Author Archives: Research Blog

Distill: Supporting Clarity in Machine Learning

Posted by Shan Carter, Software Engineer and Chris Olah, Research Scientist, Google Brain Team

Science isn't just about discovering new results. It’s also about human understanding. Scientists need to develop notations, analogies, visualizations, and explanations of ideas. This human dimension of science isn't a minor side project. It's deeply tied to the heart of science.

That’s why, in collaboration with OpenAI, DeepMind, YC Research, and others, we’re excited to announce the launch of Distill, a new open science journal and ecosystem supporting human understanding of machine learning. Distill is an independent organization, dedicated to fostering a new segment of the research community.

Modern web technology gives us powerful new tools for expressing this human dimension of science. We can create interactive diagrams and user interfaces the enable intuitive exploration of research ideas. Over the last few years we've seen many incredible demonstrations of this kind of work.
An interactive diagram explaining the Neural Turing Machine from Olah & Carter, 2016.
Unfortunately, while there are a plethora of conferences and journals in machine learning, there aren’t any research venues that are dedicated to publishing this kind of work. This is partly an issue of focus, and partly because traditional publication venues can't, by virtue of their medium, support interactive visualizations. Without a venue to publish in, many significant contributions don’t count as “real academic contributions” and their authors can’t access the academic support structure.

That’s why Distill aims to build an ecosystem to support this kind of work, starting with three pieces: a research journal, prizes recognizing outstanding work, and tools to facilitate the creation of interactive articles.
Distill is an ecosystem to support clarity in Machine Learning.
Led by a diverse steering committee of leaders from the machine learning and user interface communities, we are very excited to see where Distill will go. To learn more about Distill, see the overview page or read the latest articles.

Announcing Guetzli: A New Open Source JPEG Encoder

Posted by Robert Obryk and Jyrki Alakuijala, Software Engineers, Google Research Europe

(Cross-posted on the Google Open Source Blog)

At Google, we care about giving users the best possible online experience, both through our own services and products and by contributing new tools and industry standards for use by the online community. That’s why we’re excited to announce Guetzli, a new open source algorithm that creates high quality JPEG images with file sizes 35% smaller than currently available methods, enabling webmasters to create webpages that can load faster and use even less data.

Guetzli [guɛtsli] — cookie in Swiss German — is a JPEG encoder for digital images and web graphics that can enable faster online experiences by producing smaller JPEG files while still maintaining compatibility with existing browsers, image processing applications and the JPEG standard. From the practical viewpoint this is very similar to our Zopfli algorithm, which produces smaller PNG and gzip files without needing to introduce a new format, and different than the techniques used in RNN-based image compression, RAISR, and WebP, which all need client and ecosystem changes for compression gains at internet scale.

The visual quality of JPEG images is directly correlated to its multi-stage compression process: color space transform, discrete cosine transform, and quantization. Guetzli specifically targets the quantization stage in which the more visual quality loss is introduced, the smaller the resulting file. Guetzli strikes a balance between minimal loss and file size by employing a search algorithm that tries to overcome the difference between the psychovisual modeling of JPEG's format, and Guetzli’s psychovisual model, which approximates color perception and visual masking in a more thorough and detailed way than what is achievable by simpler color transforms and the discrete cosine transform. However, while Guetzli creates smaller image file sizes, the tradeoff is that these search algorithms take significantly longer to create compressed images than currently available methods.
Figure 1. 16x16 pixel synthetic example of a phone line hanging against a blue sky — traditionally a case where JPEG compression algorithms suffer from artifacts. Uncompressed original is on the left. Guetzli (on the right) shows less ringing artefacts than libjpeg (middle) and has a smaller file size.
And while Guetzli produces smaller image file sizes without sacrificing quality, we additionally found that in experiments where compressed image file sizes are kept constant that human raters consistently preferred the images Guetzli produced over libjpeg images, even when the libjpeg files were the same size or even slightly larger. We think this makes the slower compression a worthy tradeoff.
Figure 2. 20x24 pixel zoomed areas from a picture of a cat’s eye. Uncompressed original on the left. Guetzli (on the right)
shows less ringing artefacts than libjpeg (middle) without requiring a larger file size.
It is our hope that webmasters and graphic designers will find Guetzli useful and apply it to their photographic content, making users’ experience smoother on image-heavy websites in addition to reducing load times and bandwidth costs for mobile users. Last, we hope that the new explicitly psychovisual approach in Guetzli will inspire further image and video compression research.

An Upgrade to SyntaxNet, New Models and a Parsing Competition

Posted by David Weiss and Slav Petrov, Research Scientists

At Google, we continuously improve the language understanding capabilities used in applications ranging from generation of email responses to translation. Last summer, we open-sourced SyntaxNet, a neural-network framework for analyzing and understanding the grammatical structure of sentences. Included in our release was Parsey McParseface, a state-of-the-art model that we had trained for analyzing English, followed quickly by a collection of pre-trained models for 40 additional languages, which we dubbed Parsey's Cousins. While we were excited to share our research and to provide these resources to the broader community, building machine learning systems that work well for languages other than English remains an ongoing challenge. We are excited to announce a few new research resources, available now, that address this problem.

SyntaxNet Upgrade
We are releasing a major upgrade to SyntaxNet. This upgrade incorporates nearly a year’s worth of our research on multilingual language understanding, and is available to anyone interested in building systems for processing and understanding text. At the core of the upgrade is a new technology that enables learning of richly layered representations of input sentences. More specifically, the upgrade extends TensorFlow to allow joint modeling of multiple levels of linguistic structure, and to allow neural-network architectures to be created dynamically during processing of a sentence or document.

Our upgrade makes it, for example, easy to build character-based models that learn to compose individual characters into words (e.g. ‘c-a-t’ spells ‘cat’). By doing so, the models can learn that words can be related to each other because they share common parts (e.g. ‘cats’ is the plural of ‘cat’ and shares the same stem; ‘wildcat’ is a type of ‘cat’). Parsey and Parsey’s Cousins, on the other hand, operated over sequences of words. As a result, they were forced to memorize words seen during training and relied mostly on the context to determine the grammatical function of previously unseen words.

As an example, consider the following (meaningless but grammatically correct) sentence:
This sentence was originally coined by Andrew Ingraham who explained: “You do not know what this means; nor do I. But if we assume that it is English, we know that the doshes are distimmed by the gostak. We know too that one distimmer of doshes is a gostak." Systematic patterns in morphology and syntax allow us to guess the grammatical function of words even when they are completely novel: we understand that ‘doshes’ is the plural of the noun ‘dosh’ (similar to the ‘cats’ example above) or that ‘distim’ is the third person singular of the verb distim. Based on this analysis we can then derive the overall structure of this sentence even though we have never seen the words before.

ParseySaurus
To showcase the new capabilities provided by our upgrade to SyntaxNet, we are releasing a set of new pretrained models called ParseySaurus. These models use the character-based input representation mentioned above and are thus much better at predicting the meaning of new words based both on their spelling and how they are used in context. The ParseySaurus models are far more accurate than Parsey’s Cousins (reducing errors by as much as 25%), particularly for morphologically-rich languages like Russian, or agglutinative languages like Turkish and Hungarian. In those languages there can be dozens of forms for each word and many of these forms might never be observed during training - even in a very large corpus.

Consider the following fictitious Russian sentence, where again the stems are meaningless, but the suffixes define an unambiguous interpretation of the sentence structure:
Even though our Russian ParseySaurus model has never seen these words, it can correctly analyze the sentence by inspecting the character sequences which constitute each word. In doing so, the system can determine many properties of the words (notice how many more morphological features there are here than in the English example). To see the sentence as ParseySaurus does, here is a visualization of how the model analyzes this sentence:
Each square represents one node in the neural network graph, and lines show the connections between them. The left-side “tail” of the graph shows the model consuming the input as one long string of characters. These are intermittently passed to the right side, where the rich web of connections shows the model composing words into phrases and producing a syntactic parse. Check out the full-size rendering here.

A Competition
You might be wondering whether character-based modeling are all we need or whether there are other techniques that might be important. SyntaxNet has lots more to offer, like beam search and different training objectives, but there are of course also many other possibilities. To find out what works well in practice, we are helping co-organize a multilingual parsing competition at this year’s Conference on Computational Natural Language Learning (CoNLL) with the goal of building syntactic parsing systems that work well in real-world settings and for 45 different languages.

The competition is made possible by the Universal Dependencies (UD) initiative, whose goal is to develop cross-linguistically consistent treebanks. Because machine learned models can only be as good as the data that they have access to, we have been contributing data to UD since 2013. For the competition, we partnered with UD and DFKI to build a new multilingual evaluation set consisting of 1000 sentences that have been translated into 20+ different languages and annotated by linguists with parse trees. This evaluation set is the first of its kind (in the past, each language had its own independent evaluation set) and will enable more consistent cross-lingual comparisons. Because the sentences have the same meaning and have been annotated according to the same guidelines, we will be able to get closer to answering the question of which languages might be harder to parse.

We hope that the upgraded SyntaxNet framework and our the pre-trained ParseySaurus models will inspire researchers to participate in the competition. We have additionally created a tutorial showing how to load a Docker image and train models on the Google Cloud Platform, to facilitate participation by smaller teams with limited resources. So, if you have an idea for making your own models with the SyntaxNet framework, sign up to compete! We believe that the configurations that we are releasing are a good place to start, but we look forward to seeing how participants will be able to extend and improve these models or perhaps create better ones!

Thanks to everyone involved who made this competition happen, including our collaborators at UD-Pipe, who provide another baseline implementation to make it easy to enter the competition. Happy parsing from the main developers, Chris Alberti, Daniel Andor, Ivan Bogatyy, Mark Omernick, Zora Tung and Ji Ma!

Quick Access in Drive: Using Machine Learning to Save You Time

Posted by Sandeep Tata, Software Engineer, Google Research

At Google, we research cutting-edge machine learning (ML) techniques that allow us to provide products and services aimed at helping you focus on what’s important. From providing language translations to understanding images to helping you respond to emails, it is our goal to help you save time, making life — and work — a little more convenient.

Recent studies have shown that finding information is second only to managing email as a drain on workplace productivity. To help address this, last year we launched Quick Access, a feature in Google Drive that uses ML to surface the most relevant documents as soon as you visit the Google Drive home screen. Originally available only for G Suite customers on Android, Quick Access is now available for anyone who uses Google Drive (on the Web, Android, and iOS), saving you from having to enter a search or to browse through your folders. Our metrics show that Quick Access takes you to the documents you need in half the time compared to manually navigating or searching.
Quick Access uses deep neural networks to determine patterns from various signals, such as activity in Drive, meetings on your Calendar, and more, to anticipate your needs and show the appropriate documents on the Drive home screen. Traditional ML approaches require domain experts to derive complex features from data, which are in turn used to train the model. For Quick Access, however, we constructed thousands of simple features from the various signals above (for instance, the timestamps of the last 20 edit events on a document would constitute 20 simple input features), and combined them with the power of deep neural networks to learn from the aggregated activity of our users. By using deep neural networks we were able to develop accurate predictive models with simpler features and less feature engineering effort.
Quick Access suggestions on the top row in Drive on a desktop browser.
The model computes a relevance score for each of the documents in Drive and the top scoring documents are presented on the home screen. For example, if you have a Calendar entry for a meeting with a coworker in the next few minutes, Quick Access might predict that the presentation you’ve been working on with that coworker is more relevant compared to your monthly budget spreadsheet or the photos you uploaded last week. If you’ve been updating a spreadsheet every weekend, then next weekend, Quick Access will likely display that spreadsheet ahead of the other documents you viewed during the week.

We hope Quick Access helps you use Drive more effectively, allowing you to save time and be more productive. To learn more, watch this talk from Google Cloud Next ‘17 that dives into more details on the ML behind Quick Access.

Acknowledgements
Thanks to Alexandrin Popescul and Marc Najork for contributions that made this application of machine learning technology possible. This work was in close collaboration with several engineers on the Drive team including Sean Abraham, Brian Calaci, Mike Colagrosso, Mike Procopio, Jesse Sterr, and Timothy Vis.

Assisting Pathologists in Detecting Cancer with Deep Learning

Posted by Martin Stumpe, Technical Lead, and Lily Peng, Product Manager

A pathologist’s report after reviewing a patient’s biological tissue samples is often the gold standard in the diagnosis of many diseases. For cancer in particular, a pathologist’s diagnosis has a profound impact on a patient’s therapy. The reviewing of pathology slides is a very complex task, requiring years of training to gain the expertise and experience to do well.

Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient, which can lead to misdiagnoses. For example, agreement in diagnosis for some forms of breast cancer can be as low as 48%, and similarly low for prostate cancer. The lack of agreement is not surprising given the massive amount of information that must be reviewed in order to make an accurate diagnosis. Pathologists are responsible for reviewing all the biological tissues visible on a slide. However, there can be many slides per patient, each of which is 10+ gigapixels when digitized at 40X magnification. Imagine having to go through a thousand 10 megapixel (MP) photos, and having to be responsible for every pixel. Needless to say, this is a lot of data to cover, and often time is limited.

To address these issues of limited time and diagnostic variability, we are investigating how deep learning can be applied to digital pathology, by creating an automated detection algorithm that can naturally complement pathologists’ workflow. We used images (graciously provided by the Radboud University Medical Center) which have also been used for the 2016 ISBI Camelyon Challenge1 to train algorithms that were optimized for localization of breast cancer that has spread (metastasized) to lymph nodes adjacent to the breast.

The results? Standard “off-the-shelf” deep learning approaches like Inception (aka GoogLeNet) worked reasonably well for both tasks, although the tumor probability prediction heatmaps produced were a bit noisy. After additional customization, including training networks to examine the image at different magnifications (much like what a pathologist does), we showed that it was possible to train a model that either matched or exceeded the performance of a pathologist who had unlimited time to examine the slides.
Left: Images from two lymph node biopsies. Middle: earlier results of our deep learning tumor detection. Right: our current results. Notice the visibly reduced noise (potential false positives) between the two versions.
In fact, the prediction heatmaps produced by the algorithm had improved so much that the localization score (FROC) for the algorithm reached 89%, which significantly exceeded the score of 73% for a pathologist with no time constraint2. We were not the only ones to see promising results, as other groups were getting scores as high as 81% with the same dataset. Even more exciting for us was that our model generalized very well, even to images that were acquired from a different hospital using different scanners. For full details, see our paper “Detecting Cancer Metastases on Gigapixel Pathology Images”.
A closeup of a lymph node biopsy. The tissue contains a breast cancer metastasis as well as macrophages, which look similar to tumor but are benign normal tissue. Our algorithm successfully identifies the tumor region (bright green) and is not confused by the macrophages.
While these results are promising, there are a few important caveats to consider.
  • Like most metrics, the FROC localization score is not perfect. Here, the FROC score is defined as the sensitivity (percentage of tumors detected) at a few pre-defined average false positives per slide. It is pretty rare for a pathologist to make a false positive call (mistaking normal cells as tumor). For example, the score of 73% mentioned above corresponds to a 73% sensitivity and zero false positives. By contrast, our algorithm’s sensitivity rises when more false positives are allowed. At 8 false positives per slide, our algorithms had a sensitivity of 92%.
  • These algorithms perform well for the tasks for which they are trained, but lack the breadth of knowledge and experience of human pathologists — for example, being able to detect other abnormalities that the model has not been explicitly trained to classify (e.g. inflammatory process, autoimmune disease, or other types of cancer).
  • To ensure the best clinical outcome for patients, these algorithms need to be incorporated in a way that complements the pathologist’s workflow. We envision that algorithm such as ours could improve the efficiency and consistency of pathologists. For example, pathologists could reduce their false negative rates (percentage of undetected tumors) by reviewing the top ranked predicted tumor regions including up to 8 false positive regions per slide. As another example, these algorithms could enable pathologists to easily and accurately measure tumor size, a factor that is associated with prognosis.
Training models is just the first of many steps in translating interesting research to a real product. From clinical validation to regulatory approval, much of the journey from “bench to bedside” still lies ahead — but we are off to a very promising start, and we hope by sharing our work, we will be able to accelerate progress in this space.



1 For those who might be interested, the Camelyon17 challenge, which builds upon the 2016 challenge, is currently underway.↩

2 The pathologist ended up spending 30 hours on this task on 130 slides.↩


Google Research Awards 2016

Posted by Maggie Johnson, Director of Education and University Relations, Google

We’ve just completed another round of the Google Research Awards, our annual open call for proposals on computer science and related topics including machine learning, machine perception, natural language processing, and security. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 876 proposals covering 44 countries and over 300 universities. After expert reviews and committee discussions, we decided to fund 143 projects. Here are a few observations from this round:


Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is September 30th), please visit our website for more information.

Preprocessing for Machine Learning with tf.Transform

Posted by Kester Tong, David Soergel, and Gus Katsiapis, Software Engineers

When applying machine learning to real world datasets, a lot of effort is required to preprocess data into a format suitable for standard machine learning models, such as neural networks. This preprocessing takes a variety of forms, from converting between formats, to tokenizing and stemming text and forming vocabularies, to performing a variety of numerical operations such as normalization.

Today we are announcing tf.Transform, a library for TensorFlow that allows users to define preprocessing pipelines and run these using large scale data processing frameworks, while also exporting the pipeline in a way that can be run as part of a TensorFlow graph. Users define a pipeline by composing modular Python functions, which tf.Transform then executes with Apache Beam, a framework for large-scale, efficient, distributed data processing. Apache Beam pipelines can be run on Google Cloud Dataflow with planned support for running with other frameworks. The TensorFlow graph exported by tf.Transform enables the preprocessing steps to be replicated when the trained model is used to make predictions, such as when serving the model with Tensorflow Serving.

A common problem encountered when running machine learning models in production is "training-serving skew", where the data seen at serving time differs in some way from the data used to train the model, leading to reduced prediction quality. tf.Transform ensures that no skew can arise during preprocessing, by guaranteeing that the serving-time transformations are exactly the same as those performed at training time, in contrast to when training-time and serving-time preprocessing are implemented separately in two different environments (e.g., Apache Beam and TensorFlow, respectively).

In addition to facilitating preprocessing, tf.Transform allows users to compute summary statistics for their datasets. Understanding the data is very important in every machine learning project, as subtle errors can arise from making wrong assumptions about what the underlying data look like. By making the computation of summary statistics easy and efficient, tf.Transform allows users to check their assumptions about both raw and preprocessed data.
tf.Transform allows users to define a preprocessing pipeline. Users can materialize the preprocessed data for use in TensorFlow training, and also export a tf.Transform graph that encodes the transformations as a TensorFlow graph. This transformation graph can then be incorporated into the model graph used for inference.
We’re excited to be releasing this latest addition to the TensorFlow ecosystem, and we hope users will find it useful for preprocessing and understanding their data.

Acknowledgements
We wish to thank the following members of the tf.Transform team for their contributions to this project: Clemens Mewald, Robert Bradshaw, Rajiv Bharadwaja, Elmer Garduno, Afshin Rostamizadeh, Neoklis Polyzotis, Abhi Rao, Joe Toth, Neda Mirian, Dinesh Kulkarni, Robbie Haertel, Cyril Bortolato and Slaven Bilac. We also wish to thank the TensorFlow, TensorFlow Serving and Cloud Dataflow teams for their support.


Headset “Removal” for Virtual and Mixed Reality

Posted by Vivek Kwatra, Research Scientist and Christian Frueh, Avneesh Sud, Software Engineers


Virtual Reality (VR) enables remarkably immersive experiences, offering new ways to view the world and the ability to explore novel environments, both real and imaginary. However, compared to physical reality, sharing these experiences with others can be difficult, as VR headsets make it challenging to create a complete picture of the people participating in the experience.

Some of this disconnect is alleviated by Mixed Reality (MR), a related medium that shares the virtual context of a VR user in a two dimensional video format allowing other viewers to get a feel for the user’s virtual experience. Even though MR facilitates sharing, the headset continues to block facial expressions and eye gaze, presenting a significant hurdle to a fully engaging experience and complete view of the person in VR.

Google Machine Perception researchers, in collaboration with Daydream Labs and YouTube Spaces, have been working on solutions to address this problem wherein we reveal the user’s face by virtually “removing” the headset and create a realistic see-through effect.
VR user captured in front of a green-screen is blended with the virtual environment to generate the MR output: Traditional MR output has the user face occluded, while our result reveals the face. Note how the headset is modified with a marker to aid tracking.
Our approach uses a combination of 3D vision, machine learning and graphics techniques, and is best explained in the context of enhancing Mixed Reality video (also discussed in the Google-VR blog). It consists of three main components:

Dynamic face model capture
The core idea behind our technique is to use a 3D model of the user’s face as a proxy for the hidden face. This proxy is used to synthesize the face in the MR video, thereby creating an impression of the headset being removed. First, we capture a personalized 3D face model for the user with what we call gaze-dependent dynamic appearance. This initial calibration step requires the user to sit in front of a color+depth camera and a monitor, and then track a marker on the monitor with their eyes. We use this one-time calibration procedure — which typically takes less than a minute — to acquire a 3D face model of the user, and learn a database that maps appearance images (or textures) to different eye-gaze directions and blinks. This gaze database (i.e. the face model with textures indexed by eye-gaze) allows us to dynamically change the appearance of the face during synthesis and generate any desired eye-gaze, thus making the synthesized face look natural and alive
On the left, the user’s face is captured by a camera as she tracks a marker on the monitor with her eyes. On the right, we show the dynamic nature of reconstructed 3D face model: by moving or clicking on the mouse, we are able to simulate both apparent eye gaze and blinking.
Calibration and Alignment
Creating a Mixed Reality video requires a specialized setup consisting of an external camera, calibrated and time-synced with the headset. The camera captures a video stream of the VR user in front of a green screen and then composites a cutout of the user with the virtual world to create the final MR video. An important step here is to accurately estimate the calibration (the fixed 3D transformation) between the camera and headset coordinate systems. These calibration techniques typically involve significant manual intervention and are done in multiple steps. We simplify the process by adding a physical marker to the front of the headset and tracking it visually in 3D, which allows us to optimize for the calibration parameters automatically from the VR session.

For headset “removal”, we need to align the 3D face model with the visible portion of the face in the camera stream, so that they would blend seamlessly with each other. A reasonable proxy to this alignment is to place the face model just behind the headset. The calibration described above, coupled with VR headset tracking, provides sufficient information to determine this placement, allowing us to modify the camera stream by rendering the virtual face into it.

Compositing and Rendering
Having tackled the alignment, the last step involves producing a suitable rendering of the 3D face model, consistent with the content in the camera stream. We are able to reproduce the true eye-gaze of the user by combining our dynamic gaze database with an HTC Vive headset that has been modified by SMI to incorporate eye-tracking technology. Images from these eye trackers lack sufficient detail to directly reproduce the occluded face region, but are well suited to provide fine-grained gaze information. Using the live gaze data from the tracker, we synthesize a face proxy that accurately represents the user’s attention and blinks. At run-time, the gaze database, captured in the preprocessing step, is searched for the most appropriate face image corresponding to the query gaze state, while also respecting aesthetic considerations such as temporal smoothness. Additionally, to account for lighting changes between gaze database acquisition and run-time, we apply color correction and feathering, such that the synthesized face region matches with the rest of the face.

Humans are highly sensitive to artifacts on faces, and even small imperfections in synthesis of the occluded face can feel unnatural and distracting, a phenomenon known as the “uncanny valley.” To mitigate this problem, we do not remove the headset completely, instead we have chosen a user experience that conveys a ‘scuba mask effect’ by compositing the color corrected face proxy with a translucent headset. Reminding the viewer of the presence of the headset helps us avoid the uncanny valley, and also makes our algorithm robust to small errors in alignment and color correction.

This modified camera stream, displaying a see-through headset, with the user’s face revealed and their true eye-gaze recreated, is subsequently merged with the virtual environment to create the final MR video.

Results and Extensions
We have used our headset removal technology to enhance Mixed Reality, allowing the medium to not only convey a VR user’s interaction with the virtual environment but also show their face in a natural and convincing fashion. The example below demonstrates our tech applied to an artist using Google Tilt Brush in a virtual environment:
An artist creates 3D art using Google Tilt Brush, shown in Mixed Reality. On the top is the traditional MR result where the face is hidden behind the headset. On the bottom is our result, which reveals the entire face and eyes for a more natural and engaging experience.
While we have shown the potential of our technology, its applications extend beyond Mixed Reality. Headset removal is poised to enhance communication and social interaction in VR itself with diverse applications like VR video conference meetings, multiplayer VR gaming, and exploration with friends and family. Going from an utterly blank headset to being able to see, with photographic realism, the faces of fellow VR users promises to be a significant transition in the VR world, and we are excited to be a part of it.

The CS Capacity Program – New Tools and SIGCSE 2017

Posted by Chris Stephenson, Head of Computer Science Education Strategy

The CS Capacity program was launched in March of 2015 to help address a dramatic increase in undergraduate computer science enrollments that is creating serious resource and pedagogical challenges for many colleges and universities. Over the last two years, a diverse group of universities have been working to develop successful strategies that support the expansion of high-quality CS programs at the undergraduate level. Their work focuses on innovations in teaching and technologies that support scaling while ensuring the engagement of women and underrepresented students. These innovations could provide assistance to many other institutions that are challenged to provide a high-quality educational experience to an increasing number of introductory-level students.

The cohort of CS Capacity institutions include George Mason University, Mount Holyoke College, Rutgers University, and the University California Berkeley which are working individually, and Duke University, North Carolina State University, the University of Florida, and the University of North Carolina which are working together. These institution each brings a unique approach to addressing CS capacity challenges. Two years into the program, we're sharing an update on some of the great projects and ideas to emerge so far.

At George Mason, for example, computer science professor Jeff Offutt and his team have developed an online system to provide self-paced learning for CS1 and CS2 classes that allows learners through the learning materials wore quickly or slowly depending on their needs. The system, called SPARC, includes course content, practice and assessment exercises (including automated testing), mini-lectures, and daily inspirations. This team has also launched a program to recruit and train undergraduate tutorial assistants to increase learning support. For more information on SPARC, contact Jeff Offutt at [email protected].

The MaGE Peer Mentor program at Mount Holyoke College is addressing its increasing CS student enrollment by preparing undergraduate peer mentors to provide effective feedback on coding assignments and contribute to an inclusive learning environment. One of the major elements of these program is an online course that helps to recruit and train students to be undergraduate peer mentors. Mount Holyoke has made their entire online course curriculum for the peer mentor program available so that other institutions can incorporate all or part of it to assist with preparing their own student tutors. For more information on the MaGE curriculum, contact Heather Pon-Barry at [email protected].
MaGE Program Students and Faculty from Mount Holyoke College
At University of California, Berkeley, the CS Capacity team is focused on providing access to increased and better tutoring. They’ve instituted a small-group tutoring program that includes weekend mastery learning sessions, increased office hours support, designated discussions section, project checkpoint deadlines, exam/homework/lab/discussion walkthrough videos, and a new office hours app that tracks student satisfaction with office hours. For more information on Berkeley’s interventions, contact Josh Hug at [email protected].

The CS Capacity team at Rutgers has been exploring the gender gap at multiple levels using a longitudinal study across four required CS classes (paper to be published in the proceedings of the SIGCSE 2017 Technical Symposium). They’re investigating several factors that may impact the retention of women and underrepresented student populations, including intention to major in CS, grades, and prior experience. They’ve also been defining an additional set of feature set to improve their use of Autolab (a course management system with automated grading). This work includes building a hint system to provide more information for students who are struggling with a concept or assignment, crowd-sourcing grading, and studying how students think about CS content and the kinds of errors they are making. The Rutgers team will be publishing their study results in the proceedings of the SIGCSE 2017 Technical Symposium. For more information on these tools, contact Andrew Tjang at [email protected].

The team consisting of Duke, NCSU, UNC, and UF have produced and plan to share tools to improve the student learning experience. My Digital Hand (MDH) is a free online tool for managing and tracking one-to-one peer teaching sessions (for example, helping to keep track of how many hours peer mentors are spending with mentees). MDH supports best practice in peer teaching and mitigates some of the observed challenges in taking peer teaching to scale. The team has also been working on ASCEND (Adaptive Student Computing Environment with Natural Language Dialogue), an Eclipse plug-in designed to facilitate remote synchronous peer teaching sessions. Students can share their projects with a peer teaching fellow (PTF) and chat as the PTF leads the student through a session. ASCEND helps instructors better understand current practice by logging all programming actions and textual chats in real time to a database. For more information on these tools, contact Jeff Forbes at [email protected].

Several of the CS Capacity principle investigators will be presenting papers on these new interventions and tools at the SIGCSE conference in March. Faculty from the CS capacity program will also be presenting a panel and roundtable discussion session called “New Tools and Solutions to Address the CS Capacity Crunch.” If you’re attending SIGCSE this year, we hope you’ll join us on Thursday, March 9, from 3:45-5:00 pm.

Given the likelihood that CS undergraduate enrollments will continue to climb, it is critical that the CS education community continue to find, test, and share solutions and tools that enable institutions to effectively teach more students while maintaining the quality of the education experience for students. Faculty from the CS Capacity program will continue to share their solutions and results with the community via CS education conferences and publications.

An updated YouTube-8M, a video understanding challenge, and a CVPR workshop. Oh my!

Posted by Paul Natsev, Software Engineer

Last September, we released the YouTube-8M dataset, which spans millions of videos labeled with thousands of classes, in order to spur innovation and advancement in large-scale video understanding. More recently, other teams at Google have released datasets such as Open Images and YouTube-BoundingBoxes that, along with YouTube-8M, can be used to accelerate image and video understanding. To further these goals, today we are releasing an update to the YouTube-8M dataset, and in collaboration with Google Cloud Machine Learning and kaggle.com, we are also organizing a video understanding competition and an affiliated CVPR’17 Workshop.

An Updated YouTube-8M
The new and improved YouTube-8M includes cleaner and more verbose labels (twice as many labels per video, on average), a cleaned-up set of videos, and for the first time, the dataset includes pre-computed audio features, based on a state-of-the-art audio modeling architecture, in addition to the previously released visual features. The audio and visual features are synchronized in time, at 1-second temporal granularity, which makes YouTube-8M a large-scale multi-modal dataset, and opens up opportunities for exciting new research on joint audio-visual (temporal) modeling. Key statistics on the new version are illustrated below (more details here).
A tree-map visualization of the updated YouTube-8M dataset, organized into 24 high-level verticals, including the top-200 most frequent entities, plus the top-5 entities for each vertical.
Sample videos from the top-18 high-level verticals in the YouTube-8M dataset.
The Google Cloud & YouTube-8M Video Understanding Challenge
We are also excited to announce the Google Cloud & YouTube-8M Video Understanding Challenge, in partnership with Google Cloud and kaggle.com. The challenge invites participants to build audio-visual content classification models using YouTube-8M as training data, and to then label ~700K unseen test videos. It will be hosted as a Kaggle competition, sponsored by Google Cloud, and will feature a $100,000 prize pool for the top performers (details here). In order to enable wider participation in the competition, Google Cloud is also offering credits so participants can optionally do model training and exploration using Google Cloud Machine Learning. Open-source TensorFlow code, implementing a few baseline classification models for YouTube-8M, along with training and evaluation scripts, is available at Github. For details on getting started with local or cloud-based training, please see our README and the getting started guide on Kaggle.

The CVPR 2017 Workshop on YouTube-8M Large-Scale Video Understanding
We will announce the results of the challenge and host invited talks by distinguished researchers at the 1st YouTube-8M Workshop, to be held July 26, 2017, at the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) in Honolulu, Hawaii. The workshop will also feature presentations by top-performing challenge participants and a selected set of paper submissions. We invite researchers to submit papers describing novel research, experiments, or applications based on YouTube-8M dataset, including papers summarizing their participation in the above challenge.

We designed this dataset with scale and diversity in mind, and hope lessons learned here will generalize to many video domains (YouTube-8M captures over 20 diverse video domains). We believe the challenge can also accelerate research by enabling researchers without access to big data or compute clusters to explore and innovate at previously unprecedented scale. Please join us in advancing video understanding!

Acknowledgements
This post reflects the work of many others within Machine Perception at Google Research, including Sami Abu-El-Haija, Anja Hauth, Nisarg Kothari, Joonseok Lee, Hanhan Li, Sobhan Naderi Parizi, Rahul Sukthankar, George Toderici, Balakrishnan Varadarajan, Sudheendra Vijayanarasimhan, Jiang Wang, as well as Philippe Poutonnet and Mike Styer from Google Cloud, and our partners at Kaggle. We are grateful for the support and advice from many others at Google Research, Google Cloud, and YouTube, and especially thank Aren Jansen, Jort Gemmeke, Dan Ellis, and the Google Research Sound Understanding team for providing the audio features in the updated dataset.