On Our Nonexistence as Entities: The Social Organism

This chapter begins by taking a look at various perspectives on evolution. First they address the conflict between creationism, and evolution in schools, and then began to discuss how evolution on Earth may have taken place. Then they begin talking about life on different scales. The first scale they examine is macro. This perspective is called Gaia, and focuses on the earth as a life form itself. They talk about how life forms on a planet primarily serve to keep the planet stable in someway. In a simulation called Daisyworld, they examine how different colors of daisies can help regulate the surface temperature of a planet.

They then examine differential selection by which it is believed that evolution selects against animals that reproduce too often to prevent over population (and thus eliminating a food source). Next they discuss attempts to understand behavior that seems to contradict the idea of self-preservation: the process of inclusive fitness whereby individuals try to protect others with similar genetics. Their underlying point throughout the section is that scientists need to stop looking for individual selectionism, and focus on group selection.

The smallest level of interest lies within cells. They look at organelles, and put for the suggestion that the only purpose of humans is to preserve the life, and facilitate these “tiny masters.” This entry only appears to have been entered in order to show the importance of finding the right scope. The book puts forth the belief that you should not look at individuals, but rather societies, or “super organisms.”

Self-organization and flocking behavior is the focus of the following section. Here they examine swarm (flocking) behavior as a certain form of optimization. They talk about how it becomes easier for the individuals to survive in a group, rather then alone, and the social supports that are established to facilitate basic functions, such a raising young, and finding food. The discussion begins with bacteria, examines insects, and finishes with animals. The first discussion of potential optimization was with ants, where they looked at the traveling salesman problem. With animals, they begin to look at the impact individual agents had on the group. Additionally, they tried to establish some fundamental rules for flocking behavior.

The culmination of the previous section provided the baseline of then next: robot societies. Here they first talk about the old paradigm established as MIT, “Gold Old-Fashioned Artificial Intelligence.” GOFAI is a symbol processing system, whereby the AI is intended to understand its surroundings, and then act accordingly. The new style of AI is based on subsumption architecture. These robots intelligences are built from the bottoms up. They have a simple set of rules that they follow that allows them to appear more purposeful than they really are. From this they began talking about the nature of the mind, and how it is separated from the brain. They also look into considering whether it is worthwhile to consider the mind as a society of agents (which the deem it is not). They talk about how swarms are more nearly a sum of the parts. There is then discussion about using small robots to complete various tasks such as cleaning the television screen to taking readings from a volcano. Virtual robots are considered to simulate actions that defy laws of physics. Kerstin Dautenhahn’s research is examined regarding social intelligence, and social robotics.

The following section talks about another aspect of AI: shallow understanding. There is discussion about deep processing, which is performed through operations on symbolic representations within the computer’s native mode. Things such a processing complex databases and proving theorems seem “deep.” However, programs that simply talk with the user, and don’t seem to do anything important are called “shallow.” But, they are ironically more difficult to code. They go into example using ELIZA, and they point out that a machine can make convincing chatter based on a dataset of pre-formulated responses by on key words, but they do not actually understand any of the content they are receiving.

The final section looks into what agency means. They examine various peoples research, such as Stan Franklin and Art Graeseer to determine what exactly an agent is. Essentially they determine that it is an entity that acts according to its environment, and the people often anthropomorphize the actions of agents as more meaningful things than they really are. Then, going full circle they return to evolutionary concepts, where by they think speech may have evolved from primate grooming behaviors.

Well, after a week of bumbling incompetence the bastards at UPS had finally got my package out for delivery today. Unfortunately, the goddamn fucker of a delivery asshole “forgot” to deliver it. It is not like it was a small box that could have slipped between the cracks. It has a fucking 19″ LCD in it, and it is double boxed. Damnit! Since this happened on a Friday, I now how to wait until Monday, assuming they pull their heads out of their ass and actually deliver it. I am so pissed off.

These are a mixed culmination of what I think will win, and what I would like to win. Sometimes they are the same, sometimes they intermingle, and sometimes they are starkly different. I admit that I have not seen all the movies that have nominations, but I can read so I know at least a little.

Best Picture: Mystic River [though, it wouldn't break my heart if Lord of the Rings won]

Actor (Leading): Sean Penn (Mystic River)

Actor (Supporting): Tim Robbins (Mystic River)

Actress (Leading): Charlize Theron (Monster)

Actress (Supporting): Marcia Gay Harden (Mystic River)

Animated Feature Film: Finding Nemo [I hated this movie]

Art Direction: Lord of the Rings: Return of the King

Cinematography: Master and Commander: The Far Side of the World

Costume Design: Lord of the Rings: Return of the King

Directing: Mystic River [again, it wouldn't hurt my feelings if LotR won]

File Editing: Seabiscuit [I feel dirty]

Foreign Language Film: The Barbarian Invasion! [hell, why not?]

Makeup: Lord of the Rings: Return of the King

Original Score: Lord of the Rings: Return of the King [I have purchased all of the LotR soundtracks!]

Original Song: “Into the West” (Lord of the Rings: Return of the King)

Short Film (Animated): Destino! [at least it is fun to say]

Short Film (Live Action): Two Soldiers

Sound: Lord of the Rings: Return of the King [Master and Commander was good too though]

Sound Editing: Master and Commander: Far Side of the World [aka honorable mention]

Visual Effects: Lord of the Rings: Return of the King
Documentary Feature: Capturing the Friedmans [they desire what they are getting]

Documentary Short Subject: Ferry Tales

Screenplay (Adaptation): American Splendor

Screenplay (Original): Lost in Translation

Symbols, Connections, and Optimizations by Trial and Error

This chapter begins by looking into symbols in trees and networks. The first example of the first philosophies regarding intelligence: symbol processing. This was the first approach that was used for artificial intelligence. This methodology relies on symbols to be clearly categorized. They discussed the grounding problem for tree structures as well. The second assumption they look into is fuzzy logic. Which means that connections are not either true or false. They are based on a degree of accuracy. Another method that was discussed was constraint-satisfaction models.

Commonly in traditional symbol processing, symbols are arranged in tree structures. With this structure, each step leads to another decision. However, since fuzzy logic does not rely on clear-cut decision making, it may look erratic, where it is strong in some places, and week in others. Trees cannot depict feedback. So, network matrices are used to depict feedback relationships. There is further discussion about representing networks using matrices (include mapping of trees on matrices). There is also discussion about graph patterns that cause feedback. Paul Smolensky has an equation to find the optimal state of such networks. There is discussion about maintaining harmony of a network. Particularly, there is discussion about a method of optimization formulated by John Hopfield.

The discussion of discovering patterns of connections among elements (learning) is covered. They begin by taking a look at what it means for two things to be correlated. They then begin to talk about how this is implemented using correlation matrices. The two types of organization they talk about are symmetrical and asymmetrical connections. They begin to talk about how you can use complex logical interactions to achieve different results. They focused on creating feed forward networks, and used them to create logical propositions. Interpretation is then talked about; the sigmoid function is used to manipulate information into a tolerable range.

Neural networks are then discussed. These networks are used to try to provide a theory of cognition referred to as connectionism. This method assumes that cognitive elements are made up as patterns distributed through a network of connections. This model more closely resembles human thinking, since it does not require elements to be separated for categorization.

The next section begins to take a look at problem solving, and optimization. They begin by trying to establish that characteristics of problems can be used to generate a degree of goodness by which an answer can be estimated. In this example they look at methodologies for selecting a solution to satisfy an algebra equation. Then they begin to look at the three spaces of optimization. The first is parameter space, which is the legal value of all elements. The function space contains the results of operations on the parameters. The fitness space contains the degrees of success by which patterns of parameters are used in function space. This is measured as a degree of goodness, or badness. They then begin to talk about evaluating solutions by establishing a fitness landscape. The idea is to reach the highest point on the graph (which can be multi-dimensional).

High dimensional cognitive space and word meanings are examined in the next section. This section begins by talking about computer algorithms used for word meaning recognition. The first approach is called semantic differential (Osgood, Suci, and Tannenbaum – 1950). They gave people words, and had extensive questionnaires about their feelings pertaining to the word (57). They linked words into groups by “halo effect,” which results in the categories: evaluation, potency, and activity. Another group created a database of word relations based on Usenet samplings in order to remove the factor of tester bias (Colorado 1990). They organized word associations into a matrix so they could establish more relationships, such ad Euclidean distance. They talk about how this closely relates to the way human’s process word definitions by understanding context rather than consulting a dictionary.

NK Landscapes, and factors of complexity are discussed in the following section. This section deals with interdependent variables and problem space. The foundations of complexity lie in N, the size of the problem, and K, the amount of interconnectedness of the variables (Stuart Kauffman). Increasing N results in combinational explosion, and increasing K results in epistasis. There are extensive examples using bit strings to illustrate various problem complexities.

Combinational optimization is the focal point of the next section. The idea here is to either minimize or maximize a result. First simple permutations are talked about. Then, they talk about breadth-first and depth-first search of simple permutation diagrams (trees). Heuristics are used as shortcuts to reduce the search space. The whole idea appears to boil down to trying to find the best way to guess where you are going to find the right answer.

The next section deals with developing binary optimizations. They first discuss how various things can be encoded in binary so they can be used for binary optimizations. Then there is discussion about how binary search space doubles for every additional element, and how you represent binary strings in various dimensions. There is also discussion about the meaning of hamming distance. Since binary strings can become intractable it becomes necessary to determine which bits are the most important in order to narrow the search space. They then go into discussion of various searches such as random and greedy, hill climbing, and simulated annealing. Additionally, they talk about various implementation concepts such as binary vs. gray coding, and examining step sizes and granularity.

The final section is a brief overview of optimization using real numbers. Essentially these problems appear to be similar to combination, and binary optimization methods. However, distance (include step size) is no longer determined in Hamming distance. Typically they are represented using Euclidean distance as calculated in n-dimensional space.

Traditional tales are still very relevant today. The reason for this, because they all boil down to some kind of cultural ideas, quite typically they seem to be designed to reinforce personal integrity, and/or ethics/morals. All of which are usually achieved by overcoming some kind of hardship.

Perhaps one of the most contemporarily relevant stories is the story of, Snow White. I believe that it was originally intended to help children cope with the loss of their mothers, since in the “old days” there was such a high childbirth mortality rate. However, now that story can be used to help children cope with similar issues, such as divorce.

Another story that seems to be universally accepted is that of, Cinderella. Since, it seems that all people would like to believe that anyone can escape their circumstance, and raise themselves to a great plateau.

However, I think that there are some aspects of tales that are not quite useful as they used to be. For example, many stories make a clear distinction between good and evil, which was useful in the past, and thinks were designed to be clear-cut. If you were at war with the French, you wanted everyone to think that all the French were evil. But, in our modern society the distinctions between good and evil are very gray. And, by attempting to create generalized good verse evil situations, we often mislabel entire groups of people, because we are unable to make the proper distinctions.