[YeGoblynQueenne]

>> The AI was the search algorithm to find an effecient solution to the maze, not the mouse being able to navigate it later in a second run.

But that's not the whole story! The program can update its solution of the maze when the maze changes, but it is capable of only changing that part of the solution that has actually changed. When Shannon changes the maze and places Theseus in the modified part of the maze, I kind of rolled my eyes, sure that it was going to start a new search, all over again, but I was wrong: it searches until it finds where the unmodified part of the maze begins, then it continues on the path it learned before.

It seems that, in solving the maze, the program is building some kind of model of its world, that it can then manipulate with economy. For comparison, neural nets cannot update their models - when the world changes, a neural net can only train its model all over again, from scratch, just like I thought Theseus would start a whole new search when Shannon changed the maze. And neural nets can certainly not update parts of their models!

This demonstration looks primitive because everything is so old (a computer made with telephone relays!), but it's actually attacking problems that continue to tie AI systems of today into knots. It is certainly AI. And, in "early 1950's", it's AI avant la lettre.

[sarthakjain]

Great observation. The solution to the update problem is relatively simple. It doesn't do a search again on update. Instead everytime it encounters an update in what it knows, it just changes the data stored in memory. All it is doing is updating its learned representation. After this it still knows what the other obstacles are without having to do DFS or BFS again. If the solution was a graph, it just deleted a edge it still knows what all the other edges are. If it encounters another change it updates the state of the graph again.

With regards to Neural Networks, if they are given a reward function, which can be dynamically evaluated (in this case did I reach the end or not) they are pretty good at learning without feedback.

[YeGoblynQueenne]

You make it sound simple, but from my point of view the ability to update one's learned representation requires a representation that can withstand being updated. I mentioned John McCarthy's concept of "elaboration tolerance" in another comment, i.e. the ability of a representation to be modified easily. This was not a solved problem in McCarthy's time and it's not a solved problem today either (see my sibling comment about "catastrophic forgetting" in neural nets). For shannon's time it was definitely not a solved problem, perhaps not even a recognised problem. That's the 1950's we're talking about, yes? :)

Sorry, I didn't get what you mean about the dynamically evaluated reward function.

[marcinzm]

>For comparison, neural nets cannot update their models - when the world changes, a neural net can only train its model all over again, from scratch

I mean, sure they can. Training a neural network is literally nothing but the network's model being updated one batch of training examples at a time. You can stop, restart, extend or change the data at any point in the process. There's whole fields of transfer learning and online learning which extend that to updating a trained model with new data.

edit: Also in a way reinforcement learning where the model controls the future data it sees and updates itself on.

[YeGoblynQueenne]

The problem I'm describing is formally known as "catastrophic forgetting". Quoting from wikipedia:

Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to completely and abruptly forget previously learned information upon learning new information.

https://en.wikipedia.org/wiki/Catastrophic_interference

Of course neural nets can update their weights as they are trained, but the problem is that weight updates are destructive: the new weights replace the old weights and the old state of the network cannot be recalled.

Transfer learning, online learning and (deep) reinforcement learning are as susceptible to this problem as any neural network techniques.

This is a widely recognised limitation of neural network systems, old and new, and overcomging it is an active area of research. Many approaches have been proposed over the years but it remains an open problem.

[jmoss20]

What is transfer learning if not partially updating the model...?