When large organizations require translation services, there’s no room for the amusing errors often produced by automated apps. That’s where Lilt, an AI-powered enterprise language translation company, comes in.
Lilt CEO Spence Green spoke with AI Podcast host Noah Kravitz about how the company is using a human-in-the-loop process to achieve fast, accurate and affordable translation.
Lilt does so with a predictive typing software, in which professional translators receive AI-based suggestions of how to translate content. By relying on machine assistance, Lilt’s translations are efficient while retaining accuracy.
However, including people in the company’s workflow also makes localization possible. Professional translators use cultural context to take direct translations and adjust phrases or words to reflect the local language and customs.
Lilt currently supports translations of 45 languages, and aims to continue improving its AI and make translation services more affordable. The company is a member of NVIDIA Inception, a program that helps startups during critical stages of product development, prototyping and deployment.
Key Points From This Episode:
- Green’s experience living in Abu Dhabi was part of the inspiration behind Lilt. While there, he met a man, an accountant, who had immigrated from Egypt. When asked why he no longer worked in accounting, the man explained that he didn’t speak English, and accountants who only spoke Arabic were paid less. Green didn’t want the difficulty of adult language learning to be a source of inequality in a business environment.
- Lilt was founded in 2015, and evolved from a solely software company into a software and services business. Green explains the steps it took for the company to manage translators and act as a complete solution for enterprises.
“We’re trying to provide technology that’s going to drive down the cost and increase the quality of this service, so that every organization can make all of its information available to anyone.” — Spence Green [2:53]
“One could argue that [machine translation systems] are getting better at a faster rate than at any point in the 70-year history of working on these systems.” — Spence Green [14:01]
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