Samsung Electronics has announced that it has agreed to acquire Viv Labs, "the intelligent interface to everything." Viv has developed an open artificial intelligence (AI) platform that gives third-party developers the power to use and build conversational assistants and integrate a natural language-based interface into applications and services. It claims to be well advanced over other language interfaces like Siri or Google Now where accuracy cannot be relied on. Viv's creator, Dag Kittlaus should know as he co-created Siri too. He said, "Viv would breathe life into the inanimate objects of our life through conversation."

The article examines in some detail the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the standard stochastic gradient method with a re-scaled (larger) step-size value. The size of the re-scaling is determined by the value of the momentum parameter. The equivalence result is established for all time instants and not only in steady-state. The analysis is carried out for general strongly convex and smooth risk functions, and is not limited to quadratic risks. One notable conclusion is that the well-known bene ts of momentum constructions for deterministic optimization problems do not necessarily carry over to the adaptive online setting when small constant step-sizes are used to enable continuous adaptation and learn- ing in the presence of persistent gradient noise. From simulations, the equivalence between momentum and standard stochastic gradient methods is also observed for non-differentiable and non-convex problems.

Nearly 140 private companies working to advance artificial intelligence technologies have been acquired since 2011, with over 40 acquisitions taking place in 2016 alone (as of 10/7/2016). Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce, are competing in the race to acquire private AI companies, with Samsung emerging as a new entrant this month with its acquisition of startup Viv Labs, which is developing a Siri-like AI assistant. Google has been the most prominent global player, with 11 acquisitions in the category under its belt (follow all of Google's M&A activity here through our real-time Google acquisitions tracker). In 2013, the corporate giant picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.

Automatically identifying that an image is not suitable/safe for work (NSFW), including offensive and adult images, is an important problem which researchers have been trying to tackle for decades. Since images and user-generated content dominate the Internet today, filtering NSFW images becomes an essential component of Web and mobile applications. With the evolution of computer vision, improved training data, and deep learning algorithms, computers are now able to automatically classify NSFW image content with greater precision. Defining NSFW material is subjective and the task of identifying these images is non-trivial. Moreover, what may be objectionable in one context can be suitable in another.

Authors: Dr. Vijay Srinivas Agneeswaran, Director and Head, Big Data Labs, Impetus [email protected]} Deep learning is becoming an important AI paradigm for pattern recognition, image/video processing and fraud detection applications in finance. The computational complexity of a deep learning network dictates need for a distributed realization. Our intention is to parallelize the training phase of the network and consequently reduce training time. We have built the first prototype of our distributed deep learning network over Spark, which has emerged as a de-facto standard for realizing machine learning at scale. Geoffrey Hinton presented the paradigm for fast learning in a deep belief network [Hinton 2006].

Guest blog post by Christopher Dole and other contributors, originally posted here. Deep Learning is one of the most revolutionary and disruptive technologies ever developed in Data Science. Essentially, this is a class of algorithms inspired by how the human brain works, and it has the ability to automate and replace most of the world's jobs. This is what enables self-driving cars to function and what allows Spotify to create very customized playlists and recommendations. This is how YouTube is able to identify faces and animals in videos and how Siri can understand and process free speech in milliseconds.

Additionally, computer processing power has grown at an incredible rate while the cost of processing this data has decreased significantly, making AI more accessible. In fact, AI is everywhere, in nearly every app and device that we use every day. Apple's Siri leverages natural language processing to recognize voice commands. Facebook's deep learning facial recognition algorithm can instantly identify a person with nearly 98 percent accuracy. And Amazon, Netflix and Spotify all utilize machine learning to understand how each item in their massive catalogs not only relate to one another, but also each customer's preferences.

With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. Looking for one central source where you can learn key findings on machine learning? Deep Learning: A Practitioner's Approach provides developers and data scientists with the most practical information available on the subject, including deep learning theory, best practices, and use cases. Authors Adam Gibson and Josh Patterson present the latest relevant papers and techniques in a non academic manner, and implement the core mathematics in their DL4J library.

How can you get better performance from your deep learning model? It is one of the most common questions I get asked. What can I do if my neural network performs poorly? I often reply with "I don't know exactly, but I have lots of ideas." Then I proceed to list out all of the ideas I can think of that might give a lift in performance. Rather than write out that list again, I've decided to put all of my ideas into this post. The ideas won't just help you with deep learning, but really any machine learning algorithm. How To Improve Deep Learning Performance Photo by Pedro Ribeiro Simões, some rights reserved. This list of ideas is not complete but it is a great start.

Deep learning is becoming increasingly used throughout the world of technology, and there are now endless blogs, books, courses and other resources available for those to use. If that's still not quite good enough for you and you don't want to implement deep learning yourself, there are now several machine learning API services available that will do it for you. But, where has this rise in deep learning stemmed from you may be wondering? Big companies use deep learning techniques in various practices throughout their businesses. They generate lots of data, and this is mega important to them as they can learn from this data which will ultimately lead to increased revenue.