##txt : A Comparison Of Facebook's Deeptext

Translation services: This service can help with changing the written text from one language to another.

Translation refers to transferring the words from one language to another; this can be done using apps that you can type/speak into using a language you a familiar with i.e. English and it will then turn what you are trying to say in the desired language of your choice i.e. Chinese.

The features trained for one task can be useful for related tasks. The MTL leverages this idea in a systematic manner. Models for all task of interest (POS,CHUNK,NER) are jointly trained.All models share the look-up table , first linear layer parameters along with certain few parameters..MTL could produce a single unified architecture that performs well for all tasks, no significant improvements(only marginal) were observed with this approach when compared to training separate architectures per task.[ http://arxiv.org/pdf/1103.0398v1.pdf

On basic level Machine translation simply substitute word in source language for word in target language, but only substation of word is not give good result for translating whole phrase from source language to target language. We can improve this with the corpus and statistical techniques.

Machine Translations. Machine translations and computer-assisted translations (CAT) are not security risks in-and-of themselves. The issue is the term base and translation memory used by the computers. When these time-saving databases are updated with your personal, proprietary, confidential, or classified information, you need to ensure you know where that information is going to go. Often, term bases and translation memories are uploaded, shared, or simply used by the same company on different projects (perhaps a competitor's project) without your knowledge.

With evolving technology comes new types of social media, people have begin to question the internet’s effect on our mentality. It is a bit difficult to compare the pros and cons of social media, it has more to do with an individual’s experience.  Some could be having a horrible time due to social media, but others could be thriving off of it.  The more negative implications of using social media could be the way that we share our personal lives and how much of it we give to the public, regardless of social relationships, to consume.  Along with this, we are deeply impacted by what we see from others based on our viewing of what they share because of the ability to create falsified identities and unrealistic interpretations of who we are

There are various options out there to take care of all your translation needs. If you ever get bored of Google's version or frankly, think it sucks, maybe it's time to move one. Experimentation can be the foundation for growth so don't be afraid to get out there and try out other options. Without further adieu, here are 9 translators that just might be better than Google's effort. Remember, none of them are exactly perfect. If you're

Marin Kaste wrote the article “A Look Into Facebook’s Potential To Recognize Anybody’s Face.” This article discusses the challenge of using facial recognition software to determine an individual’s name. The software used for facial recognition has numerous defects, which causes problems when trying to identify an individual using a poor quality photo. Another problem with facial recognition software is if an individual is not in the database, then they cannot be found using the software. The article explains that the problems of facial recognition could be solved by collecting all of the photos of individuals on Facebook to create a universal database.

Lori Andrews, non-fiction, “Facebook Is Using You” (2012); laments that the United States is one of the few nations to lack data privacy laws and thus Google searches and Facebook activity can be used as data for purchase by companies and even the government. Andrew blends a variety of sources ranging from articles, to specific examples, hypothetical instances, and the details of particular pieces of legislation. Andrews’ purpose is to warn against the moral dilemma data mining presents in order to call the public to speak out and seek change in policy. Andrews develops a disgruntled and foreboding tone to clearly highlight how the lack of data privacy laws can affect the American public’s personal lives unless they choose to speak out against

Despite having 10+ years of experience in the field of translation, I am still a beginner at "People per Hour" and that is the reason I am offering my services at a discounted rate.

In previous years, there seemed to be guidelines on what was appropriate to reveal to the general public. Any decorum we may have had before has been abandoned now. Sharing intimate details on the internet has become a normal part of daily life. Caring more about ourselves than other people has become the most important thing to us. Social media has been more of a hindrance than a helper. With the arrival of social media, the principles that dictate the output of private information on social media have deteriorated.

In addition to supporter shaming being abundant in daily face-to-face interactions, social media, such as Twitter and Facebook, become a breeding ground for supporter shaming. On Twitter, Kevin Allred, a professor at Rutgers University, wrote, “If you’re white & claiming being called racist for supporting Trump is a ‘bias incident,’ you 've lived the most privileged life ever! Ever!” The supporter shaming on social media, however, did not stop at the individual level; it grew to a systematic banning of vocal and enthusiastic Trump supporters on Twitter. Charlie Nash wrote in a Breitbart article, “…It appears that Twitter’s war on free speech is directed at a specific generation of libertarians and alternative conservative voices, many of whom are…supporters of Donald Trump…they’re going after the social network’s most influential Trump supporters…” While vocal Trump supporters were being banned on Twitter, there did not seem to be an issue with death threats being posed toward Donald Trump and his supporters, seeing as composers of such tweets were not banned. This type of behavior, engaged in by social media platforms, made political discourse hostile, but the opposite should be the goal of such media platforms. It should be a duty of the media to encourage political discourse and to encourage people to actively participate in politics, since it affects everyone, not to punish those who have opposing beliefs.

Twitter is one of the best social networking stage where millions of users can communicate with each other, post their thoughts and keep a track on all the on-going topics. Its popularity has reached millions of people all round the world. Popularity of the twitter apart, there are few disadvantages that are connected with this social networking platform. Few of them include spammers, hacking or forging of accounts. This paper mainly deals with testing the already existing methods for discovering useless activities on twitter, and to identify the twitter spammers.

The proposed study aims to fill this gap in the related research. I will focus on literal translation in Chinese-English public sign. I will collect data of public sign from several

 In December 2015, Facebook dropped Bing and started using its own translation technology. The issue was that Bing was built to translate more properly written website text, not human slang. Facebook stated that the disconnect between the languages people speak and the content they want to connect to on the Internet is what made them want to create their own neural network-based machine translation (MT) system.