The problem of relevance

In chapter 4, Hoftstadter discusses artificial intelligence and the problem of representation. He defines representation as the end product of the process of perception, which is when a set of raw data has been organized into a coherent and structured whole. There are both long-term and short-term representations that hold importance at a given moment during a mental or computational process.

Of course, there are obvious questions as to how this could be implemented in a program so that representations affect a program's decisions in a human-like fashion. For example, a long-term representation could be a heuristic for solving a certain problem, like a crypto problem with 0 as the goal and one of the numbers. However, implementing the representations is not the only problem to deal with.

Constructing representations poses problems because of the problem of relevance and the problem of organization. The problems basically entail deciding what parts of the virtually unlimited number of environmental aspects are to be included in the representation, and how the representation is actually itself represented in the database.

These ideas are interesting to read about because I had wondered how one would develop a program that learns with experience. A program like that would need to have the ability to decide how much to include from its environment, and it would have to know how to represent that data, if it were to create its own representations over time. This is, after all, what people do.

The Eliza Effect

In Preface 4, Hofstadter discusses the Eliza effect and why it had worried him and his team back in the 1980's. The Eliza effect is basically defined, as Hoftstadter says, as: "the susceptibility of people to read far more understanding than is warranted into strings of symbols--especially words-- strung together by computers." In other words, it also refers to how people mistakingly believe that computers understand things, or can empathize with things, like humans far more than they actually do.

It is because of this that Hoftstadter and his team were losing morale. Other AI research teams were being credited with achieving goals in AI development which Hoftstadter believes was unwarranted, at least as much as they got. I can understand his thinking, and although this preface does seem to be a bit on the complaining side, his complaints seem valid.

From reading about his work with CopyCat and Jumbo, it is very obvious that he is aiming to truly model human cognitive processes with his work in artificial intelligence. This is in contrast to many other AI research groups who develop AI with the goal of achieving the image of intelligence with their projects. For some people, it simply is not important whether the computer truly understands the domains it is meant to be dealing with. For example, AI in video games is meant to mimic human behavior, but with as little overhead as possible. For these purposes, truly modeling human cognitive processes is not entirely important for most people, I guess. However, for people claiming to have a computer program that truly understands something like creating analogies, it has no purpose to claim the goal has been achieved if it has not truly been achieved in a way that mirrors a human approach.

Numbo

In chapter three of the reading, Daniel Defays discusses the Numbo program. He explains the goals of the game of Numble, which is to construct a given number, designated as the goal, from a group of other given numbers, using the three mathematical operations of addition, subtraction, and multiplication. This is a lot like crypto problems, except for that division is not used, and that all numbers do not need to be used to reach the designated goal number in the Numble game.

Still, the same basic principles seem to apply to the two different number games, and much of what Defays discusses reminds me yet again of the way in which I attempted to solve my first set of crypto problems. In Numbo, there are Pnodes which basically serve as markers stored in the Pnet meant to be close enough to specific ranges to be of use. In the first example that he walks the reader through, the goal is 114. In this case, the Pnode “100”, which has been programmed in as part of Numbo, is activated due to its relative closeness to 114.

The system seems to model the way that people think when approaching these problems, but even as Defays himself admits, the Pnet (the area storing the permanent network of knowledge that the program holds) does not know and recognize all of the same types of things that a human would recognize.

Temperature

In this section of the reading, Hofstadter continues to discuss the workings of his Jumbo project. The most interesting notion he brings up is how he incorporates a "temperature" into the system. Using the cell analogy, he discusses how the "cytoplasm" may incorporate a temperature, which serves as a quick global feedback to the state of the system. More precisely, it serves as a kind of barometer of the “happiness” of the gloms within this cytoplasm mixture.

The temperature is low, in fact, freezing, if there is only one single happy glom. The freezing analogy is great because the system does not break up the glom due to the fact that the cytoplasm is frozen. As expected, as the temperature increases, the chance of gloms dissolving increases. I just thought it was neat.

Hofstadter also discusses some classic illusions, relating to the fact that humans only perceive one possibility at a time, and citing things such as the vase-or-faces picture. Completely coincidentally, I was also recently shown a video on YouTube of a dancer’s silhouette that is meant to appear to be constantly spinning. However, the illusion is that it can be seen as the dancer spinning left, or the dancer spinning right. This particular example is very strong, because even after perceiving both possibilities, I was unable to make my perception change at will.

http://www.youtube.com/watch?v=uBTvKboX84E

Unique

In this section of the reading, Hofstadter continues discussing Jumbo. He details its purpose and writes about the importance of the thought processes that Jumbo was aimed to model. When I first read about Jumbo, I was initially thinking about how much effort Hofstadter went through to create a program that does something that, on the outside at least, seems like a very trivial thing—solving “insignificant word games.” However, he makes sure to remind the reader that the real purpose is much different than that.

Attempting to model these thought processes is very interesting, and that might be because I did so many of these types of puzzles when I was younger, as I am sure many other people have as well. I also suppose that not many of those people actually think about their own thought processes either during or after the solving of the problem at hand. Clearly, he is interested in things that most people do not even think to think about.

I find it interesting how he points out ideas that he is currently discussing in the reading by referring to text that the reader has just read in the very same paragraph. Occasionally he points out how he worded something in a previous sentence, which I feel helps to connect the reader to the material. It also shows how he truly does think about these kinds of things quite frequently.

Introduction to Jumbo

In this section of the book, from page 87 to 95, Hofstadter introduces the reader to the basic ideas of his Jumbo project. He talks much about anagrams and discusses the types of methods used for solving them. He focuses on the idea that these thought processes are not actually something that happens consciously, but happen under the surface, at least for people solving these problems at an expert level--not novices uses a brute-force approach.

He also discusses the issues of perception of words while reading, and how it is surprising that people do not make more mistakes in grouping letters in a given word, given that the process may be fairly complex if one actually thinks about all of the possible ways the letters of any word can be perceived.

This all reminded me of something I read a long time ago about reading, although it is not quite the same idea:

"Aoccdrnig to a rscheearch at Cmabridge Uinervtisy, it deosn't mttaer in what order the ltteers in a word are, the only iprmoatnt thing is that the frist and lsat ltteer msut be in the rghit pclae.

The rset can be a taotl mses and you can still raed it wouthit a porbelm. This is bcuseae the human mind deos not raed ervey lteter by istlef, but the word as a wlohe."

I always thought this was interesting, and I think it somehow connects to the mental processes he is discussing in the book. In fact, it almost seems to go against what he is discussing, in that it completely dismisses the idea that the mind actually even processes the groupings of letters at all.

Me, too!

This reading mainly discussed the idea of conceptual spheres. Hofstadter describes these as being formed by the central idea or theme at the core, with variations of the theme, or generalizations, making up the outer layers of the sphere. The examples he gave were very interesting. He was trying to demonstrate how it can be difficult to judge when an idea gets stretched so far that it no longer actually has anything to do with the original theme or event. The examples mainly dealt with how people react to given events and if their reactions are really appropriate. The best example, I thought, was how the FDA responded when there was a serious of deaths due to tampering with Tylenol. The FDA, at first, only decided to mandate new regulations for drug bottles, with deadlines given sooner to drugs that are most similar to Tylenol.

At first glance, this seems completely understandable, but does it actually make sense to target drugs that are simply more similar to Tylenol? Surely food or drugs not related to Tylenol could have been tampered with as well, but this is how the FDA reacted.

Apart from this, the most interesting part of the reading was his discussion of how “Me, too” fits into human tendency to generalize. Often times, when someone says “me too,” they do not actually mean they will be doing the exact same thing, as shown in many of the examples he gave. This is of course interesting because everyone probably hears someone say this at least once every day.