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TADSummit, Cognitive Telecom Services Thomas Quintana, Telecoms Geek & Entrepreneur


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Cognitive Telecom Services
Thomas Quintana, Telecoms Geek & Entrepreneur: Fort Lauderdale Machine Learning Meetup, Inteliquent, Better Voice, Health Sense, Telestax, and more
Cognitive Computing seems to be taking over the world. The technology is helping identify illnesses, composing music, driving cars, and the list goes on. In this talk we explore how it is impacting telecoms.

Google has created an immense valuation using your search information, but its without context and meaning. Search for an item and you’re blasted by webpages full of the same advert for that item. It lacks context and meaning. While all the conversations businesses have with their customer are full of context and meaning that simply go to waste. And will not be used to fill webpages full of the same advert.
Cognitive telecom services has massive potential, and the technologies and necessary price points are only just falling into place.

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TADSummit, Cognitive Telecom Services Thomas Quintana, Telecoms Geek & Entrepreneur

  1. 1. Cognitive Telecom Services Thomas Quintana
  2. 2. Agenda ● Cognitive Computing Is Taking Over! ● The Building Blocks ● The Potential ● Q/A
  3. 3. Cognitive Computing is taking over!
  4. 4. What is a cognitive service? Cognitive computing (CC) describes technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, among other technologies.
  5. 5. The Building Blocks
  6. 6. Speech Synthesis (WaveNet) ● One model learns to synthesize many languages. ● Better than both of Google’s state of the art parametric and concatenative models. ● Is now the voice of the Google assistant.
  7. 7. Speech Transcription (DeepSpeech) ● One model learns to transcribe many languages. ● The state of the art model is comparable to people transcribers on Amazon’s Mechanical Turk. ● The model is accurate in noisy environments.
  8. 8. Translation (Neural Machine Translation) ● Models are getting close to human level performance. ● One model can learn many languages simultaneously. ● The more languages the model learns the better it gets at learning to translate between new languages.
  9. 9. The potential
  10. 10. Possible Applications in Telecom ● Audio Search ● Chat Bots ● IoT ● NLP ● Personal Assistants ● Speech Translation ● Sentiment Analysis ● The list goes on...
  11. 11. Conclusion E-Mail: Twitter: @thomasquintana