Immersive branded experiences in YouTube and display ads

As a three-dimensional, visual medium, augmented reality (AR) is a powerful tool for brands looking to tell richer, more engaging stories about their products to consumers. Recently, we brought AR to Google products like Search, and made updates to our developer platform, ARCore, to help creators build more immersive experiences. Starting this week, we’re also bringing AR to YouTube and interactive 3D assets to display ads.

Helping YouTube beauty fans pick their next lipstick

Many consumers look to YouTube creators for help when deciding on new products to purchase. And brands have long been teaming up with creators to connect with audiences. Now, brands and creators can make that experience even more personalized and useful for viewers in AR.

Today, we’re introducing AR Beauty Try-On, which lets viewers virtually try on makeup while following along with YouTube creators to get tips, product reviews, and more. Thanks to machine learning and AR technology, it offers realistic, virtual product samples that work on a full range of skin tones. Currently in alpha, AR Beauty Try-On is available through FameBit by YouTube, Google’s in-house branded content platform.

M·A·C Cosmetics is the first brand to partner with FameBit to launch an AR Beauty Try-On campaign. Using this new format, brands like M·A·C will be able to tap into YouTube’s vibrant creator community, deploy influencer campaigns to YouTube’s 2 billion monthly active users, and measure their results in real time.

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Viewers will be able to try on different shades of M·A·C lipstick as their favorite beauty creator tries on the same shades. After trying on a lipstick, they can click to visit M·A·C’s website to purchase it.

We tested this experience earlier this year with several beauty brands and found that 30 percent of viewers activated the AR experience in the YouTube iOS app, spending over 80 seconds on average trying on lipstick virtually.

Bringing three-dimensional assets to display ads

We're also offering brands a new canvas for creativity with Swirl, our first immersive display format. Swirl brings three-dimensional assets to display advertising on the mobile web, which can help educate consumers before making a purchase. They can directly zoom in and out, rotate a product, or play an animation. Swirl is available exclusively through Display and Video 360.
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In this example from New Balance, people can rotate to explore the Fresh Foam 1080 running shoe. Objects like a mobile phone (right) can expand to show additional layered content.

To help brands more easily edit, configure and publish high-quality, realistic models to use in Swirl display ads, we’re introducing a new editor on Poly, Google’s 3D platform. It provides more editorial control over 3D objects, including new ways to change animation settings, customize backgrounds, and add realistic reflections.

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The new Poly editor lets you easily edit photorealistic three-dimensional objects for use in Swirl display ads.

These new tools will be available to brands and advertisers this summer. We think they’ll help brands and advertisers make content more engaging, educational, and ultimately effective in driving purchase decisions. If you’re interested, check out our getting started guide for tips. We look forward to seeing you bring your products to life!

Deliver more interactive ad experiences with Display & Video 360

Marketers have more opportunities than ever before to deliver engaging ad experiences through immersive creative. Many companies are investing in creating 3D assets to bring their products to life and allow consumers to interact with products as they would in real life. For example, a person can explore the interior and exterior of a car before taking it for a test drive, all from the comfort of their home. But it can be challenging to scale these experiences. Now you can extend the reach of these 3D assets to produce more captivating ads, with two new updates coming today.

Showcase your products with Immersive Display

Swirl is a new immersive display format designed for mobile web and available on Display & Video 360. People can explore every angle of your product by rotating the 3D object in all directions and zooming in and out, interacting from their device as if the product was in front of them. Customers like Guerlain, a leading perfume and cosmetics company, are using Swirl to deliver better ad experiences that draw people’s attention and let them interact with the perfume bottle directly and discover more about the scent.
Guerlain Swirl example
Swirl is opening up a whole new creative canvas for us. We're able to tell a more dynamic story about our products and give customers a powerful new way to interact with them. Jean-Denis Mariani
Chief Digital Officer of Guerlain

Brands that already have 3D assets can easily create a Swirl ad unit by using the 3D/Swirl component in Google Web Designer, our creative authoring tool. And with a new editor coming to Poly, Google’s 3D platform, it’s easier for brands and agencies to edit, configure, and publish high-quality, photorealistic models to use in immersive display ads. You can learn more in this post. If you’re interested in exploring Swirl but need help building 3D assets, we also have certified 3D production partners to help.

Expand the reach of your YouTube live streams

Increasingly, people are tuning in to live events like concerts, sports and shows through live streams. Brands are noticing this shift and are investing in live stream content through sponsorships and their own branded content. We know it takes a lot of time and resources to build these assets and we want to make it easier to get more out of your live stream investment.

The new live stream format in Display & Video 360 allows you to run your YouTube live stream content in display ads across screens and devices. You can quickly get started by using assets from your existing YouTube live stream campaigns with a new template in Google Web Designer.

With the live stream format people will be able to interact with the video using familiar YouTube player controls. People can preview your live stream, watch full screen, and exit when they’re done, giving them full control over how they interact with your content.

CBalm_Livestream

Building better ad experiences

Live stream and Swirl are just two examples of how we’re enabling brands to deliver more interactive ad experiences at scale with Display & Video 360. We want to make it easier for you to build ads that are engaging and valuable to consumers. We’ll continue to share creative solutions to help you build beautiful creative and deliver better ad experiences to users wherever they are online.

Both Swirl and the new live stream format are in a limited beta. To learn more about these new interactive formats, reach out to your Display & Video 360 account manager.

Carmen Sandiego is back for a final assignment in Google Earth

Nothing gets past you, super sleuth! You helped Carmen Sandiego recover the stolen Crown Jewels of England and Tutankhamun’s Mask. Now we need you for a third and final assignment: Recover the Keys to the Kremlin in Google Earth.

We’ve teamed up with Carmen Sandiego and learning company Houghton Mifflin Harcourt once again to track down a new VILE operative—Paperstar, master origamist—and return this treasure to the people of Moscow.  

Carmen Sandiego: Keys to the Kremlin Caper

To get your assignment, look for the special edition Pegman icon in Google Earth for Chrome, Android and iOS. And catch us next week at ISTE 2019 in Philadelphia, where we’ll be talking with teachers about how to use these educational capers in the classroom.

Meet David Feinberg, head of Google Health

Dr. David Feinberg has spent his entire career caring for people’s health and wellbeing. And after years in the healthcare system, he now leads Google Health, which brings together groups from across Google and Alphabet that are using AI, product expertise and hardware to take on big healthcare challenges. We sat down with David to hear more about his pre-Google life, what he’s learned as a “Noogler” (new Googler), and what’s next for Google Health.

You joined Google after a career path that led you from child psychiatrist to hospital executive. Tell us how this journey brought you to Google Health.

I’m driven by the urgency to help people live longer, healthier lives. I started as a child psychiatrist at UCLA helping young patients with serious mental health needs. Over the course of my 25 years at UCLA, I moved from treating dozens of patients, to overseeing the UCLA health system and the more than a million patients in our care. Then, at Geisinger, I had the opportunity to support a community of more than 3 million patients.

I recall my mom being very confused by my logic of stepping away from clinical duties and moving toward administrative duties as a way of helping more people. However, in these roles, the impact lies in initiatives that have boosted patient experience, improved people’s access to healthcare, and (I hope!) helped people get more time back to live their lives.

When I began speaking with Google, I immediately saw the potential to help billions of people, in part because I believe Google is already a health company. It’s been in the company’s DNA from the start.

You say Google is already a health company. How so?

We’re already making strides in organizing and making health data more useful thanks to work being done by Cloud and AI teams. And looking across the rest of Google’s portfolio of helpful products, we’re already addressing aspects of people’s health. Search helps people answer everyday health questions, Maps helps get people to the nearest hospital, and other tools and products are addressing issues tangential to health—for instance, literacysafer driving, and air pollution.

We already have the foundation, and I’m excited by the potential to tap into Google’s strengths, its brilliant people, and its amazing products to do more for people’s health (and lives).

I believe Google is already a health company. It’s been in the company’s DNA from the start.

This isn’t the first time Google has invested directly in health efforts. What has changed over the years about Google’s solving health-related problems? 

Some of Google’s early efforts didn’t gain traction due to various challenges the entire industry was facing at the time. During this period, I was a hospital administrator and no one talked about interoperability—a term familiar to those of us in the industry today. We were only just starting to think about the behemoth task of adopting electronic health records and bringing health data online, which is why some of the early projects didn’t really get off the ground. Today we take some of this for granted as we navigate today’s more digitized healthcare systems.

The last few years have changed the healthcare landscape—offering up new opportunities and challenges. And in response, Google and Alphabet have invested in efforts that complement their strengths and put users, patients, and care providers first. Look no further than the promising AI research and mobile applications coming from Google and DeepMind Health, or Verily’s Project Baseline that is pushing the boundaries of what we think we know about human health. And there’s so much more we can and will do.

Speaking of AI, it features prominently in many of Google’s current health efforts. What’s next for this research?

There’s little doubt that AI will power the next wave of tools that can improve many facets of healthcare: delivery, access, and so much more.

When I consider the future of research, I see us continuing to be deliberate and thoughtful about sharing our findings with the research and medical communities, incorporating feedback, and generally making sure our work actually adds value to patients, doctors and care providers.

Of course, we have to work toward getting solutions out in the wild, and into the hands of the pathologist scanning slides for breast cancer, or the nurse scanning a patient’s record for the latest lab results on the go. But this needs to be executed safely, working with and listening to our users to ensure that we get this right.

Now that you’ve been here for six months, what’s been most surprising to you about Google or the team?

I can’t believe how fantastic it is to not wear a suit after decades of formal business attire. When I got the job I ended up donating most of my suits. I kept a few, you know, for weddings.

On a more serious note, I’m blown away every day by the teams I’m surrounded by, and the drive and commitment they have for the work they do. I’m thrilled to be a part of this team.

What's your life motto?

I know this sounds cheesy, but there are three words I really do say every morning when I arrive in the parking lot for work: passion, humility, integrity. These are words that ground me, and also ground the work we are doing at Google Health.

Passion means we have to get this right, and feel that health is a cause worth fighting for, every day. We need humility, because at the end of the day, if we move too quickly or mess up, people’s lives are on the line. And integrity means that we should come to work with the aim of leaving the place—and the world—better than when we found it.

Enterprise app management made simpler with managed Google Play iframe

Managed Google Play lets enterprise organizations distribute and administer apps for their teams to use at work. By using managed Google Play, IT departments can help to reduce the security risks that come from sideloading applications. Admins can give their teams full access to the Android app ecosystem or curate just the right apps for getting the job done.

Managed Google Play iframe makes app distribution even easier, as IT admins can do so without leaving the Enterprise Mobility Management (EMM) console. The iframe has tools for publishing private and web apps, as well as curating public applications into collections. Admins can then configure apps and securely distribute them to their teams.

Google Play work apps

The managed Google Play iframe showing the Search apps page.

To help users find the apps they need, IT admins can now group whitelisted Android apps into “collections” that users can access from the managed Google Play store on their device. For example, admins can create a collection for frequently used apps or one for apps in a category related to expenses. They can then change the order in which those collections appear and the order of the apps bundled in those collections.

Admins can now publish a private Android app directly from an EMM Admin console. Simply upload the APK and give the app a title. It will then appear in the managed Play store —  within minutes as opposed to the hours previously required by using the Google Play Console.

Admins can also distribute web applications to their managed Google Play store—these run in a standalone mode in Chrome and provide similar functionality to a dedicated Android app. The UI can be customized to fill the entire screen or show the device’s navigation bars.

Managed Play web apps

Admins can publish a web app for their teams and customize display elements.

Enterprise mobility developers can visit the Google Developers documentation to add the iframe to the console and get specifics on implementing app management, distribution, permissions, and other essential features. 

We recommend that customers contact their EMM provider to determine their support for the managed Google Play iframe. To get started with device management, explore the Android Enterprise Solutions Directory.

Google at CVPR 2019

Andrew Helton, Editor, Google AI Communications

This week, Long Beach, CA hosts the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019), the premier annual computer vision event comprising the main conference and several co-located workshops and tutorials. As a leader in computer vision research and a Platinum Sponsor, Google will have a strong presence at CVPR 2019—over 250 Googlers will be in attendance to present papers and invited talks at the conference, and to organize and participate in multiple workshops.

If you are attending CVPR this year, please stop by our booth and chat with our researchers who are actively pursuing the next generation of intelligent systems that utilize the latest machine learning techniques applied to various areas of machine perception. Our researchers will also be available to talk about and demo several recent efforts, including the technology behind predicting pedestrian motion, the Open Images V5 dataset and much more.

You can learn more about our research being presented at CVPR 2019 in the list below (Google affiliations highlighted in blue)

Area Chairs include:
Jonathan T. Barron, William T. Freeman, Ce Liu, Michael Ryoo, Noah Snavely

Oral Presentations
Relational Action Forecasting
Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, Cordelia Schmid

Pushing the Boundaries of View Extrapolation With Multiplane Images
Pratul P. Srinivasan, Richard Tucker, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng, Noah Snavely

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan L. Yuille, Li Fei-Fei

AutoAugment: Learning Augmentation Strategies From Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le

DeepView: View Synthesis With Learned Gradient Descent
John Flynn, Michael Broxton, Paul Debevec, Matthew DuVall, Graham Fyffe, Ryan Overbeck, Noah Snavely, Richard Tucker

Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, Leonidas J. Guibas

Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, Quoc V. Le

TextureNet: Consistent Local Parametrizations for Learning From High-Resolution Signals on Meshes
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Niessner, Leonidas J. Guibas

Diverse Generation for Multi-Agent Sports Games
Raymond A. Yeh, Alexander G. Schwing, Jonathan Huang, Kevin Murphy

Occupancy Networks: Learning 3D Reconstruction in Function Space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger

A General and Adaptive Robust Loss Function
Jonathan T. Barron

Learning the Depths of Moving People by Watching Frozen People
Zhengqi Li, Tali Dekel, Forrester Cole, Richard Tucker, Noah Snavely, Ce Liu, William T. Freeman

Composing Text and Image for Image Retrieval - an Empirical Odyssey
Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, James Hays

Learning to Synthesize Motion Blur
Tim Brooks, Jonathan T. Barron

Neural Rerendering in the Wild
Moustafa Meshry, Dan B. Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, Ricardo Martin-Brualla

Neural Illumination: Lighting Prediction for Indoor Environments
Shuran Song, Thomas Funkhouser

Unprocessing Images for Learned Raw Denoising
Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, Jonathan T. Barron

Posters
Co-Occurrent Features in Semantic Segmentation
Hang Zhang, Han Zhang, Chenguang Wang, Junyuan Xie

CrDoCo: Pixel-Level Domain Transfer With Cross-Domain Consistency
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang

Im2Pencil: Controllable Pencil Illustration From Photographs
Yijun Li, Chen Fang, Aaron Hertzmann, Eli Shechtman, Ming-Hsuan Yang

Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Qi Mao, Hsin-Ying Lee, Hung-Yu Tseng, Siwei Ma, Ming-Hsuan Yang

Revisiting Self-Supervised Visual Representation Learning
Alexander Kolesnikov, Xiaohua Zhai, Lucas Beyer

Scene Graph Generation With External Knowledge and Image Reconstruction
Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, Mingyang Ling

Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks
Kuan Fang, Alexander Toshev, Li Fei-Fei, Silvio Savarese

Spatially Variant Linear Representation Models for Joint Filtering
Jinshan Pan, Jiangxin Dong, Jimmy S. Ren, Liang Lin, Jinhui Tang, Ming-Hsuan Yang

Target-Aware Deep Tracking
Xin Li, Chao Ma, Baoyuan Wu, Zhenyu He, Ming-Hsuan Yang

Temporal Cycle-Consistency Learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman

Depth-Aware Video Frame Interpolation
Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang

MnasNet: Platform-Aware Neural Architecture Search for Mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le

A Compact Embedding for Facial Expression Similarity
Raviteja Vemulapalli, Aseem Agarwala

Contrastive Adaptation Network for Unsupervised Domain Adaptation
Guoliang Kang, Lu Jiang, Yi Yang, Alexander G. Hauptmann

DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality
Chloe LeGendre, Wan-Chun Ma, Graham Fyffe, John Flynn, Laurent Charbonnel, Jay Busch, Paul Debevec

Detect-To-Retrieve: Efficient Regional Aggregation for Image Search
Marvin Teichmann, Andre Araujo, Menglong Zhu, Jack Sim

Fast Object Class Labelling via Speech
Michael Gygli, Vittorio Ferrari

Learning Independent Object Motion From Unlabelled Stereoscopic Videos
Zhe Cao, Abhishek Kar, Christian Hane, Jitendra Malik

Peeking Into the Future: Predicting Future Person Activities and Locations in Videos
Junwei Liang, Lu Jiang, Juan Carlos Niebles, Alexander G. Hauptmann, Li Fei-Fei

SpotTune: Transfer Learning Through Adaptive Fine-Tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogerio Feris

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le

Class-Balanced Loss Based on Effective Number of Samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, Serge Belongie

FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation
Paul Voigtlaender, Yuning Chai, Florian Schroff, Hartwig Adam, Bastian Leibe, Liang-Chieh Chen

Inserting Videos Into Videos
Donghoon Lee, Tomas Pfister, Ming-Hsuan Yang

Volumetric Capture of Humans With a Single RGBD Camera via Semi-Parametric Learning
Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello

You Look Twice: GaterNet for Dynamic Filter Selection in CNNs
Zhourong Chen, Yang Li, Samy Bengio, Si Si

Interactive Full Image Segmentation by Considering All Regions Jointly
Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari

Large-Scale Interactive Object Segmentation With Human Annotators
Rodrigo Benenson, Stefan Popov, Vittorio Ferrari

Self-Supervised GANs via Auxiliary Rotation Loss
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lučić, Neil Houlsby

Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, Konstantinos Bousmalis

Using Unknown Occluders to Recover Hidden Scenes
Adam B. Yedidia, Manel Baradad, Christos Thrampoulidis, William T. Freeman, Gregory W. Wornell

Workshops
Computer Vision for Global Challenges
Organizers include: Timnit Gebru, Ernest Mwebaze, John Quinn

Deep Vision 2019
Invited speakers include: Pierre Sermanet, Chris Bregler

Landmark Recognition
Organizers include: Andre Araujo, Bingyi Cao, Jack Sim, Tobias Weyand

Image Matching: Local Features and Beyond
Organizers include: Eduard Trulls

3D-WiDGET: Deep GEneraTive Models for 3D Understanding
Invited speakers include: Julien Valentin

Fine-Grained Visual Categorization
Organizers include: Christine Kaeser-Chen
Advisory panel includes: Hartwig Adam

Low-Power Image Recognition Challenge (LPIRC)
Organizers include: Aakanksha Chowdhery, Achille Brighton, Alec Go, Andrew Howard, Bo Chen, Jaeyoun Kim, Jeff Gilbert

New Trends in Image Restoration and Enhancement Workshop and Associated Challenges
Program chairs include: Vivek Kwatra, Peyman Milanfar, Sebastian Nowozin, George Toderici, Ming-Hsuan Yang

Spatio-temporal Action Recognition (AVA) @ ActivityNet Challenge
Organizers include: David Ross, Sourish Chaudhuri, Radhika Marvin, Arkadiusz Stopczynski, Joseph Roth, Caroline Pantofaru, Chen Sun, Cordelia Schmid

Third Workshop on Computer Vision for AR/VR
Organizers include: Sofien Bouaziz, Serge Belongie

DAVIS Challenge on Video Object Segmentation
Organizers include: Jordi Pont-Tuset, Alberto Montes

Efficient Deep Learning for Computer Vision
Invited speakers include: Andrew Howard

Fairness Accountability Transparency and Ethics in Computer Vision
Organizers include: Timnit Gebru, Margaret Mitchell

Precognition Seeing through the Future
Organizers include: Utsav Prabhu

Workshop and Challenge on Learned Image Compression
Organizers include: George Toderici, Michele Covell, Johannes Ballé, Eirikur Agustsson, Nick Johnston

When Blockchain Meets Computer Vision & AI
Invited speakers include: Chris Bregler

Applications of Computer Vision and Pattern Recognition to Media Forensics
Organizers include: Paul Natsev, Christoph Bregler

Tutorials
Towards Relightable Volumetric Performance Capture of Humans
Organizers include: Sean Fanello, Christoph Rhemann, Graham Fyffe, Jonathan Taylor, Sofien Bouaziz, Paul Debevec, Shahram Izadi

Learning Representations via Graph-structured Networks
Organizers include: Ming-Hsuan Yang

Source: Google AI Blog


Dev Channel Update for Desktop

The dev channel has been updated to 77.0.3824.6 for Windows, Mac, and Linux.



A partial list of changes is available in the log. Interested in switching release channels? Find out how. If you find a new issue, please let us know by filing a bug. The community help forum is also a great place to reach out for help or learn about common issues.
Lakshmana Pamarthy
Google Chrome

Coming soon to the Lone Star State: more office space and a data center

We're expanding in Texas. Austin has been home to Google for over a decade and today, we’re extending our commitment to the state with a new data center in Midlothian, and the lease of two new buildings for our Austin workforce. These new commitments are part of our larger $13 billion investment in offices and data centers across the United States, which we announced earlier this year.

We’re investing $600 million to develop the Midlothian site, which will create a number of full-time jobs, as well as hundreds of construction jobs to build the new data center. As part of this investment, we’re also making a $100,000 grant to the Midlothian Independent School District to support the continued growth and development of the region’s STEM programs in schools.


In Austin, we already have more than 1,100 employees working across Android, G Suite, Google Play, Cloud, staffing and recruiting, people operations, finance and marketing. As we continue to grow, we’ve leased additional office space at Block 185 and Saltillo—located in downtown Austin and east Austin, respectively—to accommodate our short and long-term growth.

Our current downtown Austin office on W 2nd Street

Our current downtown Austin office on W 2nd Street. We will maintain our presence there while expanding to new locations at Saltillo and Block 185.

The Lone Star state has become a hub for tech innovation and we’ve been fortunate to be a part of its growth from the very beginning. It’s the amazing talent and spirit of work and play that brought us to Texas 12 years ago and it’s what keeps us here today. We look forward to meeting our new neighbors in the Midlothian-Dallas Metro area and we’re excited to be a part of these communities for many years to come.

A father-son team uses technology to grow a 144-year-old business

Founded in 1875, Merz Apothecary is considered a Chicago landmark. For five generations, the pharmacy has been home to a collection of unique, hard-to-find goods from all over the world. Abdul Qaiyum bought the business in 1972, managing to grow the business during a time when most independent pharmacies were giving way to large chain drug stores. Abdul’s three sons worked there growing up and today, Merz Apothecary is run by Abdul and his son, Anthony. “We’re not your traditional pharmacy,” says Anthony. “We carry everything from natural remedies to grooming products to home fragrances.”


One of the secrets to their continued success? Quickly embracing the power of technology. They turned to the internet while it was still in its early days, taking their first online order in 1997 and starting an e-commerce site, smallflower.com, in 1998. In 2001 they started using Google Ads to reach customers searching for their specialty products and their business began to double. They now have 60 employees and the web continues to play a critical role in their business. A YouTube channel has expanded their reach—videos sharing fun and informative product reviews have garnered over 1.5 million views. “I view the web as a way that we can deliver what we do, to everybody,” says Anthony. “Everyone is going online searching for information all the time. Google is the place where most of that gets done, so I want to be there and I want to be seen.”
Merz Apothecary

Abdul & Anthony in 1973 and in 2018


Check out their video to learn more about how this father-son duo continues to grow a business and preserve an American landmark.

Applying AutoML to Transformer Architectures



Since it was introduced a few years ago, Google’s Transformer architecture has been applied to challenges ranging from generating fantasy fiction to writing musical harmonies. Importantly, the Transformer’s high performance has demonstrated that feed forward neural networks can be as effective as recurrent neural networks when applied to sequence tasks, such as language modeling and translation. While the Transformer and other feed forward models used for sequence problems are rising in popularity, their architectures are almost exclusively manually designed, in contrast to the computer vision domain where AutoML approaches have found state-of-the-art models that outperform those that are designed by hand. Naturally, we wondered if the application of AutoML in the sequence domain could be equally successful.

After conducting an evolution-based neural architecture search (NAS), using translation as a proxy for sequence tasks in general, we found the Evolved Transformer, a new Transformer architecture that demonstrates promising improvements on a variety of natural language processing (NLP) tasks. Not only does the Evolved Transformer achieve state-of-the-art translation results, but it also demonstrates improved performance on language modeling when compared to the original Transformer. We are releasing this new model as part of Tensor2Tensor, where it can be used for any sequence problem.

Developing the Techniques
To begin the evolutionary NAS, it was necessary for us to develop new techniques, due to the fact that the task used to evaluate the “fitness” of each architecture, WMT’14 English-German translation, is computationally expensive. This makes the searches more expensive than similar searches executed in the vision domain, which can leverage smaller datasets, like CIFAR-10. The first of these techniques is warm starting—seeding the initial evolution population with the Transformer architecture instead of random models. This helps ground the search in an area of the search space we know is strong, thereby allowing it to find better models faster.

The second technique is a new method we developed called Progressive Dynamic Hurdles (PDH), an algorithm that augments the evolutionary search to allocate more resources to the strongest candidates, in contrast to previous works, where each candidate model of the NAS is allocated the same amount of resources when it is being evaluated. PDH allows us to terminate the evaluation of a model early if it is flagrantly bad, allowing promising architectures to be awarded more resources.

The Evolved Transformer
Using these methods, we conducted a large-scale NAS on our translation task and discovered the Evolved Transformer (ET). Like most sequence to sequence (seq2seq) neural network architectures, it has an encoder that encodes the input sequence into embeddings and a decoder that uses those embeddings to construct an output sequence; in the case of translation, the input sequence is the sentence to be translated and the output sequence is the translation.

The most interesting feature of the Evolved Transformer is the convolutional layers at the bottom of both its encoder and decoder modules that were added in a similar branching pattern in both places (i.e. the inputs run through two separate convolutional layers before being added together).
A comparison between the Evolved Transformer and the original Transformer encoder architectures. Notice the branched convolution structure at the bottom of the module, which formed in both the encoder and decoder independently. See our paper for a description of the decoder.
This is particularly interesting because the encoder and decoder architectures are not shared during the NAS, so this architecture was independently discovered as being useful in both the encoder and decoder, speaking to the strength of this design. Whereas the original Transformer relied solely on self-attention, the Evolved Transformer is a hybrid, leveraging the strengths of both self-attention and wide convolution.

Evaluation of the Evolved Transformer
To test the effectiveness of this new architecture, we first compared it to the original Transformer on the English-German translation task we used during the search. We found that the Evolved Transformer had better BLEU and perplexity performance at all parameter sizes, with the biggest gain at the size compatible with mobile devices (~7 million parameters), demonstrating an efficient use of parameters. At a larger size, the Evolved Transformer reaches state-of-the-art performance on WMT’ 14 En-De with a BLEU score of 29.8 and a SacreBLEU score of 29.2.
Comparison between the Evolved Transformer and the original Transformer on WMT’14 En-De at varying sizes. The biggest gains in performance occur at smaller sizes, while ET also shows strength at larger sizes, outperforming the largest Transformer with 37.6% less parameters (models to compare are circled in green). See Table 3 in our paper for the exact numbers.
To test generalizability, we also compared ET to the Transformer on additional NLP tasks. First, we looked at translation using different language pairs, and found ET demonstrated improved performance, with margins similar to those seen on English-German; again, due to its efficient use of parameters, the biggest improvements were observed for medium sized models. We also compared the decoders of both models on language modeling using LM1B, and saw a performance improvement of nearly 2 perplexity.
Future Work
These results are the first step in exploring the application of architecture search to feed forward sequence models. The Evolved Transformer is being open sourced as part of Tensor2Tensor, where it can be used for any sequence problem. To promote reproducibility, we are also open sourcing the search space we used for our search and a Colab with an implementation of Progressive Dynamic Hurdles. We look forward to seeing what the research community does with the new model and hope that others are able to build off of these new search techniques!

Source: Google AI Blog