pages 112 to 126

Thermodynamics and Thinkodynamics
or
Organized Disorder

One central question of research on cognition is how precisely we must model the human brain in order to model human cognition.
In thermodynamics we don't have to know about the individual atoms and molecules. Rather we apply statistical measures to calculate the macro level of a system.
Hofstadter considers applying this concept to thoughts and calls this principle "thinkodynamics".
What does this mean with respect to cognition?

The traditional holy grail of AI has always been to describe thoughts at their own level without having to resort to describing any biological (i.e., cellular) underpinnings of them.

(page 125)

Future research will have to show if this claim is actually true. So far neither the low nor the high levels of the essence of cognition have truly been found.

However it does seem quite plausible to imagine some kind of "organized disorder" takes place on lower levels of cognition. Perhaps various sub-structures of a cognitive system constantly run in parallel and produce educated guesses, led by statistical analysis. Depending on their actual relevance with respect to any current situation the mental building blocks created in this manner are transferred to higher levels.
Hofstadter's theory is that the higher the level of cognition is the less parallel the processing is.
Processes on the highest level of consciousness may occur fully in serial.
This claim seems absolutely valid. Of course, we can do several things in parallel.
But although depending on the situation each thought may follow another in extremely short succession, it does seem to be the case that each conscious thought actually occurs one after another.

I want to end this entry with the following question:
Could the seemingly organized structure of our top-level cognition be a merely coincidental result of emergent properties of more or less chaotic structures on the lower levels of our cognitive system.

pages 97 to 111

From this section of the book I most enjoyed the concept of a "terraced scan".
Hostadter uses the example of going into a bookstore and rather than reading each and every book cover to cover before deciding which book one is actually interested in, one tends to sort out so very many books by the blink of an eye. We are able to categorize things at extremely high speeds. Of course we are sometimes wrong but in general this kind of approach facilitates our everyday life quite a lot. In principle only the "interesting" reach higher levels of cognition.
However, Hofstadter pointed out that this sorting out is actually not a binary decision; instead every "thread" returns a value indicating how interesting or not interesting an occurrence is.
Therefore if at first glance, occurrence A seems to be the most interesting but after more thorough research turns out to actually not be, one can anytime return to occurrence B which at first was categorized as less interesting.
This is one of the core principles that Hofstadter is trying to implement into his artificial intelligence research project "Jumbo".
Basically this approach is "merely" dealing with search strategies. I know that various A.I. researches believe that intelligence is "merely" achieved by efficient search strategies.
But I am not so sure about this. I am sure that research on search strategies will be able to significantly enhance artificial systems. But I doubt that it will be sufficient in order to create anything truly intelligent. Emotions and intuition seem to me to be equally important.

pages 87 to 95

In this section Hofstadter uses the word play with anagrams as an example for learning something about the way human cognition works.
A lot - if not most - of our cognition occurs unconsciously. If we see the character string "4 + 5" somewhere we automatically parse it into the meaning of adding a quantity of five to a quantity of four and calculate the result. Of course this only occurs if we are able to read and have learned during some previous time about the concept of numbers and addition.
But as soon as one has gained this kind of knowledge one is not only able to perform this task automatically; in fact one can't even prevent this automatic analysis of happening.
Some subconscious levels of our cognitive system constantly analyse incoming signals and if these signals match what the area in question is specialized for a message is sent to higher cognitive areas bringing this finding into consciousness.
The same thing happens with anagrams. Hofstadter describes this phenomenon as to "sit back and watch" - because the level of influence we can take on the type of processing occuring during the building of anagrams is not really as high as one might tend to think.
Modern programs tend to use exhaustive search algorithms for all kinds of problems. These programs are not only capable of finding all possible solutions within their designated domain but they can also perform this task at incredible speeds.
However this type of processing is not even remotly similar to the way human cognition works.
Therefore Hostadter writes with respect to these types of programs:

I have little interest in them, aside from genuinely admiring the clever hacks
involved in programming them.

I strongly believe that this claim is very important. Because it becomes ever more obvious that our human mind works in a completely different manner than that.
Hofstadter introduces a concept of different levels of analysis, with the most fine-grained level running in parallel by using a bottom-up approach while specific higher levels running in a top-down manner are only triggered when appropriate.
All this happens totally automatically and intuitively. The structures underlying these automatisms and intuitions are precisely where the emphasis of research must lie.

pages 70 to 86

Generalization, i.e. inductive reasoning, is undoubtedly one of the key capabilities of human intelligence or intelligent systems in general.
How does generalization work? First of all one must form concepts to begin with.
I like to think of a concept as a collection of ideas governing one entity or another.
Very deliberately I chose the word entity. Because concepts don't even have to be representations of real world occurrences. One must also consider all the kinds of mythological and theological thoughts that can arise.
However I enjoyed reading about Hofstadter's view on concepts very much, interpreting them as spheres with representations of most typical conceptual examples in the core and less typical examples forming the outer layers. Least typical examples which may already count as examples for other concepts form the sphere's blurry edges.
There is undoubtedly quite a lot of generalization going on in the everyday life of each and everyone of us. Just think about the abundance of incomplete sentences uttered in natural language.

Computer scientist: I'm in artificial intelligence because it's a mixture of psychology, philosophy, linguistics and computer science.
Architect: That's the reason I'm in architecture.
(page 76)

And I believe that this that is exactly the reason why artificial systems will never be able to grasp what a human being is trying to actually convey with his or her utterances before it actually has some intelligence of its own.
This that in question does not even refer to any specific linguistic constituent of the utterance of the computer scientist. It rather refers to a concept - the concept of enjoying one's job due to enjoying its underlying intellectual framework.

As far as I know so far nobody has come up with any solid idea on how to model this kind of ability to generalize in an artificial system.
But in order to be able to create something truly intelligent this is definitely one of the starting points we have to look at.

pages 55 to 70

In this part of the book Hofstadter writes about pattern-finding, analogy-making and the difficulty of finding out which parts of any given information are central and which are not.
Research has shown that there are many, many processes going on in parallel. Hofstadter describes these individual processes as islands (acquired by bottom-up processes, i.e. data-driven) which have to be connected by perceiving relationships (by applying top-down processing, i.e. goal-driven).
I believe that modeling this massive parallel processing is the key to enable an artificial system to act in an intelligent manner.
We will need to find out just what exactly are the smallest information units (i.e. islands) and which rules govern the process of deciding which islands are to be linked and which not.
I also enjoyed Hofstadter's analogy with respect to comparing human cognition to an ant colony very much. It seems dazzling to find out just what exactly which member of an ant colony is doing and which goal each member is trying to achieve. It is even ever more fascinating to find out what the colony as whole is capable of doing by merging the actions of the various members into a larger framework.
This kinds of thoughts shall be our starting point for further research...

pages 35 to 55

One important aspect addressed in this part of the book is the question what the key to intelligence is; is it knowledge, a knowledge-independent core, a combination of both or perhaps even something different?
Whatever the answer is, I believe that so much is probably true: We won't be able to put our fingers on any single aspect. Given the extremely complex structure of our brain, a simple answer simply doesn't appear conceivable.
However, it seems likely that intelligence emerges from a very sophisticated interconnectedness of various underlying substructures.
My first thought was that knowledge must be one of these substructures. After all it is the case that without knowledge one is lost in time and space - without being able to integrate sensory inputs into meaningful patterns. But then again I can imagine a system to start out completely without any knowledge at all and to emerge into an actually intelligent system merely by analyzing the incoming patterns via a "knowledge-independent core". Immediately after birth/creation of such a system the agent may not be intelligent yet in the way we think of intelligence. But I am willing to call a system intelligent even if it is only potentially intelligent, i.e. if it has the inherent ability to gain intelligence.

Introduction

Studying Cognitive Science means having the wish to understand how cognition actually works.
One major cornerstone in this endeavor is Douglas Hofstadter. He believes that the core principle of intelligence is the ability to form analogies.
Concepts are not static but rather "fluid". Ideas and theories about one entity or another can therefore also be applied to previously unseen entities.
Trying to apply this principle to realm of artificial intelligence remains widely unsolved:
Computers simply do not "understand" in a strong sense what they are actually computing.
Nowadays it is therefore merely impossible for a computer system to abstract knowledge from one domain and apply it to another.