#199 The State-of-the-Art in Machine Translation with Language Weaver’s Bart Maczynski

SlatorPod

In this week’s SlatorPod, we are joined by Bart Maczynski, the VP of Machine Learning at Language Weaver, the translation tech brand of Super Agency RWS, to talk about the challenges and advancements in enterprise-grade machine translation (MT).
The discussion delves into the distinctions between enterprise and consumer-grade MT, with challenges including data security, scalability, adaptability, user experience, and risk mitigation.
Bart touches on the impact of large language models (LLMs) on the landscape, noting potential risks, such as deceptive fluency, and the need for control in enterprise settings.
The VP discusses the recent launch of Evolve, an automated post-editing solution that combines auto-adaptive neural MT, machine translation quality estimation, and a secure, private LLM.
Bart talks about the evolving landscape of language AI and the integration of MT into broader workflows, driven by innovations in orchestration and automation platforms.
Bart shares insights into the future plans of Language Weaver, with a primary focus on bringing Evolve to the market and broadening its applications, supporting more languages, and exploring improvements and adaptations in various components.

Kapitel

1. Intro (00:00:00)

2. Career Background (00:00:44)

3. Language Weaver Journey (00:01:52)

4. Interesting Client Projects (00:05:24)

5. Government Solutions (00:07:21)

6. Enterprise vs. Consumer-Grade MT (00:09:47)

7. Cloud vs. Edge vs. On-Prem (00:11:55)

8. Top Changes with MT Since ChatGPT (00:15:02)

9. Multilingual eDiscovery Solutions (00:19:58)

10. Evolve Launch (00:22:17)

11. Open-Source Movement in Machine Translation (00:26:49)

12. Impact of Large Language Models (00:29:19)

13. Multimodal Machine Translation (00:35:37)

14. Roadmap for 2024 (00:36:56)