Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it implies the transformation of the data from a sparse high-dimensional space, to higher level meaningful abstractions. This dissertation employs Neural Networks for distributed paragraph representations, and Latent Dirichlet Allocation to capture higher level features of paragraph vectors. Although Neural Networks for distributed paragraph representations are considered the state of the art for extracting paragraph vectors, we show that a quick topic analysis model such as Latent Dirichlet Allocation can provide meaningful features too. We evaluate the two methods on the CMU Movie Summary Corpus, a collection of 25,203 movie plot summaries extracted from Wikipedia. Finally, for both approaches, we use K-Nearest Neighbors to discover similar movies, and plot the projected representations using T-Distributed Stochastic Neighbor Embedding to depict the context similarities. These similarities, expressed as movie distances, can be used for movies recommendation. The recommended movies of this approach are compared with the recommended movies from IMDB, which use a collaborative filtering recommendation approach, to show that our two models could constitute either an alternative or a supplementary recommendation approach.

This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network generates individual words, thus reaping the benefits of discriminatively-trained vector representations. The discourse relations are represented with a latent variable, which can be predicted or marginalized, depending on the task. The resulting model can therefore employ a training objective that includes not only discourse relation classification, but also word prediction. As a result, it outperforms state-of- the-art alternatives for two tasks: implicit discourse relation classification in the Penn Discourse Treebank, and dialog act classification in the Switchboard corpus. Furthermore, by marginalizing over latent discourse relations at test time, we obtain a discourse informed language model, which improves over a strong LSTM baseline.

A big shakeup happening in the world of deep learning, as Salesforce announced that it has acquired startup darling MetaMind. As part of the acquisition, MetaMind will shut down on May 4 for unpaid users and June 4 for paid users. Don't miss our biggest TNW Conference yet! With MetaMind and Salesforce coming together, we'll be able to offer customers real AI solutions with breakthrough capabilities that further automate and personalize customer support, marketing automation, and many other business processes. We'll extend Salesforce's data science capabilities by embedding deep learning within the Salesforce platform.

Dave Perkon is technical editor for Control Design. He has engineered and managed automation projects for Fortune 500 companies in the medical, automotive, semiconductor, defense and solar industries. Put simply, the IoT provides the connection, the cloud provides online storage and convenient applications, and big data provides analysis, management and maintenance of information, which, when combined, can overwhelm the data users and decision makers. Fortunately computers and specifically machine-learning applications, although in their early stages, can help. From the industry or manufacturing side of business, machine learning can be applied to just about any control system that is smart enough to actually alter how it controls a machine in response to changing conditions, but there is much more to it than that.

Catan is a fantasy island, uninhabited except for a few scattered settlements of humans. German robot maker Kuka recently partnered with the Regensburg University of Applied Sciences to make an A.I.-controlled robotic arm to play Settlers of Catan, a widely popular modern board game. Fortunately, the world champions of Catan won't have to worry about any surprise machine upsets. The robot right now is focused on placing pieces and connected roads. In the future, it might learn AlphaGo levels of strategy, but that's not for a long time.

Consider the artificially intelligent voices you hear on a regular basis. Are any of them men? Whether it's Apple's Siri, Microsoft's Cortana, Amazon's Alexa, or virtually any GPS system, chances are the computerized personalities in your life are women. This gender imbalance is pervasive in fiction as well as reality. Films like "Her" and "Ex Machina" reflect our anxieties about what intelligent machines mean for humanity.

This article, 10 artificial intelligence insiders to follow on Twitter, originally appeared on TechRepublic.com. With the flood of news on self-driving cars, drones, caring robots, and more, it isn't always easy to keep up-to-date with the latest in the artificial intelligence universe. For the insiders' view on what's happening in AI, follow these 10 researchers, professors, institutions, and other great thinkers who offer human insight into the world of machines. Author of the recently released Rise of the Robots: Technology and the Threat of a Jobless Future, Ford is an important voice in conversations about the future of the workplace and the debate over whether automation will replace humans in the job market. Zhang, a leading software engineer, was a recent speaker at the Grace Hopper Celebration of Women in Computing 2015 conference.

Bengaluru-based Bazmi Husain heads the research & development (R&D) vertical of Swiss engineering major ABB that spends 1.5 billion annually on R&D. Bazmi took charge as the chief technology officer of the group in January 2016 after being the managing director of ABB India. Bazmi, who also heads the venture capital arm of the group, talks to Jyoti Mukul about the global technology trends and how India is uniquely placed. Edited excerpts: How important is ABB's India centre in its R&D operations? India is the largest and fastest growing R&D location for ABB, with footprints across Bengaluru, Chennai, Vadodara and Nashik.

With a number of recent Hollywood films representing intelligent machines as either hero or villain, the subject of Artificial Intelligence (AI) has attracted the imagination of the media and popular culture. On the practical engineering side, over the past decade humanity's age-old dream of developing intelligent machines has experienced rapid development. The prospect of expert systems being increasingly able to outperform humans in a growing set of disciplines has led to deep soul-searching concerning the evolving future of labor. Meanwhile, cognitive computing platforms such as IBM's Watson are demonstrating a powerful ability to support and guide humans in complex activities as diverse as oncology diagnostics, investment management, biomedical research, and even the introduction of novel culinary recipes as Chef Watson.

During the dot-com boom, online clothing sales were predicted to grow to 40% -50% of total sales. Although online sales of some other kinds of merchandise, such as books, have reached 50% of the market in the past 15 years, the percentage of online clothing sales hovers around 20%. The difficulty in finding the correct size and fit is one of the primary reasons that consumers are reluctant to buy clothes online. And their concern is not groundless; sizing varies among clothing manufacturers, and it is difficult to ascertain fit from online images. Consequently, 30%-40% of online clothing purchases are returned.