Google Research Blog
The latest news from Research at Google
App Inventor for Android
Friday, July 31, 2009
Posted by Hal Abelson, Visiting Faculty
At Google Research, we are making it easy to build mobile applications, and we're collaborating with faculty from a dozen colleges and universities to explore whether this could change the nature of introductory computing. With the support of
Google University Relations
, the faculty group will work together this fall to pilot courses where beginning students, including non-computer science majors, create
Android applications
that incorporate social networking, location awareness, and Web-based data collections.
Mobile applications are triggering a fundamental shift in the way people experience computing and use mobile phones. Ten years ago, people "went to the computer" to perform tasks and access the Internet, and they used a cell phone only to make calls. Today, smartphones let us carry computing with us, have become central to servicing our communication and information needs, and have made the web part of all that we do. Ten years ago, people's use of computing was largely dissociated from real life. With the ubiquity of social networking, online and offline life are becoming fused. This fall's exploration is motivated by the vision that open mobile platforms like Android can bring some of that same change to introductory Computer Science, to make it more about people and their interactions with others and with the world around them. It's a vision where young people—and everyone—can engage the world of mobile services and applications as creators, not just consumers. Through this work, we hope to do the following:
Make mobile application development accessible to anyone.
Enhance introductory learning experiences in computing through the vehicle of Android’s open platform.
Encourage a community of faculty and students to share material and ideas for teaching and exploring.
The collaborative experiment kicked off with a three-day workshop at Google's Mountain View campus in June, where invited faculty shared their plans for the courses they will offer this fall. The group also got an advance look at App Inventor for Android, the prototype development platform that Google is working on and that the faculty and their students will use in their courses. App Inventor for Android lets people assemble Android applications by arranging "components" using a graphical drag-and-drop-interface. One of the goals of the fall experiment is to further shape the system in response to the experience and feedback of students and faculty.
The schools participating in this fall's collaboration are Ball State University, University of Colorado Boulder, Georgia Tech, Harvard, Indiana University, Mills College, MIT, Olin College, University of California at Berkeley, University of Michigan, University of Queensland, University of San Francisco, and Wellesley College.
Questions or comments? Please send us
feedback
. We look forward to hearing from you!
Predicting Initial Claims for Unemployment Benefits
Wednesday, July 22, 2009
Posted by Hal Varian, Chief Economist and Hyunyoung Choi, Sr. Economist
One of the strongest leading indicators of economic activity is the number of people who file for unemployment benefits. Macroeconomists
Robert Gordon
and
James Hamilton
have recently examined the historical evidence. According to Hamilton's summary: "...in each of the last six recessions, the recovery began within 8 weeks of the peak in new unemployment claims."
In an
earlier blog post
, we suggested that Google Trends/Search Insights data could be useful in short term predictions of economic variables. Given the importance of initial claims as a macroeconomic predictor, we thought it would be useful to try to forecast this economic metric. The initial claims data is available from the
Department of Labor
, while the Google Trends data for relevant categories is available
here
.
We applied the methodology outlined in our earlier paper, building a model to forecast initial claims using the past values of the time series, and then added the Google Trends variables to see how much they improved the forecast. We found a 15.74% reduction in mean absolute error for one-week ahead out, of sample forecasts. Most economists would consider this to be a significant boost. Details of our analysis may be found in
this paper
.
The bottom line is that initial claims have been generally declining from their peak and that, so far at least, the Google query data is forecasting further short term declines. It would be good news indeed if this particular Google trend continues.
ACM EC Conference and Workshop on Ad Auctions
Tuesday, July 21, 2009
By
Jon Feldman
and
Vahab Mirrokni
, Google Research, NY
This month, the 10th ACM Conference on Electronic Commerce (
EC 2009
) and the
5th Workshop on Ad Auctions
took place at Stanford University. This is one of the major forums for economists and computer scientists to share their ideas about mechanism design and algorithmic game theory. Other than co-authoring several papers in the conference and workshops, Google contributed significantly in presenting tutorials.
Among the four
tutorials
given at the ACM EC conference, we participated in presenting two of them:
In a joint tutorial with Google researcher
Muthu Muthukrishnan
, we explored research problems in sponsored search inspired by taking the advertiser's perspective. Emphasizing a cross-disciplinary approach, we presented sample research directions in keyword selection, traffic prediction and bidding strategy, encouraging the research community to build upon known auction models in order to tackle these even more challenging domains. We explored in more detail specific examples of research in bidding strategies.
Moreover, in a joint tutorial with
H. Roeglin
, we presented results in convergence of game dynamics, both to equilibria and nearly-optimal solutions. This was a more algorithm-oriented variant of the
tutorial at ICML
(which is described in a
previous blog post
.)
The
Ad Auctions Workshop
brought together many industry and academic research leaders to discuss ongoing challenges in online advertisement. The topics presented at the workshop included the role of externalities in ad auctions, new truthful ad auction mechanisms with budget constraints, efficiency loss of generalized second-price ad auctions, and complex combinatorial ad auctions. Google researchers co-organized, participated in the discussions, and contributed the following presentations:
Google's chief economist,
Hal Varian
, gave an enlightening invited talk about using
Google Trends
data for "
predicting the present
." In this work, Google Trends data is used to help improve forecasts of various economic time series. Examples illustrating this technique were drawn from the auto industry, real estate, and unemployment. He emphasized that Google Trends data is publicly available and encouraged people to use this data for their research.
We presented two papers in the workshop, one about
optimal pricing mechanisms over social networks
and one about using
offline optimization in stochastic online ad allocation problems
. In the latter talk, we presented an algorithm in which we use an optimal offline solution in an "expected instance", and use this solution as a signal in online decision making. Using the idea of the power of two choices from the CS literature, we give a novel theoretical analysis of our method, improving the best previously known result. We also gave some practical insight about using these methods in online ad allocation.
The theoretical results
will appear in the upcoming
FOCS 2009
conference.
Motivated by our various ad systems, there is a large research effort at Google around areas at the intersection of Economics, Computer Science and Machine Learning. The Ad Auctions Workshop and ACM Conference on Electronic Commerce are among the best forums for stimulating ideas and collaboration in these interdisciplinary areas.
Google's Research Awards Program Update
Tuesday, July 14, 2009
Posted by Posted by Juan E. Vargas, University Relations
When we think about innovation, it is easy to forget that it took about 55 years to spread automobile usage to 1/4 of the US population, ... 35 years for the telephone, ... 20 years for the radio, ... 15 years for the PC, ... 10 years for the cell phone, ... 7 years for the Internet (Council of Competiveness,
Innovate America
, 2004).
Recognizing that innovation holds the key to many of the unique technical challenges we face, we remain committed to maintaining strong relations with the academic research community. Our Research Awards Program has experienced phenomenal growth. Of the total number of applications that we received since the program's inception in 2005, more than half were submitted in the past year. To cope with the increased level of interest worldwide, we reorganized the program to accept submissions three times per year: April 15th, August 15th, and December 15th. Proposals are evaluated by teams of engineers and researchers, who make recommendations for funding. We try to move fast. Investigators receive a response about three months after their submission.
Here are some highlights from a recent round of applications:
"Recognition and Modeling of Objects from Street View Scans of Cities"
Thomas Funkhouser
, Princeton University
Professor Funkhouser aims to develop methods for automatic construction of semantically-labeled, detailed, and photorealistic 3D models of cities from Street View data. The main efforts will be towards the segmentation and recognition of small objects (e.g. mailboxes, fire hydrants, parking meters, etc.) in Lidar data based on shape classification and contextual reasoning. A second objective will be to construct seamless, photorealistic 3D models of complete cities by extracting and fitting parts from repositories of polygonal models.
"An Ad Auctions Trading Agent Competition"
Michael Wellman
, University of Michigan
The University of Michigan will introduce and operate a new game in the Trading Agent Competition (TAC) series of research competitions, in the domain of sponsored search. The TAC Ad Auctions (TAC/AA) game challenges participants to develop bidding strategies for advertisers in a simulated retail home entertainment market. The aim is to spur research and generate insights about advertiser bidding strategy, in a scenario more complex than those considered in the research literature to date. The TAC/AA environment features multiple interrelated keywords, a structured search user model, rich data availability, and a dynamic market context. Since 2000, the annual TAC series has catalyzed research on trading agent design and analysis, produced by a diverse group of researchers from academia and industry.
"A Suite of Automated Tools for Efficient Management and Search in Web-based Arabic Documents"
Adnan Yahya
, Birzeit University, Palestine.
This research aims to design text mining and processing tools that are able to efficiently index, process, search, and categorize large quantities of Arabic data. This research addresses the challenges Arabic poses for NLP and information retrieval, automatic Arabic document categorization, root extraction, language detection, and Arabic query correction, suggestion and expansion. The PIs employ a statistical/Corpus-based approach based on contemporary data initially obtained from a local newspaper.
"When Children Search: Understanding what they do and what they could do with Google Search"
Allison Druin
, University of Maryland
Children ages 5-13 are among the most frequent users of the Internet; yet, searching and browsing the web can present many challenges. Spelling, typing, query formulation, and deciphering results are all barriers for children in attempting to find the information they need. Professor Druin is trying to understand these issues in more diverse ages of children by focusing on current and ubiquitous search tools, namely, keyword-based web search engines.
"An Educational Camera for Kids"
Shree Nayar
, Columbia University
Professor Nayar is desiging a novel digital camera that could be used as an innovative educational medium. His target audience is students between the ages of 10 and 13 years living in poor communities across the globe. The camera, named “Bigshot,” will be presented to students as a kit to expose them to diverse science and engineering concepts. Once assembled, the camera will be used so that students can share their photos with students in other cultures using Picasa and Google Groups.
"Discovering semantic concepts and their relations in large image collections"
Bernt Schiele
, Technische Unversitat Darmstadt, Germany
Professor Schiele will investigate to what extent meaningful structures can be discovered from large sets of images both in a fully unsupervised fashion as well with minimal human supervision. To this end, Professor Schiele's work will try to first discover structure and learn multi-feature distance metrics in large collections of images and then to enrich such structure by weak annotations in order to link discovered structure and to derive semantic concepts.
"Dataspace Metrics: Measuring Progress for Pay-as-you-go Information Integration"
Michael Franklin
, University of California
The goal of this project is to develop a measurement framework for gauging progress in terms of the quality and accuracy of information integration. The starting point is the development of a set of metrics for judging the “goodness’ of information integration across a number of information types and use cases. These metrics will then be analyzed and where possible, unified, so that a more general measurement framework can be developed. Such a framework will serve as a key component for future Dataspace management systems, and could provide a grounding for other collaborative information integration solutions.
For more information about this program, including submission guidelines, please visit the
Research Awards Program
page.
International Conference on Machine Learning (ICML 2009) in Montreal
Thursday, July 02, 2009
Posted by
Eyal Even Dar
and
Vahab Mirrokni
, Google Research, NY
The 26th International Conference on Machine Learning (
ICML 2009
) was recently held in Montreal in conjunction with the 22nd Conference On Learning Theory (
COLT 2009
) and the 25th Conference on Uncertainty in Artificial Intelligence (
UAI 2009
). This is one of the major forums for researchers from both industry and academia to share the recent developments in the area of machine learning and artificial intelligence. Machine learning is a central area for Google as it has many applications in extracting useful information from a vast amount of data available on the web. In addition to sponsoring this scientific event, Google contributed intellectually to several scientific forums. Here's a short report of those activities:
There were ten papers co-authored by Googlers in these conferences, which covered several areas of machine learning including domain adaption, online learning, bandits, boosting, sparsity and kernel learning.
Corinna Cortes
, the head of Google Research NY gave one of the three invited talks of ICML. She surveyed the last decade of
research in learning kernels
and highlighted both the successes and the failures in learning kernels with a focus on applications of convex optimization for this purpose. Corinna concluded with a call for applying new ideas and novel techniques to overcome the current obstacles.
We presented a tutorial on
Convergence of Natural Game Dynamics
. This topic has received a lot of attention recently as it stands at the conflux of many fields such as economics, machine learning and theoretical computer science. In the tutorial, we surveyed the convergence properties of the most natural game dynamics such as the Nash dynamics or the best-response dynamics to the popular no-regret learning-based dynamics. The tutorial highlighted similarities and differences between the approaches in both the time of convergence, the point of convergence, and the quality of the outcome. We believe that the influence of the learning algorithms on the behavior of the users is an exciting and intriguing topic of research for many, and in particular for the analysis of ad auctions.
Google's main mission is "to organize the world's information and make it universally accessible and useful," and machine learning plays a fundamental role in both of these aspects. As a result, Google has invested significant resources in this area of research, and we look forward to continued participation and collaboration at these conferences for many more years.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
Awards
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
YouTube
Archive
2018
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Google
on
Follow @googleresearch
Give us feedback in our
Product Forums
.