Blog
The latest from Google Research
First Robots
miércoles, 22 de marzo de 2006
Posted by Sumit Agarwal, Maryam Kamvar, & Michael Stoppelman
With 4 seconds left to go, the Team
Cheesy Poofs
robot shouldered its way onto the 3 foot platform, pivoted 90 degrees into scoring position, and rapid-fired 10 balls directly into the 3-point goal. They won the match, and the Google Silicon Valley Regional Championship for US FIRST, a non-profit "For the Inspiration and Recognition of Science and Technology" (
FIRST
).
Google jumped at the opportunity to sponsor this organization after Dean Kamen (inventor of the Segway and the first implantable dialysis pump) spoke to a packed Google audience about his lifelong crusade to improve education in the United States. Dean founded US FIRST over 15 years ago, and from humble beginnings in the Northeast, FIRST has now grown to involve over 60,000 high school students all over the United States and the world.
FIRST was a natural partner for Google, given their focus on science and technology, their passion for changing the world for the better, and their single-minded focus on making education fun for students. When the final buzzer rang at the recent championship match the students jumped and hugged like they'd won the Superbowl. And in a way, they had. This event has all the excitement, tension, and drama of a major sporting event and then some.
Beyond sponsoring the FIRST tournament, Google also funded half a dozen teams in the Bay Area, ranging from East Palo Alto High School to Notre Dame High School. Several dozen employees also served as team mentors, meeting the students once a week to help construct the competition robots over the frantic six-week design/build cycle. Others volunteered at the Regional event as judges, coordinators, and referees, and plenty of Googlers were on hand to spectate the exciting matches.
We congratulate all the teams at the regional tournament for their hard work and innovation. We wish the six bay-area teams who qualified for the finals in Atlanta the best of luck . Bring home the gold!
Hiring: The Lake Wobegon Strategy
sábado, 11 de marzo de 2006
Posted by Peter Norvig, Director, Google Research
You know the Google story: small start-up of highly-skilled programmers in a garage grows into a large international company. But how do you maintain the skill level while roughly doubling in size each year? We rely on the Lake Wobegon Strategy, which says
only hire candidates who are above the mean of your current employees.
An alternative strategy (popular in the dot-com boom period) is to justify a hire by saying "this candidate is clearly better than at least one of our current employees." The following graph compares the mean employee skill level of two strategies: hire-above-the-mean (or Lake Wobegon) in blue and hire-above-the-min in red. I ran a simulation of 1000 candidates with skill level sampled uniformly from the 0 to 100th percentile (but evaluated by the interview process with noise of ±15%) starting from a core team of 10 employees with mean 75 and min 65. You can see how hire-above-the-min leads to a precipitous drop in skill level; one we've been able to avoid.
Another hiring strategy we use is
no hiring manager
. Whenever you give project managers responsibility for hiring for their own projects they'll take the best candidate in the pool, even if that candidate is sub-standard for the company, because every manager wants some help for their project rather than no help. That's why we do all hiring at the company level, not the project level. First we decide which candidates are above the hiring threshold, and then we decide what projects they can best contribute to. The orange line in the graph above is a simulation of the hiring-manager strategy, with the same candidates and the same number of hires as the no-hiring-manager strategy in blue. Employees are grouped into pools of random size from 2 to 14 and the hiring manager chooses the best one. We're pleased that these little simulations show our hiring strategy is on top. You can learn more about our
hiring and working philosophy
.
An experimental study of P2P VoIP
martes, 7 de marzo de 2006
Posted by Neil Daswani & Ravi Jain, Google; and Saikat Guha, Cornell University
VoIP (Voice-over-IP) systems are one of the fastest growing means of communication on the Internet, enabling free or low-cost phone calls. But to date, researchers have had little data to work with to learn how to build VoIP systems better. Some of these systems are proprietary, and obtaining data about their operational characteristics has been particularly challenging. For instance, even though the Skype network has tens of millions of users, it has been hard for researchers to benefit from its commercial success.
Data was collected from a Skype 'supernode' running at Cornell. Skype is a Peer-to-Peer (P2P) system in which clients (for example, a home user's PC) communicate directly to exchange voice packets with other clients (also called peers). However, their communication is facilitated by special peers called supernodes that can allow the peers to connect even if they are behind firewalls or other network elements such as NATs (Network Address Translators). P2P in Skype already connects millions of users behind NATs today. Prior to our research, not much has been known about how Skype users and clients behave, and how supernodes are selected or what kinds of demands they place on the network they reside in.
We learned a couple things from the data. For example, we found that Skype users typically keep their client software open during the workday, as opposed to users of file-sharing P2P systems (such as KaZaa) where users typically join and leave the network with much greater frequency. In further contrast to P2P file-sharing applications, which typically tend to be bandwidth hogs, Skype clients and supernodes use relatively little bandwidth and CPU even when they relay VoIP calls. So this means you can run Skype without having it slow down your Internet connection.
You'll find even more results discussed in
the paper
. In addition to better P2P systems, researchers can use the data to design a better Internet. Based on what we've learned, perhaps researchers can design a next-generation P2P-friendly Internet that is commercially viable.
Teamwork for problem-solving
sábado, 4 de marzo de 2006
Posted by Corinna Cortes, Head, Google Research NY
Google Research is about teamwork with outstanding engineers to solve novel and challenging problems that have an impact. But it's also about being at the forefront of scientific innovations. We're an active part of the research community, and we like to interact with researchers and scientists in academia. We're happy to serve as a hub for researchers to come and discuss their latest findings and get exposed to the large-scale problems and challenges that we face.
Robert Tarjan
,
John Lafferty
, and
Brian Kernighan
are among the professors that have spent time here.
We host world-renowned scientists spanning diverse areas including neuroscience, climatology, internet security and e-commerce -- and of course, computer science. In the fall, our Research Seminars attracted such prominent figures as
John Hopcroft
and
Michael Rabin
. This spring we're welcoming
Christos Papadimitriou
and
Vladimir Vapnik
, to name just a few.
So if you're curious about the
latest meteor findings in Antarctica
or interested in
high-end computing and scientific visualization at NASA
, do check out our
"tech talks" on Google Video
. You don't actually need to work at Google to "attend" the talks -- but if you're interested,
we're always looking.
Etiquetas
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
2022
jun
may
abr
mar
feb
ene
2021
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2020
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2019
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2018
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2017
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2016
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2015
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2014
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2013
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2012
dic
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2011
dic
nov
sep
ago
jul
jun
may
abr
mar
feb
ene
2010
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2009
dic
nov
ago
jul
jun
may
abr
mar
feb
ene
2008
dic
nov
oct
sep
jul
may
abr
mar
feb
2007
oct
sep
ago
jul
jun
feb
2006
dic
nov
sep
ago
jul
jun
abr
mar
feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.