Spence shares his experience creating a product around human-in-the-loop machine translation, and explains how machine translation has evolved over the years.
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Spence Green is co-founder and CEO of Lilt, an AI-powered language translation platform. Lilt combines human translators and machine translation in order to produce high-quality translations more efficiently.
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🌟 Show notes:
- http://wandb.me/gd-spence-green
- Transcription of the episode
- Links to papers, projects, and people
⏳ Timestamps:
0:00 Sneak peak, intro
0:45 The story behind Lilt
3:08 Statistical MT vs neural MT
6:30 Domain adaptation and personalized models
8:00 The emergence of neural MT and development of Lilt
13:09 What success looks like for Lilt
18:20 Models that self-correct for gender bias
19:39 How Lilt runs its models in production
26:33 How far can MT go?
29:55 Why Lilt cares about human-computer interaction
35:04 Bilingual grammatical error correction
37:18 Human parity in MT
39:41 The unexpected challenges of prototype to production
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We've got an expert on AI powered translation joining us on the show today. Spence Green is CEO at Lilt, a Silicon Valley based AI-powered enterprise translation software and services company. Lilt's mission is to make more of the world's information accessible to more of the world's people, regardless of where they were born and what language they speak. Spence is here to talk to us about how they're doing it.
In the inaugural TWiML Live, Sam Charrington is joined by Amanda Askell (OpenAI), Anima Anandkumar (NVIDIA/CalTech), Miles Brundage (OpenAI), Robert Munro (Lilt), and Stephen Merity to discuss the controversial recent release of the OpenAI GPT-2 Language Model.
We cover the basics like what language models are and why they’re important, and why this announcement caused such a stir, and dig deep into why the lack of a full release of the model raised concerns for so many.
In this episode, I chat with Rob Munro, CTO of the newly branded Figure Eight, formerly known as CrowdFlower. Figure Eight’s Human-in-the-Loop AI platform supports data science & machine learning teams working on autonomous vehicles, consumer product identification, natural language processing, search relevance, intelligent chatbots, and more. Rob and I had a really interesting discussion covering some of the work he’s previously done applying machine learning to disaster response and epidemiology, including a use case involving text translation in the wake of the catastrophic 2010 Haiti earthquake. We also dig into some of the technical challenges that he’s encountered in trying to scale the human-in-the-loop side of machine learning since joining Figure Eight, including identifying more efficient approaches to image annotation as well as the use of zero shot machine learning to minimize training data requirements. Finally, we briefly discuss Figure Eight’s upcoming TrainAI conference, which takes place on May 9th & 10th in San Francisco. Train AI you can join me and Rob, along with a host of amazing speakers like Garry Kasparov, Andrej Karpathy, Marti Hearst and many more and receive hands-on AI, machine learning and deep learning training through real-world case studies on practical machine learning applications. For more information on TrainAI, head over to figure-eight.com/train-ai, and be sure to use code TWIMLAI for 30% off your registration! For those of you listening to this on or before April 6th, Figure Eight is offering an even better deal on event registration. Use the code figure-eight to register for only 88 dollars. The notes for this show can be found at twimlai.com/talk/125.
