In this week’s SlatorPod, Fireflies.ai CEO Krish Ramineni joins us to talk about scaling the AI meeting assistant and building on the latest advances in large language models.

Krish starts with his journey to co-founding Fireflies, which began as a drone delivery service and as a result of conversations with customers and investors, evolved into an AI meeting assistant to solve their own pain point.

The CEO shares how they found their product-market fit after focusing on automated transcripts over human-assisted note-taking. He discusses the early days of AI investment and how with the rise of APIs and large language models (LLMs), you no longer need multiple PhDs to attract investors.

Krish explains how Fireflies leverages technologies like Whisper to improve their language transcription, allowing them to be more accessible to global companies. He talks about their decision to improve their Super Summaries feature through GPT technology.

The CEO shares his excitement about the potential for LLMs and how Fireflies are building a Chrome extension that uses LLMs to summarize any article or video on the internet. He advises that simply building a wrapper on top of OpenAI is not a defensible moat for companies, but rather you should build a unique platform with a unique angle into the industry you’re selling to.

Kirsh talks about the current fundraising environment where there is a lot of money being thrown around for generative AI companies, but only a few will weather the storm. When it comes to hiring machine learning talent, Krish doesn't believe in prompt engineering and also holds the view that machine learning companies may no longer need to hire large cohorts of ML PhDs to scale.

The pod rounds off with the company’s roadmap for 2023, which includes creating an ecosystem of extensions on top of Fireflies. These extensions will offer powerful functionalities to users in different sectors like healthcare and recruiting. 

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.

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.

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.

In this week’s SlatorPod, we are joined by Nikola Nikolov, an experienced researcher, engineer, YouTuber, and consultant in natural language processing (NLP) and machine learning.

Nikola talks about the evolution of large language models (LLMs), where the core technology remains the same, but the number of parameters has grown exponentially and the capacity to fine-tune models on human data via reinforcement learning from human feedback has turbocharged the models’ capabilities.

Nikola unpacks the rapid increase in front-end use cases with companies like Google and Microsoft already integrating LLMs into their products. At the same time, he speculates about what will happen to the hundreds of startups that are using APIs to build similar tools like writing assistance or summarization.

Nikola shares the limitations of an API-only approach, which include using a model limited in data it has collected from the internet and that is not fine-tuned to a domain or specific use case.

He discusses how LLMs perform when it comes to machine translation (MT). Although GPT is trained on large amounts of multilingual data, it’s not specialized in translation, so machine translation providers will retain their edge over ChatGPT for now.

Nikola predicts two different scenarios when it comes to the future of LLMs: the first is where large corporations quickly integrate LLMs into their products, competing with startups and putting many of them out of business. The second scenario is where startups will create novel use cases and integrate multimodal technology to build something completely new and different from big companies.

Florian and Esther are back for a packed news pod. The two catch up with OpenAI announcing the release of its much anticipated GPT-4 large language model. They unpack OpenAI’s research on GPT or GPT-powered systems’ impact on the labor force, including specifics around how exposed translators and interpreters are to large language models.

Over at the University of Edinburgh, researchers looked at hallucinations in large multilingual translation models. The research looked at hallucinations across models of different scale, translation directions, and data conditions.

Lilt announced the launch of Contextual AI Engine, a new GPT-style model which Lilt claims has higher accuracy than Google Translate and GPT-4 for many enterprise contexts. Esther talks about Acolad appointing Bertrand Gstalder as the Super Agency’s new CEO.

At the Google for Games Developer Summit 2023, the Tech Giant announced that developers now have access to free machine translation (MT) for their Android apps. Keynote Speaker, Greg Hartrell, also revealed an early access program for machine translation in the Play Console.

Meanwhile, Disney lawyers have filed a subpoena request to force Reddit to identify the person or people responsible for leaking a movie dialogue transcript. It was revealed that a Reddit community shared a Google Doc transcript in Portuguese of the film “Ant-Man and the Wasp: Quantumania” weeks before it was released.

Voiseed CEO and Co-founder Andrea Ballista joins SlatorPod to discuss the machine dubbing startup’s approach to operating and developing their AI-based virtual voice engine, Revoiceit.

Andrea talks about how his passion for music as a child led him to founding audio localization studio Binari Sonori, which he sold to Keywords Studios in 2014, and why he is now launching Voiseed.

He shares his impressions from this year’s Game Developers Conference where there was a lot of interest in new technologies, voice cloning solutions, and the development of emotional synthetic voices.

Andrea unpacks Revoiceit’s ability to understand the voice and emotional profile of the user and transfer both profiles into the target language. Voiseed has been profiling vocal delivery and creating a data set, so the system can have a wider knowledge of human emotion in terms of voice and language.

On the topic of large language models (LLMs), Andrea is not worried about the implications of LLMs like ChatGPT as they have had two years to build a dataset on vast amounts of voice content. He talks about Voiseed’s early financial backers and shares the story behind applying for blended finance (grants and equity) from the European Innovation Council Fund and LIFTT.

Andrea shares his experience hiring during the lockdown and their approach to investing in ‘cool’ people and helping them grow through their incentive plan. The pod wraps up with Voiseed’s product roadmap, where they aim to improve features with more emotion control and work on how to create new voices that can be used in multiple projects.

Liane Kirsch, CEO of Cinescript, joins SlatorPod to talk about specializing in the audiovisual industry and working with global TV and film productions.

Liane discusses her route into the language industry, from studying philology and translating contracts to working as an on-set interpreter and dialogue coach for international productions. Throughout this journey she founded Cinescript and for five years she built her client base parallel to her other work.

Liane shares her experience as a polyglot fluent in six languages and how she uses the melodies of languages in her learning process. She talks about the complexities behind translating screenplays, where the process involves one translator and one proofreader each for the source and target language.

She reveals how they supported the production of Netflix’s series 1899 by translating the synopsis, polishing 8 different screenplays, dialogue coaching for Cantonese, and working on additional dialogue recording in post-production.

The pod rounds off with Liane’s initiatives for 2023, including offering internships for international students and strengthening their main team in the office. 

Mark Howorth, CEO of VSI, joins SlatorPod to talk about his plans for leading and scaling the media localization provider.

Mark discusses his route into the media and entertainment (M&E) and language space as well as his path to joining VSI. Mark took up his role at the leading media localization provider in January 2023 after spending five years as the CEO of SDI Media including overseeing the company’s sale to Iyuno in 2021. He outlines how the media localization industry has evolved with the streaming boom impacting turnaround times.

Mark also shares some of the challenges of hiring and retaining media localization talent internally and externally post-Covid. He offers his thoughts on the use of machine translation (MT) and automatic speech recognition (ASR) as productivity enhancers, rather than a replacement for human subtitling.

Mark talks about VSI’s historically self-funded growth and highlights the company’s plans to scale further in 2023 and beyond. Mark shares some of the growth drivers behind VSI on the back of +20% growth in 2022.

The CEO reveals key initiatives for VSI in 2023, such as building dubbing capacity, widening its customer base, and geographical expansion. The pod rounds off with Mark’s 2-3 year outlook on the media localization market in terms of customer demands. 

In this week’s SlatorPod, we are joined by David Orrego-Carmona, Assistant Professor at the University of Warwick to discuss his research on language technologies, audiovisual translation, and users of translation.

David shares his background studying translation in Colombia and how it led to the formation of a research group focusing on audiovisual translation and subtitling. He outlines how he is developing Translation Studies at Warwick, not only to teach students about translation and culture, but so they can have a direct link to the industry.

David reveals the key findings from his PhD on the production of interlingual subtitles, where he used eye-tracking to track reading behaviors of non-professional and professional subtitles. He talks about how multilingual content like Netflix’s Sense8 and 1899 is changing the perception of subtitling.

David challenges the idea of the invisibility of subtitles as users between the age of 18 and 24 in the UK are more likely to use intralingual subtitles in English.

David gives his thoughts on the age-old debate of subtitling versus dubbing, where there is no right answer as both modes of translation are efficient and can convey meaning. He talks about how non-professional translators are implementing machine translation in a more informed and educated way through pre-editing.

David discusses why it’s important for students to learn about the requirements of different media in subtitling, including short-form content like YouTube and TikTok. He touches on the impact of ChatGPT on academia, from plagiarism to integrating large language models into the curriculum.

The pod rounds off with David’s current research projects, the first on understanding how people watch subtitles and the second on how machine translation is used by local authorities, NGOs, and charities in the UK.