Application of HTM in today’s ML frameworks

[rhyolight]

@Cairo

The reason I call it an odd choice though, is that it doesn’t really use or prove (if that is actually what the method is) any other htm theory concept. It is just taking advantage of the sparsity of a matrix, which is already a well known concept in machine learning. I was under the assumption that numenta was trying to move away from the optimizations arena and into cortical algorithms, so I’m really curious to know what the purpose of the paper is when it comes out.

For the past couple years, we have not been focused on applications at all. We’ve changed that a bit recently. @Subutai talked about how we are now looking for ways to apply components of our theory into ML frameworks in Numenta's 2018 Year in Review. The most obvious place to start is with SDRs. And the way the SP creates sparsity retains semantic meaning, so it is more valuable than something like dropout. Instead of removing random connections, the SP is learning.