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Research at Google
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inFORM: interact with digital information in a tangible way

Imagine if you could physically interact with digital data. What if you had 3D physical displays of remote video conference participants that enabled action at a distance? What if you could manipulate digital paraboloids with your hands, observing the parameters change as you touch its surface? 

Check out inFORM, a Dynamic Shape Display from researchers at MIT’s Tangible Media Group that can render 3D content physically, and also interact with the physical world around it.  
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Cloud-Based Robot Grasping with the Google Object Recognition Engine

What if robots were not limited by onboard computation, algorithms did not need to be implemented on every class of robot, and model improvements from sensor data could be shared across many robots? What if one could use the Internet, with the millions of photos that are uploaded and made publicly available every day, as a potential source for computation and data about objects, their semantics, and how to manipulate them. 

In Cloud-Based Robot Grasping with the Google Object Recognition Engine, presented at the 2013 IEEE International Conference on Robotics and Automation ( and recently highlighted in the list of influential Google papers from 2013 (, Googlers +Sal Candido and +James Kuffner along with UC Berkeley researchers Ben Kehoe, +Akihiro Matsukawa and +Ken Goldberg detailed a system architecture, an implemented prototype, and initial experimental data for a cloud-based robot grasping system.

Using a Willow Garage PR2 robot with onboard color and depth cameras, object recognition was performed in the cloud by a using a variant of the Google Goggles object recognition engine, while the Point Cloud Library (PCL) was used for pose estimation, and Columbia University’s GraspIt! toolkit and OpenRAVE implemented for grasping. 

To learn more, read the full paper at
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I am bender please insert MUSTARD
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Computer Vision and Machine Learning and Robotics (and more)! Oh My!

Googlers across the company actively engage with the scientific community by publishing technical papers, contributing open-source packages, working on standards, introducing new APIs and tools, giving talks and presentations, participating in ongoing technical debates, and much more. 

Today, the Google Research Blog posted a list of especially influential papers co-authored by Googlers in 2013, which sample a variety of technical and algorithmic advances, feature aspects we learn as we develop novel products and services, and shed light on some of the technical challenges we face at Google.

Head over to the Research Blog to dig into just a few of the papers published from 2013!
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I love this liste of papers! I will check them all out!
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”We're excited about Carnegie Mellon's work on mechanisms to allow online courses to adapt automatically to the learning needs of individual students. We believe this research will make online courses much more engaging, and benefit both students and educators around the world."
-+Alfred Spector, Google Vice President, Engineering

In a multi-year program supported by a Google Focused Research Award (, researchers at Carnegie Mellon University (CMU) are working to unlock the educational potential of massive open online courses (MOOCs).

The research explores data-driven approaches to improving MOOCs through a variety of methods, including the personalization of the MOOC experience, making available course work more interactive and engaging, and lessening the issue of student attrition.

Learn more in the CMU press release, linked below 
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Hippocratic Abbreviation Expansion: A Machine Learning Model for Text-To-Speech Synthesis 

This week, Baltimore hosts the 52nd annual meeting of the Association for Computational Linguistics (, drawing researchers from around the world who work on the computational and linguistic properties of language. As a Gold Sponsor of #acl14nlp , Google is on hand to take part in the conversation surrounding the latest technologies that enable the interaction between computers and human languages.

One of the papers being presented at #acl14nlp  is Hippocratic Abbreviation Expansion (, in which Google Research Scientists +Brian Roark and +Richard Sproat present a high precision approach to learning non-standard word (NSW) abbreviation expansion models for text-to-speech synthesis (TTS). 

Users who rely on TTS for most or all of their information needs require a “do no harm” approach to abbreviation expansion; If an abbreviation is unrecognized, the system defaults to a character by character reading rather than pronouncing a potentially incorrect, and misleading, expansion. 

Investigating methods for training classifiers to establish whether a particular expansion is appropriate, this research seeks to reliably improve the prediction and expansion of NSW for TTS application. Using in-domain unannotated data that depends on only a small amount of annotated data, the methods outlined in the paper offer dramatic improvement in correct abbreviation expansion, along with a substantial reduction in incorrect expansions.
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D Moore
I myself am willing to take advantage of this message
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Supporting the Future of Computer Science

Nurturing and maintaining strong relations with the academic community is a top priority at Google. Today, we’re announcing the 2014 Google PhD Fellowship recipients. These students, recognized for their incredible creativity, knowledge and skills, represent some of the most outstanding graduate researchers in computer science across the globe. We’re excited to support them, and we extend our warmest congratulations. 

For the list of 2014 Fellows, and a highlight of two past recipients, visit the Google Research blog, linked below.
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I am honored to be bestowed with such a prestigious award. Thank you!
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Crowdsourcing, the distribution of jobs to a workforce of willing individuals, is becoming an increasingly popular way to efficiently accomplish tasks that are difficult for computers but easy for humans, such as tagging photos or writing reviews for products. But what happens when crowdsourcing is used to create false information or support? 

Known as “crowdturfing”, misuse of crowdsourcing platforms can spread malicious URLs in social media, post fake reviews and ratings, form artificial grassroots campaigns, manipulate search engine results, and create false reputations on social networks. 

In The Dark Side of Micro-Task Marketplaces: Characterizing Fiverr and Automatically Detecting Crowdturfing (, Utah State University Assistant Professor Kyumin Lee ( and co-authors present an analysis of active tasks and users in a crowdsourcing platform, developing statistical classification models to automatically differentiate between legitimate tasks and crowdturfing tasks.
Learn more about Kyumin’s research, presented at the 8th International AAAI Conference on Weblogs and Social Media (ICWSM, and supported in part by a 2013 Google Faculty Research Award (, in the MIT Technology Review article, linked below.
It’s possible to buy a good reputation on the Internet for a modest price, but some are trying to put an end to that.
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How can I get fake F2F friends? Anyone got any tech for that like androids or something?
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Sibyl: A System for Large Scale Machine Learning at Google

Last week, at the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN,, Google Software Engineer +Tushar Chandra gave a keynote address outlining the systems aspects of Sibyl, a supervised machine learning system that is used for solving a variety of prediction challenges, such as YouTube video recommendations.

To learn how Sibyl is being used at Google to solve internet-scale problems while using reasonable resources, watch the video below.
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Open Source?
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Maps for good: Saving trees and saving lives with petapixel-scale computing

Satellites have been systematically collecting imagery of our changing planet for more than 40 years, yet until recently this data has not been online and available for high-performance analysis. 

At #io14 , Google Engineering Manager for Earth Outreach ( and Earth Engine ( +Rebecca Moore spoke about the new Earth Engine technology and experimental API for massively-parallel geospatial analysis on daily-updating global datasets such as Landsat satellite imagery.

Scientists and other domain experts are developing new applications utilizing Earth Engine which map, measure and monitor our changing planet in unprecedented detail, for the benefit of people and the environment. 
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Google I/O: Biologically inspired models of intelligence

Tune into #io14  at 3PM PST today to hear Google Director of Engineering +Ray Kurzweil  ( speak on the exploration of developing artificial intelligence based on biologically inspired models of the neocortex, enhancing functions such as search, answering questions, interacting with the user, and language translation.
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It was amazing.
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Entity Recognition and Disambiguation (ERD) Challenge Results Announced

In March, we announced a call for participation in the 2014 Entity Recognition and Disambiguation (ERD) Challenge (, a competition to build a working system to recognize mentions of entities in a given text, disambiguate them, and map them to the known entities in a given collection or knowledge base.

Today the results were announced, with participating teams having the past 3 months to test run their systems using development datasets hosted by the ERD Challenge website. The final evaluations and the determination of winners was performed on held-out datasets that have similar properties to the development sets. 

We congratulate the SMAPH team as the top scoring team in the short-text track (i.e., web search queries), consisting of Marco Cornolti and +Paolo Ferragina of the University of Pisa, Google Research Scientist +Massimiliano Ciaramita, and Hinrich Shütze and Stefan Rüd of the University of Munich.

Each participating team will be offered a spot at the Entity Recognition and Disambiguation Workshop at the 37th Annual ACM Special Interest Group On Information Retrieval (SIGIR) conference ( July 6-11 in Golf Coast, Australia.
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Thermal Touch: an augmented reality interface via body heat

Computer vision and augmented reality has the potential to change the way in which we interact with digital information. +metaio - Augmented Reality Solutions  is experimenting with Thermal Touch, a prototype that explores ways to interact with smartphones and laptops using the residual heat from a finger when a surface is touched.

Utilizing a thermal camera to detect the heat from a touch, along with a standard camera to determine the location of the object being touched, Thermal Touch could enable the use of a wide variety of surfaces as an interactive touch interface.

What potential applications do you see for this technology? 
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woow that"s great...its cracking me
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Have them in circles
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∀x, CS+x
Google is full of smart people working on some of the most difficult problems in computer science today. Most people know about the research activities that back our major products, such as search algorithms, systems infrastructure, machine learning, and programming languages. Those are just the tip of the iceberg; Google has a tremendous number of exciting challenges that only arise through the vast amount of data and sheer scale of systems we build.

What we discover affects the world both through better Google products and services, and through dissemination of our findings by the broader academic research community.  We value each kind of impact, and often the most successful projects achieve both.