SlatorPod - #162 The Great ChatGPT and Translation Debate

#162 The Great ChatGPT and Translation Debate

04/19/23 • 67 min

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This week, SlatorPod hosts its very first panel debate with guests Adam Bittlingmayer, CEO of ModelFront, Varshul Gupta, Co-founder of Dubverse, and Mihai Vlad, General Manager of Language Weaver.
To start off, the panel participants reflect on their recent experience with ChatGPT since its launch in November 2022 and how this shapes their views on large language models (LLMs). Varshul and Adam talk about how clients view ChatGPT.
Mihai agrees with the idea that the language services industry is exceptionally well-prepared for the launch of ChatGPT due to its experience with human-machine interaction. Varshul discusses how LLMs have influenced startups like Dubverse to build prototypes that can handle edge cases.
Mihai shares the challenges of deploying LLMs in large enterprises. Adam and Varshul highlight how parameters such as security, data privacy, latency, throughput, and cost are essential to consider in an enterprise setting.
Varshul and Mihai talk about the potential of multilingual content generation from scratch and how it will affect production costs. Varshul shares how they continue to attract users throughout this AI hype and the importance of adding a UX on top of LLMs.
Adam discusses the potential for LLMs to assist translators in their work, although the implementation of this tech may take some time to become the new normal. Varshul and Mihai debate how services-focused companies should react to the rapid advancements in LLMs, whether you wait to see how things pan out or go all in to stay ahead of the curve.
The panel rounds off with emerging use cases for LLMs, from building prompt-based systems for more concise translations to addressing long-tail languages that are often overlooked by machine learning due to the fragmentation of the language industry.

Previous Episode

#161 Microsoft’s Christian Federmann on the Translation Quality of Large Language Models

In this week’s SlatorPod, we are joined by Christian Federmann, Principal Research Manager at Microsoft, where he works on machine translation (MT) evaluation and language expansion.
Christian recounts his journey from working at the German Research Center for Artificial Intelligence under the guidance of AI pioneer Hans Uszkoreit to joining Microsoft and building out Microsoft Translator.
He shares how Microsoft Translator evolved from using statistical MT to neural MT and why they opted for the Marian framework.
Christian expands on Microsoft’s push into large language models (LLMs) and how his team is now experimenting with NMT and LLM machine translation systems. He then explores how LLMs translate and the role of various prompts in the process.
Christian discusses the key metrics historically and currently used to evaluate machine translation. He also unpacks the findings from a recent research paper he co-authored investigating the applicability of LLMs for automated assessment of translation quality.
Christian describes how Microsoft’s custom translator fine-tunes and improves the user’s MT model through customer-specific data, which degrades more general domain performance.
He shares Microsoft’s approach to expanding its support for languages with the recent addition of 13 African languages. Collaboration with language communities is an integral step in improving the quality of the translation models
To round off, Christian believes that the hype around LLMs may hit a wall within the next six months, as people realize the limitations of what they can achieve. However, in a year or two, there will be better solutions available, including LLM-enhanced machine translation.

Next Episode

#163 The Future of Live Multilingual Captioning Ai-Media CEO Tony Abrahams

Tony Abrahams, CEO and Co-founder of Ai-Media, joins SlatorPod to talk about the journey to building a market leader in multilingual live captioning.
Tony discusses his transition from working in finance to co-founding Ai-Media with Alex Jones and introducing large-scale captioning to Australian Pay TV. He gives an overview of Ai-Media’s technology stack, which delivers high-quality automatic captioning through three key elements: encoding, the iCap network, and LEXI.
The CEO talks about the use of respeaking versus LEXI in settings where captioning accuracy is critical, and where there are multiple speakers, mixed-quality audio, or background noise. He discusses how Ai-Media measures live-captioning quality using the NER model, which weights the types of errors as editing errors or recognition errors.
Touching on the multilingual component of Ai-Media, Tony explores the possibility of using AI instead of respeakers and having a fully-automated translation product in the near future. He believes that large language models are an opportunity as the technology has enabled them to interpret sentences more accurately, resulting in a better outcome with LEXI 3.0.
Tony gives his thoughts on growing through M&A and the strategy behind acquiring EEG to gain a competitive advantage in terms of its technology and product suite. He shares his rationale for taking AI-Media public.
The CEO reveals Ai-Media’s roadmap for 2023, such as improving the iCap network and launching the LEXI Library, which allows customers to search their media library by captions.