Category Archives: Introduction

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

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Why Hypergraphs?

I’ve recently been hacking on creating a new parser for the Link Grammar theory of natural language parsing. I want to couple parsing to machine learning (ML), to that I can use ML to learn natural languages. To do that, I need to place everything in a certain abstract data representation framework that allows graph rewrite rules, logical reasoning, and Bayesian probabilistic reasoning to be combined. This framework exists in OpenCog, but few people know or understand this. That this framework also has a firm foundation in model theory, category theory (even n-categories!) and type theory is even less well known. To explain all this, I just wrote a simple, easy introduction to all of these ideas, and how they come together. Follow the link for more. Continue reading

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Genetic Crossover in MOSES

MOSES is a system for learning programs from input data. ¬†Given a table of input values, and a column of outputs, MOSES tries to learn a program, the simplest program that can reproduce the output given the input values. The … Continue reading

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The AGI Summer School 2009

In the middle of last year, Xiamen University hosted the first international summer school on Artificial General Intelligence. While several of the core OpenCog developers, and Ben Goertzel were there to teach, it passed by somewhat quietly on our blog … Continue reading

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