The Inefficiencies of Using a Machine for Translation

The Shallowness of Google Translate
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Hofstadter has outlined the inefficiencies of using the machine for translation. This article gives a comprehensive review of how machine translators and human translators compare. At the beginning of the article, he came across his friends’ guest. The guest was Danish while Frank majorly relied on English (Hofstadter, 2018, p. Parag.1). In the event of getting to communicate, both of them resort to the Google Translate for chit-chat across the two languages. To his amusement, the two were intelligent but still used the automated translator to communicate. As the article articulates, the author has developed much skepticism an about Google Translate and other engines. In his defense, he argues that human translators are unmatched. Such individuals can convey the same message with its originality in entirety.
This article uses a statement made by Norbert Wiener, who was a machine-translation advocate in the 1940s. He stated that “no reasonable person thinks that a machine translation can ever achieve elegance and style.”  (Hofstadter, 2018, p. Parag.3). This statement shows that human translators keep the language simple with both elegance and style. Since the days of the advocate, there has been a tremendous improvement in the efficiency of translation engines. However, this implies that human translators are becoming marginalized over time. This group of people would only remain with simple tasks such as glitch fixers as well as mere controllers. This is contrary to the expectation of the author in the sense that machines cannot outdo humans. Therefore, human translators should have the opportunity to produce fresh new tests in the process of translation, a task entrusted to automated engines.
The replacement of human translators with inanimate objects comes at a cost. The human translators are likely to suffer a mental breakdown, loss of experience in life over the years and loss of creativity. However, human translators still stand a chance against the machines. According to the author, most words put across the engines lose intended meaning as soon as they are decoded in the desired language. This is one of the reasons why people would still require translators. For instance, he checks out a paragraph from English to French. First, he breaks down the word “deep” as used by Google to describe the machine translator as DeepMindCITATION Dou18 \p "Parag. 2" \l 1033 (Hofstadter, 2018, p. Parag. 2). From a different standpoint, one would imagine how powerful the engine has become. On the contrary, the word has been used simply because the neural network has a greater number of layers as compared to older versions.
Without discrediting Google Translate for the operations it can perform, the author indicates that the engine is globally accessible at no fees. Additionally, it can translate over 100 languages via the internet. He argues that the translator has ever...