The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for the expansion of image data using a stochastic, pipeline-based approach which effectively allows for images to be sampled from a distribution of augmented images at runtime. Augmentor provides methods for most standard augmentation practices as well as several advanced features such as label-preserving, randomised elastic distortions, and provides many helper functions for typical augmentation tasks used in machine learning.

Deriving an effective facial expression recognition component is important for a successful human-computer interaction system. Nonetheless, recognizing facial expression remains a challenging task. This paper describes a novel approach towards facial expression recognition task. The proposed method is motivated by the success of Convolutional Neural Networks (CNN) on the face recognition problem. Unlike other works, we focus on achieving good accuracy while requiring only a small sample data for training. Scale Invariant Feature Transform (SIFT) features are used to increase the performance on small data as SIFT does not require extensive training data to generate useful features. In this paper, both Dense SIFT and regular SIFT are studied and compared when merged with CNN features. Moreover, an aggregator of the models is developed. The proposed approach is tested on the FER-2013 and CK+ datasets. Results demonstrate the superiority of CNN with Dense SIFT over conventional CNN and CNN with SIFT. The accuracy even increased when all the models are aggregated which generates state-of-art results on FER-2013 and CK+ datasets, where it achieved 73.4% on FER-2013 and 99.1% on CK+.

Tableau, a Seattle-based business intelligence and analytics provider, announced this morning that it has acquired ClearGraph. Based in Palo Alto, Calif., ClearGraph provides smart data discovery and data analysis natural language query solutions. The purchase price has not been disclosed. ClearGraph was founded in 2014 by investment specialist Andrew Vigneault (CEO) and CTO Ryan Atallah. It is expected that ClearGraph's entire team will join Tableau and help integrate the technologies.

Mapping 1,100 years of metaphoric English language, researchers at UC Berkeley and Lehigh University in Pennsylvania have detected patterns in how English speakers have added figurative word meanings to their vocabulary. Researchers called the original semantic domain the "source domain" and the domain that the metaphorical meaning was extended to, the "target domain." More than 1,400 online participants were recruited to rate semantic domains such as "water" or "mind" according to the degree to which they were related to the external world (light, plants), animate things (humans, animals), or intense emotions (excitement, fear). In comparing their computational predictions against the actual historical record provided by the Metaphor Map of English, researchers found that their models correctly forecast about 75 percent of recorded metaphorical language mappings over the past millennium.

Mapping 1,100 years of metaphoric English language, researchers at UC Berkeley and Lehigh University in Pennsylvania have detected patterns in how English speakers have added figurative word meanings to their vocabulary. Researchers called the original semantic domain the "source domain" and the domain that the metaphorical meaning was extended to, the "target domain." More than 1,400 online participants were recruited to rate semantic domains such as "water" or "mind" according to the degree to which they were related to the external world (light, plants), animate things (humans, animals), or intense emotions (excitement, fear). In comparing their computational predictions against the actual historical record provided by the Metaphor Map of English, researchers found that their models correctly forecast about 75 percent of recorded metaphorical language mappings over the past millennium.

Nvidia, which has been diversifying into newer technologies including self-driving cars and artificial intelligence, originally came into prominence in the gaming industry for designing graphics processing chips, that are also used for cryptocurrency mining.

We arrive a few minutes late, thanks to a traffic jam that is now routine on the Outer Ring Road in Bengaluru. In the lobby of a hotel in Cessna Business Park, which houses Cisco, Flipkart and InMobi, D J Patil settles into a chat with my colleague Sriram Sharma. Patil comes across as an easygoing guy for a scientist. He was the first chief data scientist at the White House, handpicked by then US president Barack Obama. Patil and Jeff Hammerbacher coined the term "data scientist" in 2008.

Data released by West Midlands Fire Service appears to show the city of Birmingham has too many fire stations, with 15 compared with neighbouring Solihull's two. The service's online map of attendance times shows many parts of Solihull, a suburban and rural area, have to wait much longer for firefighters to arrive. Even on the basis of relative population sizes, Solihull looks under-served. You forgot to provide an Email Address. This email address doesn't appear to be valid.

If you haven't heard, artificial intelligence (AI) startups are sort of a big deal. It's a tech category that has left the halls of academia in favor of entrepreneurs' innovative embrace. In turn, those entrepreneurs are holding their hands out to investors who have shown an increasing willingness to invest. Whether the current cohort of AI startups find market traction, and, eventually, the exits their backers anticipate, is an open question. Of course, for AI startups being funded, there are two paths to exit for founders and their investors: going public or being acquired.

Google has been pretty far ahead of the curve when it comes to its artificial intelligence research. The world was shocked when its AI beat a top human player at the game of Go. More recently the company taught AI to use imagination and make predictions. Google is making its AI more human -- to a startling degree. At first glance, it might seem counter-intuitive to build an artificial agent that needs to'sleep' – after all, they are supposed to grind away at a computational problem long after their programmers have gone to bed.