Just finished the first 72 minute Minsky interview here, https://www.youtube.com/watch?v=DrmnH0xkzQ8 . It was lovely to listen to. Only a couple multi-agent threads came out, but yea, they were highly visible clarion calls.

Minsky went way overboard & decided to tackle all the problems at once & make a robot that could visually pick up & handle blocks & it was a disaster, and each program would have different capabilities. It was a hugely wrong too-big approach, but the lesson still resonates with me, and there's a ton of other similar threads in this first suggested interview about eskewing perfection, about not trying to be perfect, but being instead dynamic & managing multiple streams of thought/reasoning/judgement.

> that we should stop looking for a very good vision program and this concept came to Peppard and me around the same time we were working together. the idea was: all right let's see if we could get 10 pretty good vision programs and get them and manage them and see which of them seemed to be working in different situations. so the idea of the society of mind was that in the brain or in the mind or in a computer you shouldn't look for perfection, and you shouldn't go around trying to debug programs and find the best possible way to do something. you should find a lot of different ways and have different resources, and then you should make managers that can decide under which circumstances to use which ones now.

> I still think this is the way to make big programs work better but no one does it and so this idea which is now from about the late 1960s and now it's the early 1990s in spite of how simple and clearly correct this idea is it hasn't caught on and I'm very disappointed in my colleagues and people in the field of AI in general for some reason they've gotten fixed on the idea: "let's get it right"

> And that's wrong; there is no right

Divine. There is no right. I think we miss that a lot. Right changes. https://www.youtube.com/watch?v=DrmnH0xkzQ8&t=23m

To right changing again,

>> would you characterize the difference between- does he [John McCarthy] still believe you can get at these things through logic?

> it's rather tricky to describe just where we agree and disagree. we both agree very much and always have from the beginning that in order- to for a machine to be smart it would have to have common-sense knowledge. where we differed I think was on how that common-sense knowledge would best be represented and on what are the reasoning processes that use it?

> now he's maintained that it would be good to have a uniform logical reasoning process. but in order to do that you have to find ways of dealing with exceptions and suppositions and things like that and he's been working on technical subproblems of that sort for some thirty years. how do you make a logical system- how do you have an axiom and tolerate a few exceptions? how can you do reasoning of the form what if this were not true for a moment, what could I learn from it? it's very difficult.

> and my feeling is that there are other ways to reason by analogy using frames and defaults that are more lifelike and more productive, and that you don't have to struggle quite so hard with these logical difficulties if you start with a more flexible system. in the long run though it would be nice if we were using these other informal kinds of reasoning to have theorists come along and clean them up and say well certain places we can replace it by much more efficient perfect procedure. I suspect that most situations that can never be done, but it doesn't matter. so we differ on what problems to work on. John likes to McCarthy likes to prove things get them settled if you have a good theorem it lasts a lifetime if you have a practical theory you just never know what its status is from one year to the next.

This is one of the most overwhelming lessons I want humanity to wake up & hear, everywhere, and in software. Clutching to correctness & right answers is anti-the-point. Software ought/should/must be malleable.

It's also amazing how much things are changing since this time. The expert systems mania has given way to much more general & complex machine learning view. Yet still I think we focus a lot on loss calculations, on discerning truth, & Minksy's essence here is of higher order complexity that must be tackled & wrangled & jumped into, and how cowardly it has been that his field has been for their conservative clinging to singular notions of truth.