Revolution #4 Automation & AI
The fiction spun by writers of the past is now becoming a reality woven into our lives. In 1979, “Hitchhiker’s Guide to the Galaxy” playfully imagined the Babel fish’s instant translation ability, unknowingly inspiring future innovators like those at Google. The result? The release of the impressive Pixel Buds—a device not quite magical but certainly adept in languages.
The incredible feats achieved by modern AI often mask the challenging path that led to their existence. To truly understand this journey, let’s glance back, shedding light on the evolutionary road:
In the 1960s and 1970s, Rule-Based Machine Translation (RBMT) emerged, translating word by word with Transfer-Based Machine Translation for syntax correction
The 1980s brought Example-Based Machine Translation (EBMT), using existing examples to guide translation. EBMT could extract a sentence, find a translation match, and navigate nuances with dictionaries.
The 1990s and 2000s introduced Statistical Machine Translation (SMT), a numerical approach. SMT consumed texts in various languages, estimating how words or phrases correlated in meaning.
Word-based SMT focused on single words, evolving in the 1990s and early 2000s to consider word order and contextual words.
Phrase-based SMT took this further, incorporating phrases and contextual cues.
Then came 2016 and Neural Machine Translation (NMT), a seismic shift. Google Translate, powered by NMT, mimicked human brain learning patterns. NMT used neural networks to predict word sequences, understanding their significance in source and target sentences.
NMT was a monumental game-changer, rendering SMT obsolete due to its precision, speed, and growth potential. It transformed translation, driving a wave of NMT integration by providers using human-translated content tailored to specific domains and languages.
But NMT’s potential goes beyond translation, fostering remarkable AI advancements:
- Customer Service: AI-driven chatbots excel in multilingual sentiment analysis, boosting customer service efficiency and empathy.
- Recruitment: AI assigns tasks, like selecting a translator experienced in IPO transactions for a share purchase agreement.
- Machine Quality Evaluation: AI’s maturity could enable self-aware assessment of machine translation quality, flagging areas needing human input.
AI has made significant progress in translation beyond the initial expectations of rule-based machine translation. Yet, amid this advancement, a humbling Google-generated translation from Sesotho to English serves as a reminder that AI’s journey is still a work in progress.
TextUnited navigates the world of machine translation post-editing (MTPE), balancing AI’s speed and precision with human translators’ refinement. This harmony, an industry standard on translation platforms, yields rapid, precise results.
Beyond translation, AI’s rise drives streamlined automation, calculation, and integration, promising an efficient, ingenious future.