Hi Linas, Please share those PDFs. お願い 🙏
I’ve been searching for a unifying theory that can encompass both formal reasoning systems and neural nets for some time, and I suspect you might have it. Or at the very least you’re much closer than I am. My project (Hippocampus) was/is a value-flow network that could represent programs. Not unlike Atomspace. I opted for connectivity rules that I called “flux attenuation”. That is to say each linkage could express a value between 0 and 1, and collectively the “conductance” of any path through the graph could be evaluated using Ohm’s law. For example, a CondLink (in Atomese parlance) could be thought of as a semiconductor with the conductance changing depending on the value passing through it. I had two reasons for this design choice: 1.) I wanted to be able to mix and match subgraphs with predictable results. (first and foremost HC is aiming to be a programming language) and 2.) I wanted to be able to apply Quasi-Monte-Carlo methods (low-discrepancy sequence sampling, e.g. Halton, etc.) to creating a probability-distribution-function, solving an entire graph. But, without the ability to tweak biases, HC networks are pretty much untrainable, compared with neural networks. At least I haven’t been able to train them to do much beyond some very simple toy problems. Maybe I’m a bad teacher. HC allows NNs to be embedded inside HC nodes, e.g. a classifier could use a SoftMax to normalize the outputs into attenuation values, but it feels like I’m missing something important. Thank you. -Luke > On Jan 17, 2021, at 3:21 PM, Linas Vepstas <[email protected]> wrote: > ... > So I've attempted to build the AtomSpace as a place to store and connect-up > axioms/sequents/assertions/rules with connections that are > probabilities/weights/fuzzy-logic values/etc. -- that is, numbers, or > number-like things: qubits/homogenous spaces/etc. > > If you study neural networks, you can see that they are densely connected > networks, with nodes, and almost all weights between almost all nodes being > non-zero. If you study formal mathematical proofs, you can see that they are > extremely sparse networks, where every node is connected to only 1 to 3 or 4 > others, where the weights are exactly true/false/0/1. If you study natural > language, and biochemistry and many other natural phenomena, you find a > scale-free network that is neither dense, nor is it sparse, but somewhere in > the middle. > > I am deeply interested in converting time-ordered expressions of that network > into the underlying structure. (and back). So, by analogy: a seismologist, > all they have are some time-series recordings of Earth's vibrations; from > that they try to reconstruct the structure of Earth. I have a time series of > words, I want to reconstruct the structure of the brain that wrote those > words. And, once reconstructed, what else might that "brain" have said? Just > like the Earth model: what other kinds of earthquakes might it produce? > > I've got half-a-dozen PDFS all 20 to 100 pages long, that spin out each of > the above paragraphs into great detail. I think they're important, but I > can't get anyone to read them :-) So it goes... -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/8CD8FFA9-45D4-430C-AC76-7715BB4B6ED1%40gmail.com.
