Brooks argues that instead of AI having an influence on computer architectures, the converse is true, that architectures have had a strong influence on our models of thought and intelligence. Particularly from the Von Neumann model of computation.

Brooks avoids a formal definition of intelligence explaining that it can lead to philosophical regress. Instead, “therefore I prefer to stay with a more informal notion of intelligence being the sort of stuff that humans do, pretty much all the time.” This is nice, but unfortunately can lead to wildly varying interpretations.

The classical account of AI is built from the top-down, starting with thought and reason. This naturally leads into abstract approaches to cognition such as knowledge representation, planning, and problem solving. Brooks’ approach aims start at the bottom level, starting with physical systems that are situated in physical environments: robots. This approach is aimed to reflect the evolutionary path of human development. The running comparison is between artificial intelligence as compared to robots and biological systems.

Early approaches to robotics made use of robots with onboard computers that would form models of their environments and then form plans to act in relation to them. This set of models is refered to as the Sense, Model, Plan, Act framework, or SMPA. This approach was influenced by the traditional AI models, and was not very successful. Brooks explains that the assumption behind SMPA was that once the problem of performing tasks in a static environment had been solved, then the more difficult problem of acting in a dynamic environment would fall into place. This echoes the claims of Newell and Simon in their assertion that once reasoning were solved, then issues of emotion, human interaction, and the like would naturally follow.

Around 1984, roboticists realized several things of importance:

• Most of what people do ordinarily is neither problem solving nor planning, but activity in a benign but dynamic world. Objects are not defined by symbols, but by interactions. (Agre and Chapman)
• An observer may be able to describe an agent’s beliefs and goals, but those do not need to be reflected in the agent itself. (Kaelbling and Rosenschein)
• In order to test ideas of intelligence, it is necessary to build agents in the real world. Agents can exhibit behavior that appears intelligent even without having internal data structures. (Brooks)

This new approach to robotics signifies a significant departure from traditional AI. It also contains several new values for how to think about intelligence and robots. The approach values both situatedness and embodiment. Agents are present in the world and interact with real situations as opposed to abstractions. The intelligence of robots is given from their repoire with the world, as opposed to from abstract reasoning. Robotics is also characterized by emergent behavior from the component elements.

Brooks compares the systematic behavior of both computers and biological systems. Biology operates in parallel, with low speed. By comparison, AI systems run on Von Neumann machines, which have serial calculations, large spaces of memory, and narrow channels with which to access that memory. In AI research, there is a strong trend of associating the current models of computation as the pinnacle of computational technology. This is perhaps a straw-man argument, but it is reflected in the way that human problem solving has been associated to computational models. Brooks argues that this is extremely foolhardy.

Particularly, Brooks is critical of the approach of Turing that cognition and computation are independent of embodiment. Turing’s examples of computational intelligence also encouraged disembodied activities (especially chess), the prevalence of which has continued in AI research. Brooks continues to describe several AI movements, each of which have met with little success.

Brooks gives an overview of some biological perspectives of how cognition and intelligence work. Early appraoches to biology, notably ethology, were heavily influential in early AI. These approaches propose hierarchical models of behavior selection, but have largely been discredited by modern evidence. Particularly, modern approaches to psychology, especially neurophysiology, suggest a flexibility present in the workings of human brains which is dramatically contrary to ideas used by AI, notably the notion of the brain as a knowledge storage system, and heirarchical models of cognition. Brooks argues that the models used by traditional AI are not reflective of how human brains are built.

Brooks’ critique of AI is severe: He asserts that it is necessary to focus on robots that are situated within physical space. The robots should not hold internal representations, but instead use the world as a model. Systems would also need to work within the constraints of its physical components. Robots are not only present in physical space, but, like real bodies, they are also imperfect and subject to limitations, deficiencies, and drifting calibrations.

While these arguments are not particularly helpful for the purpose of developing a simulation of a cultural world, they are important for considering ways to think of agents within a world. Brooks’ focus is on intelligence, not in the sense of the metaphysical, or the ability to solve complex abstract problems, but rather in the empirical sense. Intelligence is determined by interactions within the world, and by the eye of the observer. In light of this, his essay provides an anchoring to the empirical and demonstrable. Even in the case of a simulated world, agents are still situated (though in a significantly reduced sense), and thus their intelligence is still determined by their engagement with that world.

This article elucidates some material which later appears in Turkle’s book, The Second Self. The subject of this essay is the culture of simulation and its effect on pedagogy. The article is tied between the competing ideas of a computer as a creative tool versus an appliance, and between the role of education as teaching mastery or usage.

Turkle’s article was published in 1997, which gives it some historical distance from the current trends in education, but the state of affairs in 1997 seems to strongly resemble the state of affairs now, at least as pertains to simulation. I think that modern education has become overtaken by the cultural effects of the internet and mass information.

Computer education in the 1980s relied on teaching students programming, and using the metaphor of the computer as a machine or calculator. Educators aimed to portray the internals of the computer as something to be understood and manipulated. This moment was seminally infleunced by Seymour Papert’s Mindstorms. This style of thinking culminates in an example of an exhibit in the Boston Computer Museum, of a computer visually blown up, so that children can see the insides.

Gradually, there is a shift in the way that people think about computers, which is heralded by the desktop metaphor of the Macintosh user interface. Instead of seeing transparency as looking into the lower operations of the computer, the role of transparency changes to the immediacy of metaphors and interfaces. Transparency becomes the value of being able to look at a computer screen and immediately see a document, a spreadsheet, or a desktop.

This shift brings in an educational change, where educators become motivated to teach the computer as an appliance, and instruct children in the operation of programs. Instead of being taught how to build machines, children are being taught to use them. This is partially motivated by the prevalence of computers in the workplace and as a means of training students for future employment. Along this way, students begin to get used to thinking of the computer as a black box, with the internals hidden and unknowable, rather than something to be learned and mastered. The pinnacle of this new moment is simulation, which is all about black boxes.

Turkle gives an example of a child playing SimLife, who does not attempt to ask what the meaning of the terms in simulation are. Instead, he understands things functionally. This is depicted with some degree of terror. Turkle fears that simulation shuts down questions rather than answering them, but I disagree, and say that simulation instead demonstrates answers by playing them out, by exposing procedural and functional relationships. Instead of telling, simulation shows.

Another element is that this demonstrates in kids a comfortability (in simulation culture) to working with partial and incomplete information. This is also a gender issue. In Western culture, Girls are traditionally less comfortable with working with partially understood systems, and prefer having a more complete understanding.

Turkle exposes this question further about simulation culture: why should kids use virtual magnets to pick up virtual pins? However, I think the reason is exactly the same as using real magnets to pick up real pins. Interaction is playful, but is illustrative of relationships.

Describes that concerns over simulation in college education. Simulation results in students detachment from their work. With simulation, educators are concerned that students do not understand importance and effects of the subjects they are learning. It does however enable students to do work and experiments they would not have been able to do before. This is still a subject of some controversy in education today. There is a heretical/blasphemous element to simulation in science, where educators fear that students will mistake world of simulation for the real world. This fear goes back to Baudrillard.

This article discusses Turkle’s ideas of simulation resignation and denial, and poses a third mode of criticism, which examines and challenges internal assumptions. She argues that it would be possible to develop a readership for culture of simulation. This would emphasize a way of distinguishing between the world of the simulation and the real world.

The way to build this would be to have children create their own simulations, to develop authorial skill to learn how to critique and read the simulations. We have centuries-long history of readership for written text, a similar tradition must be made for understanding simulations.

I am working away on an analysis, a kind of deep reading of one of the chapters in the book. My goal behind this is to learn what things are happening symbolically within the story world, and find ways of abstracting those into some kind of format that can be easily modeled. My plan has been to do cycles of analysis and then design, and repeat the process, getting in a few good cycles. I hoped to get the analysis done last week, and the design done by Thursday, but I am only halfway through with the chapter analysis. Part of the problem is my fault for simply writing too much. Hopefully this will not prove to be problematic.

However, I did have a few preliminary notes:

Social variables / currencies are:

• money
• happiness
• social status (this is moral standing in social landscape)

core character traits are:

• moral standing (gentlemanliness) in Austen’s moral landscape
• pride – sense of self value
• prejudice – disposition toward (usually against) other characters. Prejudice is also an indication of the rate of change of one’s opinions
• adherence to social codes

notes:

• prejudice offsets moral interpretation of others
• pride offsets moral interpretation of self ?
• high adherence involves interpretation of social status as morality, otherwise treats real moral standing (evidenced by actions) as such

Types of conversation atoms:

gestures:

• offer: offer a topic of conversation
• observation: special form of offer, commenting on something in situation
• press: press an offer (if it has been rejected)
• reject: decline an offer, potentially giving an excuse
• pause: make no response to an offer
• accept: accept the offer and respond to it in some form
• respond: an accept in response to an observation
• display emotion: blushing, growing pale, looking struck or taken aback
• proposition: a kind of observation, suggest that something is true, generally of the conversation partner. This may be a raise or lower among others
• agree: accept and respond positively to a proposition
• disagree: deny and reject negatively a proposition
• request: a special kind of offer, ask a character to engage in some activity or situation
• gossip: a special kind of observation where the speaker shares some potentially hidden information
• inquire: an offer that asks the conversation partner to gossip or make a proposition
• intrigue: make an ambiguous statement which would require an inquiry to make sense, also may require a lowering of status to ask
• defer: an accept that is accompanied with either an agreement or an inquiry for the partner to continue on

status related:

• raise: compliment someone in some direct manner
• lower: tease or lower someone socially
• tease: special form of lower, indicate other’s failure to abide by social convention of conduct
• misdirect: redirect a tease to the teaser, ignoring the accusation and reconstructing it
• grab: attempt to raise own status by claiming authority in situation
• threat: generally has the form of an inquiry, which suggests a grave violation of conduct were the threat transgressed. This may be used to refer to events in the past
• pull together: associate two characters by comparison
• distance: dissociate two characters through refuting comparison