Blog
The latest news from Google AI
Groundbreaking simulations by Google Exacycle Visiting Faculty
Monday, December 16, 2013
Posted by David Konerding, Staff Software Engineer
In April 2011, we
announced
the
Google Exacycle for Visiting Faculty
, a new academic research awards program donating one billion core-hours of computational capacity to researchers. The Exacycle project
enables massive parallelism for doing science in the cloud
, and inspired multiple
proposals
aiming to take advantage of cloud scale. Today, we would like to share some exciting results from a project built on Google’s infrastructure.
Google Research Scientist
Kai Kohlhoff
, in collaboration with Stanford University and Google engineers, investigated how an important
signalling protein
in the membrane of human cells can switch off and on by changing its three-dimensional structure following a sequence of local
conformational changes
. This research can help to better understand the effects of certain chemical compounds on the human body and assist future development of more potent drug molecules with fewer side effects.
The protein, known as the
beta-2 adrenergic receptor
, is a G protein-coupled receptor (
GPCR
), a primary drug target that plays a role in several debilitating health conditions. These include asthma, type-2 diabetes, obesity, and hypertension. The receptor and its close GPCR relatives bind to many familiar molecules, such as epinephrine, beta-blockers, and caffeine. Understanding their structure, function, and the underlying dynamics during binding and activation increases our chances to decode the causes and mechanisms of diseases.
To gain insights into the receptor’s dynamics, Kai performed detailed molecular simulations using hundreds of millions of core hours on Google’s infrastructure, generating hundreds of terabytes of valuable molecular dynamics data. The Exacycle program enabled the realization of simulations with longer sampling and higher accuracy than previous experiments, exposing the complex processes taking place on the nanoscale during activation of this biological switch.
The paper summarizing the results of Kai’s and his collaborators’ work is featured on the January cover of
Nature Chemistry
, with artwork by Google R&D UX Creative Lead Thor Lewis, to be published on December 17, 2013. The online version of his paper was published on their
website
today.
We are extremely pleased with the results of this program. We look forward to seeing this research continue to develop.
Googler Moti Yung elected as 2013 ACM Fellow
Wednesday, December 11, 2013
Posted by Alfred Spector, VP of Engineering
Yesterday, the Association for Computing Machinery (ACM)
released
the list of those who have been elected ACM Fellows in 2013. I am excited to announce that Google
Research Scientist Moti Yung
is among the distinguished individuals receiving this honor.
Moti was chosen for his contributions to computer science and cryptography that have provided fundamental knowledge to the field of computing security. We are proud of the breadth and depth of his contributions, and believe they serve as motivation for computer scientists worldwide.
On behalf of Google, I congratulate our colleague, who joins the 17 ACM Fellow and other professional society awardees at Google, in exemplifying our extraordinarily talented people. You can read a more detailed summary of Moti’s accomplishments below, including the official citations from ACM.
Dr. Moti Yung: Research Scientist
For contributions to cryptography and its use in security and privacy of systems
Moti has made key contributions to several areas of cryptography including (but not limited to!) secure group communication, digital signatures,
traitor tracing
,
threshold cryptosystems
and
zero knowledge proofs.
Moti's work often seeds a new area in theoretical cryptography as well as finding applications broadly. For example, in 1992, Moti co-developed a protocol by which users can commonly compute a group key using their own private information that is secure against coalitions of rogue users. This work led to the growth of the broadcast encryption research area and has applications to pay-tv, network communication and sensor networks.
Moti is also a long-time leader of the security and privacy research communities, having mentored many of the leading researchers in the field, and serving on numerous program committees. A prolific author, Moti routinely publishes 10+ papers a year, and has been a key contributor to principled and consistent anonymization practices and data protection at Google.
Free Language Lessons for Computers
Tuesday, December 3, 2013
Posted by Dave Orr, Google Research Product Manager
Not everything that can be counted counts.
Not everything that counts can be counted.
-
William Bruce Cameron
50,000 relations from Wikipedia. 100,000 feature vectors from YouTube videos. 1.8 million historical infoboxes. 40 million entities derived from webpages. 11 billion Freebase entities in 800 million web documents. 350 billion words’ worth from books analyzed for syntax.
These are all datasets that we’ve shared with researchers around the world over the last year from Google Research.
But data by itself doesn’t mean much. Data is only valuable in the right context, and only if it leads to increased knowledge. Labeled data is critical to train and evaluate machine-learned systems in many arenas, improving systems that can increase our ability to understand the world. Advances in natural language understanding, information retrieval, information extraction, computer vision, etc. can help us
tell stories
, mine for valuable insights, or
visualize information
in beautiful and compelling ways.
That’s why we are pleased to be able to release sets of labeled data from various domains and with various annotations, some automatic and some manual. Our hope is that the research community will use these datasets in ways both straightforward and surprising, to improve systems for annotation or understanding, and perhaps launch new efforts we haven’t thought of.
Here’s a listing of the major datasets we’ve released in the last year, or you can subscribe to our
mailing list
. Please tell us what you’ve managed to accomplish, or send us pointers to papers that use this data. We want to see what the research world can do with what we’ve created.
50,000 Lessons on How to Read: a Relation Extraction Corpus
What is it
: A human-judged dataset of two relations involving public figures on
Wikipedia
: about 10,000 examples of “place of birth” and 40,000 examples of “attended or graduated from an institution.”
Where can I find it
:
https://code.google.com/p/relation-extraction-corpus/
I want to know more
: Here’s a
handy blog post
with a broader explanation, descriptions and examples of the data, and plenty of links to learn more.
11 Billion Clues in 800 Million Documents
What is it
: We took the ClueWeb corpora and automatically labeled concepts and entities with
Freebase concept IDs
, an example of entity resolution. This dataset is huge: nearly 800 million web pages.
Where can I find it
: We released two corpora:
ClueWeb09 FACC
and
ClueWeb12 FACC
.
I want to know more
: We described the process and results in a recent blog post.
Features Extracted From YouTube Videos for Multiview Learning
What is it
: Multiple feature families from a set of public YouTube videos of games. The videos are labeled with one of 30 categories, and each has an associated set of visual, auditory, and and textual features.
Where can I find it
: The data and more information can be obtained from the
UCI machine learning repository (multiview video dataset)
, or from
Google’s repository
.
I want to know more
: Read more about the data and uses for it
here
.
40 Million Entities in Context
What is it
: A disambiguation set consisting of pointers to 10 million web pages with 40 million entities that have links to Wikipedia. This is another entity resolution corpus, since the links can be used to disambiguate the mentions, but unlike the ClueWeb example above, the links are inserted by the web page authors and can therefore be considered human annotation.
Where can I find it
: Here’s the
WikiLinks corpus
, and tools can be found to help use this data on our partner’s page:
Umass Wiki-links
.
I want to know more
: Other disambiguation sets, data formats, ideas for uses of this data, and more can be found at our
blog post announcing the release
.
Distributing the Edit History of Wikipedia Infoboxes
What is it
: The edit history of 1.8 million infoboxes in Wikipedia pages in one handy resource. Attributes on Wikipedia change over time, and some of them change more than others. Understanding attribute change is important for extracting accurate and useful information from Wikipedia.
Where can I find it
:
Download from Google
or from
Wikimedia Deutschland
.
I want to know more
: We
posted
a detailed look at the data, the process for gathering it, and where to find it. You can also read a
paper
we published on the release.
Note the change in the capital of Palau.
Syntactic Ngrams over Time
What is it
: We automatically syntactically analyzed 350 billion words from the 3.5 million English language books in
Google Books
, and collated and released a set of fragments -- billions of unique tree fragments with counts sorted into types. The underlying corpus is the same one that underlies the recently updated
Google Ngram Viewer
.
Where can I find it
:
http://commondatastorage.googleapis.com/books/syntactic-ngrams/index.html
I want to know more
: We discussed the nature of dependency parses and describe the data and release in a
blog post
. We also published a
paper about the release
.
Dictionaries for linking Text, Entities, and Ideas
What is it
: We created a large database of pairs of 175 million strings associated with 7.5 million concepts, annotated with counts, which were mined from Wikipedia. The concepts in this case are Wikipedia articles, and the strings are anchor text spans that link to the concepts in question.
Where can I find it
:
http://nlp.stanford.edu/pubs/crosswikis-data.tar.bz2
I want to know more
: A description of the data, several examples, and ideas for uses for it can be found in a
blog post
or in the
associated paper
.
Other datasets
Not every release had its own blog post describing it. Here are some other releases:
Automatic
Freebase annotations
of Trec’s Million Query and Web track queries.
A
set of Freebase triples
that have been deleted from Freebase over time -- 63 million of them.
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
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
.
