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March 22, 2013

Google Adds Another Big Name In Artificial Intelligence To Its Employee Roster

Geoffrey Hinton Machine Learning

 Artificial Intelligence
Google's recent acquisition of DNNresearch, a startup founded by professor Geoffrey Hinton and two of his graduate students at the University of Toronto, Alex Krizhevsky and Ilya Sutskever could mean vast search capability may be near on the horizon. 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 recognize more types of content including video.
Google has recently acquired DNNresearch, an artificial intelligence startup founded by University of Toronto professor Geoffrey Hinton and two of his graduate students, Alex Krizhevsky and Ilya Sutskever.

Incorporated last year, the startup’s website does not reveal much besides a blank black screen.

The financial terms of the deal have not been disclosed, however Google was eager to acquire DNN's research on neural networks and the expertise behind them 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 , the University of Toronto said that the team’s research “has profound implications for areas such as speech recognition, computer vision and language understanding.”

It is unclear at this time if Hinton and his colleagues will be working for Ray Kurzweil at Google, or if Kurzweil had any involvement in the deal. Kurzweil also recently joined Google closely following the publication of his book, How to Create a Mind.

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.” 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.”

facial recogntion
Facial recognition in video may be one area Hinton and colleagues could work on at Google.

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.

Undoubtedly these technologies fit in to the vision Google has for its Glass and other products, incorporating augmented reality and object recognition into the devices and software.

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 new search engine algorithms. Google already has deep image search capabilities on the web, but, going forward, as smartphones and other devices 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.”

That sounds like a profound statement.

Also in the paper, the authors state, "Ultimately we would like to use very large and deep convolutional nets on video sequences where the temporal structure provides very helpful information that is missing or far less obvious in static images."  With the merger of this research into Google's intellectual property mix it seems as though YouTube videos being searched and recognized will be one potential avenue.

The acquisition of DNNresearch follows a $600K gift that Google awarded to Hinton and his research team to support their work in neural nets. Following this, 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.”

Hinton also wrote a Google+ post 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.”
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.

SOURCE  TechCrunch

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