54 STREAMING MEDIA INDUSTRY SOURCEBOOK 2018
ability to determine what content will give a specific
ad, say an ad for a white sneaker, the most impact.
“Our system collects different parameters about the
video content and looks for the possible correlation
between ad performance and video content. Based
on this analysis, the brand can optimize its media buy
or optimize which creative ad is delivered against
what type of content,” says Zvika Netter, CEO and co-founder, Innovid.
These capabilities are especially relevant given industry challenges in delivering ads safely, at scale.
“[Our] artificial intelligence analyzes billions of online
activities per day across social platforms and websites, powered by RSS feeds, to understand the topics
and content driving the most engagement among digital audiences.” This is nirvana for brands, being able to
better understand where their engaged audience is.
Data Boasting Rights
Remember that white sneaker? A well-trained system will have the exact product number for that white
sneaker. Azimi says that right now he doesn’t have the
ability to train the Google Cloud Vision API, and so
Cantemo is planning on supporting other frameworks.
An IBM feature he likes is that the company offers use
of an isolated container, so while the file proxy is being
sent to Watson, the data is not being used by IBM. “The
customers that have sensitive content would probably
want to train a machine-learning system from scratch
and have that maintained on-prem or maybe in their
own virtual private cloud so that their data is not leaving their VPC environment,” he says.
This brings up two good questions: If a company is
starting to train an AI model, do they own the intelli-
gence that they are providing, and where is that data
stored? “We would love for you to share your train-
ing with the world, but most people don’t want to do
that,” says Kulczar. The challenge is that systems be-
come smart based on the aggregate of data being con-
sumed. For anyone who has used a system that has
returned horrible results for image recognition, the
idea of pooling customer data is certainly appealing.
With Conviva, publishers own their raw data, but
Conviva owns the analytics on that data, which it uses
across its whole customer base. “We call [this] transfer
learning, where a global data set trains an algorithm for
application use on a local dataset,” says Haslam. “We
have access to this huge amount of data on behalf of our
publishers.” “That algorithm is smarter for ESPN because it got trained by ESPN data, HBO data, Sky data,
and CBS data.” All that data is kept secure via a cloud-based, containerized architecture for each customer.
AI is now everywhere. It’s a prevalent technological
paradigm, and the expectation is technology will benefit from data and become smarter. There are a number of other use cases we’ll leave for a future article,
including image compression, content rating, denial
of service filtering, and even automated video editing.
Moving forward, there are a number of questions to
consider, especially for content owners and publishers looking to leverage AI: How much data does a system need to become relatively accurate? What kind of
software interface is there to work with a system, and
what skills do staff members need to do this training?
Is the data owned by the user? Will there be a larger
pool of data training the system? Is the data transferable to another system? How long will it take to train
a system? What is available now, and what’s on the
roadmap for the next year? Is there any benchmarking information available?
We’re nowhere near the point where the fear that
“the machines are taking over” is warranted. Common
sense, curiosity, and abstract reasoning are still in the
domain of human intelligence. Considering how much
video content is being generated in the world today,
AI is the only way content can be managed, parsed,
and optimized in the future.
While AI can certainly help with labor-intensive processes, there’s no way AI can take over these tasks
without human training for the systems and human
operators to check the output. The biggest benefit
is that companies can free up resources to focus on
more interesting problems that need to be solved in
order to deliver great streaming content at scale.
Nadine Krefetz ( firstname.lastname@example.org) is a digital media consultant.
Her background is in writing, multimedia software development, and project
management. She thinks everything naturally is better in video, plus there’s
something rewarding about getting engineers to speak in plain English
about complex technology.
Comments? Email us at email@example.com, or check the
masthead for other ways to contact us.
AI can be used to generate analytics that help brands determine which ads are most
relevant to the audiences watching particular types of content.