Modern multicore computers provide an opportunity to parallelize plan-recognition algorithms to decrease run time. Viewing plan recognition as parsing based on a complete breadth first search, makes ELEXIR (engine for lexicalized intent recognition) (Geib 2009, Geib and Goldman 2011) particularly suited for parallelization. This article documents the extension of ELEXIR to utilize such modern computing platforms. We will discuss multiple possible algorithms for distributing work between parallel threads and the associated performance wins. We will show that the best of these algorithms provides close to linear speedup (up to a maximum number of processors), and that features of the problem domain have an impact on the achieved speedup.

Kieran O'Neill, CEO of London fashion startup Thread, is building a new way of shopping for clothes. His site uses online stylists, as well as artificial inteligence (AI) and machine learning, to create a personalised way to shop. But that can cause problems when he visits standard clothes shops. "We went to Liberty for a bit and I went to the men's section," O'Neill said in an interview at Thread's London office. "[I] went to the rack and was browsing and I looked at it and it wasn't my size. I felt this rage, this offence that why would you bother showing it to me if it's not actually in my size?"

Asking effective questions is a powerful social skill. In this paper we seek to build computational models that learn to discriminate effective questions from ineffective ones. Armed with such a capability, future advanced systems can evaluate the quality of questions and provide suggestions for effective question wording. We create a large-scale, real-world dataset that contains over 400,000 questions collected from Reddit "Ask Me Anything" threads. Each thread resembles an online press conference where questions compete with each other for attention from the host. This dataset enables the development of a class of computational models for predicting whether a question will be answered. We develop a new convolutional neural network architecture with variable-length context and demonstrate the efficacy of the model by comparing it with state-of-the-art baselines and human judges.

Kieran O'Neill, CEO of London fashion startup Thread, is building a new way of shopping for clothes. His site uses online stylists, as well as artificial inteligence (AI) and machine learning, to create a personalised way to shop. But that can cause problems when he visits standard clothes shops. "We went to Liberty for a bit and I went to the men's section," O'Neill said in an interview at Thread's London office. "[I] went to the rack and was browsing and I looked at it and it wasn't my size. I felt this rage, this offence that why would you bother showing it to me if it's not actually in my size?"