Was asked a question regarding the history of AI and why so much jumping around. That is, why did research begin, then end suddenly. And then begin again, decades later, and then again end suddenly.

It's like with deep learning. Selfridge et al were doing conferences on it in the 1950s. Then it was popular again in the 1980s. And then in 2012--2018.

Researchers are mostly professionals and follow the funding, Meanwhile funding depends on the current hobbyhorse of the funders. Otherwise the discretionary budget in most places is not enough to hire even one engineer. Only enough for two students to have free tuition and a stipend however.

That's one reason. But there's another reason. And that's the math.

Both Marvin Minsky and John Holland objected to the lack of formal methods for reasoning about complex systems. In general there are no shortcuts, as Wolfram showed, but abstracting out the irrelevant, there are often statistical shortcuts and approaches, such as those championed by Watanabe.

For example, I'm using tricks like Schwartz distributions to get useful models. But most engineers don't know about such things. Why would they? No, really; why would they? People seem to have gotten stuck for about a year, and then have issues with funding, and move in other directions.

Mathematics a huge rabbit hole. If not dedicating enough time to it, you just won't learn enough to make the time spent useful, and yet the time spent in it could be extended forever, and yet most tools of mathematics are not useful tools. Only some are useful. Yet to know which ones are useful or not requires maturity, having enough broad and deep knowledge of what is available and where things are going.

For example, suppose we have a typical construction from the 1970s. Agent loading up with goals from a buffer which it empties periodically, and equipped with some heuristics and operations/methods which it encapsulates. (This is even before Smalltalk, and probably inspired it.)

It doesn't have any specific output that's desired, but rather an output within the target class. So it has inputs in Class I. And it wants outputs in Class II. Not outside it. Which point in class II results doesn't really matter, all count as solutions. The target class can be varied by the user. Set as a parameter. Or changed by another process.

So a typical problem people were solving was similar to the iterative calculation of derivatives in finance.

O_n ( O_n-1 ( ... O_2 ( O_1 ( INPUT) ) ) ) and the program must for each O_i select one method from list L.
It must not take longer than time T to do this, or post an error.

Looking ahead and trying all combinations is not efficient. So for example, there's a Guard or list of heuristics that narrows down list L.

Some kind of backup or backtracking is possible. If at step O_i it looks like the input is less likely, given further operations remaining, to end up in Class II than what the input to O_i-1 was, it undoes the operation and tries again from a backup.

Like in finance, where the results of a previous step, which also yields an approximation, are used to select the next calculation procedure from a list, using the approximation in the last step as input, to get a better approximation, and the additional accuracy and precision gained is compared with the anticipated cost of each further procedure. But it's easier, because they can do things like discount distant future events and their effect increasingly much.

And later many agents of this type are added. And the question for practical purposes is then how to predict what they do in fact.

Without just knowing a lot of mathematical tricks from outside computer science, it becomes clear that useful prediction is very hard.

Even when one correct approach, such as floating marks and locking some operation until enough marks arrive in the right places to unlock it, is known in the 1960s. Or after tagging everything was invented in the 1990s.

Most mathematical tricks come down to knowing how to aggregate variables will losing only irrelevant information. (Constructing the appropriate algebraic structures.)

Then you get few variables and can reason about it productively.

 

 

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