victorHeard about this: https://github.com/numenta/nupic in a podcast, it sounded very interesting. If I got it correctly, you could train (unsupervised) two models on two different corpus and resulting "semantic fingerprints" would still be comparable. I'm wondering what replicating some of the word embedding art with HTM would lead to.
This podcast btw: https://www.oreilly.com/ideas/natural-language-analysis-using-hierarchical-temporal-memory and it didn't mention numenta/nupic -- what I meant was that I found out about HTM through there, and then googled HTM and found an OSS project in Python.
Michael_Spencer@vhf With hashtags dead even on the birdsite, have to admit I'm enjoying using them here at on LinkedIn (via their new "trends" feature).
:two_hearts:Euphoria@Michael_Spencer @vhf Hastags are dead? Man, how can I be so behind on some things? Ah well. I came to them late and now I really enjoy inventing them, so I guess I'll keep it up.
Hmm.. now where did I put the window with your LinkedIn piece that I opened when you tooted it whenever the heck that was? Looking forward to reading it!
@Euphoria @vhf If you lose the URL, just check my bio and go to articles. Yes apparently machine learning now can recognize keywords everywhere without the need for the old "#". I still like them innately as signals for targeting.
#s are generally considered bad for Facebook, old for Twitter, but I still find them cute!
Well done, Michael! So well written and you really said something new and fresh that I hadn't seen anyone else say about Mastodon yet. I will be sharing it wherever possible.
@Michael_Spencer You're welcome! Thanks for taking the time to write yours and for sharing it.
I, personally, have a fondness for cheesy, but we also need crackers with our cheese.