#dnnresearch

DeepMind - combine “the best techniques from machine learning and systems neuroscience to build powerful general-purpose learning algorithms.” Aritificial Intelligence.

Re/Code - Artificial Intelligence firm.

DNNresearch - object recognition.

Wavii - understanding natural language.

Boston Dynamics

New Post has been published on http://projectopenhand.biz/google-scoops-up-neural-networks-startup-dnnresearch-to-boost-its-voice-and-image-search-tech-2/2856

Google Scoops Up Neural Networks Startup DNNresearch To Boost Its Voice And Image Search Tech

Well, Google’s M&A strategy is nothing if not diverse in focus. In November, it acquired package delivery startup Bufferbox. Last month, Google it made its first acquisition of the year, buying eCommerce startup Channel Intelligence. Today, Google dug into the Computer Science department at The University of Toronto to acquire DNNresearch, a young startup founded by professor Geoffrey Hinton and two of his grad students, Alex Krizhevsky and Ilya Sutskever.

Incorporated last year, the startup’s website is conspicuously devoid of any identifying information — just a blank black screen. While the financial terms of the deal were not disclosed, Google was eager to acquire the startup’s research on neural networks — as well as the talent behind it — to help it go beyond traditional search algorithms in its ability to identify pieces of content, images, voice, text and so on. In its announcement today, the University of Toronto said that the team’s research “has profound implications for areas such as speech recognition, computer vision and language understanding.”

Furthermore, Professor Hinton is the founding director of the Gatsby Computational Neuroscience Unit at University College in London, holds a Canada Research Chair in Machine Learning and is the director of the Canadian Institute for Advanced Research-funded program on “Neural Computation and Adaptive Perception.” Also a fellow of The Royal Society, Professor Hinton has become renowned for his work on neural nets and his research into “unsupervised learning procedures for neural networks with rich sensory input.”

In its statement, the University of Toronto said that both Krizhevsky and Sutskever will be moving to Google, while Hinton will “divide his time between his university research and his work at Google,” both in Google’s Toronto offices and at Google headquarters in Mountain View.

For Google, this means getting access, in particular, to the team’s research into the improvement of object recognition, as the company looks to improve the quality of its image search and facial recognition capabilities. The company recently acquired Viewdle, which owns a number of patents on facial recognition, following its acquisition of two similar startups in PittPatt in 2011 and Neven Vision all the way back in 2006.

In addition, Google has been looking to improve its voice recognition, natural language processing and machine learning, integrating that with its knowledge graph to help develop a brave new search engine. Google already has deep image search capabilities on the web, but, going forward, as smartphones proliferate, it will look to improve that experience on mobile.

In a recent paper published by the three founders of DNNresearch, the team found that “despite the attractive qualities of CNNs [convolutional neural networks], and despite the relative efficiency of their local architecture, they have still been prohibitively expensive to apply in large scale to high-resolution images … [However, the results of its research] show that a large, deep convolutional neural network is capable of achieving recordbreaking results on a highly challenging dataset using purely supervised learning.”

Get that?

The acquisition of DNNresearch also follows a $ 600K gift that Google awarded to Hinton and his research team to support their work in neural nets. Following its do-good thesis, the company pledged to “support ambitious research in computer science and engineering” through its “Focused Research Awards program,” which offer unrestricted, two-to-three-year grants and give recipients access to Google “tools, technologies and expertise.”

So, it looks like Google discovered DNNresearch through its award program and, seeing the implications that the team’s work could have on the fields of speech recognition, language processing and image recognition — all central to its core products — decided that a grant wasn’t enough.

“Geoffrey Hinton’s research is a magnificent example of disruptive innovation with roots in basic research,” University of Toronto President David Naylor said in a statement. “The discoveries of brilliant researchers, guided freely by their expertise, curiosity, and intuition, lead eventually to practical applications no one could have imagined, much less requisitioned.”

More in the University of Toronto’s statement here.

Update: Professor Hinton penned a Google+ post today that offers his take on joining Google officially, in which he says he is betting on “Google’s team to be the epicenter of future breakthroughs.”

Full post below:

Last summer, I spent several months working with Google’s Knowledge team in Mountain View, working with Jeff Dean and an incredible group of scientists and engineers who have a real shot at making spectacular progress in machine learning. Together with two of my recent graduate students, Ilya Sutskever and Alex Krizhevsky (who won the 2012 ImageNet competition), I am betting on Google’s team to be the epicenter of future breakthroughs. That means we’ll soon be joining Google to work with some of the smartest engineering minds to tackle some of the biggest challenges in computer science. I’ll remain part-time at the University of Toronto, where I still have a lot of excellent graduate students, but at Google I will get to see what we can do with very large-scale computation.

New Post has been published on http://projectopenhand.biz/google-scoops-up-neural-networks-startup-dnnresearch-to-boost-its-voice-and-image-search-tech/2852

Google Scoops Up Neural Networks Startup DNNresearch To Boost Its Voice And Image Search Tech

Well, Google’s M&A strategy is nothing if not diverse in focus. In November, it acquired package delivery startup Bufferbox. Last month, Google it made its first acquisition of the year, buying eCommerce startup Channel Intelligence. Today, Google dug into the Computer Science department at The University of Toronto to acquire DNNresearch, a young startup founded by professor Geoffrey Hinton and two of his grad students, Alex Krizhevsky and Ilya Sutskever.

Incorporated last year, the startup’s website is conspicuously devoid of any identifying information — just a blank black screen. While the financial terms of the deal were not disclosed, Google was eager to acquire the startup’s research on neural networks — as well as the talent behind it — to help it go beyond traditional search algorithms in its ability to identify pieces of content, images, voice, text and so on. In its announcement today, the University of Toronto said that the team’s research “has profound implications for areas such as speech recognition, computer vision and language understanding.”

Furthermore, Professor Hinton is the founding director of the Gatsby Computational Neuroscience Unit at University College in London, holds a Canada Research Chair in Machine Learning and is the director of the Canadian Institute for Advanced Research-funded program on “Neural Computation and Adaptive Perception.” Also a fellow of The Royal Society, Professor Hinton has become renowned for his work on neural nets and his research into “unsupervised learning procedures for neural networks with rich sensory input.”

In its statement, the University of Toronto said that both Krizhevsky and Sutskever will be moving to Google, while Hinton will “divide his time between his university research and his work at Google,” both in Google’s Toronto offices and at Google headquarters in Mountain View.

For Google, this means getting access, in particular, to the team’s research into the improvement of object recognition, as the company looks to improve the quality of its image search and facial recognition capabilities. The company recently acquired Viewdle, which owns a number of patents on facial recognition, following its acquisition of two similar startups in PittPatt in 2011 and Neven Vision all the way back in 2006.

In addition, Google has been looking to improve its voice recognition, natural language processing and machine learning, integrating that with its knowledge graph to help develop a brave new search engine. Google already has deep image search capabilities on the web, but, going forward, as smartphones proliferate, it will look to improve that experience on mobile.

In a recent paper published by the three founders of DNNresearch, the team found that “despite the attractive qualities of CNNs [convolutional neural networks], and despite the relative efficiency of their local architecture, they have still been prohibitively expensive to apply in large scale to high-resolution images … [However, the results of its research] show that a large, deep convolutional neural network is capable of achieving recordbreaking results on a highly challenging dataset using purely supervised learning.”

Get that?

The acquisition of DNNresearch also follows a $ 600K gift that Google awarded to Hinton and his research team to support their work in neural nets. Following its do-good thesis, the company pledged to “support ambitious research in computer science and engineering” through its “Focused Research Awards program,” which offer unrestricted, two-to-three-year grants and give recipients access to Google “tools, technologies and expertise.”

So, it looks like Google discovered DNNresearch through its award program and, seeing the implications that the team’s work could have on the fields of speech recognition, language processing and image recognition — all central to its core products — decided that a grant wasn’t enough.

“Geoffrey Hinton’s research is a magnificent example of disruptive innovation with roots in basic research,” University of Toronto President David Naylor said in a statement. “The discoveries of brilliant researchers, guided freely by their expertise, curiosity, and intuition, lead eventually to practical applications no one could have imagined, much less requisitioned.”