February 18, 2020

375 words 2 mins read

Make Alexa and Siri speak with each other: Toward a universal grammar in AI

Make Alexa and Siri speak with each other: Toward a universal grammar in AI

More than 50% of all interactions between humans and machines are expected to be speech-based by 2022. The challenge: Every AI interprets human language slightly different. Tobias Martens details current issues in NLP interoperability and uses Chomsky's theory of universal hard-wired grammar to outline a framework to make the human voice in AI universal, accountable, and computable.

Talk Title Make Alexa and Siri speak with each other: Toward a universal grammar in AI
Speakers Tobias Martens (whoelse.ai)
Conference O’Reilly Artificial Intelligence Conference
Conf Tag Put AI to Work
Location London, United Kingdom
Date October 15-17, 2019
URL Talk Page
Slides Talk Slides

Universal Namespace (UNS) is a new kind of cocreation lab for deep tech innovations. Join Tobias Martens to discover the company’s first project, whoelse.ai—a universal grammar to make AIs talk to each other. It’s also a privacy-by-design standard for language processing in online services and the first intent-marketplace for affiliates. Together with DIN e. V., the public agency for industry standards in Germany, UNS is developing a standardized API norm for NLP AIs. The goal of this DIN (later: ISO) standard is to make NLP systems interoperable and voice commands, as privacy-by-design encoded intents, dispatchable across different AI ecosystems. Bias is a huge challenge for interpreting human language in AI, and different NLPs recognize the same voice command each at least slightly differently. Similarly, every user speaks differently—human language is arbitrary. New ideas to efficiently compute human language in AI are needed. Just as hypertext had to be invented for text internet, solutions are needed to make the content of human language linkable as voice internet. Tobias outlines UNS’s findings in developing a publicly accredited standard for human language in AI and presents the company’s first results. As part of the standardization process, the company currently organizes a multistakeholder working group together with partners from industry and academia. Insights from the standardization commission’s work provide a snapshot of current industry NLP R&D challenges and collaboration opportunities, an introduction to the implications of explainable language in AI for technology governance, and outline new approaches toward the standardization of human language from a social science point of view. Get involved in the development of the standard and check out further information and background information.

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