Google will buy London-based artificial intelligence company DeepMind. The Information reports that the acquisition price was more than $500 million, and that Facebook was also in talks to buy the startup late last year. DeepMind confirmed the acquisition to us, but couldn't disclose deal terms. The acquisition was originally confirmed by Google to Re/code. Google's hiring of DeepMind will help it compete against other major tech companies as they all try to gain business advantages by focusing on deep learning.

Deeper neural nets often yield harder optimization problems. At the heart of deep learning lies a hard optimization problem. So hard that for several decades after the introduction of neural networks, the difficulty of optimization on deep neural networks was a barrier to their mainstream usage and contributed to their decline in the 1990s and 2000s. Since then, we have overcome this issue. In this post, I explore the "hardness" in optimizing neural networks and see what the theory has to say.

THAT a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year they spent some $8.5 billion on deals, says Quid, a data firm. That was four times more than in 2010.

Microsoft has announced a new partnership with OpenAI, the $1 billion artificial intelligence research nonprofit cofounded by Tesla CEO Elon Musk and Y Combinator President Sam Altman. Under the terms of the partnership, OpenAI will use the Microsoft Azure cloud computing service as the "preferred" place to do its AI experiments. In return, Microsoft gets increased access to OpenAI's deep bench of robotics and experts, making Azure a better place for building AI-powered software. "You want to get a good feedback loop going," says Microsoft Executive VP of Cloud and Enterprise Scott Guthrie. "That helps you ultimately build a better platform."

The Ishikawa Watanabe Laboratory, the University of Tokyo laboratory has all rights to the materials shown in the video. The paper "Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup" and its online demo is available here: http://hi.cs.waseda.ac.jp/ esimo/en/r... http://hi.cs.waseda.ac.jp:8081/ Recommended for you: Rocking Out With Convolutions - https://www.youtube.com/watch?v JKYQO... Separable Subsurface Scattering - https://www.youtube.com/watch?v 72_iA... WaveNet by Google DeepMind - https://www.youtube.com/watch?v CqFIV... WE WOULD LIKE TO THANK OUR GENEROUS PATREON SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: Sunil Kim, Julian Josephs, Daniel John Benton, Dave Rushton-Smith, Benjamin Kang. Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_c... Image credits: Bitmap and vector images (two of them): Wikipedia - https://en.wikipedia.org/wiki/Vector_... and https://en.wikipedia.org/wiki/Image_t... Image resolution: Wikipedia - https://en.wikipedia.org/wiki/Image_r... Vectorization: Wikipedia - https://en.wikipedia.org/wiki/Image_t... Thumbnail background - https://pixabay.com/photo-1281718/ Music: Dat Groove by Audionautix is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/...) Artist: http://audionautix.com/

IBM is developing a platform that uses deep learning to diagnose melanoma from images of skin lesions in collaboration with dermatologists from Memorial Sloan Kettering Cancer Center. Over the past few years, we've seen IBM dive into healthcare including the recent launch of Watson Health. From helping guide cancer therapy decisions to simply collecting & collating unbelievable amounts of health data, IBM has undertaken a number of exciting endeavors. In this work, IBM researchers describe development of a platform that uses deep learning technology to analyze images of skin lesions and identify those that could be melanoma. The technical details of how they accomplished this are described in detail in a paper recently published online by IBM in detail that goes way beyond my understanding of this space.

With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings. Coupled with breakneck speeds in AI-research, the new wave of popularity shows a lot of promise for solving some of the harder problems out there. That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks. A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved. This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production.

Microsoft researchers recently built an artificially intelligent system that seems to recognize conversational speech as effectively as a human. Yes, this research comes with caveats, but it's part of a very real and very rapid leap in artificial intelligence over the past several years, a leap driven by deep neural networks. These sweepingly complex algorithms can teach themselves very particular tasks by analyzing vast amounts of data. Microsoft's system learned to recognize words by looking for patterns in old tech support calls. But it's not just the algorithms that are driving the recent revolution in AI. Microsoft's speech rec system relies on large farms of GPU processors, chips that were originally designed for rendering graphics but have proven remarkably adept at running artificial intelligence models.

This event is part of a series designed to bring together artists, developers, designers, technologists and industry professionals to discuss the applications of artificial intelligence in the creative industries.