May 11, 2016

Highlights from NAB Show 2016

Valossa at NAB Show
“This is by far the most advanced system I’ve seen in NAB”

NAB Show 2016 was notable for many things that packed 103,000 people into the Las Vegas Convention Center from April 18-21. However, among the most notable of events was the unveiling of the next generation Artificial Intelligence for the broadcast/production industry. Valossa, attracting an uninterrupted stream of visitors, was in the spotlight with a live recognition demo. Using GPU-accelerated server as its forge, Val.ai recognized places and objects from 25 simultaneous broadcast video streams, earning much earned praise from industry experts.

This was the debut appearance of Valossa at NAB, the world’s largest professional audio/video conference, and it confirmed what we have known for the past several months: we have a product that is in high demand for automated content curation, and on several levels. From the many meetings and demos we had at NAB 2016, it is clear that our platform satisfies clear needs across the content production, management and monetization ecosystems, from ingest to editing, archiving to discovery, and advertising.

Valossa at NAB Show

 

Our NAB display was prominently located in front of the Echostreams booth, demonstrating 25 concurrent content streams, which impressively showed our real-time content analysis engine. Thanks to our partners, Echostreams and Orange Silicon Valley, the demo was ideal for broadcasters. We did not go small at NAB; our live content indexing demo attracted attention, and by Day 3 of the show, it was clear that word had gone around the ranks of studio and network engineers that we had something to see.

Not surprisingly, we heard many questions about Valossa’s emotion and expression recognition engine. We have a clear mission to respond to the many potential customers with whom we met at NAB, and in the coming months, our initial product will be in the hands of several leading players in the broadcast/production industry.

Real-time stream analysis