Blog
The latest news from Google AI
Data and code open sourced from Google's Renewable Energy Cheaper than Coal project
Monday, January 30, 2012
Posted by Ross Koningstein, Engineer, Google RE<C team
Cross-posted
with the Open Source at Google Blog
Google’s
RE<C
renewable energy research project has recently open sourced a new tool and a significant amount of data to support future CSP (concentrating solar power)
heliostat
development.
HOpS Open Source Site
HOpS
,
h
eliostat
op
tical
s
imulation, is an open source software tool for accurately and efficiently performing optical simulations of fields of heliostats, the actuated mirror assemblies that direct sunlight onto a target in CSP applications.
Google used this tool to help
evaluate heliostat field layouts
and calculate heat input into a CSP receiver for power production. HOpS works by passing "packets" of light between optical elements (the sun, heliostats, and elements of the target surface), tracking shadowing and blocking masks along the way. For our analysis goals, this approach gave our researchers more flexibility and accuracy than analytic tools (such as DELSOL or HFLCAL), and it was easier to set up for thousands of runs than using ray tracers. Output from the simulation includes heliostat efficiency, target irradiance, and more, while an included shell script facilitates plotting heat maps of the output data using gnuplot.
REC-CSP Open Source Site
The
REC_CSP
open source project contains data sets and software useful for designing cheaper heliostats.
Available on the project site are:
1. Thirty days of three-dimensional wind measurement data taken with ultrasonic anemometers (sampled at ~7 Hz), recorded at several near surface elevations. The data is presented in the
RE<C wind data collection document
and is available for download on the open source site
here
.
2. A collection of
heliostat aerodynamic load data
obtained in a NASA wind tunnel and graphically represented in the
appendix
. This data is available for download on the open source site
here
.
3. Matlab software for high-precision, on-target heliostat control with built-in simulation for testing. This is essentially the same software used in the
RE<C heliostat control demonstrations
and described in the
accelerometer sensing
and
control system design
documents. The source code is available for download
here
.
Video: Demonstrating single and multiple heliostat control
Open-sourcing Sky Map and collaborating with Carnegie Mellon University
Friday, January 20, 2012
Posted by John Taylor and Kevin Serafini
In May 2009
we launched Google Sky Map: our “window on the sky” for Android phones. Created by half a dozen Googlers at the
Pittsburgh office
in our 20% time, the app was designed to show off the amazing capabilities of the sensors in the first generation Android phones. Mostly, however, we wrote it because we love astronomy. And, thanks to Android’s broad reach, we have managed to share this passion with over 20 million Android users as well as with our local community at events such as the
Urban Sky Party
.
Today, we are delighted to announce that we are going to share Sky Map in a different way: we are donating Sky Map to the community. We are collaborating with Carnegie Mellon University in an exciting partnership that will see further development of Sky Map as a series of student projects. Sky Map’s development will now be driven by the students, with Google engineers remaining closely involved as advisors. Additionally, we have
open-sourced
the app so that other astronomy enthusiasts can take the code and augment it as they wish.
The Google Sky Map team would like to thank all of our users who have taken the time to send us comments over the past 3 years. You tell us that Sky Map has helped you show off your phone, enabled you to see the stars when the urban light pollution or weather obscured them and even find romance! The feedback that touched us most though can be summarized by this short email:
“sat down with my son and looked around at the planets for about 45 minutes...time well spent, thanx”
CDC Birth Vital Statistics in BigQuery
Friday, January 13, 2012
Posted by Dan Vanderkam, Software Engineer
Google’s
BigQuery Service
lets enterprises and developers crunch large-scale data sets quickly. But what if you don’t have a large-scale data set of your own?
To help the data-less masses, BigQuery offers several
large, public data sets
. One of these is the
natality
data set, which records information about live births in the United States. The data is derived from the
Division of Vital Statistics
at the
Centers for Disease Control and Prevention
, which has collected an electronic record of birth statistics
since 1969
. It is one of the longest-running electronic records in existence.
Each row in this database represents a live birth. Using simple queries, you can discover fascinating trends from the last forty years.
For example, here’s the average age of women giving birth to their first child:
The average age has increased from 21.3 years in 1969 to 25.1 years in 2008. Using more complex queries, one could analyze the factors which have contributed to this increase, i.e. whether it can be explained by changing racial/ethnic composition of the population.
You can see more
examples
like this one on the BigQuery site.
Google Correlate expands to 49 additional countries
Tuesday, January 3, 2012
Posted by Matt Mohebbi, Software Engineer
In May of this year we
launched
Google Correlate on Google Labs.
This system
enables a correlation search between a user-provided time series and millions of time series of Google search traffic. Since our initial launch, we've graduated to Google Trends and we've seen a number of great applications of Correlate in several domains, including economics (
consumer spending
,
unemployment rate
and
housing inventory
),
sociology
and
meteorology
. The correspondence of
gas prices and search activity for fuel efficient cars
was even briefly discussed in a
Fox News presidential debate
and NPR recently
covered
correlations related to political commentators.
Health has always been an area of particular interest to our team (Matt Mohebbi, Julia Kodysh, Rob Schonberger and Dan Vanderkam). Correlate was inspired by Google Flu Trends and many of us worked on both systems. So we were very excited when the BioSense division at the CDC
published
a page which shows correlations between some of their national trends in patient diagnosis activity and Google search activity. With just three years of weekly data, relevant search terms are surfaced. For example, the time series for
bloody nose
surfaces "bloody snot" and "blood in snot".
While these terms shouldn't come as a surprise, there are others which are more interesting, including searches related to static electricity, dry skin, and red cheeks. Of course, correlation is not causation but we hope that Correlate can be used as a method for researchers to generate new hypotheses with their data.
To help researchers outside the United States, we're pleased to announce support for 49 additional countries in Google Correlate. It's now possible to see correlations like
"snorkeling" in Australia
,
"cherry blossoms" in Japan
, and
"beer garden" in Germany
. We look forward to seeing what new correlations researchers can find with this data!
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
2021
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
.
