Category Archives: Theory

“Wave function collapse” for procedural generation

I’ve just been notified about an exciting and important algorithm for procedural generation: the “wave function collapse algorithm“, described by Marian Kleineberg in 2019. I want to talk about it here, from the viewpoint of natural language generation. It’s a … Continue reading

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Everything is a Network

The goal of AGI is to create a thinking machine, a thinking organism, an algorithmic means of knowledge representation, knowledge discovery and self-expression. There are two conventional approaches to this endeavor. One is the ad hoc assembly of assorted technology … Continue reading

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COVID-19 Modelling and Random Social Networks

Seems like everyone wants to be an epidemiologist these days, so why not OpenCog? After all, diseases spread through networks, propagating from one node to the next. A network is a graph, the AtomSpace is a graph database, and the … Continue reading

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Value Flows

Graphs and graphical databases are now accepted as a very good (the best?) way of capturing the relationship between things. Yet, many of the “things” represented graphically are actually processes.  An example might be the water cycle in nature: rain … Continue reading

Posted in Documentation, Introduction, Theory | Tagged , | 2 Comments

Symbolic and Neural Nets: Two Sides of the Same Coin

Deep learning and neural nets are all the rage, today, and have displaced symbolic AI systems in most applications. It’s commonly believed that the two approaches have nothing to do with each other; that they’re just completely different, and that’s … Continue reading

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