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Futuregazing translationRoberto Silva, April 2015

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How translators used to work in the past?

Antonio da Fabriano II: St. Jerome in study (The Walters Art Museum).

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How translators used to work in the past?

Source: http://www.ucg.org/vertical-thought/types-of-bible-translations

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How translators used to work in the past?

Source: Champollion’s Description of the Ptolemy and Cleopatra Cartouche. http://traveltoeat.com/the-rosetta-stone-british-museum-london/

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How translators work today?

Local Terminology search (dictionaries, glossaries)

Online Terminology search (databases, translation resources)

Reference documents

Online collaborative translation

Project management, including quoting and invoicing

Online and offline research

CAT tools, including translation memory

Machine translation

Dictation

Training

Solve issues (file format, DTP, etc.)

Certifications

Marketing, social networking

Source: Google image search

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Is it enough?

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Why?

Source: DOMO (http://www.domo.com/blog/2014/04/data-never-sleeps-2-0/, data compiled from many original sources)

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Present efforts

Cloud based collaborative translation, sharing

translation and terminology

Authoring + Translation + Revision in parallel

Learning from other industries and cooperating with

them

Working together customers, agencies, translators,

researchers, developers

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Present efforts

Technology convergence: touch translation + voice

recognition + eye tracking + AI driven big data analysis

Deep learning (machine learning from data)

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Disruptive technologies

Mobile computing

Wearables (smart bands, watches, glasses, clothing, furniture)

Roll-up digital screens (Source-Gated-Transistor based or similar) and graphene-based apps

Body embedded nanotechnology

Universal translator as a goal for helping humans

Permanent connectivity

Source: Own research

3D aware devices will create new services

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Disruptive trends

Kids are 100% digital. They will drive the new generation of products and will expect them to be fully and instantly multilingual.

Security will require complex interaction between human translators and computers

As companies become more and more global, shareholders and decision makers will require translation of all kind of materials at a much faster rate

On demand services and applications will require new translation services, included online help

Webrooming will become more common than showrooming. Millennials prefer it.

Younger generations use privacy to exchange. Traditional view on privacy is gone.

Open source in translation

Source: Own research

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Why translators won’t be

replaced (soon)

Disfluency (the difference between how people write and talk) makes voice recognition very difficult

Humans understand a concept that does not have direct translation or does not exist in another language. Computers don’t.

Mixing speech recognition, machine translation and human translation today is not achievable

Skype translator and Microsoft Translator are not good enough

Persuasive technology needs human translation

Virtual reality will require demanding translation and interpreting services

Source: Own research

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The future?

Translation (text/voice) embedded in every device

Multimodal (including Brain to computer interaction),

multidevice, multisource translation input

Automatic translation of adapted content based on

human profile recognition

Real time high quality cross language information

retrieval

Real-time on demand translation

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Google Word lens

Source: https://plus.google.com/explore/wordlens

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Deep learning

Source: Own research

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Deep learning

Source: Own research

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Deep learning

Source: LEVAN project, http://levan.cs.washington.edu/

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Deep learning

Source: Google research blog, http://googleresearch.blogspot.co.uk/2014/11/a-picture-is-worth-thousand-coherent.html

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Deep learning

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Deep learning

Source: Microsoft Image Recognition research

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Deep learning

Source: http://www.freegreatpicture.com/kitty/cat-and-goldfish-3062

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Deep learning

Real time examples:

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Roberto Silva

roberto.silva@conversis.com