pages 195 to 204

After the fact, I can understand the "logic" behind the Italien breakup of "in fact" into in effetti, di fatto, infatti, in realtà, anzi, però, tant'è vero che, per esempio, a dire il vero, and so forth, but I never would have thought of it myself.

(page 200)

Obviously no exact number of concepts exist for any occurrence whatsoever.
The quality and quantity of concepts depend strongly on one's culture and environment.

In an environment with very frequent snowfall one may have more words for the different kinds of snow than in other places; a society living in an environment with many different sorts of sand most likely will come up with a respectable amount of words describing these different types.

Also worthwile pointing out is Hofstadter's idea of conceptual slippage:
For example, the Italian question "Lei ha fratelli?" actually means "Do you have brothers?" (page 199). But what if one has two sisters instead? Will one simply answer "No."?
Of course not; In English one would answer "No, but I have two sisters.".
And in Italian one even answers "Sì, due sorelle" - meaning "Yes, two sisters.".

Why is this? The reason is that many statements and questions (like here the "Lei ha fratelli?") are merely to be understood metaphorically.
Every real-world occurrence is more or less "fuzzy". There is (practically) nothing that is 100% this way or 100% that way. Therefore, our concepts must be "fuzzy" as well.

An artificial system that should even remotely be able to interact with its environment in a human-like manner must account for this fuzziness in order to be successful.
This fuzziness must also include the ability of constantly being able update one's concepts, if necessary.

pages 169 to 193

Now that I am writing, it is essential that I conceive my paper as a surface for inscription.... But if I wished to light a fire, and no other materials were by, the essential way of conceiving the paper would be as a combustible material....

(page 174)

This quote shows very clearly that our active mental representations at any given time are highly context-dependent.

We can - and we do - constantly perceive similar objects in completely different ways:
a liquid may be drinkable in one situation and used to distinguish a fire in another;
a glass may be something to drink out of in one situation and used as a weapon in another.

It is highly unlikely that we constantly are "loading" all concepts into our active mental representations of present occurences. Instead it seems plausible that we only use those concepts that are relevant to a current situation.

Perception and cognition are constantly influencing each other.
If we are thirsty, we will search for drinkable fluids. If we have to put out a fire, we will search for non-inflammable fluids. If we want a container to drink out of, we will usually search for different objects than if we need something to mix glue in.

Many A.I. research projects provide pre-coded representations to their programs.
But due to the above mentioned statements this, most likely, may not be the best approach.

In order to model the human mind, perception and cognition must interact with each other in order to create context-dependent - i.e. flexible - representations.

Trying to create this type of artificial system from scratch would pose immense computational problems because the real world offers an unbelievable amount of information every second throughout various modalities.
Therefore Hofstadter suggests to create microdomains in which artificial systems have a manageable amount of data available and where perception and the creation of representations can interact with each other.

After having read this chapter I definitely agree that this approach sounds highly promising.

pages 155 to 168

a compound word like "knows-truth-or-falsity", so transparently evocative to readers of English, might as well be, for all the computer could care, "xjs-beuglh?" or "doesn't-give-a-damn-about" or the digit "8" or any other alphanumeric string.

(page 164)

This excerpt shows most obviously that todays' computers simply are not able to actually understand anything at all even in a remotely similar way that any average human is able to.
We can pass arbitrary values to computers which can store them in variables. Then the computer can do various computations on these variables; but no matter how complex these computations may be, there isn't even a spark of actual understanding involved in this process.

The only things today's computers are capable of doing is "symbol manipulation".
When we read the output of computer programs we often are impressed and have the feeling that what we read actually makes a lot of sense. We tend to believe that the program which created this output is very clever.

But we may never forget that however smart the output may appear to us, it has absolutely no meaning at all to the computer program which created it.

pages 139 to 154

Fighting Codelets

There seems to be no central processing center in our brain but rather a

probabilistically-biased parallel exploration of possibilities.

(page 150).

The principle of collective intelligence is based on the principle that many individuals will nearly always come up with a better solution than any individual on his or her own.
Constant generation, regrouping and chaining together of new ideas has the potential of always finding a satisfying solution.

This is exactly what is going on in our brain:
We are constantly producing "codelets" (small parts of information), regrouping them and chaining them together in various fashions until the solution to a problem is found.
Codelets are constantly competing with each other, trying to come closer to solving a given problem.

For our purposes the only obvious difference between humans interacting with each other and codelets interacting with each other is that codelets are not self-aware. They do not have the "big picture". Rather the interaction amongst them is actually creating the global picture.

The precise strategies of these interactions remain unknown.
But Numbo is definitely a good starting point in order to try to understand more about this emergent phenomenon.

pages 131 to 138

Human Problem Solving: Knowledge or Randomness?

Some problems can be solved by applying knowledge alone: If we are experts in one field or another, we have no doubt that this or that problem can be solved in a very specific way which are previously aware of.
On the other hand completely unknown situations may make it necessary to generate strategies on a random basis and simply hope for the best.
But on a more general basis it seems quite straight-forward that most problems we have to deal with are neither solved solely by applying previously acquired knowledge nor solely on a random basis.
Almost always we at least have some kind of intuition what may be the best way to deal with whatever situation we may encounter.
Intuitions seem to consist of small knowledge fragments which may or may not be applicable to the current situation. But what is important is that intuitions very often lead is in the right direction. One may call this an "educated guess".
This is exactly what happens when we try to solve problems:
We try to use as many fragments of previously stored knowledge as possible; and whatever may still be missing is added on a random basis.