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Deep Learning in the Media Supply Chain

Deep Learning in the Media Supply Chain

Although many media professionals are skeptical about , recent studies find they would be comfortable with -generated news like traffic or weather reports. But without the right strategy, more automation can quickly become a nightmare.

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SwissCognitive: WHAT’S WHAT?

No other topic has dominated industry conversation in recent years like . But what exactly does it mean when we speak of

Artificial intelligence is the generic term for a machine simulation of human cognitive abilities. Machine Learning, in turn, describes a series of mathematical methods that can identify certain patterns in data from learned examples. Deep Learning is a subset of and uses artificial neural networks that enable the system to learn autonomously. 

DEEP LEARNING IN MEDIA

Deep Learning enables the processing of amounts of data that is not practical to process manually. The strength of lies in capturing patterns and structures of different data types, as well as in tagging and enriching data. With its daily flow of current facts, figures and data, the media sector is ideal for the application of

Although many media professionals are skeptical about , recent studies find they would be comfortable with -generated news like traffic or weather reports. But without the right strategy, more automation can quickly become a nightmare.

IN THE MEDIA SUPPLY CHAIN

How do we integrate our existing systems with the rapidly growing field of providers with pre-trained models, frameworks and environments ready to be used as services?

First, we need to look at where we might apply them. There are opportunities throughout the media supply chain. A few examples include: 

  • Ingest—Automatic QC, compliance, deep fake recognition, copyright monitoring  
  • Production—Tagging, entity recognition, topic clustering and (soon) rough cuts, automatic highlight cuts, journalism 
  • Planning—Automatic program planning, based on licensing or marketing patterns 
  • Marketing—Rating prediction, imitation of buying patterns 
  • Distribution—Automated playout or packaging

A GOOD STRATEGY

 

Deep Learning helps us to gain insights into media objects at a level that wasn’t practical without automation and helping us toward our vision of wanting to know “everything about every frame.”

To support to the multitude of services available and bridge the data and organizational silos that segregate both content and business intelligence, we implement an “-specific” intelligence layer that manages all communication, but also adds value through:

  • Normalization—Bringing results into a unified format 
  • Cross-media analysis—Video, stills, audio, text 
  • Multicloud—Connect many different providers 
  • Training—Especially in the field of computer vision 
  • Knowledge graph—Build contextual data models from different data silos and query them in real time with dynamic requests   […]

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