We're still figuring out probabilistic knowledge representation. So much depends on context. Earlier approaches are fraught with problems. Expressing an enco

We're still figuring out probabilistic knowledge representation. So much depends on context. Earlier approaches are fraught with problems. Expressing an encoded fact that translates to "Obama is the president." is far too simplistic. It does not take into account the proper context (president of the USA), and time period (from January 20th, 2009 to the current time) and so on. These contexts: conversation (were we talking about politics?), location (USA, Russia, etc.), all need to be captured as best as possible and appropriately considered when trying to apply a learned fact. Maybe the context is hypothetical (talking about an alternate history where McCain won the election) or fantastical (talking about President Garrett Walker in the TV show House of Cards). The existing systems I've seen can't really handle all of that. Somehow we humans are generally good at figuring out which sets of contexts are appropriate to apply in a given situation, and doing it quickly. What's tricky is that even in a single conversation with one person, the context may switch from historical, to hypothetical and back again quickly.