We recently partnered with Awwwards, an awards platform for web development and web design, to launch a Mobile Excellence Badge on awwwards.com and a Mobile Excellence Award to recognize great mobile web experiences.
Starting this month, every agency and digital professional that submits their website to Awwwards can be eligible for a Mobile Excellence Badge, a guarantee of the performance of their mobile version. The mobile website's performance will be evaluated by a group of experts and measured against specific criteria based on Google's mobile principles on speed and usability. When a site achieves a minimum score, it will be recognized with the new Mobile Excellence Badge. All criteria are listed at the Mobile Guidelines.
The highest scoring sites with the Mobile Excellence Badge will be nominated for Mobile Site of the Week. One of them will then go on to win Mobile Site of the Month.
All Mobile Sites of the Month will be candidate for Mobile Site of the Year, with the winner receiving a physical award at the Awwwards Conference in Berlin, 8-9 February 2018.
In a time where mobile is playing a dominant role in how people access the web, it is necessary that web developers and web designers build websites that meet users' expectations. Today, 53% of mobile site visits are abandoned if pages take longer than 3 seconds to load1 and despite the explosion of mobile usage, performance and usability of existing mobile sites remain poor and are far from meeting those expectations. At the moment, the average page load time is 22s globally2, which represents a massive missed opportunity for many companies knowing the impact of speed on conversion and bounce rates3.
If you created a great mobile web experience and want it to receive a Mobile Excellence Badge and compete for the Mobile Excellence Award submit your request here.
Google Data, Aggregated, anonymized Google Analytics data from a sample of mWeb sites opted into sharing benchmark data, n=3.7K, Global, March 2016 ↩
Google Research, Webpagetest.org, Global, sample of more than 900,000 mWeb sites across Fortune 1000 and Small Medium Businesses. Testing was performed using Chrome and emulating a Nexus 5 device on a globally representative 3G connection. 1.6Mbps download speed, 300ms Round-Trip Time (RTT). Tested on EC2 on m3.medium instances, similar in performance to high-end smartphones, Jan. 2017. ↩
Akamai.com, Online Retail Experience Report 2017 ↩
When we started API.AI, our goal was to provide developers like you with an API to add natural language processing capabilities to your applications, services and devices. We've worked hard towards that goal and accomplished a lot partnering with all of you. But as we've taken a look at our work over the past year and where we're heading, from new features like our Analytics tool to the 33 prebuilt agents, we realized that we were doing so much more than just providing an API. So with that, we'd like to introduce Dialogflow – the new name for API.AI.
Our new name doesn't change the work we're doing with you or our mission. Our mission continues to be that Dialogflow is your end-to-end platform for building great conversational experiences and our team will help you share what you've built with millions of users. In fact, here are 2 new features we've just launched to help you build those great experiences:
Thanks for being a part of API.AI – we can't wait to see what we do together with Dialogflow. Head over to your developer console and give these new features a try. And, as always, contact us if you have any questions.
As you may have seen, it's a big day for the Google Assistant with new features, new devices and new languages coming soon. But it's also a big day for developers like you, as Actions on Google is also coming to new devices and new languages, and getting better for building and sharing apps.
Actions on Google is already available in English in the US, UK and Australia and today, we're adding new languages to the mix—German (de-DE), French (fr-FR), Japanese (ja-JP), Korean (kr-KR), and both French and English in Canada (en-CA, fr-CA). Starting this week, you can build apps for the Google Assistant in these new languages and soon, they'll be available via the Assistant! Users will soon be able to talk to apps like Zalando, Namatata and Drop the Puck, with more apps on the way. We can't wait to see what you build!
Along with the new Pixelbook come apps for the Assistant. As soon as the Pixelbook hits shelves later this year, your apps will just work, with no extra steps from you! With that said, as with every new surface, especially one with a screen, it's good to make sure that your app is in tip top shape, including using high quality images or adding images to make your conversations more visual.
With apps on Pixelbook, you'll be able to reach a whole new audience and give users the chance to explore your app on a bigger screen, while they get things done throughout their day.
And, in case you missed it, we also recently introduced apps on headphones optimized for the Google Assistant and with the Assistant on Android TV.
Today we shared how the Assistant is great for families—giving people the chance to connect, explore, learn and have fun with the Assistant. And from trivia to storytelling, you can now build Apps for Families and get a dedicated badge via the Assistant on your phone, letting people know your app is family friendly! Soon, users will be able to say "Ok Google, what's my Justice League superhero?" or "Ok Google, play Sports Illustrated Kids Trivia" if you're looking for a game. Or 'Ok Google, let's learn" for some educational fun.
To participate, you first need to make sure your app complies with the program policies and, after that, simply submit it for review. Once approved it will be live for anyone to try! You can learn more about that here. Apps for Families will only be available in US English at the start.
It's easier than ever to make your first (or fifth!) app. With new templates, you can create your own trivia game, flash card app or personality quiz for the Google Assistant without writing code or doing any complex server configurations. All you have to do is add some questions and answers via a Google Sheet. Within minutes, voilà, you can try it out on your Google Assistant and publish it! And if you want to try one today, just say "Ok Google, Play Planet Quiz"
We even provide pre-defined personalities when you create an app from the templates, offering a voice, tone and natural conversational feel for your app's users, without any additional work on your end.
If you prefer to code your own apps, we put a fresh coat of paint on our Actions Console UI to make it easier to create apps with tools like API.AI.
In May we announced that you could start building transactional apps for the Google Assistant on phones and starting this week in the US, you can submit your apps for review! To get a first look at how transactions will work, you'll soon be able to try out 1-800-Flowers, Applebee's, Panera and Ticketmaster.
Ready to give it a try for yourself? You can build and test transactional apps that include payments, status updates and follow-on actions here.
In addition to paying, with transactional apps, a user can see their order history, get status updates and more.
And, last up, to support your efforts in building apps for the Google Assistant and celebrate your accomplishments, we created a new developer community program. Starting with up to $200 in monthly Google Cloud credit and an Assistant t-shirt when you publish your first app, the perks and opportunities available to you will grow as you hit milestone after milestone including your chance to earn a Google Home. And if you've already created an app, don't fret! Your perks are on the way!
Thanks for everything you do to make the Assistant more helpful, fun and interactive! It's been an exciting 10 months to see the platform expand to new languages and devices and to see what you've all created.
Today we're excited to launch Cloud Firestore, a fully-managed NoSQL document database for mobile and web app development. It's designed to easily store and sync app data at global scale, and it's now available in beta.
Key features of Cloud Firestore include:
And of course, we've aimed for the simplicity and ease-of-use that is always top priority for Firebase, while still making sure that Cloud Firestore can scale to power even the largest apps.
Managing app data is still hard; you have to scale servers, handle intermittent connectivity, and deliver data with low latency.
We've optimized Cloud Firestore for app development, so you can focus on delivering value to your users and shipping better apps, faster. Cloud Firestore:
As you may have guessed from the name, Cloud Firestore was built in close collaboration with the Google Cloud Platform team.
This means it's a fully managed product, built from the ground up to automatically scale. Cloud Firestore is a multi-region replicated database that ensures once data is committed, it's durable even in the face of unexpected disasters. Not only that, but despite being a distributed database, it's also strongly consistent, removing tricky edge cases to make building apps easier regardless of scale.
It also means that delivering a great server-side experience for backend developers is a top priority. We're launching SDKs for Java, Go, Python, and Node.js today, with more languages coming in the future.
Over the last 3 years Firebase has grown to become Google's app development platform; it now has 16 products to build and grow your app. If you've used Firebase before, you know we already offer a database, the Firebase Realtime Database, which helps with some of the challenges listed above.
The Firebase Realtime Database, with its client SDKs and real-time capabilities, is all about making app development faster and easier. Since its launch, it has been adopted by hundred of thousands of developers, and as its adoption grew, so did usage patterns. Developers began using the Realtime Database for more complex data and to build bigger apps, pushing the limits of the JSON data model and the performance of the database at scale. Cloud Firestore is inspired by what developers love most about the Firebase Realtime Database while also addressing its key limitations like data structuring, querying, and scaling.
So, if you're a Firebase Realtime Database user today, we think you'll love Cloud Firestore. However, this does not mean that Cloud Firestore is a drop-in replacement for the Firebase Realtime Database. For some use cases, it may make sense to use the Realtime Database to optimize for cost and latency, and it's also easy to use both databases together. You can read a more in-depth comparison between the two databases here.
We're continuing development on both databases and they'll both be available in our console and documentation.
Cloud Firestore enters public beta starting today. If you're comfortable using a beta product you should give it a spin on your next project! Here are some of the companies and startups who are already building with Cloud Firestore:
Get started by visiting the database tab in your Firebase console. For more details, see the documentation, pricing, code samples, performance limitations during beta, and view our open source iOS and JavaScript SDKs on GitHub.
We can't wait to see what you build and hear what you think of Cloud Firestore!
Posted by Wesley Chun (@wescpy), Developer Advocate, G Suite
Today, we announced a collection of exciting new features in Google Slides—among these is support for Google Apps Script. Now you can use Apps Script for Slides to programmatically create and modify Slides, plus customize menus, dialog boxes and sidebars in the user interface.
Presentations have come a long way—from casting hand shadows over fires in caves to advances in lighting technology (magic lanterns) to, eventually, (in)famous 35mm slide shows of your Uncle Bob's endless summer vacation. More recently, we have presentation software—like Slides—and developers have been able to write applications to create or update them. This is made even easier with the new Apps Script support for Google Slides. In the latest G Suite Dev Show episode, we demo this new service, walking you through a short example that automatically creates a slideshow from a collection of images.
To keep things simple, the chosen images are already available online, accessible by URL. For each image, a new (blank) slide is added then the image is inserted. The key to this script are two lines of JavaScript (given an existing presentation and a link to each image):
var slide = presentation.appendSlide(SlidesApp.PredefinedLayout.BLANK); var image = slide.insertImage(link);
The first line of code adds a new slide while the other inserts an image on the new slide. Both lines are repeated for each image in the collection. While this initial, rudimentary solution works, the slide presentation created doesn't exactly fit the bill. It turns out that adding a few more lines make the application much more useful. See the video for all the details.
To get started, check the documentation to learn more about Apps Scripts for Slides, or check out the Translate and Progress Bar sample Add-ons. If you want to dig deeper into the code sample from our video, take a look at the corresponding tutorial. And, if you love watching videos, check out our Apps Script video library or other G Suite Dev Show episodes. If you wish to build applications with Google Slides outside of the Apps Script environment and want to use your own development tools, you can do so with the Slides (REST) API—check out its documentation and video library.
With all these options, we look forward to seeing the applications you build with Google Slides!
Originally posted by Paul McReynolds, Product Manager (@pauljmcr), Apps Script and Wesley Chun (@wescpy), Developer Advocate, G Suite on the G Suite Developers Blog
Apps Script is just as popular inside Google as it is among external users and developers. In fact, there are more than 70,000 weekly active scripts written by thousands of Googlers. One of our many uses for Apps Script at Google is to automate and monitor our internal issue tracker.
In spring of this year, we migrated our G Suite issue trackers to a new system based on our internal tracker. This carries a lot of benefits, including improving our ability to track how issues reported from outside of Google relate to bugs and features we're working on internally. We also have an internal Apps Script API that talks to our issue tracker, which we can now use to work with issues reported from outside of Google.
As soon as the migration was finished, we put Apps Script to work monitoring…itself. Now we have a script in place that monitors Apps Script issues as they are reported and upvoted on the public tracker. When we see an issue that's having widespread or sudden impact, the script generates an alert that we can then investigate. With the help of our large, active community of developers, and leveraging Apps Script, we're now able to identify and respond to issues more quickly.
There's no substitute for independent monitoring, and our Apps Script-based approach isn't the first or the last line of defense. Instead, this new script helps us catch anything that our monitoring systems miss by listening to what developers are saying on the tracker.
Please help us keep Apps Script humming! When you notice a problem, search the issue tracker for it and file an issue if it's new. Click the star to let us know you're affected and leave a comment with instructions to reproduce, along with any other relevant details. Those instructions and other details help us respond to the issues more effectively, so please be sure to include them.
Happy scripting!
Coca-Cola's core loyalty program launched in 2006 as MyCokeRewards.com. The "MCR.com" platform included the creation of unique product codes for every Coca-Cola, Sprite, Fanta, and Powerade product sold in 20oz bottles and cardboard "fridge-packs" purchasable at grocery stores and other retail outlets. Users could enter these product codes at MyCokeRewards.com to participate in promotional campaigns.
Fast-forward to 2016: Coke's loyalty programs are still hugely popular with millions of product codes having been entered for promotions and sweepstakes. However, mobile browsing went from non-existent in 2006 to over 50% share by the end of 2016. The launch of Coke.com as a mobile-first web experience (replacing MCR.com) was a response to these changes in browsing behavior. Thumb-entering 14-character codes into a mobile device could be a difficult enough user experience to impact the success of our programs. We want to provide our mobile audience the best possible experience, and recent advances in artificial intelligence opened new opportunities.
For years Coke attempted to use off-the-shelf optical character recognition (OCR) libraries and services to read product codes with little success. Our printing process typically uses low-resolution dot-matrix fonts with the cap or fridge-pack media running under the printhead at very high speeds. All of this translates into a low-fidelity string of characters that defeats off-the-shelf OCR offerings (and can sometimes be hard to read with the human eye as well). OCR is critical to simplifying the code-entry process for mobile users: they should be able to take a picture of a code and automatically have the purchase registered for a promotional entry. We needed a purpose-built OCR system to recognize our product codes.
Our research led us to a promising solution: Convolutional Neural Networks. CNNs are one of a family of "deep learning" neural networks that are at the heart of modern artificial intelligence products. Google has used CNNs to extract street address numbers from StreetView images. CNNs also perform remarkably well at recognizing handwritten digits. These number-recognition use-cases were a perfect proxy for the type of problem we were trying to solve: extracting strings from images that contain small character sets with lots of variance in the appearance of the characters.
In the past, developing deep neural networks like CNNs has been a challenge because of the complexity of available training and inference libraries. TensorFlow, a machine learning framework that was open sourced by Google in November 2015, is designed to simplify the development of deep neural networks.
TensorFlow provides high-level interfaces to different kinds of neuron layers and popular loss functions, which makes it easier to implement different CNN model architectures. The ability to rapidly iterate over different model architectures dramatically reduced the time required to build Coke's custom OCR solution because different models could be developed, trained, and tested in a matter of days. TensorFlow models are also portable: the framework supports model execution natively on mobile devices ("AI on the edge") or in servers hosted remotely in the cloud. This enables a "create once, run anywhere" approach for model execution across many different platforms, including web-based and mobile.
Any neural network is only as good as the data used to train it. We knew that we needed a large set of labeled product-code images to train a CNN that would achieve our performance goals. Our training set would be built in three phases:
The pre-launch training phase began by programmatically generating millions of simulated product-code images. These simulated images included variations in tilt, lighting, shadows, and blurriness. The prediction accuracy (i.e. how often all 14 characters were correctly predicted within the top-10 predictions) was at 50% against real-world images when the model was trained using only simulated images. This provided a baseline for transfer-learning: a model initially trained with simulated images was the foundation for a more accurate model that would be trained against real-world images.
The challenge now turned to enriching the simulated images with enough real-world images to hit our performance goals. We created a purpose-built training app for iOS and Android devices that "trainers" could use to take pictures of codes and label them; these labeled images were then transferred to cloud storage for training. We did a production run of several thousand product codes on bottle caps and fridge-packs and distributed these to multiple suppliers who used the app to create the initial real-world training set.
Even with an augmented and enriched training set, there is no substitute for images created by end-users in a variety of environmental conditions. We knew that scans would sometimes result in an inaccurate code prediction, so we needed to provide a user-experience that would allow users to quickly correct these predictions. Two components are essential to delivering this experience: a product-code validation service that has been in use since the launch of our original loyalty platform in 2006 (to verify that a predicted code is an actual code) and a prediction algorithm that performs a regression to determine a per-character confidence at each one of the 14 character positions. If a predicted code is invalid, the top prediction as well as the confidence levels for each character are returned to the user interface. Low-confidence characters are visually highlighted to guide the user to update characters that need attention.
This user interface innovation enables an active learning process: a feedback loop allows the model to gradually improve by returning corrected predictions to the training pipeline. In this way, our users organically improve the accuracy of the character recognition model over time.
To meet user expectations around performance, we established a few ambitious requirements for the product-code OCR pipeline:
We initially explored an architecture that used a single CNN for all product-code media. This approach created a model that was too large to be distributed to mobile apps and the execution time was longer than desired. Our applied-AI partners at Quantiphi, Inc. began iterating on different model architectures, eventually landing on one that used multiple CNNs.
This new architecture reduced the model size dramatically without sacrificing accuracy, but it was still on the high end of what we needed in order to support over-the-air updates to mobile apps. We next used TensorFlow's prebuilt quantization module to reduce the model size by reducing the fidelity of the weights between connected neurons. Quantization reduced the model size by a factor of 4, but a dramatic reduction in model size occurred when Quantiphi had a breakthrough using a new approach called SqueezeNet.
The SqueezeNet model was published by a team of researchers from UC Berkeley and Stanford in November of 2016. It uses a small but highly complex design to achieve accuracy levels on par with much larger models against popular benchmarks such as Imagenet. After re-architecting our character recognition models to use a SqueezeNet CNN, Quantiphi was able to reduce the model size of certain media types by a factor of 100. Since the SqueezeNet model was inherently smaller, a richer feature detection architecture could be constructed, achieving much higher accuracy at much smaller sizes compared to our first batch of models trained without SqueezeNet. We now have a highly accurate model that can be easily updated on remote devices; the recognition success rate of our final model before active learning was close to 96%, which translates into a 99.7% character recognition accuracy (just 3 misses for every 1000 character predictions).
Advances in artificial intelligence and the maturity of TensorFlow enabled us to finally achieve a long-sought proof-of-purchase capability. Since launching in late February 2017, our product code recognition platform has fueled more than a dozen promotions and resulted in over 180,000 scanned codes; it is now a core component for all of Coca-Cola North America's web-based promotions.
Moving to an AI-enabled product-code recognition platform has been valuable for two key reasons:
Our product-code recognition platform is the first execution of new AI-enabled capabilities at scale within Coca-Cola. We're now exploring AI applications across multiple lines of business, from new product development to ecommerce retail optimization.
Last month we announced that UK users can access apps for the Google Assistant on Google Home and their phones—and starting today, we're bringing Actions on Google to Australia. From Perth to Sydney, developers can start building apps for the Google Assistant, giving their users even more ways to get things done.
Similar to our launch in the UK, your English apps will appear in the local directory automatically. With that said, there are a few things to help make your app a true blue Aussie:
Our developer tools, documentation and simulator have all been updated to make it easy for you to create, test and deploy your app. So what are you waiting for?
UK and Aussie users are just the start, we'll continue to make the Actions on Google platform available in more languages over the coming year. If you have questions about internationalization, please reach out to us on Stackoverflow and Google+.
