Author Archives: Tariq Shaukat

NAB 2017: How AI is remaking Hollywood

Greetings from Las Vegas, where the National Association of Broadcasters is having its annual conference. At NAB, 1,700 exhibitors and more than 100,000 attendees take over the Las Vegas Convention Center, representing a dozen industries including TV, movies, radio — and now, virtual reality.

And everybody here agrees. This is a big year for media.

Media/entertainment and cloud technologies are coming together. This changes the economics of the business, the ways people make and distribute content and how they relate to their audience. As the NAB put it introducing this year’s show, “It’s redesigning the very nature of how we live, work and play.”

Large-scale computing systems, next gen software and ubiquitous networks simplify and enable the recording, editing and transmission of content to billions of personal devices. Companies now broadcast more content than ever, in a direct relationship with each audience member. The quality of this relationship relies heavily on the seamlessness and personalization of the experience. The cost benefits and ease of use of the cloud-based model is driving change in all aspects of the business.

NAB-gif2

As president of the customer team at Google Cloud, this is a familiar and exciting story. In media, our customers are seeing cost and time to market reductions of 90 percent or better, with substantial performance improvements, by taking advantage of Google Cloud. Spotify, has seen up to 35x improvement in analytic performance, allowing them to greatly improve their personalization experience. For example, on-premises, their algorithms to identify top tracks took five hours; on BigQuery in Google Cloud it takes eight minutes.

Lead VFX studio on Disney's The Jungle Book, MPC artists built a complex photo-real world

Scripps Networks Interactive saw its livestream TV Everywhere video plays grow by 844 percent in 2016. 

NAB-scripps

They use the cloud to not only run their multiscreen video experiences on mobile and connected devices, but also deliver personalized ads targeted to each and every user.

What excites me most is not simply that our customers have new ways to create, personalize or monetize their content, or that they have a new level of agility in their business, with storage and network charges below what they're paying just for the real estate where they keep their own servers.

These are both important, but most exciting is the way their digital assets are, like all data-rich businesses, coming into the age of artificial intelligence, particularly through machine learning.

NAB-gif1

In the case of media, machine learning allows customers to greatly scale activities that have historically been time-consuming and hard — for example, high quality translation and captioning to make content accessible to more audiences everywhere. It also enables completely new experiences — for example, companies can automatically create and deliver highlight reels of multi-hour sports matches for consumption on mobile devices, and build recommendation systems to ensure that their vast unmonetized long tail of content gets discovered by eager fans.    

This isn't science fiction or a long-term research project. It's here now. Those examples are just a few of the ways our customers already use machine learning.

We look forward to doing much, much more, and hope you'll join us on the journey.

Source: Google Cloud


NCAA teams up with Google Cloud

Sports have the power to bring friends and family together, unite communities and inspire future generations. That’s why we’re so excited to be partnering with the NCAA® to make Google Cloud its official public cloud provider.

As part of its journey to the cloud, the NCAA is migrating 80+ years of historical and play-by-play data, from 90 championships and 24 sports, to Google Cloud Platform (GCP). To start, the NCAA will tap into decades of historical basketball data using BigQuery, Cloud Spanner, Datalab, Cloud Machine Learning and Cloud Dataflow, to power the analysis of team and player performance. In partnership with Turner Sports, our team will build a data-driven bracketology competition using historic NCAA data that will be integrated with public datasets, and data captured from live broadcasts. Fans and NCAA members will be able to search, compare and analyze team and player performance, as well as receive near real-time simulations for tournament analysis and forecasting. This will all kick off ahead of March Madness in 2018.

The NCAA also plans to use this data to create analysis workflows to build descriptive, predictive and diagnostic outputs that will help objectively determine and analyze the selection and seeding process across men’s and women’s sports. As part of this collaboration, we’ve also become the official NCAA Cloud Partner, in partnership with Turner Sports and CBS Sports, starting with the 2017-18 NCAA Division I men’s and women’s basketball seasons.

The mission of the NCAA has long been about serving the needs of schools, their teams and students. We’re proud to support that mission by helping the NCAA use data and machine learning to better engage with its millions of fans, nearly half-million college athletes and more than 19,000 teams. Game on!

Source: Google Cloud


NCAA teams up with Google Cloud

Sports have the power to bring friends and family together, unite communities and inspire future generations. That’s why we’re so excited to be partnering with the NCAA® to make Google Cloud its official public cloud provider.

As part of its journey to the cloud, the NCAA is migrating 80+ years of historical and play-by-play data, from 90 championships and 24 sports, to Google Cloud Platform (GCP). To start, the NCAA will tap into decades of historical basketball data using BigQuery, Cloud Spanner, Datalab, Cloud Machine Learning and Cloud Dataflow, to power the analysis of team and player performance. In partnership with Turner Sports, our team will build a data-driven bracketology competition using historic NCAA data that will be integrated with public datasets, and data captured from live broadcasts. Fans and NCAA members will be able to search, compare and analyze team and player performance, as well as receive near real-time simulations for tournament analysis and forecasting. This will all kick off ahead of March Madness in 2018.

The NCAA also plans to use this data to create analysis workflows to build descriptive, predictive and diagnostic outputs that will help objectively determine and analyze the selection and seeding process across men’s and women’s sports. As part of this collaboration, we’ve also become the official NCAA Cloud Partner, in partnership with Turner Sports and CBS Sports, starting with the 2017-18 NCAA Division I men’s and women’s basketball seasons.

The mission of the NCAA has long been about serving the needs of schools, their teams and students. We’re proud to support that mission by helping the NCAA use data and machine learning to better engage with its millions of fans, nearly half-million college athletes and more than 19,000 teams. Game on!

NAB 2017: How AI is remaking Hollywood

Greetings from Las Vegas, where the National Association of Broadcasters is having its annual conference. At NAB, 1,700 exhibitors and more than 100,000 attendees take over the Las Vegas Convention Center, representing a dozen industries including TV, movies, radio—and now, virtual reality.

And everybody here agrees. This is a big year for media.

Media/entertainment and cloud technologies are coming together. This changes the economics of the business, the ways people make and distribute content and how they relate to their audience. As the NAB put it introducing this year’s show, “It’s redesigning the very nature of how we live, work and play.”

Large-scale computing systems, next gen software and ubiquitous networks simplify and enable the recording, editing and transmission of content to billions of personal devices. Companies now broadcast more content than ever, in a direct relationship with each audience member. The quality of this relationship relies heavily on the seamlessness and personalization of the experience. The cost benefits and ease of use of the cloud-based model is driving change in all aspects of the business.

NAB-gif2

As president of the customer team at Google Cloud, this is a familiar and exciting story. In media, our customers are seeing cost and time to market reductions of 90 percent or better, with substantial performance improvements, by taking advantage of Google Cloud. Spotify, has seen up to 35x improvement in analytic performance, allowing them to greatly improve their personalization experience. For example, on-premises, their algorithms to identify top tracks took five hours; on BigQuery in Google Cloud it takes eight minutes.

Lead VFX studio on Disney's The Jungle Book, MPC artists built a complex photo-real world

Scripps Networks Interactive saw its livestream TV Everywhere video plays grow by 844 percent in 2016. 

NAB-scripps

They use the cloud to not only run their multiscreen video experiences on mobile and connected devices, but also deliver personalized ads targeted to each and every user.

What excites me most is not simply that our customers have new ways to create, personalize or monetize their content, or that they have a new level of agility in their business, with storage and network charges below what they're paying just for the real estate where they keep their own servers.

These are both important, but most exciting is the way their digital assets are, like all data-rich businesses, coming into the age of artificial intelligence, particularly through machine learning.

NAB-gif1

In the case of media, machine learning allows customers to greatly scale activities that have historically been time-consuming and hard — for example, high quality translation and captioning to make content accessible to more audiences everywhere. It also enables completely new experiences — for example, companies can automatically create and deliver highlight reels of multi-hour sports matches for consumption on mobile devices, and build recommendation systems to ensure that their vast unmonetized long tail of content gets discovered by eager fans.    

This isn't science fiction or a long-term research project. It's here now. Those examples are just a few of the ways our customers already use machine learning.

We look forward to doing much, much more, and hope you'll join us on the journey.