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Oxford Scientists Predict the Future of Machine Translation

Machine translation is becoming increasingly relevant in the translation industry. As the list of languages supported by Google Translate expands, many media outlets predict the imminent disappearance of the translator profession. However, experts are reluctant to agree with such categorical statements and instead strive to provide a more accurate picture of the future based on existing data.

Researchers from the University of Oxford and the Yale School of Political Science published an article titled "When Will AI Surpass Human Effectiveness? The Opinion of Experts in the Field of Artificial Intelligence." In a survey, 352 machine learning experts attempted to determine when machines will reach the level of HLMI (high-level machine intelligence) and will be able to perform any tasks better and cheaper than humans independently.

Among the diverse tasks, such as "writing a school essay," "assembling any LEGO set," and "driving a truck," machine translation is of particular interest. The scientists identified three types of translation to form a complete picture of the future development of machine intelligence in this area.

Oxford Scientists Predict the Future of Machine Translation

The first type of translation is "amateur," which can be performed by a person fluent in two languages without professional translation skills. The surveyed experts believe that machines will surpass humans in this type of translation by 2024. By 2026, machines will learn to translate speech into new languages better than humans, using only an unlimited number of movies with subtitles. By 2032, according to the respondents, computers will be able to translate English text into a previously unknown language based on a single translated document as effectively as a group of professionals.

These forecasts regarding machine translation cover the relatively near future. The study's authors plan to periodically return to the obtained results to verify their predictions, which will help conclude the success of machine learning development and the forecasting abilities of scientists working in this field. Thus, the results of this study may influence our understanding of the future of the translation profession and the development of machine intelligence as a whole.

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