Artificial Intelligence Blog

I had a good talk with some of the Metaweb developers last year and started playing with their Python APIs for accessing structured data. I wanted to be able to use this structured data source in a planned Ruby project and was very pleased to see Christopher Eppstein's new project that provides an ActiveRecord style API on top of Freebase. Here is the web page for Christopher's Freebase API project. Assuming that you do a "gem install freebase", using this API is easy; some examples:

require 'rubygems'
require "freebase"
require 'pp'

an_asteroid = Freebase::Types::Astronomy::Asteroid.find(:first)
#pp "an_asteroid:", an_asteroid
puts "name of asteroid=#{an_asteroid.name}"
puts "spectral type=#{an_asteroid.spectral_type[0].name}"

#all_asteroids = Freebase::Types::Astronomy::Asteroid.find(:all)
#pp "all_asteroids:", all_asteroids

a_company = Freebase::Types::Business::Company.find(:first)
#pp "a_company:", a_company
puts "name=#{a_company.name}"
puts "parent company name=#{a_company.parent_company[0].name}"

You will want to use this API interactively: use the Freebase web site to find type hierarchies that you are interested in, fetch the first object matching a type hierarchy (e.g., Types -> Astronomy -> Asteroid) and pretty print the fetched object to see what data fields are available.

Labels: knowledge representation, Ruby, semantic web

Bill Clementson mentioned Conrad Barski's new open content article on Knowledge Representation. Nice! Conrad provides not only a great overview, but just as important, the motivation for treating Knowledge Representation as a science.

Labels: knowledge representation

Our own intelligence is defined by our abilities to predict and generalize. As Jeff Hawkins points out, we live our lives constantly predicting what will happen to us in the next few seconds. (See Numenta.com - Hawkin's company - for the source code to NTA hierarchical temporal memory system.)

We also generalize by learning to recognize patterns and ignore noise.

AI systems need to implement prediction and generalization, and do this in a way that scales so that we can move past small toy problems. Scalability is most important in prediction because of the size of data required to model the environment that an AI will live in and the real time requirements (prediction does us little good if the calculation takes too long).

Knowledge Engineering is not AI, it is the engineering discipline for the understanding and re-implementation in software of human level expertise in narrow problem domains.

Labels: knowledge representation, neural networks