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Article talks about the growing field of forensic linguistics: that is, the study of language to identify who said or wrote that verbal sample. Applications are enormous, from evaluating the identity of potential asylum seekers to pinpointing the Unabomber or perpetrators of crimes where language data is involved, but critics maintain that computer scans alone to evaluate language are insufficient and that overreliance can pose dangers.

"Humans and many primates clearly recognize individual voices ... [but] it's been surprisingly difficult to design quantitative studies for truly wild animals ... they played recorded calls from one individual on one side of a target meerkat, and then from the other ... The meerkats reacted with a prolonged vigilance, paying much closer attention than they did to other recorded calls. The situation didn't compute."

Voice-based interactions require sophisticated programming and AI to enable a machine to understand and talk to you. In other words, it's a really big deal to program a computer to have a conversation. Walter says the challenge now is to process massive amounts of dialogue-related data to facilitate human-like intellectual and speech functionality in machines. "We are developing the technology that will give machines the cognitive ability to understand the semantics and context of a conversation, respond to topic changes and, in essence, carry on everything from a complex conversation to small talk."

This article talks about the academic learning gaps between deaf and hearing students and how new technologies could be the solution to this problem. Although this animation could be a new translator, it is a difficult process as English and ASL are just as different as English and Chinese.

A team of researchers at the Cincinnati Children's Hospital use computers to analyze the language of suicide notes, in the hope that they can better identify those at risk. By comparing patient interview responses to suicide notes, they can identify how similar or divergent their language is from the language of suicide.Here are three patterns researchers have identified in their corpus of authentic suicide notes: 1. Loss of hope. When hope is gone, when hopelessness emerges - and that's in most of the notes 2. Practical instruction, e.g.. "Remember to change the tires. Remember to change the oil. I drew a check, but I didn't put the money in. Please go ahead and make the deposit." 3. The presence of the following emotions: depression, a little bit of anger, abandonment, and the sense of "I just can't go on any longer. I can't deal with this any longer."

Why do Jane Austen's literary works endure some 200 years after her death? The Stanford Literary Lab ran computational analysis on the words contained in Austen's work. Eschewing the fantastical and dramatic elements typical of novels both in Austen's lifetime and ours, Austen is more concerned with social realities and human nature. Her works display emotional intelligence; she employs more words about emotions and time, as well as abstract words connected to states of mind and social relationships.

In the age of founding fathers and type writers, typists used to place two spaces after each full stop in a sentence, because letters of uniform width looked cramped without that extra space. However, with modern word processors and computers, which were designed to have the perfect/variable amount of spacing, the majority of people now only place one space after full stops. This article explains a scientific study run by three psychology researchers from Skidmore college, which shows that placing two spaces after a full stop is better. They found out that the extra space does not necessarily allow people to read faster, but spend fewer milliseconds staring at the full stop and making the reading process smoother.

This article is about how a team of researchers has discovered the distinct computations that occur when we switch between different languages.

Languages often adapt naturally to the world around them. Speakers create new words to communicate new concepts. But when a language isn't spoken widely enough to adapt on its own - as with Hawaiian - it may need help to move things along. The Hawaiian language has nearly 30,000 words. But up until the late 1980s, the language didn't have words for subjects like soccer, computer or recycling. So a group of linguists and language advocates formed a lexicon committee in 1987 to invent new words. The committee has created at least 7,500 new words since its inception. Many of the committee's entries have been published in a modern Hawaiian language dictionary called Māmāka Kaiao. Much of the group's work helped to make Hawaiian teachable in language immersion schools. But some are skeptical of the committee's work. One interviewee noted that there is a small group creating words that we "need" now, but it's unclear why that word was chosen or how. Even the pronunciation of new words can be confusing, she adds. Disagreements among Hawaiian speakers may seem like bad news for spreading the language. But Larry Kimura, UH-Hilo Hawaiian language professor, says it's a sign that the language is growing. He said the lexicon committee helps speed up what would have been an otherwise natural process of ʻōlelo Hawaiʻi adapting to the world around it.

This article explains the new system created for air traffic controllers and pilots. AcListant is a system that will listen to air controllers' radio conversations to help make suggestions for commands that fit the situation. The system can filter through basic greetings like "Hello" and "Good Morning" and focus on commands instead. AcListant can help with better communication especially with pilots who speak very fast or with an accent.

Youngsters and adults alike will benefit from a new tool designed to keep the Nlaka' pamux language alive and well in the Nicola Valley. The eight First Nations that make up the Citxw Nlaka' pamux Assembly (CNA) are each receiving six computers equipped with licenses to an online program designed to teach the language orally.

A traveling journalist in Beijing utilizes both Baidu (China's version of Google) and Google voice-translation apps with mixed results. You speak into the apps, they listen and then translate into the language you choose. They do it in writing, by displaying text on the screen as you talk; and out loud, by using your phone's speaker to narrate what you've said once you're done talking. Typically exchanges are brief: 3-4 turns on average for Google, 7-8 for Baidu's translate app. Both Google and Baidu use machine learning to power their translation technology. While a human linguist could dictate all the rules for going from one language to another, that would be tedious, and yield poor results because a lot of languages aren't structured in parallel form. So instead, both companies have moved to pattern recognition through "neural machine translation." They take a mountain of data - really good translations - and load it into their computers. Algorithms then mine through the data to look for patterns. The end product is translation that's not just phrase-by-phrase, but entire thoughts and sentences at a time. Not surprisingly, sometimes translations are successes, and other times, epic fails. Why? As Macduff Hughes, a Google executive, notes, "there's a lot more to translation than mapping one word to another. The cultural understanding is something that's hard to fully capture just in translation."

This article explains how machines such as "Siri" and "Echo" are able to speak the human language. "Language technologies teach themselves, via a form of pattern-matching. For speech recognition, computers are fed sound files on the one hand, and human-written transcriptions on the other. The system learns to predict which sounds should result in what transcriptions."

In order to fill the shortage of jobs that is to come in Australia, they may have to start learning a new language, but it may not be what you think. The new language that many think is going to be the most common language in the world is computer coding. A program called, Code Camp, has been teaching primary school students, through after school and in school classes, how to write code. They are saying that there will be a shortage of Australians skilled enough to fill these positions.

R.I.P. to the understatement. Welcome to death by Internet hyperbole, the latest example of the overly dramatic, forcibly emotive, truncated, simplistic and frequently absurd ways chosen to express emotion in the Internet age (or sometimes feign it). The trend toward hyperbole appears to echo a broader belief among experts that young women are its first adopters. One explanation for the use of hyperbole (OMG!) With the increase in digital, vs. face to face communication, we must come up with increasingly creative ways to express tone and emphasis when facial cues are not an option. There's a performative element to our social media interactions, too: We are expressing things with an audience in mind. Tyler Schnoebelen, a linguist and founder of Idibon, a company that uses computer data to analyze language, notes "Performance generally requires the performer to be interesting. So do likes, comments and reshares. Exaggeration is one way to do that."

In modern society, there is sarcasm all around us. This article describes sarcasm and how we are so surrounded by it, that it is practically the "primary language". It also discusses how we detect sarcasm and how it is naturally picked up from a young age. Lastly, researchers found that some people have a difficult time detecting sarcasm so some computer scientists have actually developed a sarcasm detection device.

This study examining the online identity and language use of teens who maintain personal blogs. Expression of gender, identity, and use of language were examined in terms of disclosed personal information (sexual identity, emotive features, and semantic themes).

As AI advances, so does its language. A chat bot by Google uses language to speak with others in conversation. However, it runs into some problematic issues, especially regarding its ethics. For instance, it will associate doctors as men and nurses as women. Research is being put in place to make it a safer program.

Siri uses a variety of advanced machine learning technologies to be able to understand your command and return a response - primarily natural language processing (NLP) and speech recognition. NLP primarily focuses on allowing computers to understand and communicate in human language. In terms of programming, languages are split up into three categories - syntax, semantics, and pragmatics. Whereas syntax describes the structure and composition of the phrases, semantics provide meaning for the syntactic elements. Pragmatics, on the other hand, refers to the composition and context in which the phrase is used.

This article describes a recent study in which scientists were able to use CT scans to generate 3-D models of Neanderthal ear structures. In the past, attempts to determine whether Neanderthals used language hinged on the hyoid bone, a single piece of the Neanderthal vocal tract. However, these scientists took a different approach by looking at the ears of Neanderthals to give clues about Neanderthal language. By running the ear models through computer programs, scientists were able to determine that the Neanderthal ear's "sweet spot" included higher frequencies characteristic of consonant production, and therefore human language. This is exciting because it gives scientists another piece in the puzzle of early human language development.