Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropriate to leverage such correlations. In this paper, we propose to use the von Mises-Fisher distribution to model the density of words over a unit sphere. Such a representation is well-suited for directional data. We use a Hierarchical Dirichlet Process for our base topic model and propose an efficient inference algorithm based on Stochastic Variational Inference. This model enables us to naturally exploit the semantic structures of word embeddings while flexibly discovering the number of topics. Experiments demonstrate that our method outperforms competitive approaches in terms of topic coherence on two different text corpora while offering efficient inference.

Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics. We propose a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. This model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Furthermore, we also develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. We experimented with both synthetic data and data gathered from Twitter, and show that our model provides a good fit to the data as well as more accurate predictions than alternatives.

An enormous volume of software development is done overseas. The labor is cheaper and many countries have done a good job of training a large number of people to be competent developers. But history tells a story of machines and robots replacing people. This may be what is about to happen to overseas developers. And yes, once again it is robots that are taking their place.

In today's world of big data and the internet of things, it is common for a business to find itself sitting atop a mountain of data. Possessing it is one thing, but leveraging it for data driven decision making is a much different ball game. Gut-feelings and institutionalized heuristics have traditionally been used to guide development of protocol and decision making, but the world of artificial intelligence and big disparate data is changing that. Everyone is trying to make sense of, and extract value from, their data. Those that are not will be left behind. This challenge (and opportunity) is not limited to certain industries.

Go, an open source language by Google was initially created by group of engineers who were frustrated with C . Ever since their creation, the language has gotten traction for its simplicity. It ranked highly in the programming popularity indexes of Redmonk & TiOBE.

We are truly passionate about Machine Learning's promise to make The World a better place. As such, we are committed to playing our part in elevating the teaching and practice of Machine Learning in education institutions. As a token of our support, we have enabled a special education promotion by offering FREE PRO SUBSCRIPTION access to all educators and students worldwide for a year. Be sure to claim your account today by registering with your education institution email address. If your university or school uses .edu

Visual effects rendering is a computationally intensive process where one second of screen-time can require thousands of cores and terabytes of frame data. Learn how Academy Award-winning and recognized studios take advantage of cloud economics and Google's on-demand computing to realize their creative visions and expand this digital medium for storytelling.

Artificial intelligence is on the rise in the world of machine translation. A string of recent news about tech giants bolstering machine translation engines with deep learning underscores just how central integrating deep learning into machine translation products has become for companies like Google and Microsoft. Slator reached out to a representative of Beijing-based Baidu, who is authorized to speak for the company, to get an exclusive look at what the Chinese tech leader has in store for its translation technology. Baidu began R&D on Baidu Translate in 2010, launching the product in June 2011. The company felt that translation was in line with what their search users needed.

Microsoft Build, the company's annual conference with software developers, gets underway Wednesday. Microsoft will be talking about its plans for the year, and encouraging developers to think up new applications for its products. Two of the most important things Microsoft is highlighting are artificial intelligence and machine learning. If you use Microsoft products, chances are you already experience machine learning, said spokesman Frank Shaw, who gave the example of Microsoft virtual assistant Cortana. "Cortana will say, currently, 'Hey, you sent email and promised a reply by tomorrow. One of the key elements of the Microsoft Build developer conference is thinking about how to incorporate that kind of intelligence throughout the Microsoft ecosystem. "Microsoft is very good, and they're getting better.

The Bank of Russia is using machine learning technology to identify unlicensed money lenders, and the websites hosting them. The technology, developed by Yandex Data Factory, has helped to reveal 2,500 suspicious organisations. The system uses algorithms to search out websites hosting illegal cash loan providers and unregulated financial activity by indexing web pages related to microfinance and consumer loans. Yandex uses keyword analysis across a search index of some seven million web pages related to finance topics. In order to help build the specialised search model, Bank of Russia experts sorted through and categorised 8,000 web pages.