent design, but only to rely on many generations interacting with their environment within the limits of their “bodily” functions.

One aspect of artificial life shown in chapter 6 is the work done on flocking. A computer program was written that simulated a group of boids, modeled after birds that were required to only follow three rules. The rules were based around interaction with the rest of the flock. Amazingly, not only did the boids resemble the sort of behavior we see in a group of flocking birds, but they even parted and re-grouped when faced with an obstacle in their path. This work done showed remarkable behavior in patterning. Similarly, the robots modeled after termites further show the ability for A.I. to problem solve not by being designed individually with extreme intelligence, but in a group activity with external factors leading the response. While these fears are astonishing, it is noted that much behavior of humans is done without a physical of environmental limitation to guide the action. These A.I. are lacking that cognitive capacity, the capacity to respond and detect a non-nomic property. These examples are categorized as emergence as collective self-organization. The collective system performs an activity without a “self.” This activity appears to be being performed by the “self” but is really more explained by the interaction with its physical properties and an external environment placed upon it. The “self” works because it is a group that acts in a pattern and continually falls into that pattern as more members of the group influence their neighbor to follow in the group direction until the group becomes a mass collective-self.

I was interested in chapter 6 when the flocking behavior of animals was said to be almost perfectly depicted by boids following a set of basic rules, and the question was raised as to whether or not the boids were truly flocking. Undoubtedly animals who flock are also following a simple set of rules which have been programmed into their minds through the process of evolution, but it seems as though these rules (such as stay near the mass of others, match speed with others, never get too close or too far from your neighbors) are merely tools used to accomplish the overall goal: to flock for survival. In this way the definition of flocking holds a double meaning; 1)to follow a set of rules (the functional basis of flocking) and also 2)to move in a way which meets certain criteria of survival (such as confusing predators). It seems that the boids are missing the second part of this definition of flocking. While they are showing flocking behavior by exhibiting the rules, they are not truly flocking because their movement is not aiding in survival, which is the true essence of natural flocking.

I think Marr’s computational approach to understanding cognition in chapter 5 is a better representation of the way humans use logic to go about solving problems than it is an effective way to describe how nature has developed systems to solve similar problems. It seems natural that when a human goes about tackling a problem, that person first identifies the task, develops a corresponding algorithm, and lastly implements that algorithm. Just because this is the process we use as cognitive beings to understand nature doesn’t necessitate that it is the same process by which nature solves problems. It seems that we’re imposing the limitations of our knowledge on natural systems. That or we’re assuming that all such systems have this capacity to reason. If we reject this notion, the lines between task, algorithm, and implementation are substantially blurred. Take, for example, your eyes. Would not the computational analysis of information also be the implementation of that system’s function? In this sense, it is not only difficult but impossible to distinguish between what constitutes task, algorithm, and implementation in this model.
In stepping away from Marr’s approach, scientists have begun mimicking biological means of engineering. In order to truly understand this process of incremental tweaking however, we must first expand our means of comprehension. If Marr’s task/algorithm/implementation structure accurately depicts the way we rationalize, then maybe it is our very system of thought that needs to change in order to fully comprehend the process by which cognition functions.

In response to Erin's comment above, concerning flocking qua flocking, I think the statement "they are not truly flocking because their movement is not aiding in survival, which is the true essence of natural flocking" assumes too much on part of the natural world. We as humans experience a richly sensible world. Geese, ducks, and other flocking animals probably do not have all of what we have. In fact, I assume it would be safe to say that they have a very different idea of what the world is like. From this, I think we can draw the conclusion that the animals, much like the boids, are simply following rules. There is no consideration of whether or not survival will happen. Natural Selection rewarded birds with strong tendencies towards flocking with longer lives, and survival just happened. So here is my distinguishing question: If we introduced a predatory boid into the simulation, would it then be flocking?

Vincent had a groundbreaking insight a little while ago that impressed me deeply; he made a comment about the “of courseness” of mechanistic responses to the physical world, and how asinine it was when building robots to attempt to start with cognition. Nature, after all, started very basically with mechanical mobility ruled by nothing more than a few neuron-like cells firing when stimulated. It seems to me as though the cog scientists in charge of building robots in order to further understand cognition should start with this notion of mechanistic interaction with the environment, and once this has been mastered a little further, then we can start introducing more cognition-based interactions.

So, What I am going to start rambling about is the craziness that is involved with figuring out how we figure out. We started out trying to compare our brains to the (hope I spell this right) Turing Machine, were direct causal effects took place to give us the end result of 2+2. As our semester has progressed we have started to see many other options to how our brain might actually work.
Neuro networks that calculate the percentage any given network has through positive reinforcement is a very good option for conceptualizing how our brain works. This is so considering that we, or at least I, can visually see something like this being able to happen and the ability of our brains to do something like this is not that far fetched. Also, when we start to compare this to the creation of things and we take the idea of creation being an overflow from one sensory input to another, this neuro network could support this idea through the cross connections of axons leading to accidental transmission of electrical pulses, “thoughts”.
I want to move more attention to what was discussed in chapter six, with the idea of emergence. How termites build their homes was explained by the book as deposits of a chemical every time a termite puts down a dirt clod. The other termites “smell” this and then decide to deposit there dirt clods wherever they chemical scent is the strongest.
Anyways, the reason that I’m writing about this is because this almost seems like the original thought process that we originally talked about. So it seems like animals go through simple causal factors that help them in determining what they are going to do while creativity comes from a short in neurons. So since humans are arguably more creative than a termite it would follow that humans have more electrical shorts in their brains. Maybe humans are a deformity.

In chapter six, Clark discusses robots and artificial life. He starts off by talking about how crickets can tell their different species apart by the frequency of the song they can produce. Crickets can also tell which direction the other cricket is by the song. When robotic crickets were made, the robots could pick up the songs from other robots, however they could not tell the difference between the different songs (i.e. the different species) nor could they tell the which direction the song was coming from. This shows that we can study nature and see the affects, and they ways in which simple tasks are done. However, at the same time it shows that we can take the information we have collected and not come up with the robot we intended to create. There is a good probability that crickets are not self-aware, so there is a good probability that our faults on creating sure a robot do not stem from the robots not being self-aware, but on the idea that we may need to take a larger look at the problem. As in we may need to look into the "brain, body, and world", or look at a larger chunk of the problem. For example, there may be exterior elements working on the interior, causing the interior to work a certain way.

In chapter seven, Clark talks about the dynamics. He starts by naming three cases. These cases, show that humans use both the mind and the biological, as one, to work through certain tasks provided. In a way, the biological introduces the inputs, or the "problem", into the mind; there the mind can evaluate the input can therefore can product an export or solution. However, it is unclear how much of the mind is actually conscious of what it is doing. For example, (for the most part) people do not have to consciously think about walking in order to walk, however one may have to be conscious of the path being walked (especially if it is unfamiliar). At the same it, Clark is not implying that with difficult tasks the mind is fully conscious, or even partially conscious. He ends with stating that real-time response and sensor motor coordination are key players for the mind, however their importance is not yet known.

I have been pondering something for awhile. I have noticed that nature solves problems as simplistically as possible, and we as humans always try to solve problems from a very high level. The question I have been pondering is "is this bad?". It is true that we can learn a lot from nature by studying how it solves problems, but do we really need to start at such a low level? Or can we "skip" billions of years of evolution and start building artificial life at a much higher level. I would think it would still be possible to replicate the "algorithms" that implement cognition if we could figure out what and how exactly the algorithms are implemented, and what they are.

The discussion on Monday about emergence was extremely interesting to me, because as I listened I began to think about life, and the possibility that life could be just an emergent process. This of course would be a naturalistic explanation, and it makes sense in the sense that of all the universes that exist(assuming the multi-verse is real), this is the only one, or just one, that allows life to be an emergent behavior, something that just occurs through random chemical processes. Though this doesn't explain why this emergent process can't be reproduced, maybe the conditions required for this emergent process are just extremly delicate and we have yet to find their exact quantities and conditions that are required to allow this emergent behavior to arise.

There is a discussion in chapter 7 in which a cat who loses a leg will quickly learn to gracefully walk on the remaining three. Pollock claims that this adaption ability does not come from an operating system of great intelligence, but rather originates from many different functioning systems working elegantly together inside the biological animal. These systems may include the physical properties in the legs and brain, a history of learning experiences the animal has been through, and even the particular nature of the animal such as energy, curiosity and a powerful will to survive. This point brought me back to my earlier discussion of the Chinese room; I wondered about the author of the book in the room, and made the point that this information must have come from some ultimately intelligent source. This shows how different a cat re-learning to use its legs is from the room- the cat has no system of superior knowledge orchestrating it, rather, its adaption ability originates from sources deep within its own working system.