Blog
The latest from Google Research
Conference Report: Workshop on Internet and Network Economics (WINE) 2012
Wednesday, December 19, 2012
Posted by Vahab Mirrokni, Research Scientist, Google Research New York
Google regularly participates in the
WINE
conference: Workshop on Internet & Network Economics. WINE’12 just happened last week in Liverpool, UK, where there is a strong
economics and computation group
. WINE provides a forum for researchers across various disciplines to examine interesting algorithmic and economic problems of mutual interest that have emerged from the Internet over the past decade. For Google, the exchange of ideas at this selective workshop has resulted in innovation and improvements in algorithms and economic auctions, such as our display ad allocation.
Googlers co-authored three papers this year; here’s a synopsis of each, as well as some highlights from invited talks at the conference:
Budget Optimization for Online Campaigns with Positive Carryover Effects
This paper first argues that ad impressions may have some long-term impact on user behaviour, and refers to an older
WWW ’10 paper
. Based on this motivation, the paper presents a scalable budget optimization algorithm for online advertising campaigns in the presence of Markov user behavior. In such settings, showing an ad to a user may change their actions in the future through a Markov model, and the probability of conversion for the ad does not only depend on the last ad shown, but also on earlier user activities. The main purpose of the paper is to give a simpler algorithm to solve a constrained Markov Decision Process, and confirms this easier solution via simulations on some advertising data sets. The paper was written when Nikolay Archak, a PhD student at NYU business school, was an intern with the New York market algorithms research team.
On Fixed-Price Marketing for Goods with Positive Network Externalities
This paper presents an approximation algorithm for marketing “networked goods” and services that exhibit positive network externalities - for example, is the buyer's value for the goods or service influenced positively by other buyers owning the goods or using the service? Such positive network externalities arise in many products like operating systems or smartphone services. While most of previous research is concerned with influence maximization, this paper attempts to identify a revenue maximizing marketing strategy for such networked goods, as follows: The seller selects a set (S) of buyers and gives them the goods for free, then sets a fixed per-unit price (p), at which other consumers can buy the item. The strategy is consistent with practice and is easy to implement. The authors use ideas from non-negative submodular maximization to find the optimal revenue maximizing fixed-price marketing strategy.
The AND-OR game: Equilibrium Characterization
Yishay Mansour, former Visiting Faculty in Google New York, presented the results; he first argued that the existence and uniqueness of market equilibria is only known for markets with divisible goods and concave or convex utilities. Then he described a simple market AND-OR game for divisible goods. To my surprise, he showed a class of mixed strategies are basically the unique set of randomized equilibria for this market (up to minor changes in the outcome). At the end, Yishay challenged the audience to give such characterization for more general markets with indivisible goods.
Kamal Jain of Ebay Research gave an interesting talk about mechanism design problems, inspired by application in companies like Ebay and Google. In one part, Kamal proposed "
coopetitive ad auctions
" for settings in which the auctioneer runs an auction among buyers who may cooperate with some advertisers, and at the same time compete with others for sealing advertising slots. He gave context around "product ads"; for example, a retailer like Best Buy may cooperate with a manufacturer like HP to put out a product ad for an HP computer sold at Best Buy. Kamal argued that if the cooperation is not an explicit part of the auction, an advertiser may implicitly end up competing with itself, thus decreasing the social welfare. By making the cooperation an explicit part of the auction, he was able to design a mechanism with better social welfare and revenue properties, compared to both first-price and second-price auctions. Kamal also discussed optimal mechanisms for intermediaries, and “surplus auctions” to avoid cyclic bidding behavior resulted from running naive variants of first-price auctions in repeated settings.
David Parkes of Harvard University discussed techniques to combine mechanism design with machine learning or heuristic search algorithms. At one point David discussed how to implement a
branch-and-bound search algorithm
in a way that results in a "monotone" allocation rule, so that if we implement a VCG-type allocation and pricing rule based on this allocation algorithm, the resulting mechanism becomes truthful. David also presented ways to compute a set of prices for any allocation, respecting incentive compatibility constraints as much as possible. Both of these topics appeared in ACM EC 2012 papers that he had co-authored.
At the business meeting, there was a proposal to change the title of the conference from “workshop” to “conference” or “symposium” to reflect its fully peer-reviewed and archival nature, keeping the same acronym of WINE. (Changing the title to “Symposium on the Web, Internet, and Network Economics” was rejected: SWINE!) WINE 2013 will be held at Harvard University in Boston, MA, and we look forward to reconnecting with fellow researchers in the field and continuing to nurture new developments and research topics.
Using online courses in Spain to teach entrepreneurship
Tuesday, December 18, 2012
Posted by Francisco Ruiz Anton, Policy Manager, Google Spain
Cross-posted with the
Policy by the Numbers Blog
At the end of the third quarter in 2012,
roughly 25%
of adults in Spain were out of work. More than half of adults under 24 years old are unemployed. Recent graduates and young adults preparing to enter the workforce face the toughest job market in decades.
The Internet presents an opportunity for growth and economic development. According to
recent research
, more than 100,000 jobs in Spain originate from the Internet and it directly contributes to the GDP with 26.7 billion euros (2.5%). That impact that could triple by 2015 under the right conditions.
One of those conditions is making high-quality education accessible, echoed by a
recent OECD
report on the youth labor market in Spain. This is no easy task. University degrees are in high demand, straining the reach of our existing institutions.
The web has become a way for learners to develop new skills when traditional institutions aren’t an option. Recent courses on platforms like
Udacity
,
Coursera
and
edX
have seen hundreds of thousands of students enroll and participate in courses taught by prestigious professors and lecturers.
Google is partnering with numerous organizations and universities in Spain to organize
UniMOOC
, an online course intended to educate citizens in Spain and the rest of the Spanish-speaking world about entrepreneurship. It was built with
Course Builder
, Google’s new open source toolkit for constructing online courses.
To date nearly 10,000 students have registered for the course, over two-thirds of them from Spain and one-third from 93 countries. It recently
won an award
for the “Most innovative project” in 2012 from the newspaper El Mundo.
Spain’s situation is not entirely unique in Europe. Policymakers across the continent are asking themselves how best to create economic opportunity for their citizens, and how to ensure that their best and brightest students are on a path toward financial success. Our hope is that the people taking this course will be more empowered with the right skills and tools to start their own businesses that can create jobs. They will push not only Spain, but Europe and the rest of the world towards economic recovery and growth.
The course is still running, and you’re able to
join today
.
Millions of Core-Hours Awarded to Science
Monday, December 17, 2012
Posted by Andrea Held, Program Manager, University Relations
In 2011 Google University Relations
launched
a new academic research awards program,
Google Exacycle for Visiting Faculty
, offering up to one billion core-hours to qualifying proposals. We were looking for projects that would consume 100M+ core-hours each and be of critical benefit to society. Not surprisingly, there was no shortage of applications.
Since then, the following seven scientists have been working on-site at Google offices in Mountain View and Seattle. They are here to run large computing experiments on Google’s infrastructure to change the future. Their projects include exploring antibiotic drug resistance, protein folding and structural modelling, drug discovery, and last but not least, the dynamic universe.
Today, we would like to introduce the Exacycle award recipients and their work. Please stay tuned for updates next year.
Simulating a Dynamic Universe with the Large Synoptic Sky Survey
Jeff Gardner
, University of Washington, Seattle, WA
Collaborators:
Andrew Connolly
, University of Washington, Seattle, WA, and
John Peterson
, Purdue University, West Lafayette, IN
Research subject
:
The Large Synoptic Survey Telescope
(LSST) is one of the most ambitious astrophysical research programs ever undertaken. Starting in 2019, the LSST’s 3.2 Gigapixel camera will repeatedly survey the southern sky, generating tens of petabytes of data every year. The images and catalogs from the LSST have the potential to transform both our understanding of the universe and the way that we engage in science in general.
Exacycle impact
: In order to design the telescope to yield the best possible science, the LSST collaboration has undertaken a formidable computational campaign to simulate the telescope itself. This will optimize how the LSST surveys the sky and provide realistic datasets for the development of analysis pipelines that can operate on hundreds of petabytes. Using Exacycle, we are reducing the time required to simulate one night of LSST observing, roughly 5 million images, from 3 months down to a few days. This rapid turnaround will enable the LSST engineering teams to test new designs and new algorithms with unprecedented precision, which will ultimately lead to bigger and better science from the LSST.
Designing and Defeating Antibiotic Drug Resistance
Peter Kasson, Assistant Professor, Departments of Molecular Physiology and Biological Physics and of Biomedical Engineering, University of Virginia
Research subject
: Antibiotics have made most bacterial infections routinely treatable. As antibiotic use has become common, bacterial resistance to these drugs has also increased. Recently, some bacteria have arisen that are resistant to almost all antibiotics. We are studying the basis for this resistance, in particular the enzyme that acts to break down many antibiotics. Identifying the critical changes required for pan-resistance will aid surveillance and prevention; it will also help elucidate targets for the development of new therapeutic agents.
Exacycle impact
: Exacycle allows us to simulate the structure and dynamics of several thousand enzyme variants in great detail. The structural differences between enzymes from resistant and non-resistant bacteria are subtle, so we have developed methods to compare structural "fingerprints" of the enzymes and identify distinguishing characteristics. The complexity of this calculation and large number of potential bacterial sequences mean that this is a computationally intensive task; the massive computing power offered by Exacycle in combination with some novel sampling strategies make this calculation tractable.
Sampling the conformational space of G protein-coupled receptors
Kai Kohlhoff, Research Scientist at Google
Collaborators: Research labs of
Vijay Pande
and
Russ Altman
at Stanford University
Research subject
: G protein-coupled receptors (
GPCRs
) are proteins that act as signal transducers in the cell membrane and influence the response of a cell to a variety of external stimuli. GPCRs play a role in many human diseases, such as asthma and hypertension, and are well established as a primary drug target.
Exacycle impact
: Exacycle let us perform many tens of thousands of molecular simulations of membrane-bound GPCRs in parallel using the
Gromacs
software. With
MapReduce
,
Dremel
, and other technologies, we analyzed the 100s of Terabytes of generated data and built
Markov State Models
. The information contained in these models can help scientists design drugs that have higher potency and specificity than those presently available.
Results
: Our models let us explore kinetically meaningful receptor states and transition rates, which improved our understanding of the structural changes that take place during activation of a signaling receptor. In addition, we used Exacycle to study the affinity of drug molecules when binding to different receptor states.
Modeling transport through the nuclear pore complex
Daniel Russel, post doc in structural biology, University of California, San Francisco
Research subject
: Our goal is to develop a predictive model of transport through the nuclear pore complex (NPC). Developing the model requires understanding how the behavior of the NPC varies as we change the parameters governing the components of the system. Such a model will allow us to understand how transportins, the unstructured domains and the rest of the cellular milieu, interact to determine efficiency and specificity of macromolecular transport into and out of the nucleus.
Exacycle impact
: Since data describing the microscopic behavior of most parts of the nuclear transport process is incomplete and contradictory, we have to explore a larger parameter space than would be feasible with traditional computational resources.
Status
: We are currently modeling various experimental measurements of aspects of the nuclear transport process. These experiments range from simple ones containing only a few components of the transport process to measurements on the whole nuclear pore with transportins and cellular milieu.
Large scale screening for new drug leads that modulate the activity of disease-relevant proteins
James Swetnam, Scientific Software Engineer, drugable.org, NYU School of Medicine
Collaborators: Tim Cardozo, MD, PhD - NYU School of Medicine.
Research subject
: We are using a high throughput, CPU-bound procedure known as virtual ligand screening to ‘dock’, or produce rough estimates of binding energy, for a large sample of bioactive chemical space to the entirety of known protein structures. Our goal is the first computational picture of how bioactive chemistry with therapeutic potential can affect human and pathogen biology.
Exacycle Impact
: Typically, using our academic lab’s resources, we could screen a few tens of thousands of compounds against a single protein to try to find modulators of its function. To date, Exacycle has enabled us to screen 545,130 compounds against 8,535 protein structures that are involved in important and underserved diseases as cancer, diabetes, malaria, and HIV to look for new leads towards future drugs.
Status
: We are currently expanding our screens to an additional 206,190 models from
ModBase. We aim to have a public dataset for the research community in the first half of 2013.
Protein Structure Prediction and Design
Michael Tyka, Research Fellow, University of Washington, Seattle, WA
Research subject
: The precise relationship between the primary sequence and the
three dimensional structure of proteins
is one of the unsolved grand challenges of computational biochemistry. The
Baker Lab
has made significant progress in recent years by developing more powerful protein prediction and design algorithms using the
Rosetta Protein Modelling suite
.
Exacycle impact
: Limitations in the accuracy of the
physical model
and lack of sufficient computational power have prevented solutions to broader classes of medically relevant problems. Exacycle allows us to improve model quality by conducting large parameter optimization sweeps with a very large dataset of experimental protein structural data. The improved energy functions will benefit the entire theoretical protein research community.
We are also using Exacycle to conduct simultaneous
docking
and
one-sided protein design
to develop novel protein binders for a number of medically relevant targets. For the first time, we are able to aggressively redesign backbone conformations at the binding site. This allows for a much greater flexibility in possible binding shapes but also hugely increases the space of possibilities that have to be sampled. Very promising designs have already been found using this method.
Continuing the quest for future computer scientists with CS4HS
Thursday, December 13, 2012
Erin Mindell, Program Manager, Google Education
Computer Science for High School (CS4HS) began five years ago with a simple question: How can we help create a much needed influx of CS majors into universities and the workforce? We took our questions to three of our university partners--University of Washington, Carnegie Mellon, and UCLA--and together we came up with CS4HS. The model was based on a “train the trainer” technique. By focusing our efforts on teachers and bringing them the skills they need to implement CS into their classrooms, we would be able to reach even more students. With grants from Google, our partner universities created curriculum and put together hands-on, community-based workshops for their local area teachers.
Since the initial experiment, CS4HS has exploded into a worldwide program, reaching more than 4,000 teachers and 200,000 students either directly or indirectly in more than 34 countries. These hands-on, in-person workshops are a hallmark of our program, and we will continue to fund these projects going forward. (For information on
how to apply
, please see our
website
.) The success of this popular program speaks for itself, as we receive more quality proposals each year. But success comes at a price, and we have found that the current format of the workshops is not infinitely scalable.
This is where Research at Google comes in. This year, we are experimenting with a new model for CS4HS workshops. By harnessing the success of online courses such as
Power Searching with Google
, and utilizing open-source platforms like the one found in
Course Builder
, we are hoping to put the
“M” in “MOOC”
and reach a broader audience of educators, eager to learn how to teach CS in their classrooms.
For this pilot, we are looking to sponsor two online workshops, one that is geared toward CS teachers, and one that is geared toward CS for non-CS teachers to go live in 2013. This is a way for a university (or several colleges working together) to create one incredible workshop that has the potential to reach thousands of enthusiastic teachers. Just as with our in-person workshops, applications will be open to college, university, and technical schools of higher learning only, as we depend on their curriculum expertise to put together the most engaging programs. For this pilot, we will be looking for MOOC proposals in the US and Canada only.
We are really excited about the possibilities of this new format, and we are looking for quality applications to fund. While applications don’t have to run on our
Course Builder platform
, we may be able to offer some additional support to funded projects that do. If you are interested in joining our experiment or just learning more, you can find information on how to apply on our
CS4HS website
(or click
here
).
Applications are open until February 16, 2013; we can’t wait to see what you come up with. If you have questions, please email us at
cs4hs@google.com
.
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
materials science
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
Responsible AI
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
2021
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
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
.
