Just FYI, for me personally this [from scratch] presumption comes from my trying to understand human brain algorithms.
Thanks for clarifying. I see how you might apply a ‘from scratch’
assumption to the neocortex. On the other hand, if the problem is to
include both learned and hard-coded parts in a world model, one might
take inspiration from things like the visual cortex, from the observation that while initial
weights in the visual cortex neurons may be random (not sure if this is biologically true though), the broad neural wiring has been hardcoded by evolution. In AI terminology, this wiring represents a
hardcoded prior, or (if you want to take the stance that you are
learning without a prior) a hyperparameter.
So, the AI pioneers wrote into their source code whether each door in the building is open or closed? And if a door is closed when the programmer expected it to be open, then the robot would just slam right into the closed door?? That doesn’t seem like something to be proud of! Or am I misunderstanding you?
The robots I am talking about were usually not completely blind, but
they had very limited sensing capabilities. The point about
hardcoding here is that the processing steps which turned sensor signals
into world model details were often hardcoded. Other necessary world
model details for which no sensors were available would have to be
hardcoded as well.
If I understand you correctly, you’re assuming that the programmer will manually set up a giant enormous Bayesian network that represents everything in the world
I do not think you not understand me correctly.
You are assuming I am talking about handcoding giant networks where each individual node might encode a
single basic concept like a dowsing rod, and then ML may even add more
nodes dynamically. This is not at all what the example networks I
linked to look like, and not at all how ML works on them.
Look, I included this link to the sequence to clarify exactly what I mean:
please click the link and take a look. The planning world causal graphs you
see there are not world models for toy agents in toy worlds, they are
plausible AGI agent world models. A single node typically represents
a truly giant chunk of current or future world state. The learned
details of a complex world are all inside the learned structural
functions, in what I call the model parameter L in the sequence.
The linked-to approach is not the only way to combine learned and hardcoded model parts, but think it shows very useful technique. My more general point is also that there are a lot of not-in-fashion historical examples that may offer further inspiration.
Well, I did try reading your posts 6 months ago, and I found them confusing, in large part because I was thinking about the exact problem I’m talking about here, and I didn’t understand how your proposal would get around that problem or solve it. We had a comment exchange here somewhat related to that, but I was still confused after the exchange … and it wound up on my to-do list … and it’s still on my to-do list to this day … :-P
Definitely my sequence of 6 months ago is not about doing counterfactual planning by modifying somewhat opaque million-node causal networks that might be generated by machine learning. The main idea is to show planning world model modifications that you can apply even when you have no way of decoding opaque machine-learned functions.
Thanks for clarifying. I see how you might apply a ‘from scratch’ assumption to the neocortex. On the other hand, if the problem is to include both learned and hard-coded parts in a world model, one might take inspiration from things like the visual cortex, from the observation that while initial weights in the visual cortex neurons may be random (not sure if this is biologically true though), the broad neural wiring has been hardcoded by evolution. In AI terminology, this wiring represents a hardcoded prior, or (if you want to take the stance that you are learning without a prior) a hyperparameter.
The robots I am talking about were usually not completely blind, but they had very limited sensing capabilities. The point about hardcoding here is that the processing steps which turned sensor signals into world model details were often hardcoded. Other necessary world model details for which no sensors were available would have to be hardcoded as well.
I do not think you not understand me correctly.
You are assuming I am talking about handcoding giant networks where each individual node might encode a single basic concept like a dowsing rod, and then ML may even add more nodes dynamically. This is not at all what the example networks I linked to look like, and not at all how ML works on them.
Look, I included this link to the sequence to clarify exactly what I mean: please click the link and take a look. The planning world causal graphs you see there are not world models for toy agents in toy worlds, they are plausible AGI agent world models. A single node typically represents a truly giant chunk of current or future world state. The learned details of a complex world are all inside the learned structural functions, in what I call the model parameter L in the sequence.
The linked-to approach is not the only way to combine learned and hardcoded model parts, but think it shows very useful technique. My more general point is also that there are a lot of not-in-fashion historical examples that may offer further inspiration.
Well, I did try reading your posts 6 months ago, and I found them confusing, in large part because I was thinking about the exact problem I’m talking about here, and I didn’t understand how your proposal would get around that problem or solve it. We had a comment exchange here somewhat related to that, but I was still confused after the exchange … and it wound up on my to-do list … and it’s still on my to-do list to this day … :-P
I know all about that kind of to-do list.
Definitely my sequence of 6 months ago is not about doing counterfactual planning by modifying somewhat opaque million-node causal networks that might be generated by machine learning. The main idea is to show planning world model modifications that you can apply even when you have no way of decoding opaque machine-learned functions.