A different point of view on building AI system

[Paul_Lamb]

@stepan

TM has only two ways to depolarize predicted patterns: by the previous pattern on the same level or by representations from a higher level. But a “banana” doesn’t connect to the “red light”. And the model doesn’t have high-level representation for “red light” yet.

So, I don’t see how the HTM could predict the appearance of “red light” after it sees a fruit.

You have hinted at the answer when you mentioned predicted patterns on the same level. The representation for the concept “fruit” doesn’t have to be higher in the hierarchy than the concepts for “apple” or “banana”. These can all three be sibling concepts (existing in the same levels of the hierarchy). What matters from the perspective of heirarchy is that the lower-level inputs or abstractions which make up these concepts and which are spread across multiple areas in the cortex need to converge up a pyramid hierarchy so that representations can be formed for them in an area of cells that are physically close to each other. This allows those representations to share semantics as overlapping bits in their SDRs.

Now, the concept for “fruit” will be semantically similar to both “banana” and “apple” (all three have some overlapping bits). If the system has learned that a red light comes on after sensing an apple, then it should be able to also predict a red light after it sees something else that is semantically similar enough to an apple. Depending on past experiences/training, a banana could be similar enough.

@stepan

Let’s say I told you some random word “апельсин”. You would certainly create SDR for this pattern. Then I tell you that “the word I told you - a fruit”. So, does it change the SDR of “апельсин” or does it change the SDR for “fruit”?

It does both. Synaptic connections change over time, so the representation for any concept is always changing (they are not static). “Object” representations are influenced by each different context in which they are encountered. This is somewhat obscured in classic HTM because the SP process is designed specifically for an inference layer. Changes have to be made when using it for a pooling layer (I’ll actually be posting more information about this pretty soon, as I am wrapping up my latest pooling implementation).

Cortical.io Semantic Folding video provides a nice (albeit very high level) idea of how they were able to generate SDRs for text words that derive their meaning from context. It’s not exactly what I am talking about here, but hopefully it will trigger the basic idea that I am trying to communicate

@stepan

About the “location signal”. My point is why would we even need a location signal? Please, could you give me an example of the task which we absolutely can’t do by current (even HTM) methods and need a “location signal”?

I recommend watching the HTM Chat with Jeff, as he explains the problem much better than I can in a forum post.