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Over the summer of 2017, Google and Microsoft raced to claim a major milestone in the history of Human-Machine Interfaces: their respective Speech Recognition engines had reached human-level accuracy. Their cloud-based solutions offered better, or nearly equivalent accuracy to professional transcribers on a reference academic dataset of informal phone conversations.

Less than 18 months later, the adoption of voice interfaces is overwhelming. It is now ordinary to have a cloud-powered microphone in our direct environment, whether it’s in our pocket, home, vehicle, etc. But naturally, concerns are rising regarding privacy and security risks associated with such centralized, ubiquitous voice interfaces…


How we achieved a private and efficient cloud-independent voice interface.

In this blog post, we introduce our machine learning team’s recent article on the architecture of the Spoken Language Understanding (SLU) system embedded into the Snips Voice Platform. This embedded inference solution is fast and accurate while enforcing Privacy by Design — no personal user data is ever collected. The resulting SLU engine runs entirely offline, is lightweight, and fast to execute, making it a fit for deployment on small devices. …


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The development of Voice Interfaces over the last years has been exhilarating. They have evolved from spotting limited and predetermined keywords in a sentence to understanding handling any formulation of given intentions. You can now simply talk to Voice Assistants using natural language. They also became much more reliable: state of the art Speech Recognition engines have reached the human level. They basically make as few mistakes as professional transcribers. Last, the speed at which the voice ecosystem evolves is breath-taking. Every day, new integrations are made with new connected devices, so they can be controlled with voice. …


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Integrating a voice or chatbot interface into a product used to require a Natural Language Understanding (NLU) cloud service. Today, we are open sourcing Snips NLU, a Private by Design, GDPR compliant NLU engine. It can run on the Edge or on a server, with minimal footprint, while performing as good or better than cloud solutions.

Natural Language Understanding

2017 was arguably the year of the AI assistant. From the 60 million messages Facebook bots process every day, to the tens of millions of users now talking to an Alexa or Google-powered device, natural language has become a preferred mode of interaction between…


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Hasta ahora, integrar una interfaz de voz o de chatbot en un producto requería un servicio de Natural Language Understanding (NLU) en la nube. Hoy, abrimos el código de Snips NLU, un motor de NLU privado por diseño y respetuoso con la GDPR . Puede ejecutarse en el Edge o en un servidor, utilizando un espacio mínimo y funcionando tan bien o mejor que las soluciones en la nube.

Comprensión del lenguaje natural

2017 fue posiblemente el año del asistente de inteligencia artificial. Desde los 60 millones de mensajes que los bots de Facebook procesan cada día, hasta las decenas de millones de usuarios…


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Integrating a voice or chatbot interface into a product used to require a Natural Language Understanding (NLU) cloud service. Today, we are open sourcing Snips NLU, a Private by Design, GDPR compliant NLU engine. It can run on the Edge or on a server, with minimal footprint, while performing as good or better than cloud solutions.

Natural Language Understanding

2017 was arguably the year of the AI assistant. From the 60 million messages Facebook bots process every day, to the tens of millions of users now talking to an Alexa or Google-powered device, natural language has become a preferred mode of interaction between…


Image for post
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Integrating a voice or chatbot interface into a product used to require a Natural Language Understanding (NLU) cloud service. Today, we are open sourcing Snips NLU, a Private by Design, GDPR compliant NLU engine. It can run on the Edge or on a server, with minimal footprint, while performing as good or better than cloud solutions.

Natural Language Understanding

2017 was arguably the year of the AI assistant. From the 60 million messages Facebook bots process every day, to the tens of millions of users now talking to an Alexa or Google-powered device, natural language has become a preferred mode of interaction between…


Let’s run a data experiment … together. Introducing the Snips Emotional Snapshot.

TL;DR

  • We are working on an assistant that reacts to the content of your messages, whatever the platform (Whatsapp, Facebook Messenger, etc).
  • We are doing so without collecting any data from our users. Your data stays on your device!
  • We still need data for our AI research, so we create experiments where we explicitly ask for data contributions in exchange for something cool.
  • Emotional Snapshot is a MacOS application where YOU can contribute with your text messages, carefully anonymized and encrypted to ensure your privacy before any data is uploaded.
  • As suggested by its name, the Emotional Snapshot app will…


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Photo: credits to Yanidel

Putting new pressure on your old barometer

There is more to location tracking than triggering avalanches of advertising when you walk by a store. Location is a key element to a specific field of artificial intelligence called context awareness. It can be used to determine where you are, where you’re heading to, and your current needs. For example, it is only natural that one expects a person to behave differently in a church and in a disco. Context awareness is about giving devices enough intelligence to tell the difference between a sacrament and a party, and adjust accordingly.

Traditional activity detection

Traditional activity detection with mobile phones relies on location…


Since we were kids, we’ve been told the future would look like sci-fi movies. At Snips, we believe it should be the other way around: technology should be so smart that it disappears into the background! The key to turning this vision into a reality is to make connected devices context-aware.

Jointly with Serge Abiteboul, a research director and INRIA, a blogger, and a former chair of the Collège de France, Snips is organizing a meetup on context awareness on March 3rd, 7:30pm (Snips HQ 18 rue Saint-Marc, 75002 Paris, France). We will be covering both theoretical and practical aspects of context awareness:

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Due to limited seating capacity, prior registration is requested. Please RSVP to let us know if you are coming by February 22nd.

We hope to see you at Snips!

Joseph Dureau

CTO at Snips

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