Stochastic Gradient Descent (SGD) has become one of the most popular optimization methods for training machine learning models on massive datasets. However, SGD suffers from two main drawbacks: (i) The noisy gradient updates have high variance, which slows down convergence as the iterates approach the optimum, and (ii) SGD scales poorly in distributed settings, typically experiencing rapidly decreasing marginal benefits as the number of workers increases. In this paper, we propose a highly parallel method, CentralVR, that uses error corrections to reduce the variance of SGD gradient updates, and scales linearly with the number of worker nodes. CentralVR enjoys low iteration complexity, provably linear convergence rates, and exhibits linear performance gains up to hundreds of cores for massive datasets. We compare CentralVR to state-of-the-art parallel stochastic optimization methods on a variety of models and datasets, and find that our proposed methods exhibit stronger scaling than other SGD variants.

Existing methods for retrieving k-nearest neighbours suffer from the curse of dimensionality. We argue this is caused in part by inherent deficiencies of space partitioning, which is the underlying strategy used by most existing methods. We devise a new strategy that avoids partitioning the vector space and present a novel randomized algorithm that runs in time linear in dimensionality of the space and sub-linear in the intrinsic dimensionality and the size of the dataset and takes space constant in dimensionality of the space and linear in the size of the dataset. The proposed algorithm allows fine-grained control over accuracy and speed on a per-query basis, automatically adapts to variations in data density, supports dynamic updates to the dataset and is easy-to-implement. We show appealing theoretical properties and demonstrate empirically that the proposed algorithm outperforms locality-sensitivity hashing (LSH) in terms of approximation quality, speed and space efficiency.

This does not want to be an exhaustive list of skills for data scientists because the field is moving at a stellar speed (and a tool that is relevant today might not be relevant in six months). It is rather an attempt to provide an extensive list of skills and tools that are useful in developing data science projects, and of course not owning one of those skills do not preclude a data scientist to be identified as such. Note: the above is an adapted excerpt from my book "Big Data Analytics: A Management Perspective" (Springer, 2016).

During Final Jeopardy in the episode that aired on February 16, 2011, a computer screen sat between long-running stars Brad Rutter and Ken Jennings. On it, swirling green and blue lines represented the thought patterns of IBM's Watson--a question-answering computer system. Watson's hardware filled a neighboring room as he worked to process natural language and sift through 200 million pages of data to find the winning answer. The category was 19th Century novelists. Alex Trebek, the show's host, read the clue: "William Wilkinson's'An Account of the Principalities of Wallachia and Moldavia' inspired this author's most famous novel." Watson had thirty seconds to find the correct response.

Dedicated Computing is co-hosting a Deep Learning Institute workshop in collaboration with NVIDIA and Milwaukee School of Engineering (MSOE). The workshop will take place at MSOE on April 13, 2017. Deep learning is a new area of machine learning that seeks to use algorithms, big data, and parallel computing to enable real-world applications and deliver results. Machines are now able to learn at the speed, accuracy, and scale required for true artificial intelligence. This technology is used to improve self-driving cars, aid mega-city planners, and help discover new drugs to cure disease.

If you're an accountant, lawyer or data analyst, a robot may soon take over your job. A new report from the International Bar Association suggests machines will most likely replace humans in high-routine occupations. The authors have suggested that governments introduce human quotas in some sectors in order to protect jobs. Gerlind Wisskirchen, a lawyer for labour and employment law, coordinated the study, which started one-and-a-half years ago. "We thought it'd just be an insight into the world of automation and blue collar sector," she said.

Starsky Robotics is retrofitting large trucks to make them driverless. By the end of the year, the startup hopes it'll be able to operate a truck without a person physically sitting in the vehicle. Starsky Robotics is retrofitting large trucks to make them driverless. By the end of the year, the startup hopes it'll be able to operate a truck without a person physically sitting in the vehicle. Driverless cars could transform the way our country moves, potentially making roads more efficient and possibly saving lives because of fewer traffic accidents.

In recent years, the rapid advance of artificial intelligence has evoked cries of alarm from billionaire entrepreneur Elon Musk and legendary physicist Stephen Hawking. Others, including the eccentric futurist Ray Kurzweil, have embraced the coming of true machine intelligence, suggesting that we might merge with the computers, gaining superintelligence and immortality in the process. As it turns out, we may not have to wait much longer. This morning, a group of research scientists at Google DeepMind announced that they had inadvertently solved the riddle of artificial general intelligence (AGI). Their approach relies upon a beguilingly simple technique called symmetrically toroidal asynchronous bisecting convolutions.

Finding the music of the moment can often be a challenging problem, even for humans with well-versed musical tastes. These challenges further explode into a myriad of complexities when attempting to construct algorithmic approaches for automatic playlist generation. A variety of factors play a role in influencing a listener's perception of what music is appropriate on a given seed (e.g. Erik Schmidt, Senior Scientist at Pandora will be presenting at the Machine Intelligence Summit in San Francisco, 23-24 March. Erik will present an overview of recommendations at Pandora, followed by a deep dive into the challenges of recommending content.

Whether you've just gotten rid of cable or want to supplement your TV package with online video, now's an excellent time to buy a media streaming device. Compared to the typical smart TV, standalone streamers such as the Roku Streaming Stick and Amazon Fire TV tend to have bigger app selections, faster performance, and more features. And with so much competition between device makers, the hardware is becoming faster, more capable, and more affordable. We constantly test all the latest devices, including Roku players, Fire TV devices, Android TV devices, Apple TV, and Chromecast. We review each new generation of hardware and constantly revisit the software and app selection so we can help you determine which platform is right for you.