This is a video lecture on chapter 17, titled “Values in a Scientific World”, of Russ Shafer-Landau’s book, ‘Whatever Happened to Good and Evil?’ The argument that this chapter considers is one that claims that if one follow’s Occam’s Razor, then one ought to reject the reality of objective moral facts or laws. This is an argument for moral skepticism, and Shafer-Landau offers several responses to it and in defense of moral objectivism. In the end, he claims that this use of occam’s razor defeats itself. This lecture is part of an introductory level philosophy course, Introduction to Ethics.

Occam’s razor applies only to descriptive facts, not normative ones.

This is a video lecture about the different between descriptive claims or laws, on the one hand, and normative claims or laws, on the other. I also explain three different varieties of normativity: the moral, the prudential, and the epistemic. This video was originally produced for use in an introductory level philosophy course, Introduction to Ethics. But it is a stand-alone explanation, so it can be used in any other context.

This is a lecture video about a short selection from book 3 of David Hume’s famous work of philosophy, A Treatise of Human Nature (1739–40). Hume was an empiricist. The lecture of basically a presentation of his argument from empiricism to the conclusion that there are no genuine, objective moral facts residing in actions themselves (rather, there are only sentiments of moral disapprobation or disapproval in us). This lecture of part of Introduction to Ethics.

The atheists aren’t saying that there was nothing before everything. They’re saying that there was no “before” because time itself started when the universe started.

A free, open-source ontology editor and framework for building intelligent systems

Protégé is supported by a strong community of academic, government, and corporate users, who use Protégé to build knowledge-based solutions in areas as diverse as biomedicine, e-commerce, and organizational modeling.

A set of ontology matching algorithms (for finding correspondences between concepts) is based on a thesaurus that provides the source data for the semantic distance calculations. In this wiki era, new resources may spring up and improve this kind of semantic search. In the paper a solution of this task based on Russian Wiktionary is compared to WordNet based algorithms. Metrics are estimated using the test collection, containing 353 English word pairs with a relatedness score assigned by human evaluators. The experiment shows that the proposed method is capable in principle of calculating a semantic distance between pair of words in any language presented in Russian Wiktionary. The calculation of Wiktionary based metric had required the development of the open-source Wiktionary parser software.

We introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in AI applications. We evaluate Wiktionary on the pervasive task of computing semantic relatedness for English and German by means of correlation with human rankings and solving word choice problems. For the first time, we apply a concept vector based measure to a set of different concept representations like Wiktionary pseudo glosses, the first paragraph of Wikipedia articles, English WordNet glosses, and GermaNet pseudo glosses. We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations.

ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images.

Freebase is an open, shared database that contains structured information on millions of topics in hundreds of categories.