Blog
The latest news from Google AI
Google joins the Global Alliance for Genomics and Health
Thursday, February 27, 2014
Posted by Jonathan Bingham, Product Manager
Generating research data is easier than ever before, but interpreting and analyzing it is still hard, and getting harder as the volume increases. This is especially true of genomics. Sequencing the whole genome of a single person produces more than 100 gigabytes of raw data, and a million genomes will add up to more than 100 petabytes. In 2003, the
Human Genome Project
completed after 15 years and $3 billion. Today, it takes closer to one day and $1,000 to sequence a human genome.
This abundance of new information carries great potential for research and human health -- and requires new standards, policies and technology. That’s why Google has joined the
Global Alliance for Genomics and Health
. The Alliance is an international effort to develop harmonized approaches to enable responsible, secure, and effective sharing of genomic and clinical information in the cloud with the research and healthcare communities, meeting the highest standards of ethics and privacy. Members of the Global Alliance include leading technology, healthcare, research, and disease advocacy organizations from around the world.
To contribute to the genomics community and help meet the data-intensive needs of the life sciences, we are introducing:
a proposal for a
simple web-based API
to import, process, store, and search genomic data at scale
a preview implementation of the API built on Google’s cloud infrastructure, including sample data from public datasets like the
1,000 Genomes Project
a collection of in-progress
open-source sample projects
built around the common API
Interoperability: One API, Many Apps
Any of the apps at the top (one graphical, one command-line, and one for batch processing) can work with information in any of the repositories at the bottom (one using cloud-based storage and one using local files). As the ecosystem grows, all developers and researchers benefit from each individual developer’s work.
With these first steps, it is our goal to support the global research community in bringing the vision of the Global Alliance for Genomics and Health to fruition. Imagine the impact if researchers everywhere had larger sample sizes to distinguish between people who become sick and those who remain healthy, between patients who respond to treatment and those whose condition worsens, between pathogens that cause outbreaks and those that are harmless. Imagine if they could test biological hypotheses in seconds instead of days, without owning a supercomputer.
We are honored to be part of the community, working together to refine the technology and evolve the ecosystem, and aligning with appropriate standards as they arise.
How you can be involved
To request access to
the API
for your research, please
fill out this simple form
to tell us about yourself and your research interests, and we will let you know when we’re ready to work with more partners.
Together with the members of the Global Alliance for Genomics and Health, we believe we are at the beginning of a transformation in medicine and basic research, driven by advances in genome sequencing and huge-scale computing. We invite you to
contact us
and share your ideas about how to bring data science and life science together.
Making Sense of Data with Google
Tuesday, February 25, 2014
Posted by John Atwood, Program Manager
In September 2013, Google announced joining forces with
edX
to contribute to their open source platform, Open edX. Since then we’ve been working together to expand this open education ecosystem. We’re pleased to announce our first online course built using Open edX.
Making Sense of Data
showcases the collaborative technology of Google and edX using
cbX
to run Open edX courses on Google App Engine.
The world is filled with lots of information; learning to make sense of it all helps us to gain perspective and make decisions. We’re pleased to share tools and techniques to structure, visualize, and analyze information in our latest self-paced, online course: Making Sense of Data.
Making Sense of Data is intended for anybody who works with data on a daily basis, such as students, teachers, journalists, and small business owners, and who wants to learn more about how to apply that information to practical problems. Participants will learn about the data process, create and use Fusion Tables (an experimental tool), and look for patterns and relationships in data. Knowledge of statistics or experience with programming is not required.
Like past courses, participants engage with course material through a combination of video and text lessons, activities, and projects. In this course, we will also introduce some new features that help create a more engaging participant experience. For example, participants will be able to access instant hangouts and live chats from the course web page for quick help or for direct feedback. As with all of our MOOCs, you’ll learn from Google experts and collaborate with participants worldwide. You’ll also have the opportunity to complete a final project and apply the skills you’ve learned to earn a certificate.
Making Sense of Data runs from
March 18 - April 4, 2014
. Visit
g.co/datasense
to learn more and register today. We look forward to seeing you make sense of all the information out there!
Monitoring the World's Forests with Global Forest Watch
Thursday, February 20, 2014
Posted by Crystal Davis, Director of Global Forest Watch, the World Resources Institute, and Dave Thau, Developer Advocate, Google Earth Engine
Cross-posted at the Google Lat Long Blog
By the time we find out about deforestation, it’s usually too late to take action.
Scientists have been studying forests for centuries, chronicling the vital importance of these ecosystems for human society. But most of us still lack timely and reliable information about where, when, and why forests are disappearing.
This is about to change with the launch of
Global Forest Watch
—an online forest monitoring system created by the World Resources Institute, Google and a group of more than 40 partners. Global Forest Watch uses technologies including
Google Earth Engine
and
Google Maps Engine
to map the world’s forests with satellite imagery, detect changes in forest cover in near-real-time, and make this information freely available to anyone with Internet access.
By accessing the most current and reliable information, everyone can learn what’s happening in forests around the world. Now that we have the ability to peer into forests, a number of telling stories are beginning to emerge.
Global forest loss far exceeds forest gain
Pink = tree cover loss
Blue = Tree cover gain
According to
data
from the University of Maryland and Google, the world lost more than 500 million acres of forest between 2000 and 2012. That’s the equivalent of losing 50 soccer fields’ worth of forests every minute of every day for the past 13 years! By contrast, only 0.8 million km2 have regrown, been planted, or restored during the same period.
The United States’ most heavily forested region is made up of production forests
Pink = tree cover loss Blue = Tree cover gain
The Southern United States is home to the nation’s most heavily forested region, making up 29 percent of the total U.S. forest land. Interestingly, the majority of this region is “production forests.” The mosaic of loss (pink) and gain (blue) in the above map shows how forests throughout this region are used as crops – grown and harvested in five-year cycles to produce timber or wood pulp for paper production.
This practice of “intensive forestry” is used all over the world to provide valuable commodities and bolster regional and national economies. WRI
analysis
suggests that if managers of production forests embrace a “
multiple ecosystem services strategy
”, they will be able to generate additional benefits such as biodiversity, carbon storage, and water filtration.
Forests are protected in Brazil’s indigenous territories
Pink = tree cover loss Dark green = forest Light green = Degraded land or pastures
The traditional territory of Brazil's Surui tribe is an island of green surrounded by lands that have been significantly degraded and deforested over the past 10+ years. Indigenous communities often rely on forests for their livelihoods and cultural heritage and therefore have a strong incentive to manage forests sustainably. However, many indigenous communities struggle to protect their lands against encroachment by illegal loggers, which may be seen in Global Forest Watch using annual data from the University of Maryland and Google, or monthly alerts from
Imazon
, a Brazilian NGO and GFW partner.
Make Your Own Forest Map
Previously, the data required to make these maps was difficult to obtain and interpret, and most people lacked the resources necessary to access, view, and analyze the the information. With Global Forest Watch, this data is now open to anyone with Internet access. We encourage you to visit Global Forest Watch and
make your own forest map
. There are many stories to tell about what is happening to forests around the world—and your stories can lead to action to protect these special and threatened places. What story will you tell?
Google Award Program stimulates Journalism and CS collaboration
Wednesday, February 19, 2014
Posted by Krishna Bharat, Distinguished Research Scientist
Last fall, Google invited academic researchers to participate in a Computational Journalism awards program focused on the intersection of Computer Science and Journalism. We solicited proposals for original research projects relevant to today’s fast evolving news industry.
As technology continues to shape and be shaped by the media landscape, applicants were asked to rethink traditional models and roles in the ecosystem, and reimagine the lifecycle of the news story in the online world. We encouraged them to develop innovative tools and open source software that could benefit readers and be game-changers for reporters and publishers. Each award includes funding of $60,000 in cash and $20,000 in computing credits on Google’s Cloud Platform.
We congratulate the recipients of these awards, whose projects are described below, and look forward to the results of their research. Stay tuned for updates on their progress.
Larry Birnbaum
, Professor of Electrical Engineering and Computer Science, and Journalism, Northwestern University
Project
: Thematic Characterization of News Stories
This project aims to develop computational methods for identifying abstract themes or "angles" in news stories, e.g., seeing a story as an instance of "pulling yourself up by your bootstraps," or as a "David vs. Goliath" story. In collaboration with journalism and computer science students, we will develop applications utilizing these methods in the creation, distribution, and consumption of news content.
Irfan Essa
, Professor, Georgia Institute of Technology
Project
: Tracing Reuse in Political Language
Our goal in this project is to research, and then develop a data-mining tool that allows an online researcher to find and trace language reuse. By language reuse, we specifically mean: Can we find if in a current text some language was used that can be traced back to some other text or script. The technical innovation in this project is aimed at (1) identifying linguistic reuse in documents as well as other forms of material, which can be converted to text, and therefore includes political speeches and videos. Another innovation will be in (2) how linguistic reuse can be traced through the web and online social networks.
Susan McGregor
, Assistant Director, Tow Center for Digital Journalism, Columbia Journalism School
Project
: InfoScribe
InfoScribe
is a collaborative web platform that lets citizens participate in investigative journalism projects by digitizing select data from scanned document sets uploaded by journalists. One of InfoScribe's primary research goals is to explore how community participation in journalistic activities can help improve their accuracy, transparency and impact. Additionally, InfoScribe seeks to build and expand upon understandings of how computer vision and statistical inference can be most efficiently combined with human effort in the completion of complex tasks.
Paul Resnick
, Professor, University of Michigan School of Information
Project
: RumorLens
RumorLens
is a tool that will aid journalists in finding posts that spread or correct a particular rumor on Twitter, by exploring the size of the audiences that those posts have reached. In the collection phase, the user provides one or a few exemplar tweets and then manually classifies a few hundred others as spreading the rumor, correcting it, or labeling it as unrelated. This enables automatic retrieval and classification of remaining tweets, which are then presented in an interactive visualization that shows audience sizes.
Ryan Thornburg
, Associate Professor, School of Journalism and Mass Communication, University of North Carolina at Chapel Hill
Project: Public Records Dashboard for Small Newsrooms
Building off our Knight News Challenge effort to bring data-driven journalism to readers of rural newspaper websites, we are developing an internal newsroom tool that will alert reporters and editors to potential story tips found in public data. Our project aims to lower the cost of finding in public data sets stories that shine light in dark places, hold powerful people accountable, and explain our increasingly complex and interconnected world. (Public facing site for the data acquisition element of the project at
http://open-nc.org
)
Google Research Awards: Winter 2014
Tuesday, February 18, 2014
Posted by Maggie Johnson, Director of Education & University Relations
We have just completed another round of the
Google Research Awards
, our biannual open call for proposals on computer science-related topics including robotics, natural language processing, systems, policy, and mobile. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.
This round we received 691 proposals, an increase of 19% over last round, covering 46 countries on 6 continents. After expert reviews and committee discussions, we decided to fund 115 projects. The subject areas that received the highest level of support were human-computer interaction, systems, and machine learning, with 25% of the funding awarded to universities outside the U.S.
We set a new record this round with over 2000 reviews done by 650 reviewers. Each proposal is reviewed by internal committees who provide feedback on merit and relevance. In many cases, the committees include some of the foremost experts in the world. All committee members are volunteers who spend a significant amount of time making the Research Award program happen twice a year.
Congratulations to the well-deserving
recipients of this round’s awards
. If you are interested in applying for the next round (deadline is April 15), please visit
our website
for more information.
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
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
.
