"Machine translation (MT) is the application of computers to the task of translating texts from one natural language to another. One of the very earliest pursuits in computer science, MT has proved to be an elusive goal, but today a number of systems are available which produce output which, if not perfect, is of sufficient quality to be useful in a number of specific domains."
– Definition from the European Association for Machine Translation (EAMT).
Artificial intelligence (AI) is surpassing human performance in a growing number of domains. However, there is limited evidence of its economic effects. Using data from a digital platform, we study a key application of AI: machine translation. We find that the introduction of a new machine translation system has significantly increased international trade on this platform, increasing exports by 10.9%. Furthermore, heterogeneous treatment effects are consistent with a substantial reduction in translation costs.
Verdict lists ten of the most popular tweets on artificial intelligence (AI) in August 2020 based on data from GlobalData's Influencer Platform. The top tweets were chosen from influencers as tracked by GlobalData's Influencer Platform, which is based on a scientific process that works on pre-defined parameters. Influencers are selected after a deep analysis of the influencer's relevance, network strength, engagement, and leading discussions on new and emerging trends. Ronald van Loon, principal analyst and CEO of Intelligent World, shared a video from the World Economic Forum on a neural machine translation technology developed by Google to provide natural translation between different languages using artificial intelligence and deep learning. The system was also used to translate two languages without using English as a bridge.
Common sense would tell us that it is necessary to understand a text in order to translate it. So can an artificial intelligence system actually understand a text in the same sense a human being can? The simplest approach to translation would be simply to have a computer translate word-by-word, utilizing a digitalized bilingual dictionary, and ignoring grammatical structures. Needless to say, the results of this simplistic procedure are often incomprehensible and useless. Translating between human languages requires intelligence in some form. An ideal field for AI to flex its muscles! One of the biggest dilemmas for machine translation (MT) lies in the fact that human language is full of ambiguities. The meanings of words or even entire sentences – and hence also their translations – cannot be determined in isolation, but only in context. The latter can include not only other words and sentences in the text, but also knowledge about the subject matter of the text.
In 1993, the Portable Document Format or the PDF was born and released to the world. Since then, companies across various industries have been creating, scanning, and storing large volumes of documents in this digital format. These documents and the content within them are vital to supporting your business. Yet in many cases, the content is text-heavy and often written in a different language. This limits the flow of information and can directly influence your organization's business productivity and global expansion strategy.
Speech recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. Speech recognition has many applications, such as home automation, mobile telephony, virtual assistance, hands-free computing, video games, and so on. This is the application of Speech recognition where the machine converts text into speech so that it could be easily listened. Ex: Speechify is a startup that focuses on creating Audiobooks from any text. Machine Translation (MT) is the task of automatically converting one natural language into another, preserving the meaning of the input text, and producing fluent text in the output language.
An end-to-end, integrated chemical research system unveiled by IBM last week gives us a glimpse of how artificial intelligence, robotics and the cloud might change the future of drug discovery. And it's a good time as any to see some a breakthrough in the field. The world is still struggling with the covid-19 pandemic, and the race to the find a vaccine for the dangerous novel coronavirus has not yet yielded reliable results. Researchers are bound by travel and social distancing limitations imposed by the virus, and for the most part, they still rely on manual methods that can take many years. While in some cases, such delays can result in inconvenience, in the case of covid-19, it means more lives lost.
Facebook continues to pour considerable resources into machine translation (MT); but, as evidenced by a recent Thai translation snafu, language technology remains a major challenge for the social media giant. In addition to improving quality estimation and various other initiatives, Facebook is currently working on two others that share information with the broader open source community, allowing developers to improve the technology. In a July 2020 blog post, Facebook AI made available CoVoST V2, a "massively multilingual" speech-to-text translation dataset. The original CoVoST was built on Mozilla's Common Voice, a database of crowdsourced voice recordings. This new version boasts 2,900 hours of speech, as well as speech translation data from 21 languages into English and from English into 15 languages.
Is the success of Google that of the algorithms or that of data? Today's fascination with artificial intelligence (AI) reflects both our appetite for data and our excitement about the new opportunities in machine learning. Here, I argue that newcomers to the field of data science are blinded by the shiny object of magical algorithms -- and that they forget the critical infrastructures that are needed to create and to manage data in the first place. There are now many companies that provide AI services. An attractive offer should affirm all of the above -- the sole expertise in analyses and algorithms is generally insufficient, as it does not necessarily address the data part of the equation.
All of us are aware of Google Translate. Today, we will provide examples of how we can use the googletrans which is a free and unlimited python library that implemented Google Translate API. This uses the Google Translate Ajax API to make calls to such methods as detect and translate . The first thing that we need to do is to install the googletrans library. I suggest to use the conda install command.