If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Data science, and numerical computing, in general, has a problem: the deep linear algebra libraries deal with pure numbers in vectors and matrices, but in the real world there is always metadata attached to those structures that needs to be carried along through the computational pipeline. Rows and columns have information attached to them--names, typically--that has to be accounted for even as we do things like remove rows or swap data around to make certain computations more tractable.
Though consistency across the population renders the extraordinarily complex functional anatomy of the human brain surveyable, the inverse inference--from common functional maps to individual behaviour--is constrained by marked individual deviation from the population mean. Such inference is fundamental to the evaluation of therapeutic interventions in focal brain injury, where the impact of an induced structural change in the brain is quantified by its behavioural consequences, inevitably refracted through the lens of lesion-outcome relations. Current therapeutic evaluations do not incorporate inferences to the individual outcome derived from a detailed specification of the lesion anatomy, relying only on reductive parameters such as lesion volume and crudely discretised location. Examining 1172 patients with anatomically registered focal brain lesions, here we show that such low-dimensional models are highly insensitive to therapeutic effects. In contrast, high-dimensional models supported by machine learning dramatically improve sensitivity by leveraging complex individuating patterns in the functional architecture of the brain.
As drones and their components get smaller, more efficient, and more capable, we've seen an increasing amount of research towards getting these things flying by themselves in semi-structured environments without relying on external localization. The University of Pennsylvania has done some amazing work in this area, as has DARPA's Fast Lightweight Autonomy program.
It took several months, but Lyft and nuTonomy have made good on their promise to test autonomous ridesharing cars in Boston. The two have launched a pilot program that gives "select" Seaport-area passengers a ride in one of nuTonomy's self-driving Renault cars. If you're one of the few to hop in (the Lyft app will make it obvious), your feedback will help refine the system to make sure it's both comfortable and safe.