98 captures
13 Jul 2018 - 28 Jun 2023
Oct
NOV
Dec
28
2019
2020
2021
success
fail
About this capture
COLLECTED BY
Collection:
Common Crawl
Web crawl data from Common Crawl.
TIMESTAMPS
The Wayback Machine - https://web.archive.org/web/20201128082931/https://ai.googleblog.com/2009/05/
Blog
The latest news from Google AI
Google Fellowships, the Nuts and Bolts
Friday, May 15, 2009
Posted by Leslie Yeh Johnson, Google University Relations
As you may have read, today we announced the recipients of the 2009 Google Fellowships. (You can read the announcement over on the
Official Google Blog
.) This is fantastic news, and the blog post makes the Google Fellowship Program sound very polished. But the truth is there was a lot more work (and scrambling) done in the background...here's a quick snapshot.
We first conceived of the idea of the fellowships late last year. Google already funds academic research through the Google Research Awards, but we really wanted to support the graduate students who are doing a lot of the research and are the future of their respective fields. Idea: why don't we search out the best and brightest PhD students and pay their tuition and expenses, plus give them an Android phone and hook them up with a Google researcher so we can all share really cool ideas? Done and done.
After we made the decision to do the fellowships in 2009, we were in for some hard work. We quickly spread the word about the fellowships in order to give the universities and students time to prepare and send us information about themselves and their research. The nominated students were doing research on a vast array of subjects: Cloud Computing, Computer Graphics, Market Algorithms, Machine Learning, Natural Language Processing, Social Computing, Information Retrieval, Compilers, and Computer Vision to name a few. I relied upon a small army of research scientists and distinguished engineers to help me review them. In addition to lending their scientific expertise to looking over the Google Research Awards, not to mention their "day job", the forty-five Googlers also were able to provide feedback on the students in record time - these guys are champs. Then a whirlwind review with Alfred Spector, VP of Research and Special Initiatives at Google, and just six months later we are proud to announce the 2009 Google Fellowship recipients.
It was a jam-packed 6 months, and I'm really proud of how the program turned out this year. That said, I'm already looking forward to our sophomore year in 2010. You should expect to see a broader program covering more areas of research, more schools, and more geographies. I can't wait.
The best and the brightest
Friday, May 15, 2009
Posted by Leslie Yeh Johnson, Google University Relations
[Also posted on the
Official Google Blog
]
I can't think of a better environment than academia for asking hard questions and trying to solve the unsolvable. It's at universities that graduate students perform some of the most exciting and game-changing research in computer science and technology. These university labs foster the students that are going to be the next innovators and leaders in research.
We started the Google Fellowship Program this year to support graduate students in their quest to discover and achieve great things. Our goal was to find the best and brightest PhD students and award them a unique fellowship that highlights their contributions to research and supports them through their graduate studies. Several top universities submitted their students for consideration by research scientists, distinguished engineers and executives at Google. The breadth of research covered by these students and the scope of their vision was astounding. Learning about them was exciting; choosing from among them was truly difficult.
After careful review, we are proud to announce the 2009 Google Fellowship recipients:
Roxana Geambasu, Google Fellowship in Cloud Computing (
University of Washington
)
Michael Piatek, Google Fellowship in Computer Networking (
University of Washington
)
David Sontag, Google Fellowship in Machine Learning (
Massachusetts Institute of Technology
)
Ali Farhadi, Google Fellowship in Computer Vision Image Interpretation (
University of Illinois at Urbana-Champaign
)
Nicholas Chen, Google Fellowship in Human-Computer Interaction (
University of Maryland
)
Siddhartha Sen, Google Fellowship in Fault Tolerant Computing (
Princeton University
)
Ryan Peterson, Google Fellowship in Distributed Systems (
Cornell University
)
Eric Gilbert, Google Fellowship in Social Computing (
University of Illinois at Urbana-Champaign
)
Micha Elsner, Google Fellowship in Natural Language Processing (
Brown University
)
Subhransu Maji, Google Fellowship in Computer Vision Object Recognition (
University of California, Berkeley
)
Nicolas Lambert, Google Fellowship in Market Algorithms (
Stanford University
)
Han Liu, Google Fellowship in Statistics (
Carnegie Mellon University
)
Lixia Liu, Google Fellowship in Compiler Technology (
Purdue University
)
These students exemplify excellence in all areas, and we look forward to the impact that they are sure to have on their fields and the world. The Google Fellowship will provide them with funding to cover their tuition and expenses, plus an Android-powered phone and a Google mentor. Our sincere congratulations to all of them!
ACM Multimedia 2009 Grand Challenges
Tuesday, May 12, 2009
Posted by Jay Yagnik, Head of Computer Vision Research
At Google Research we interact with the academic research community closely through various programs like
Research Awards
,
Visiting Faculty Program
, and by active participation in various conferences. Dealing with large quantities of data gives us some unique challenges and perspectives on various problems. In many cases entirely new problem classes begin to emerge. These problems often have not received attention from a broad part of the research community. In an effort to bridge this gap for multimedia problems, we participated in setting
Grand Challenges
for this year's
ACM Multimedia Conference
. We proposed "Robust, As-Accurate-As-Human Genre Classification for Video" as a challenge.
The majority of research in video analysis today focuses on surveillance video. While this is critical for a lot of security applications, it is incomplete in describing challenges that come up when we tackle a video retrieval and discovery application like YouTube. Analysis work beyond surveillance is often limited to specific categories like News and Sports that have well defined structures that the solution methods can explicitly work with. Our challenge aims to encourage more work in the area of semantic understanding of a broad variety of videos. Genre classification is a problem thats representative of some of the challenges that stem from the sheer diversity that can exist across video categories. The challenge will encourage new methods to solve these problems, as well as attempts at standardizing datasets to represent this problem. With internet video gaining popularity in an astounding magnitude, we believe this challenge will steer the multimedia research community towards challenges posed by the magnitude and variety of this new problem area.
We are grateful to
Mor Naaman
(Rutgers University) and
Tat-Seng Chua
(National University of Singapore) for organizing this industry challenge track at ACM Multimedia and inviting us to be a part of it.
Details of our challenge can be found
here
.
The bar-bet phenomenon: increasing diversity in mobile searches
Thursday, May 7, 2009
Posted by Maryam Kamvar, Melanie Kellar, Rajan Patel and Ya Xu, Google Research
Historically,
research
suggests that web search on mobile phones has been limited when compared to the diverse set of queries which comprise computer-based search. Researchers attribute the homogeneous mobile search behavior in part to the phone's form factor and browsing capabilities. However, our new logs-based study indicates that high-end phones, like the iPhone, are changing the landscape of mobile search. We found that search from these phones has evolved not only to mimic computer web search patterns, but to exceed the expectations set by conventional web search in some cases.
We see iPhone searches mimicking computer-based search behavior in terms of query length (~3 words per query for computer and iPhone queries, as opposed to 2.5 words per query for conventional mobile queries) and query classification (notably the percentage of Adult and Entertainment searches have decreased on the iPhone relative to conventional mobile phones). But what is most surprising to us is that frequent searchers on iPhone
surpass
frequent searchers on computers in terms of the diversity of queries they issue. In other words, people are using high-end phones to search for a more diverse set of information needs than computers are used for; we jokingly refer to this as the "
bar-bet
" phenomenon -- or the "
pub-quiz
" phenomenon for those of you in the UK.
We devised a metric for quantifying the variability of a user’s search intentions across time. This variability metric, entro-percent, is a normalized entropy metric which compares the number of search tasks issued by a user to the number of categories those search tasks fall under. This user-variability for conventional mobile web search is much lower than for computer-based search, confirming the hypothesis that mobile web users query over a much less diverse set of topics. The surprising news is that iPhone users, on the other hand, had a higher variability than computer based users, indicating their information needs are more diverse! This shows that the challenges posed by a phone's form factor can be outweighed by its "always on, always in your pocket" benefits.
To understand the meaning of the entro-percent equation, read our
full paper
summarizing the findings of our logs-based study of search patterns on conventional mobile phones, iPhones and conventional computers and get all the juicy details.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
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
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
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
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
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
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2020
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
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
Follow @googleai
Give us feedback in our
Product Forums
.
