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The latest news from Google AI
Lessons learned while protecting Gmail
Tuesday, March 29, 2016
Posted by Elie Bursztein - anti-abuse & security research, Nicolas Lidzborski - Gmail security engineering, and Vijay Eranti - Gmail anti-abuse engineering
Earlier this year in San Francisco,
USENIX
hosted their inaugural
Enigma Conference
, which focused on security, privacy and electronic crime through the lens of emerging threats and novel attacks. We were
excited to help make this conference happen
and to participate in it.
At the conference, we heard from a variety of terrific speakers including:
Ron Rivest
, Professor at MIT and inventor of RSA, who spoke about
the consequences of backdooring encryption
Rob Joyce
, Chief of the NSA Tailored Access Operations organization, who spoke about about
defending against state attackers
George “Geohot” Hotz
, Hacker extraordinaire, who discussed
state of the art software debugging
In addition, we were able to
share the lessons we’ve learned
about protecting Gmail users since it was launched over a decade ago. Those lessons are summarized in the infographic below (the talk slides are
also available
).
We were proud to sponsor this year's inaugural Enigma conference, and it is our hope that the core lessons that we have learned over the years can benefit other online products and services. We're looking forward to participating again next year when
Enigma returns in 2017
. We hope to see you there!
Machine Learning in the Cloud, with TensorFlow
Wednesday, March 23, 2016
Posted by Slaven Bilac, Software Engineer, Google Research
At Google, researchers collaborate closely with product teams, applying the latest advances in Machine Learning to existing products and services - such as
speech recognition in the Google app
,
search in Google Photos
and the
Smart Reply feature in Inbox by Gmail
- in order to make them more useful. A growing number of Google products are using
TensorFlow
, our open source Machine Learning system, to tackle ML challenges and we would like to enable others do the same.
Today, at
GCP NEXT 2016
, we
announced the alpha release
of
Cloud Machine Learning
, a framework for building and training custom models to be used in intelligent applications.
Machine Learning projects can come in many sizes, and as we’ve seen with our open source offering
TensorFlow
, projects often need to scale up. Some small tasks are best handled with a local solution running on one’s desktop, while large scale applications require both the scale and dependability of a hosted solution. Google
Cloud Machine Learning
aims to support the full range and provide a seamless transition from local to cloud environment.
The
Cloud Machine Learning
offering allows users to run custom distributed learning algorithms based on
TensorFlow
. In addition to the
deep learning
capabilities that power
Cloud Translate API
,
Cloud Vision API
, and
Cloud Speech API
, we provide easy-to-adopt samples for common tasks like linear regression/classification with very fast convergence properties (based on the
SDCA
algorithm) and building a custom image classification model with few hundred training examples (based on the
DeCAF
algorithm).
We are excited to bring the best of
Google Research
to
Google Cloud Platform
. Learn more about this release and more from GCP Next 2016 on the
Google Cloud Platform blog
.
Announcing the 2016 Google PhD Fellows for North America, Europe and the Middle East
Thursday, March 10, 2016
Posted by Michael Rennaker, Google PhD Fellowships Lead
Google created the
PhD Fellowship program
in 2009 to recognize and support outstanding graduate students doing exceptional research in Computer Science and related disciplines. Now in its eighth year, our fellowship program has supported hundreds of future faculty, industry researchers, innovators and entrepreneurs.
Reflecting our continuing commitment to supporting and building relationships with the academic community, we are excited to announce the 39 recipients from North America, Europe and the Middle East. We offer our sincere congratulations to Google’s 2016 Class of PhD Fellows.
Computational Neuroscience
Cameron (Po-Hsuan) Chen,
Princeton University
Grace Lindsay,
Columbia University
Martino Sorbaro Sindaci,
The University of Edinburgh
Human-Computer Interaction
Koki Nagano,
University of Southern California
Arvind Satyanarayan,
Stanford University
Amy Xian Zhang,
Massachusetts Institute of Technology
Machine Learning
Olivier Bachem,
Swiss Federal Institute of Technology Zurich
Tianqi Chen,
University of Washington
Emily Denton,
New York University
Yves-Laurent Kom Samo,
University of Oxford
Daniel Jaymin Mankowitz,
Technion - Israel Institute of Technology
Lucas Maystre,
École Polytechnique Fédérale de Lausanne
Arvind Neelakantan,
University of Massachusetts, Amherst
Ludwig Schmidt,
Massachusetts Institute of Technology
Shandian Zhe,
Purdue University, West Lafayette
Machine Perception, Speech Technology and Computer Vision
Eugen Beck,
RWTH Aachen University
Yu-Wei Chao,
University of Michigan, Ann Arbor
Wei Liu,
University of North Carolina at Chapel Hill
Aron Monszpart,
University College London
Thomas Schoeps,
Swiss Federal Institute of Technology Zurich
Chia-Yin Tsai,
Carnegie Mellon University
Market Algorithms
Hossein Esfandiari,
University of Maryland, College Park
Sandy Heydrich,
Saarland University - Saarbrucken GSCS
Rad Niazadeh,
Cornell University
Sadra Yazdanbod,
Georgia Institute of Technology
Mobile Computing
Lei Kang,
University of Wisconsin
Tauhidur Rahman,
Cornell University
Yuhao Zhu,
University of Texas, Austin
Natural Language Processing
Tamer Alkhouli,
RWTH Aachen University
Jose Camacho Collados,
Sapienza - Università di Roma
Privacy and Security
Kartik Nayak,
University of Maryland, College Park
Nicolas Papernot,
Pennsylvania State University
Damian Vizar,
École Polytechnique Fédérale de Lausanne
Xi Wu,
University of Wisconsin
Programming Languages and Software Engineering
Marcelo Sousa,
University of Oxford
Structured Data and Database Management
Xiang Ren,
University of Illinois, Urbana-Champaign
Systems and Networking
Andrew Crotty,
Brown University
Ilias Marinos,
University of Cambridge
Kay Ousterhout,
University of California, Berkeley
Train your own image classifier with Inception in TensorFlow
Wednesday, March 9, 2016
Posted by Jon Shlens, Senior Research Scientist
At the end of last year we released code that allows a user
to classify images with TensorFlow
models. This code demonstrated how to build an image classification system by employing a deep learning model that we had previously trained. This model was known to classify an image across 1000 categories supplied by the
ImageNet
academic competition with an error rate that approached
human performance
. After all, what self-respecting computer vision system would fail to recognize a cute puppy?
Image via
Wikipedia
Well, thankfully the image classification model would recognize this image as a
retriever
with 79.3% confidence. But, more spectacularly, it would also be able to distinguish between a
spotted salamander
and
fire salamander
with high confidence – a task that might be quite difficult for those not experts in
herpetology
. Can
you
tell the difference?
Images via
Wikipedia
The deep learning model we released,
Inception-v3
, is described in our Arxiv preprint "
Rethinking the Inception Architecture for Computer Vision
” and can be visualized with this schematic diagram:
Schematic diagram of Inception-v3
As described in the preprint, this model achieves 5.64% top-5 error while an ensemble of four of these models achieves 3.58% top-5 error on the validation set of the ImageNet whole image
ILSVRC 2012
classification task. Furthermore, in the
2015 ImageNet Challenge
, an ensemble of 4 of these models came in 2nd in the image classification task.
After the release of this model, many people in the TensorFlow community voiced their preference on having an Inception-v3 model that they can train themselves, rather than using our pre-trained model. We could not agree more, since a system for training an Inception-v3 model provides many opportunities, including:
Exploration of different variants of this model architecture in order to improve the image classification system.
Comparison of optimization algorithms and hardware setups for training this model faster or to a higher degree of predictive performance.
Retraining/fine-tuning the Inception-v3 model on a distinct image classification task or as a component of a larger network tasked with object detection or multi-modal learning.
The last topic is often referred to as
transfer learning
, and has been an area of particular excitement in the field of deep networks in the context of vision. A common prescription to a computer vision problem is to first train an image classification model with the ImageNet Challenge data set, and then transfer this model’s knowledge to a distinct task. This has been done for
object detection
,
zero-shot learning
,
image captioning
,
video analysis
and multitudes of other applications.
Today we are happy to announce that
we are releasing libraries and code for training
Inception-v3
on one or multiple GPU’s. Some features of this code include:
Training an Inception-v3 model with synchronous updates across multiple GPUs.
Employing batch normalization to speed up training of the model.
Leveraging many distortions of the image to augment model training.
Releasing a new (still experimental) high-level language for specifying complex model architectures, which we call
TensorFlow-Slim
.
Demonstrating how to perform transfer learning by taking a pre-trained Inception-v3 model and fine-tuning it for another task.
We can train a model from scratch to its best performance on a desktop with 8 NVIDIA Tesla K40s in about 2 weeks. In order to make research progress faster, we are additionally supplying a new version of a pre-trained Inception-v3 model that is ready to be fine-tuned or adapted to a new task. We demonstrate how to use this model for transfer learning on a simple flower classification task. Hopefully, this provides a useful didactic example for employing this Inception model on wide range of vision tasks.
Want to get started? See the accompanying
instructions
on how to
train
,
evaluate
or
fine-tune
a network.
Releasing this code has been a huge team effort. These efforts have taken several months with contributions from many individuals spanning research at Google. We wish to especially acknowledge the following people who contributed to this project:
Model Architecture
– Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Jon Shlens and Zbigniew Wojna
Systems Infrastructure
– Sherry Moore, Martin Wicke, David Andersen, Matthieu Devin, Manjunath Kudlur and Nishant Patil
TensorFlow-Slim
– Sergio Guadarrama and Nathan Silberman
Model Visualization
– Fernanda Viégas, Martin Wattenberg and James Wexler
Deep Learning for Robots: Learning from Large-Scale Interaction
Tuesday, March 8, 2016
Posted by Sergey Levine, Research Scientist
Update (August 23, 2016): The data used in this research is now available
here
.
While we’ve recently seen great strides in robotic capability, the gap between human and robot motor skills remains vast. Machines still have a very long way to go to match human proficiency even at basic sensorimotor skills like grasping. However, by linking learning with continuous feedback and control, we might begin to bridge that gap, and in so doing make it possible for robots to intelligently and reliably handle the complexities of the real world.
Consider for example
this robot
from
KAIST
, which won last year’s
DARPA robotics challenge
. The remarkably precise and deliberate motions are deeply impressive. But they are also quite… robotic. Why is that? What makes robot behavior so distinctly robotic compared to human behavior? At a high level, current robots typically follow a sense-plan-act paradigm, where the robot observes the world around it, formulates an internal model, constructs a plan of action, and then executes this plan. This approach is modular and often effective, but tends to break down in the kinds of cluttered natural environments that are typical of the real world. Here, perception is imprecise, all models are wrong in some way, and no plan survives first contact with reality.
In contrast, humans and animals move quickly, reflexively, and often with remarkably little advance planning, by relying on highly developed and intelligent feedback mechanisms that use sensory cues to correct mistakes and compensate for perturbations. For example, when serving a tennis ball, the player continually observes the ball and the racket, adjusting the motion of his hand so that they meet in the air. This kind of feedback is fast, efficient, and, crucially, can correct for mistakes or unexpected perturbations. Can we train robots to reliably handle complex real-world situations by using similar feedback mechanisms to handle perturbations and correct mistakes?
While servoing and feedback control have been studied extensively in robotics, the question of how to define the right sensory cue remains exceptionally challenging, especially for rich modalities such as vision. So instead of choosing the cues by hand, we can program a robot to acquire them on its own from scratch, by learning from extensive experience in the real world. In our first experiments with real physical robots, we decided to tackle robotic grasping in clutter.
A human child is able to reliably grasp objects after one year, and takes around four years to acquire more sophisticated precision grasps. However, networked robots can instantaneously share their experience with one another, so if we dedicate 14 separate robots to the job of learning grasping in parallel, we can acquire the necessary experience much faster. Below is a video of our robots practicing grasping a range of common office and household objects:
While initially the grasps are executed at random and succeed only rarely, each day the latest experiences are used to train a deep
convolutional neural network
(CNN) to learn to predict the outcome of a grasp, given a camera image and a potential motor command. This CNN is then deployed on the robots the following day, in the inner loop of a servoing mechanism that continually adjusts the robot’s motion to maximize the predicted chance of a successful grasp. In essence, the robot is constantly predicting, by observing the motion of its own hand, which kind of subsequent motion will maximize its chances of success. The result is continuous feedback: what we might call hand-eye coordination. Observing the behavior of the robot after over 800,000 grasp attempts, which is equivalent to about 3000 robot-hours of practice, we can see the beginnings of intelligent reactive behaviors. The robot observes its own gripper and corrects its motions in real time. It also exhibits interesting pre-grasp behaviors, like isolating a single object from a group. All of these behaviors emerged naturally from learning, rather than being programmed into the system.
To evaluate whether the system achieves measurable benefit from continuous feedback, we can compare its performance to an open-loop baseline that closer resembles the perception-planning-action loop described previously, albeit with a learned CNN used to determine both the open-loop grasps and the closed-loop servoing trained on the same data. This approach is most similar to
recent work by Pinto and Gupta
. With open-loop grasp selection, the robot chooses a single grasp pose from a single image, and then blindly executes this grasp. This method has a 34% average failure rate on the first 30 picking attempts for this set of office objects:
Incorporating continuous feedback into the system reduces the failures by nearly half, down to 18% from 34%, and produces interesting corrections and adjustments:
Neural networks have made great strides in allowing us to build computer programs that can process images, speech, text, and even draw pictures. However, introducing actions and control adds considerable new challenges, since every decision the network makes will affect what it sees next. Overcoming these challenges will bring us closer to building systems that understand the effects of their actions in the world. If we can bring the power of large-scale machine learning to robotic control, perhaps we will come one step closer to solving fundamental problems in robotics and automation.
The research on robotic hand-eye coordination and grasping was conducted by Sergey Levine, Peter Pastor, Alex Krizhevsky, and Deirdre Quillen, with special thanks to colleagues at Google Research and X who've contributed their expertise and time to this research. An early preprint is
available on arXiv
.
An Update on fast Transit Routing with Transfer Patterns
Wednesday, March 2, 2016
Arno Eigenwillig, Software Engineer on Google Maps Directions
What is the best way to get from A to B by public transit? Google Maps is answering such queries for over 20,000 cities and towns in over 70 countries around the world, including large metro areas like New York, São Paulo or Moscow, and some complete countries, such as Japan or Great Britain.
Since its
beginnings in 2005
with the single city of Portland, Oregon, the number of cities and countries served by Google’s public transit directions has been growing rapidly. With more and larger regions, the amount of data we need to search in order to provide optimal directions has grown as well. In 2010, the search speed of transit directions made a leap ahead of that growth and became fast enough to update the result
while you drag the endpoints
. The technique behind that speed-up is the Transfer Patterns algorithm [1], which was created at Google’s engineering office in Zurich, Switzerland, by visiting researcher Hannah Bast and a number of Google engineers.
I am happy to report that this research collaboration has continued and expanded with the
Google Focused Research Award
on
Next-Generation Route Planning
. Over the past three years, this grant has supported
Hannah Bast
’s research group at the
University of Freiburg
, as well as the research groups of
Peter Sanders
and
Dorothea Wagne
r at the
Karlsruhe Institute of Technology
(KIT).
From the project’s numerous outcomes, I’d like to highlight two recent ones that re-examine the Transfer Patterns approach and massively improve it for continent-sized networks:
Scalable Transfer Patterns
[2] and
Frequency-Based Search for Public Transit
[3] by Hannah Bast,
Sabine Storandt
and Matthias Hertel. This blogpost presents the results from these publications.
The notion of a
transfer pattern
is easy to understand. Suppose you are at a transit stop downtown, call it A, and want to go to some stop B as quickly as possible. Suppose further you brought a printed schedule book but no smartphone. (This sounded plausible only a few years ago!) As a local, you might know that there are only two reasonable options:
Take a tram from A to C, then transfer at C to a bus to B.
Take the direct bus from A to B, which only runs infrequently.
We say the first option has transfer pattern A-C-B, and the second option has transfer pattern A-B. Notice that no in-between stops are mentioned. This is very compact information, much less than the actual schedules, but it makes looking up the schedules significantly faster: Knowing that all optimal trips follow one of these patterns, you only need to look at those lines in the schedule book that provide direct connections from A to C, C to B and A to B. All other lines can safely be ignored: you know you will not miss a better option.
While the basic idea of transfer patterns is indeed that simple, it takes more to make it work in practice. The transfer patterns of all optimal trips have to be computed ahead of time and stored, so that they are available to answer queries. Conceptually, we need transfer patterns for every pair of stops, because any pair could come up in a query. It is perfectly reasonable to compute them for all pairs within one city, or even one metro area that is densely criss-crossed by a transit network comprising, say, a thousand stops, yielding a million of pairs to consider.
As the scale of the problem increases from one metro area to an entire country or continent, this “all pairs” approach rapidly becomes expensive: ten thousand stops (10x more than above) already yield a hundred million pairs (100x more than above), and so on. Also, the individual transfer patterns become quite repetitive: For example, from any stop in Paris, France to any stop in Cologne, Germany, all optimal connections end up using the same few long-distance train lines in the middle, only the local connections to the railway stations depend on the specific pair of stops considered.
However, designated long-distance connections are not the only way to travel between different local networks – they also overlap and connect to each other. For mid-range trips, there is no universally correct rule when to choose a long-distance train or intercity bus, short of actually comparing options with local or regional transit, too.
The Scalable Transfer Patterns algorithm [2] does just that, but in a smart way. For starters, it uses what is known as
graph clustering
to cut the network into pieces, called
clusters
, that have a lot of connections inside but relatively few to the outside. As an example, the figure below (kindly provided by the authors) shows a partitioning of Germany into clusters. The stops highlighted in red are
border stops
: They connect directly to stops outside the cluster. Notice how they are a small fraction of the total network.
The public transit network of Germany (dots and lines), split into clusters (shown in various colors). Of all 251,763 stops, only 10,886 (4.32%) are boundary stops, highlighted as red boxes.
Click here
to view the full resolution image.[source: S. Storandt, 2016]
Based on the clustering, the transfer patterns of all optimal connections are computed in two steps.
In step 1
, transfer patterns are computed for optimal connections inside each cluster. They are stored for query processing later on, but they also accelerate the search
through
a cluster in the following step: between the stops on its border, we only need to consider the connections captured in the transfer patterns.
The next figure sketches how the transit network in the cluster around Berlin gets reduced to much fewer connections between border stations. (The central station stands out as a hub, as expected. It is a border station itself, because it has direct connections out of the cluster.)
The cluster of public transit connections around Berlin (shown as dots and lines in light blue), its border stops (highlighted as red boxes), and the transfer patterns of optimal connections between border stops (thick black lines; only the most important 111 of 592 are shown to keep the image legible). This cuts out 96.15% of the network (especially a lot of the high-frequency inner city trips, which makes the time savings even bigger).
Click here
to view the full resolution image. [source: S. Storandt, 2016]
In step 2
, transfer patterns can be computed for the entire network, that is, between any pair of clusters. This is done with the following twists:
It suffices to consider trips from and to boundary stops of any cluster; the local transfer patterns from step 1 will supply the missing pieces later on.
The per-cluster transfer patterns from step 1 greatly accelerate the search across other clusters.
The search stops exploring any possible connection between two boundary stops as soon as it gets worse than a connection that sticks to long-distance transit between clusters (which may not always be optimal, but is always quick to compute).
The results of steps 1 and 2 are stored and used to answer queries. For any given query from some A to some B, one can now easily stitch together a network of transfer patterns that covers all optimal connections from A to B. Looking up the direct connections on that small network (like in the introductory example) and finding the best one for the queried time is very fast, even if A and B are far away.
The total storage space needed for this is much smaller than the space that would be needed for all pairs of stops, all the more the larger the network gets. Extrapolating from their experiments, the researchers estimate [2] that Scalable Transfer Patterns for the whole world could be stored in 30 GB, cutting their estimate for the basic Transfer Patterns by a thousand(!). This is considerably more powerful than the “hub station” idea from the original Transfer Patterns paper [1].
The time needed to compute Scalable Transfer Patterns is also estimated to shrink by three orders of magnitude: At a high level, the earlier phases of the algorithm accelerate the later ones, as described above. At a low level, a second optimization technique kicks in: exploiting the repetitiveness of schedules in time. Recall that finding transfer patterns is all about finding the optimal connections between pairs of stops
at any possible departure time
.
Frequency-based schedules (e.g., one bus every 10 minutes) cause a lot of similarity during the day, although it often doesn’t match up between lines (e.g., said bus runs every 10 minutes before 6pm and every 20 minutes after, and we seek connections to a train that runs every 12 minutes before 8pm and every 30 minutes after). Moreover, this similarity also exists from one day to the next, and we need to consider all permissible departure dates.
The Frequency-Based Search for Public Transit [3] is carefully designed to find and take advantage of repetitive schedules while representing all one-off cases exactly. Comparing to the set-up from the original Transfer Patterns paper [1], the authors estimate a whopping 60x acceleration of finding transfer patterns from this part alone.
I am excited to see that the scalability questions originally left open by [1] have been answered so convincingly as part of this Focused Research Award. Please see the
list of publications
on the
project’s website
for more outcomes of this award. Besides more on transfer patterns, they contain a wealth of other results about routing on road networks, transit networks, and with combinations of travel modes.
References:
[1]
Fast Routing in Very Large Public Transportation Networks Using Transfer Patterns
by H. Bast, E. Carlsson, A. Eigenwillig, R. Geisberger, C. Harrelson, V. Raychev and F. Viger
(ESA 2010). [
doi
]
[2]
Scalable Transfer Patterns
by H. Bast, M. Hertel and S. Storandt (ALENEX 2016). [
doi
]
[3]
Frequency-based Search for Public Transit
by H. Bast and S. Storandt (SIGSPATIAL 2014). [
doi
]
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