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Papercup raises $20M for AI that automatically dubs videos

TechCrunch

Dubbing is a lucrative market, with Verified Market Research predicting that film dubbing services alone could generate $3.6 million, most of which will be put toward research around expressive AI-generated voices and expanding Papercup’s support for foreign languages, Shemen told TechCrunch via email. billion annually by 2027.

Video 228
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Public broadcasting in the wake of the OTT revolution. Will technology and data save culture?

Capgemini

I will no longer speak to four million housewives under 50 but to Paul who loves documentaries on sport, Milène who watches fantasy series after midnight, or Martin who binges on the same episode of Star Wars over and over. and 4 p.m.,

Culture 98
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Sunny side up

Pandora's Brain

Major sporting competitions have three strands: robots, augmented humans, and un-augmented humans. Customised learning plans based on continuous data analysis and personal AI tutors are becoming the norm. Artificial intelligence has made companies so efficient that the cost of most non-luxury goods and services is close to zero.

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Emotional marketing and an e-mail titan walk into a bar

TechCrunch

The networks made online — either through the rise of meme culture or Substack spice — can be a competitive advantage in the world of investment, as two new funds this week showed us. And in the little-known capital lender space, Shopify is using machine learning to lend money to startups. Around TechCrunch.

Marketing 359
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101 Mobile App Ideas for startups in 2021

Openxcell

In 2020, the mobile app development industry has transformed to take on newer challenges like augmented reality, virtual reality, machine learning, and artificial intelligence. Sports Learning App 26. College Learning App 86. Language Learning App 90. Sports Learning App.

Mobile 93
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Recommender Systems: Content-based, Social recommendations and Collaborative filtering

Federico Cargnelutti

With the emergence of new technologies like AI, metadata, and machine learning, traditional content discovery approaches can’t cut the mustard anymore for content publishers. Form of the learned model : Most collaborative filtering systems to date have used k-nearest neighbour models in user-user space.

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Recommender Systems: Content-based, Social recommendations and Collaborative filtering

Federico Cargnelutti

With the emergence of new technologies like AI, metadata, and machine learning, traditional content discovery approaches can’t cut the mustard anymore for content publishers. Form of the learned model : Most collaborative filtering systems to date have used k-nearest neighbour models in user-user space.