Existing approaches to text generation fail to consider how interactions with the user may be managed within a coherent explanation or description. This paper presents an approach to generating such interactive explanations based on two levels of discourse planning - content planning and dialogue planning. The system developed allows aspects of the changing context to be monitored with an explanation, and the developing explanation to depend on this changing context. Interruptions from the user are allowed and dealt with (and resumed from) within the context of that explanation.
Given a knowledge base, expanding a query consists of determining all the ways of deriving it from atoms built on some distinguished predicates. In this paper, we address the problem of determining the expansions of a query in description logics and CARIN. Description Logics are logical formalisms for representing classes of objects (called concepts) and their relationships (expressed by binary relations called roles). Much of the research in description logics has concentrated on algorithms for checldng subsumption between concepts and satisfiability of knowledge bases (see e.g.
What do Siri and machine translation have in common? They both produce strange, sometimes ridiculous language that leave us shaking our heads with confusion. Here at IVANNOVATION we frequently use Siri as well as Google's dictation function to get our work done. Siri instantly adds items to our to do lists, adds events to our calendars, and tells us answers to important questions like, "Siri, how much wood would a woodchuck chuck if a woodchuck could chuck wood?" (Ask Siri yourself.) Likewise, Google dictation helps us avoid the ruthless onslaught of carpal tunnel syndrome by typing up our articles and emails for us.
I feel that there was a sort of explosion a couple of years ago after which the whole topic of Artificial Intelligence (AI) suddenly sprang into a wider audience's consciousness. All of a sudden we had Siri, Amazon's Alexa and we started talking about self-driving cars. Jaan Tallinn, how did it happen? There were two different explosions. I believe that a lot of the latter had to do with the works of Elon Musk and Stephen Hawking. Most importantly, the former was the revolution of deep learning.
Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since it allows to discover non-destructively spectral characteristics of plants governed primarily by scattering and absorption characteristics of the leaf internal structure and biochemical constituents. Several drought stress indices have been derived using hyper-spectral imaging. However, they are typically based on few hyper-spectral images only, rely on interpretations of experts, and consider few wavelengths only. In this study, we present the first data-driven approach to discovering spectral drought stress indices, treating it as an unsupervised labeling problem at massive scale. To make use of short range dependencies of spectral wavelengths, we develop an online variational Bayes algorithm for latent Dirichlet allocation with convolved Dirichlet regularizer. This approach scales to massive datasets and, hence, provides a more objective complement to plant physiological practices. The spectral topics found conform to plant physiological knowledge and can be computed in a fraction of the time compared to existing LDA approaches.