How Has Deep Learning Impacted the Translation Industry?

Deep Learning

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Deep learning is a subset of advanced machine learning which uses artificial neural networks. The Asia Pacific region is utilizing deep learning not only in electronics but also across medical and automotive industry. The focus of major market players has been in adopting new product developments, product launches, partnerships, and collaboration as key strategies for market growth.

The deep learning market is anticipated to grow in the forecast period owing to driving factors such as growing usage of deep learning in big data analytics along with rapid adoption of cloud-based technology. Moreover, increasing focus on Artificial Intelligence in customer-oriented services is expected to boost the market growth. However, lack of standards and protocols may hamper the growth of the deep learning market during the forecast period. On the other hand, limited structured data is likely to create demand for deep learning solutions in the coming years.

The global deep learning market is segmented on the basis of component, application, and industry vertical. Based on component, the market is segmented as hardware, software, and services. On the basis of the application, the market is segmented as signal recognition, image recognition, data mining, and others. The market on the basis of the industry vertical is classified as automotive, manufacturing, healthcare, BFSI, and others.

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Some of the leading players in global deep learning industry are Amazon, Google, IBM, Intel, Microsoft, NVIDIA, QUALCOMM, Samsung, Sensory, Xilinx

Deep Learning definition aside, the potential for the deep neural network to crack machine translation is clear. The issues with machine translation have traditionally been around the poor quality of its results in terms of word choice, grammar, and sentence structure. Essentially, machine translation software delivers language that doesn’t sound natural, despite being fed tens of thousands (if not more) examples of written language.

Neural machine translation, which replaced the use of statistical machine translation back in 2015 and marked a significant leap forward, as a result, is therefore incredibly exciting. However, it still requires the machine to be fed comparable sentences in each of the languages it learns in order to translate them.

Source: The Insight Partners