pgs one one one to one two six.

Hofstadter says that humans do not attend to two rival possibilities to a problem at exactly the same time. He notes the necker cube, vase-face, and other illusions as examples. While we are able to switch between different interpretations of the same stimuli, our perceptive systems don't allow us to use both at the same time. It may be the case, though, and Hofstadter mentions this also, that there is an unfelt parallelism going on behind the scenes -- else, where would any of these thoughts be bubbling up from?

Next, he continues his thoughts of gloms. He says that his program has different kinds of gloms -- one for syllables, vowel/consonant clusters, and others. Each of these can feel "happy" or "sad". According to how a glom feels, it will be more or less likely to be attended to (I visualized a room full of gloms in the form of crying babies for some reason...).

Hofstadter describes different ways that gloms can be arranged and more importantly, transformed. He cites the spoonerism, which is switching the consonants of the first two structures. Another noteworthy technique is exchange of syllables -- where each of the syllables is shuffled around in a different order. And also reversals, kniferisms, and other techniques he thinks would be reasonable to believe each of us might employ while doing a jumble.

A final article in the chapter uses analogies from physical science to describe how Jumbo is able to keep itself self-organizing. The happiness levels of gloms are similar to the statistical mechanics of natural rules. And the words that are formed out of this probabilistic chaos are similar to the macro-level thermodynamics we see as a result. Ideally, AI folks probably want to explain thoughts in terms of the macro-level, excluding the messiness of neurons and chemistry as much as possible. Personally, I don't think this will ever be possible. Why? Because, you can't understand how a cloud acts (how a thought is made, fades, or becomes a memory) without understanding the chemistry of water, heat or understanding how the wind alters the cloud's world.

Hofstadter is kind-of a romantic.

Jumbo is an artificial intelligence endeavor with aims to model cognition as a series of concurrent modules all sort-of aiming at the same place. At least in problem solving. Against my frowning, Hofstadter mentions that he does not seek to understand the microscopic mechanisms underlying brain activity and thought, but rather would try to pick apart the symbolic strata of information being pushed around in the mind.

Jumbo's problem domain is anagrams (a set of jumbled letters which must be arranged into a real word in order to solve the puzzle). Instead of having the program simply sort out the options from the full list of possible permutations, Hofstadter's architecture attempts to use a technique similar to chemical bonding in the natural world (also similar to symmballs..).

By visualizing that each letters has a set of "good" fits and "bad" fits, such as "t" to "h" to fit into "th", Jumbo builds words out of many smaller parts -- called gloms. Letters are weighted according to their use in the language -- English in this case. Simple on paper, but probably required alot of effort to balance in a way that produced reasonable results.

I especially like Hofstadter's intuition about imagining mind activity in terms of analogies of nature. In fact, it seems like one of the things he's best at is showing us that sort-of Zen quality of the universe -- that everything is related. He says that, like the chemical bonds found in the natural laws, a letter is able to shift between gloms should the need arise. A letter is also very attracted to some other letters but not very attracted at all to other letters -- just like certain molecules.

pgs 87-95.

Doug Hofstadter spends a bit of time talking about his work on the LISP program JUMBO, which was an anagram solver. A very interesting mention is made of his conceptual idea of the parallel terraced scan, which is a strategy used by a program to detect properties up the scale of a schema. These scales each have the ability to pattern match alongside the bottom-level program which is actually running the entire time. This provides one with the ability to conserve computer resources (or brain energy) by only running modules when the time is right (as in .. when the preconditions are met).

This scaling reminds me of the screaming demons architecture I learned about back in Cog166. The model went as follows: there is a set of demons which will scream when a specific symbol is cast before them, furthermore, there is a set of demons which will scream when a specific set of demons beneath them is screaming ... you can see the bottom-up approach visually. But those second-third-whatever level demons would only activate any screaming when the lower level gave them a specific input. I am reminded more, now, of a tree structure in computer science ... with the root giving me the answer I needed. I know that's not quite right for most data structures, but the visualization seems to fit here. My closing comment on this reading is this: the mind appears to work as a cluster of chaos that quickly can organize itself into something meaningful.

Islands

The human mind's ability to problem solve is quite different from a computer programs in the fact that humans have slippery paradigms which may be shifted when examining a problem. This is much harder to do for a computer program, as only the problem presented may tell if it is time to shift the importance of a technique forward or back on the scale. As Hofstadter says, the computer program fails at the figure/ground switch.

His reaction to this rigidness in computer program structure is an attempt to allow fluid templates to alter importance of pattern-finding techniques in Seek-Whence's architecture.

The human mind works all at once, and as needed. There is no single CPU running the tasks of the operating system, there is (to use the computer analogy further) countless processing units taking on very small subtasks and calculations, clustering together to push information to the next set of units, and so on..

By this fact, it is not hard to see that our mind's may make use of both the number-savvy (expert knowledge) and pattern-sensitivity (theory-based knowledge) techniques temporally parallel but intersecting at the right moments. This may create the illusion of a single stream of consciousness which branches along a set of possibilities that all appear to be related to one another, but I don't think this is actually what is going on in the brain.

I think that there are a very large number of solutions which are posed and logically worked out simultaneously, with almost all of them quickly being downvoted in the first few milliseconds of thought. From there, the remaining set of solutions is positioned for scrutiny in the form of truth from knowledge and also for patterns.

Chipping Away Ugly Stone

Before you lies a problem. Inside you lie the tools to solve it. You may set the problem in front of you and sit calmly, brushing over it with your eyes or zooming in on fine details. Billions of people before you have probably acted the same way.

Douglas Hofstadter has a problem. How does he act upon first meet? In Fluid Concepts, he says that his mind is drawn to elegance, simplicity, and balance (among other things) when traveling through possible solutions. Of course, many readers could say the same thing -- when writing a book report, programming a database, or plastering a wall. We all want the simplest and most direct solution to our problem.

A specifically brilliant example of our problem solving abilities, as humans, derives from our ability to sense similar structures in a pattern. This can come in the form of altering a part of a pattern to match up with another part, or by redefining "packets" (representations of parts of the pattern). This is kind of like an analogy, as Hofstadter mentions, which is a part of a human's pattern sensitivity.

To decipher the number patterns shown in the book, though, one must also know something about the properties of numbers, in order to find order between their positioning. Hofstadter calls this being "number-savvy" (as opposed to "pattern sensitive").

Between these two forms of knowledge and technique, a person can involve themselves in a pattern matching problem by examining the entire puzzle with pattern sensitivity modules, and also by being able to zoom in closely using number-savviness to report truths about the properties and relationships between the numbers. A little bit remarkable for a lump of mushy cells in our heads.

Patterns of patterns

Doug Hofstadter tells a tale about when he was sixteen years old. He was fascinated by numbers and sought out to be a grand mathematician. During his search for patterns in sets of numbers he came across an interesting repeating sequence inside of the union of squares and primes:

1, 1, 3, 4, 6, 9, 10, 15, 16, 21, 25 . . .

Between each of the squares (bold) there were either one or two primes, which become a new set of 2's and 1's. Sequences of 2 1 2 1, and so on are noted by Hofstadter. He further tries to extrapolate sequences from the new sequence, by attempting many different methods (counting breaks between a common subsequence, looking for very large periodic patterns, and more).

Eventually, he comes to the conclusion of counting 1's between the 2's and produced a new set entirely:

212112121211212112121211212112121121212112

1 2 1 1 2 1 2 1 1 2 1 2 1 2 1 1 2

Upon further musing, he decided even to apply his counting idea to the new sequence, which led to:

212112121211212112121211212112121121212112

1 2 1 1 2 1 2 1 1 2 1 2 1 2 1 1 2

2 1 2 1 1 2

The first and the third sequence are the same ! And, according to Hofstadter, they continue to be the same for quite some time.

I'd like to reflect a bit on pattern matching in humans, as I this is what Hofstadter was trying to illustrate by telling his sequence story from when he was sixteen.

Pattern matching in humans is obviously an instinct. We are driven to align the world (or microworld, such as number sets) from chaos to order. Why? Although we don't have time machines to look at our ancestor's behavior, we do have other animals which are (assumedly) of lower intelligence than us (atleast when it comes to pattern matching numbers). We can study them to learn something about how we may have evolved, specifically primates, because we share so much genetic heritage.

How do we go about deciding on patterns? It doesn't seem to be an instantaneous reaction to a set of mismatched data, but more of a mechanical process of rules that can be applied to develop a coherent result. Hofstadter used his own rules to decide where to break the sequences up, and what to count. This is probably alot of trial and error, especially without any advanced training in this sort of thing.

Is this how our ancestor's devised patterns, through trial, error, and reason? Maybe. Perhaps when they started caring about precise periodic events, such as tide, season, and constellation sequences.

Hello World

Hello World !