An Irish startup has claimed a breakthrough in text-to-speech synthesis that improves on public demonstrations by Google’s DeepMind and Facebook. 

The result is an artificial voice that lacks many of the glitches in intonation heard from digital assistants like Siri or Amazon’s Alexa. It sounds eerily human, and shows that you no longer need a multi-billion dollar R&D budget or hundreds of engineers to produce an artificial voice that’s as good as Google’s. Read More

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IT’S DIFFICULT TO remember now, but there was a moment in early 2016 when many in the tech industry believed chatbots—automated text-based virtual assistants—would be the next big platform. Messaging app Kik staked its company’s future on bots and “chatvertising.” Startup studio Betaworks launched an accelerator program called Botcamp. And at its 2016 F8 conference, Facebook pitched bots to developers as the best way to connect with 900 million Messenger users.

Few expected that voice assistants like Amazon’s Alexa and Google Assistant would thrive and text-based chatbots would become a punchline. Betaworks’ accelerator, which the company says was designed as a one-off, has moved on to other themes. Kik pivoted to blockchain technology. And now Facebook says it will shutter M, its buzzy full-service virtual assistant, on Jan. 19. Read More

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If you’ve spent much time on Nanalyze, you know that we’re passionate about technology and believe that we’re living in the most exciting times in history. We’re talking cure-for-cancer and sending-humans-to-Mars territory here. Our job is to keep you up-to-date about these changes in a variety of fields, so you can make informed financial decisions about where to invest or not—and learn some pretty cool stuff along the way. We talk about the good, the bad and ugly no matter what.

Then we came across Narrative Science and its natural language generation (NLG) platform Quill, which uses artificial intelligence technology to write everything from financial reports to sports news. In other words, it’s the competition. That’s right: Looks like we’re going from gonzo journalism to robo journalism. We knew that English degree would be obsolete someday. Read More

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Speech-to-speech translation systems have been developed over the past several decades with the goal of helping people who speak different languages to communicate with each other. Such systems have usually been broken into three separate components: automatic speech recognition to transcribe the source speech as text, machine translation to translate the transcribed text into the target language, and text-to-speech synthesis (TTS) to generate speech in the target language from the translated text. Dividing the task into such a cascade of systems has been very successful, powering many commercial speech-to-speech translation products, including Google Translate. Read More

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Over the past few years, Deep Learning (DL) architectures and algorithms have made impressive advances in fields such as image recognition and speech processing.

Their application to Natural Language Processing (NLP) was less impressive at first, but has now proven to make significant contributions, yielding state-of-the-art results for some common NLP tasks. Named entity recognition (NER), part of speech (POS) tagging or sentiment analysis are some of the problems where neural network models have outperformed traditional approaches. The progress in machine translation is perhaps the most remarkable among all.

In this article I will go through some recent advancements for NLP that rely on DL techniques. I do not pretend to be exhaustive: it would simply be impossible given the vast amount of scientific papers, frameworks and tools available. I just want to share with you some of the works that I liked the most. I think the last months have been great for our field. The use of DL in NLP keeps widening, yielding amazing results in some cases, and all signs point to the fact that this trend will not stop. Read More

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