Скачать или смотреть In 2017, AI Translation Needed Labs. Today It Runs on Your Phone | Dario Fanucchi @UCT ( Part 6)

In 2017, machine translation was one of the hardest challenges in AI. Building a system meant aligning millions of sentence pairs, often by hand, with dozens, sometimes hundreds, of linguists and engineers fine-tuning model outputs line by line. It was expensive and slow. At the time, neural networks were just beginning to replace statistical models and the shift laid the foundation for modern machine learning as we know it.

Fast forward to 2025, translation isn’t just cheaper, it’s radically more scalable. Today’s models like @Meta AI and @Google Research, like large-scale Transformers—learn from billions of multilingual sentences, with zero-shot capabilities that let them translate between languages they’ve never even seen paired together. We no longer need parallel corpora for every pair. Systems now learn meaning, structure, and intent, not just surface patterns. And they can translate spoken words in real-time, on-device, without needing internet or cloud compute.

What once required a full research lab can now run on your phone or be accessed through an API. At Isazi, we focus on using that same level of intelligence to solve real problems from supply chain planning to operational efficiency. We're not trying to make the tech sound bigger than it is, we're using it where it matters and making sure the next generation of scientists knows how to do the same.

#AI #MachineTranslation #DeepLearning #Transformers #IsaziAI #LanguageModels #AppliedAI