The Linguist

The Linguist 57,4 - August/September 2018

The Linguist is a languages magazine for professional linguists, translators, interpreters, language professionals, language teachers, trainers, students and academics with articles on translation, interpreting, business, government, technology

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8 The Linguist Vol/57 No/4 2018 Christopher Barnatt asks whether artificial intelligence poses a real threat to translators' livelihoods A s 2020 approaches, we stand at the dawn of the Cognitive Computing Age. What this means is that a few years from now every type of digital technology will be able to possess internally, or access remotely, some level of artificial intelligence (AI). In turn, this will allow computers to undertake a widening range of cognitive tasks, including complex natural language processing (NLP) and translation. It is critical to understand that most AIs do not follow hard-and-fast rules. Rather, many of the latest systems are based on artificial neural networks (ANNs). These employ layers of 'artificial neurons' that learn to recognise and react to specific patterns of data input. A lot of ANNs also employ 'deep learning'. This means that their design includes many layers of 'hidden' neurons, which establish complex patterns of connection that are very hard for a human being to interpret. In some senses, ANNs function a bit like the human brain, with many able to make decisions by comparing data input to vast stores of learnt or sample data. Future translation AIs may, for example, draw on their knowledge of hundreds of thousands of books in order to deliver the most fluid and accurate output. Many reputable organisations have attempted to predict the impact of AI. In 2016, Citi and the Oxford Martin School estimated that about 35% of jobs in the UK are "at risk of automation". 1 A year later, a report from PwC was in broad agreement, noting that 30% of UK jobs are "susceptible to automation" by 2030, 2 although PwC did stress that many jobs are destined to change, rather than be lost, due to the rise of AI. When considering the impact of AI on employment, we would be wise to focus on the range of work tasks most likely to be automated, rather than the potential replacement of entire jobs. Using this approach, my own estimate is that around 20% of work tasks will be candidates for AI automation by 2030, with these activities spread across at least 80% of human occupations. I would suggest that only a small percentage of people will lose their job to AI. However, the vast majority of us will see our work altered and restructured to some extent, as cognitive computing takes hold. The advance of AI Already AI is advancing rapidly and radically. The development of artificial general intelligences (AGIs) able to 'think' and act like humans remains a distant pipedream, yet AIs do not need to mimic people in order to automate a wide range of cognitive tasks. Recently, AI has started to be delivered over the internet. For example, IBM, Google, Microsoft and Amazon now offer cloud AI services using standardised APIs (application programming interfaces) that are turning AI into a pay-as-you-go, plug-and-play utility. Only two or three years ago, anybody wanting to integrate AI into their business needed deep pockets and a technical knowledge of machine learning (ML) techniques. Today, new users simply require some basic coding skills, and either a purchase order or a credit card that will be charged on a per-transaction basis. The cloud AI services on offer from the goliaths of computing include language processing and translation utilities that can be plugged together like Lego. IBM, for example, has invested more than a billion dollars in an AI platform called Watson. This includes modules called Language Translator, Natural Language Classifier, Natural Language Understanding and Tone Analyzer. These facilitate the interpretation and translation of text in 13 languages, with the AI able to recognise and understand the emotion, sentiment and communication style within a passage of text. Watson has been used to create chatbot interfaces for the Royal Bank of Scotland in the UK, and Staples in the US. Google indicates how its cloud AI services can be used to "inject AI into your business". For example, the company's Natural Language API can be employed "to reveal the structure and meaning of text", while the Google Cloud Translation API can be plugged into any website or AI module to offer translation in more than 100 languages. Back in its early days, Google Translate was not very accurate. But the self-learning system is vastly improved, with Google charging just US$20 for the translation of 1 million characters. Microsoft's online cognitive services are offered from its Azure cloud platform. Current modules include Language Understanding (LUIS), the Translator Text API and Linguistic Analysis API. Again, these can be mixed with other components (including decision-making, Replaced by the robot © SHUTTERSTOCK

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