News Analysis
Meta launched its multilingual and multimodal SeamlessM4T model on Aug. 22 to allow people to communicate through speech or text in close to 100 different languages. A group of business and artificial intelligence (AI) leaders believe the open-source AI translation model presents new possibilities for employees in the digital workplace.
Here are the key details on the foundational SeamlessM4T model — and various projections on its effects on the workplace that executives shared with Reworked.
Multimodal AI Translation
The SeamlessM4T model offers several core elements for translating and transcribing languages, including:
- Speech recognition: for nearly 100 languages
- Speech-to-text translation: for nearly 100 input and output languages
- Speech-to-speech translation: for nearly 100 input languages and 36 output languages
- Text-to-text translation: for nearly 100 languages
- Text-to-speech translation: for nearly 100 input languages and 35 output languages
In developing the AI translation model, Meta worked to reduce instances of toxicity and gender bias, noting it will “continuously improve” SeamlessM4T, according to a Meta blog post.
The M4T in the name stands for Massively Multilingual Multimodal Machine Translation.
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Foundational AI Model for Translation
Researchers and developers can “build on” the open-source SeamlessM4T model under the Creative Commons license CC BY-NC 4.0 — a move that is “in keeping with our approach to open science,” the blog states.
The open-source license includes access to Meta’s SeamlessAlign corpus, a data set with 270,000 hours of mined speech and text alignments.
One thing to note here is the NC of the Creative Commons license. Although anyone is free to build on and experiment with the model, the results cannot be used for commercial purposes.
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Future Workplace Uses of the AI Translation Model
Meta didn’t disclose any immediate commercial applications for SeamlessM4T. However, members of Meta’s SeamlessM4T team discuss potential uses for the model in a 102-page research paper.
The paper mentions several possible language-centric technologies that “downstream applications supported by SeamlessM4T” could complement to enable a “greater variety of multilingual experiences and in ways that feel more natural and dynamic.”
Speech interfaces
- Audio assistants
- Voice memos
- Live transcriptions
Auditory content
- Podcasts
- Audio books
- Long-form videos
In the paper, the SeamlessM4T team stresses that applications built with the model should be viewed as an “augmentation device” rather than a tool that “replaces the need for language learning or reliable human interpreters,” particularly in “high-stakes situations” involving medicine and law.
Meta called SeamlessM4T “only the latest step in our ongoing effort to build AI-powered technology that helps connect people across languages.”
“In the future, we want to explore how this foundational model can enable new communication capabilities — ultimately bringing us closer to a world where everyone can be understood,” Meta said.
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SeamlessM4T has the potential to help companies communicate more effectively with their employees, “regardless of their native language,” impacting HR communications and employee experience in positive ways, said Carolyn Clark, VP of employee experience (EX) strategy at Simpplr.
“This is a barrier many have been trying to solve at high expense and time,” she continued. “This new innovation opens the door to bolster inclusion at work by minimizing language barriers and promoting more effective cross-language communication, especially within a global landscape, creating a more cohesive and productive workplace.”
Clark shared the example of a company with employees spread across different time zones may have to schedule multiple meetings to accommodate everyone’s schedules. With SeamlessM4T-based tools, employees could participate in a single meeting with automatic translation, “reducing the number of meetings and freeing up more time for productivity."
Steve Toy, CEO of language learning app Memrise, called Meta’s multilingual and multimodal SeamlessM4T, “Quite exciting for one primary reason – it brings us another step closer to a ‘Star Trek’-like ‘universal translator.’”
“The real advancement is the ability to switch between languages in real-time, which solves a problem that until now didn’t have a good solution,” he said.
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By combining tasks that once required separate instructions and calls to separate services, SeamlessM4T adds speed and efficiency to any service it is integrated with, he continued.
It's easy to imagine how this sort of model could be deployed to offer various benefits in the workplace, said Toy, including: breaking down language barriers between collaborators and facilitating global mobility and inclusivity; enhancing learning by democratizing access to educational materials, such as content from other cultures; improving collaboration in the workplace as international teams work more fluidly; and language barriers would no longer hinder meetings, brainstorming sessions, and day-to-day communications.
However, Toy noted “some downsides” to this type of technology: taking incentive away from people to learn a new language; regardless of how advanced, the model can miss cultural nuances, idioms, or connotations; and private conversations may be subject to privacy/data disclosure issues.
SeamlessM4T has “immense potential” in the digital workplace, particularly in enhancing cross-language communication and collaboration, said Yellow.AI co-founder and CEO Raghu Ravinutala.
“Its distinctive ability to recognize code-switching facilitates natural and seamless conversations across languages, reducing global language barriers and fostering inclusive conversations,” he said.
The AI translation model would remove obstacles standing in the way of communication collaboration among employees with diverse linguistic backgrounds, he continued. "One of the most common workplace issues is a lack of coordination between different teams. But what happens if language barriers bar that inter-team collaboration? The answer lies in the continued research and development of AI models like SeamlessM4T.”
SeamlessM4T could be a vital resource for international expansion, streamlining the localization and globalization processes and “significantly benefiting” employee experience and performance, Ravinutala said.
Instant translations supported by the model could also enhance EX user friendliness and accessibility for a diverse workforce, he said.
“This can lead to higher adoption rates and greater satisfaction with digital solutions as content is presented in employees' preferred languages,” Ravinutala said.
However, Gleen co-founder and CTO Nagendra Kumar cautioned SeamlessM4T’s impact on employee learning will be a “double-edged sword.”
“People will learn more about the subject matter being taught — through better translation,” he said. “But, in the long term, people will be far less likely to learn foreign languages. Why bother learning a foreign language when you have SeamlessM4T?"
He did however note the benefits for multinational companies, namely accurate multimodal translation could mean seamless work across borders and time zones as well as expand the potential hiring pool for companies that want to expand their global teams.
Renaud Charvet, co-founder and CEO of Ringover, said SeamlessM4T could “mark something of a revolution in the potential for receiving and fully understanding international communications.”
Charvet added that Meta’s SeamlessM4T could “make a real difference” in workplace collaboration, allowing for the sharing of ideas and discussions, regardless of workers’ language proficiency.
