Did you know Buddhism describes a “layer stack” of the mind, complete with a loss/reward function—much like AI and machine learning?
Back in 2015, my friend Brian Fenton and I published an AI article (https://link.springer.com/chapter/10.1007/978-3-319-21365-1_...) at AGI Berlin conference on a cognitive architecture with self-models at multiple levels—allowing AI to reflect on its own states and capabilities. But since we wrote it in highly technical language for an AI audience, it flew under the radar.
Today, I have published an article on the same topic—this time from a Buddhist perspective (https://medium.com/@frankber/buddhas-layer-stack-software-ar...). This approach lets me explore introspection and consciousness, ideas that don’t always fit within the constraints of scientific papers.
AOLServer (now Naviserver again) is still the base for the largest open-source project management system: ]project-open[ (https://www.project-open.com/)
There was a massive parallel implementation of Prolog running on literally 256 processors: BA-Prolog (http://fraber.de/bap/). Unfortunately the hardware platform was abandoned some years later by Inmos.
Prolog is very, very dead. I love Prolog with all my heart, but it excells at problems that are solved today much more efficiently using neuronal networks. So it's utterly obsolete.
The issue of Prolog is that you need to code your rules manually. Doing ML with Prolog is possible, but very clumsy. Better stick to Python.
Speed is irrelevant, because most problems suitable for Prolog are exponential. Implementation is irrelevant, because SWI-Prolog does all you need with good integrations, except that it's a bit slower. But that's irrelevant, see above.
Learning Prolog is a great experience for any advanced computer science student. It amazes, doesn't it?
Back in 2015, my friend Brian Fenton and I published an AI article (https://link.springer.com/chapter/10.1007/978-3-319-21365-1_...) at AGI Berlin conference on a cognitive architecture with self-models at multiple levels—allowing AI to reflect on its own states and capabilities. But since we wrote it in highly technical language for an AI audience, it flew under the radar.
Today, I have published an article on the same topic—this time from a Buddhist perspective (https://medium.com/@frankber/buddhas-layer-stack-software-ar...). This approach lets me explore introspection and consciousness, ideas that don’t always fit within the constraints of scientific papers.