Category Archives: Google India Blog

The Official Google Blog for India

Exploring art (through selfies) with Google Arts & Culture

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The Google Arts & Culture platform hosts millions of artifacts and pieces of art, ranging from prehistory to the contemporary, shared by museums across the world. But the prospect of exploring all that art can be daunting. To make it easier, we dreamt up a fun solution: connect people to art by way of a fundamental artistic pursuit, the search for the self … or, in this case, the selfie.


We created an experiment that matches your selfie with art from the collections of museums on Google Arts & Culture—and over the past few days, people have taken more than 30 million selfies. Even if your art look-alike is a surprise, we hope you discover something new in the process. (By the way, Google doesn't use your selfie for anything else and only keeps it for the time it takes to search for matches.)


That’s me, Michelle, the product manager for this feature!


We're so happy people are enjoying their selfie matches, but we're even happier that people haven't stopped with the selfie. They’ve jumped—face first—into the 6,000 exhibitions hosted on Google Arts & Culture, from more than 1,500 museum partners from 70 countries, to explore their artwork and learn about their stories.


At Google Arts & Culture, our software engineers are always experimenting with new and creative ways to connect you with art and culture. That’s how this selfie feature came about too. After the overwhelming response in parts of the U.S., we are excited to bring it to users in India. We’ll continue to partner with more museums to bring diverse cultures from every part of the world online (any museum can join!), so you can explore their stories and find even more portraits.


In the meantime, you can download the Google Arts & Culture app for iOS or Android and get face to face with art!

Posted by: Michelle Luo, Product Manager, Google Arts & Culture

Cloud AutoML: Making AI accessible to every business

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When we both joined Google Cloud just over a year ago, we embarked on a mission to democratize AI. Our goal was to lower the barrier of entry and make AI available to the largest possible community of developers, researchers and businesses.


Our Google Cloud AI team has been making good progress towards this goal. In 2017, we introduced Google Cloud Machine Learning Engine, to help developers with machine learning expertise easily build ML models that work on any type of data, of any size. We showed how modern machine learning services, i.e., APIs—including Vision, Speech, NLP, Translation and Dialogflow—could be built upon pre-trained models to bring unmatched scale and speed to business applications. Kaggle, our community of data scientists and ML researchers, has grown to more than 1 million members. And today, more than 10,000 businesses are using Google Cloud AI services, including companies like Box, Rolls Royce Marine, Kewpie, and Ocado.


But there’s much more we can do. Currently, only a handful of businesses in the world have access to the talent and budgets needed to fully appreciate the advancements of ML and AI. There’s a very limited number of people that can create advanced machine learning models. And if you’re one of the companies that has access to ML/AI engineers, you still have to manage the time-intensive and complicated process of building your own custom ML model. While Google has offered pre-trained machine learning models via APIs that perform specific tasks, there's still a long road ahead if we want to bring AI to everyone.


To close this gap, and to make AI accessible to every business, we’re introducing Cloud AutoML. Cloud AutoML helps businesses with limited ML expertise start building their own high-quality custom models by using advanced techniques like learning2learn and transfer learning from Google. We believe Cloud AutoML will make AI experts even more productive, advance new fields in AI, and help less-skilled engineers build powerful AI systems they previously only dreamed of.


Our first Cloud AutoML release will be Cloud AutoML Vision, a service that makes it faster and easier to create custom ML models for image recognition. Its drag-and-drop interface lets you easily upload images, train and manage models, and then deploy those trained models directly on Google Cloud. Early results using Cloud AutoML Vision to classify popular public datasets like ImageNet and CIFAR have shown more accurate results with fewer misclassifications than generic ML APIs.


Here’s a little more on what Cloud AutoML Vision has to offer:
  • Increased accuracy: Cloud AutoML Vision is built on Google’s leading image recognition approaches, including transfer learning and neural architecture search technologies. This means you’ll get a more accurate model even if your business has limited machine learning expertise.
  • Faster turnaround time to production-ready models: With Cloud AutoML, you can create a simple model in minutes to pilot your AI-enabled application, or build out a full, production-ready model in as little as a day.
  • Easy to use: AutoML Vision provides a simple graphical user interface that lets you specify data, then turns that data into a high quality model customized for your specific needs.



    Urban Outfitters is constantly looking for new ways to enhance our customers’ shopping experience," says Alan Rosenwinkel, Data Scientist at URBN. "Creating and maintaining a comprehensive set of product attributes is critical to providing our customers relevant product recommendations, accurate search results, and helpful product filters; however, manually creating product attributes is arduous and time-consuming. To address this, our team has been evaluating Cloud AutoML to automate the product attribution process by recognizing nuanced product characteristics like patterns and necklines styles. Cloud AutoML has great promise to help our customers with better discovery, recommendation, and search experiences."


    Mike White, CTO and SVP, for Disney Consumer Products and Interactive Media, says: “Cloud AutoML’s technology is helping us build vision models to annotate our products with Disney characters, product categories, and colors. These annotations are being integrated into our search engine to enhance the impact on Guest experience through more relevant search results, expedited discovery, and product recommendations on shopDisney.”

    And Sophie Maxwell, Conservation Technology Lead at the Zoological Society of London, tells us: "ZSL is an international conservation charity devoted to the worldwide conservation of animals and their habitats. A key requirement to deliver on this mission is to track wildlife populations to learn more about their distribution and better understand the impact humans are having on these species. In order to achieve this, ZSL has deployed a series of camera traps in the wild that take pictures of passing animals when triggered by heat or motion. The millions of images captured by these devices are then manually analysed and annotated and with the relevant species such as elephants, lions, and giraffes, etc., which is a labour-intensive and expensive process. ZSL’s dedicated Conservation Technology Unit has been collaborating closely with Google’s CloudML team to help shape the development of this exciting technology, which ZSL aims to use to automate the tagging of these images—cutting costs, enabling wider-scale deployments, and gaining a deeper understanding of how to conserve the world’s wildlife effectively."


    If you’re interested in trying out AutoML Vision, you can request access via this form.

    AutoML Vision is the result of our close collaboration with Google Brain and other Google AI teams, and is the first of several Cloud AutoML products in development. While we’re still at the beginning of our journey to make AI more accessible, we’ve been deeply inspired by what our 10,000+ customers using Cloud AI products have been able to achieve. We hope the release of Cloud AutoML will help even more businesses discover what’s possible through AI.

    By Jia Li, Head of R&D, Cloud AI, and Fei-Fei Li, Chief Scientist, Cloud AI

    A New Approach to YouTube Monetization

    There’s no denying 2017 was a difficult year, with several issues affecting our community and our advertising partners. We are passionate about protecting our users, advertisers and creators and making sure YouTube is not a place that can be co-opted by bad actors. While we took several steps last year to protect advertisers from inappropriate content, we know we need to do more to ensure that their ads run alongside content that reflects their values. As we mentioned in December, we needed a fresh approach to advertising on YouTube. Today, we are announcing three significant changes.


    Stricter criteria for monetization on YouTube
    After careful consideration and extended conversations with advertisers and creators, we’re making big changes to the process that determines which channels can run ads on YouTube. Previously, channels had to reach 10,000 total views to be eligible for the YouTube Partner Program (YPP). It’s been clear over the last few months that we need the right requirements and better signals to identify the channels that have earned the right to run ads. Instead of basing acceptance purely on views, we want to take channel size, audience engagement, and creator behavior into consideration to determine eligibility for ads.


    That’s why starting today, new channels will need to have 1,000 subscribers and 4,000 hours of watch time within the past 12 months to be eligible for ads. We will begin enforcing these new requirements for existing channels in YPP beginning February 20th, 2018.


    Of course, size alone is not enough to determine whether a channel is suitable for advertising. We will closely monitor signals like community strikes, spam, and other abuse flags to ensure they comply with our policies. Both new and existing YPP channels will be automatically evaluated under this strict criteria and if we find a channel repeatedly or egregiously violates our community guidelines, we will remove that channel from YPP. As always, if the account has been issued three community guidelines strikes, we will remove that user’s accounts and channels from YouTube.


    This combination of hard-to-game user signals and improved abuse indicators will help us reward the creators who make engaging content while preventing bad actors and spammers from gaming the system in order to monetize unsuitable content. While this new approach will affect a significant number of channels eligible to run ads, the creators who will remain part of YPP represent more than 95% of YouTube's reach for advertisers.


    Those of you who want more details, can find additional information in our Help Center.


    Manually reviewing Google Preferred
    We’re changing Google Preferred so that it not only offers the most popular content on YouTube, but also the most vetted. We created Google Preferred to surface YouTube's most engaging channels and to help our customers easily reach our most passionate audiences. Moving forward, the channels included in Google Preferred will be manually reviewed and ads will only run on videos that have been verified to meet our ad-friendly guidelines. We expect to complete manual reviews of Google Preferred channels and videos by mid-February in the U.S. and by the end of March in all other markets where Google Preferred is offered.


    Greater transparency and simpler controls over where ads appear
    We know advertisers want simpler and more transparent controls. In the coming months, we will introduce a three-tier suitability system that allows advertisers to reflect their view of appropriate placements for their brand, while understanding potential reach trade offs.


    We also know we need to offer advertisers transparency regarding where their ads run. We’ve begun working with trusted vendors to provide third-party brand safety reporting on YouTube. We're currently in a beta with Integral Ad Science (IAS) and we're planning to launch a beta with DoubleVerify soon. We are also exploring partnerships with OpenSlate, comScore and Moat and look forward to scaling our third-party measurement offerings over the course of the year.


    The challenges we faced in 2017 have helped us make tough but necessary changes in 2018. These changes will help us better fulfill the promise YouTube holds for advertisers: the chance to reach over 1.5 billion people around the world who are truly engaged with content they love. We value the partnership and patience of all our advertisers to date and look forward to strengthening those ties throughout 2018.

    Posted by Paul Muret, VP, Display, Video & Analytics

    Empowering a new generation of localization professionals

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    Have you ever wondered what makes billions of users around the world use Google products in their native languages? This isn’t magic: It’s the work of a dedicated localization team. Spread over more than 30 countries, our team makes sure that all Google products are fun and easy to use in more than 70 languages.

    But localization goes beyond translation. While references to baseball and donuts work well in the United States, these are not necessarily popular concepts in India. Therefore we change these, for example, to Cricket and Gulab-jamun. Our mission is to create a diverse user experience that fits every language and every culture. We do this through a network of passionate translators and reviewers who localize Google products to make sure they sound natural to people everywhere.
    With more and more people from around the world coming online every day, the localization industry keeps growing, and so does the demand for great translators, reviewers, and localization professionals. This holds true especially for countries like India, where an increasing number of users, in an increasing number of languages, is getting online. 9 out of every 10 new internet users in India over the next 5 years are likely to be Indian language users (KPMG-Google Study 2017). Companies and government organisations might like to localize more and more products and it opens plethora of opportunities. This means the Indian localization industry is on the cusp of a strong phase of growth.

    So, as part of Google’s mission to build products for everyone and make the web globally accessible, no matter where users are, We’ve recently launched a massive open online course (MOOC) called Localization Essentials. The course is completely free to access on Udacity. Videos and course transcripts can be downloaded and used offline. The course covers all localization basics needed to develop global products and to work in this expanding industry. Indian users already welcomed this free online course enthusiastically: Their enrollment is the highest, second only to US users, with more than 700 Indian students already enrolled and taking the course and the number still growing.

    By sharing our knowledge, we hope that more culturally relevant products will become available to users in India and we hope we’re providing users, language experts, product managers and developers opportunities that they might not have had before. We’re looking forward to seeing how sharing this localization knowledge will impact users in India.


    By Bert Vander Meeren, Director, Localization, Google