Group items tagged

"Computational Linguistics became an open access journal, freely available to all online readers. ... Computational Linguistics is the longest running publication devoted exclusively to the design and analysis of natural language processing systems. From this highly-regarded quarterly, university and industry linguists, speech specialists, and philosophers get information about computational aspects of research on language, linguistics, and the psychology of language processing and performance."

This article dives into the idea of people attempting to teach computers the concept of sarcasm-that people do not always mean exactly what they say. If the computer is solely relying on the words of a message, it is extremely difficult to recognize sarcastic remarks; the accompaniment of images will be a stronger indication of if the words should be taken literally or sarcastically. A potential implication of this technology would be to gauge how the public really feels about something in particular. The article goes on to describe how researchers are tackling the issue: they have written computer algorithms based on how they've observed sarcasm being used online.

This article provides an insight into an interview with Christopher Manning, a Stanford professor of computer science and linguistics. He is focused on computational linguistics, also known as natural language processing. Natural language processing involves creating algorithms that can allow computers to understand written and spoken language and then intelligently respond. This involves systems such as Siri, Alexa, and Google Voice. These systems are pretty advanced technology, however, they are still far from perfect. Manning notes that people will probably still be working on natural learning processing in twenty years.

This article talks about how even though learning how to program a computer seems like learning a new language, computer language is actually processed through a different area of the brain than when language is processed. It says that it activates the multiple demand network, which is also activated by complex cognitive tasks such as solving math problems or crossword puzzles. However, it appears to rely on different parts of the network than math or logic problems do, which suggests that coding does not precisely replicate the cognitive demands of mathematics either. The article says that researchers didn't find any regions that appear to be exclusively devoted to programming. They also suggest that understanding computer code cannot be categorized as a language-based or math-based skill. Instead, they think it might require a combination of both skills, even though the brain regions don't reflect it.

Choosing a voice has implications for design, branding or interacting with machines. A voice can change or harden how we see each other. Research suggests that users prefer a younger, female voice for their digital personal assistant. We don't just need that computerized voice to meet our expectations, said Justine Cassell, a professor at Carnegie Mellon's Human-Computer Interaction Institute. We need computers to relate to us and put us at ease when performing a task. "We have to know that the other is enough like us that it will run our program correctly," she said. That need seems to start young. Ms. Cassell has designed an avatar of indeterminate race and gender for 5-year-olds. "The girls think it's a girl, and the boys think it's a boy," she said. "Children of color think it's of color, Caucasians think it's Caucasian." Another system Cassell built spoke in what she termed "vernacular" to African-American children, achieving better results in teaching scientific concepts than when the computer spoke in standard English. When tutoring the children in a class presentation, however, "we wanted it to practice with them in 'proper English.' Standard American English is still the code of power, so we needed to develop an agent that would train them in code switching," she said. And, of course, there are regional issues to consider when creating a robotic voice. Many companies, such as Apple, have tweaked robotic voices for localized accents and jokes.

A movement by parents and lawmakers to get computer coding classes to count toward foreign language credits in school. Supporters say that it will expose students to coding and will provide them with a valuable skill in the workplace.

A movement by parents and lawmakers to get computer coding classes to count toward foreign language credits in school hit a snag this month - but advocates aren't giving up. Sen.

Computer programs often reflect the biases of their very human creators. That's been well established. The question now is: How can we fix that problem? Adam Kalai thinks we should start with the bits of code that teach computers how to process language.

The World Health Organisation counts 430m people as deaf or hard of hearing. Many use sign languages to communicate. If they cannot also use those languages to talk to computers, they risk being excluded from the digitisation that is taking over everyday life. Sign language poses particular issues in re: its translation to either text or speech. Some challenges include improving the machine-learning algorithms that recognise signs and their meaning and developing the best methods to interpret sign languages' distinctive grammars. The applications of such technology could improve the lives of the deaf, for example, allowing them to use their cell phones to search for directions or look up the meanings of unknown signs, without resorting to the written form of a spoken language.

A new study finds that software designed to automatically read and grade essays can do as good a job as humans - maybe even better. The robo-readers are efficient, as well, and can grade 16,000 essays in 20 seconds. The caveats, however: computers don't do as well on comprehension, ascertaining facts vs. fiction, and assessing certain genres, like poetry.

"A small band of linguists, engineers and computer scientists, among others, are busy training computers to recognize hallmarks of what they call emotional speech - talk that reflects deception, anger, friendliness and even flirtation. ... Algorithms developed by Dr. Hirschberg and colleagues have been able to spot a liar 70 percent of the time in test situations, while people confronted with the same evidence had only 57 percent accuracy ... His lab has also found ways to use vocal cues to spot inebriation, though it hasn't yet had luck in making its computers detect humor - a hard task for the machines, he said."

With the advances of technology, it is presumed that humans will soon be using ear pieces and microphones to help translate or speak in a different language. However, it is argued that computers and technology will be unsuccessful at doing so. Computers and technology aims for perfection and therefore won't be able to communicate or translate the imperfect human language. In the article, the human language is compared to coding on a computer. The two are actually very different in that language is constantly changing and imperfect, however coding is pre-set and there is a general understanding for it.

Pieter Bruegel the Elder's 'The Tower of Babel' (1563). Wikimedia Commons Wouldn't it be wonderful to travel to a foreign country without having to worry about the nuisance of communicating in a different language?

Researchers are using computer-animation techniques, such as motion-capture, to make life-like computer avatars that can reliably and naturally translate written and spoken words into sign language, whether it's American Sign Language or that of another country. The signing avatars can also be used in apps and games to help deaf children get early exposure to language, which is critical for their cognitive development.

Novelist Kurt Vonnegut once opined, "There is no reason why the simple shapes of stories can't be fed into computers. They are beautiful shapes." Thanks to new text-mining techniques, this has now been done. Researchers at the University of Vermont's Computational Story Lab have analysed over 1,700 English novels to reveal six basic story types - you could call them archetypes - that form the building blocks for more complex stories. They are: 1. Rags to riches - a steady rise from bad to good fortune 2. Riches to rags - a fall from good to bad, a tragedy 3. Icarus - a rise then a fall in fortune 4. Oedipus - a fall, a rise then a fall again 5. Cinderella - rise, fall, rise 6. Man in a hole - fall, rise The researchers used sentiment analysis to get the data - a statistical technique often used by marketeers to analyse social media posts in which each word is allocated a particular 'sentiment score', based on crowdsourced data. Depending on the lexicon chosen, a word can be categorised as positive (happy) or negative (sad), or it can be associated with one or more of eight more subtle emotions, including fear, joy, surprise and anticipation. For example, the word 'happy' is positive, and associated with joy, trust and anticipation. The word 'abolish' is negative and associated with anger. Do sentiment analysis on all the words in a novel, poem or play and plot the results against time, and it's possible to see how the mood changes over the course of the text, revealing a kind of emotional narrative. While not a perfect tool - it looks at words in isolation, ignoring context - it can be surprisingly insightful when applied to larger chunks of text

Learning the intricacies of human languages is hard even for human children and non-native speakers - but it's particularly difficult for AI. Scientists have already taught computers how to do simple tasks, like translating one language to another or searching for keywords. Artificial intelligence has gotten better at solving these narrow problems. But now scientists are tackling harder problems, like how to build AI algorithms that can piece together bits of information to give a coherent answer for more complicated, nuanced questions. "Language is the ultimate frontier of AI research because you can express any thought or idea in language," states Stanford computer science professor Yoav Shoham. "It's as rich as human thinking." For Shoham, the excitement about artificial intelligence lies not only in what it can do - but also in what it can't. "It's not just mimicking the human brain in silicon, but asking what traits are so innately human that we don't think we can emulate them on a computer," Shoham said. "Our creativity, fairness, emotions, all the stuff we take for granted - machines can't even come close."

Over the past few months, the online word game Wordle has skyrocketed in popularity, with cryptic grids of gray, green and yellow squares appearing on social media.The game is challenging, but simple: Once a day, players have six guesses to identify a new five-letter word (all players receive the same word on a given day). Each guess provides color-coded hints: a letter turns green if it is in the correct spot, yellow if it is part of the word but in a different spot, and gray if it is not in the word at all. In Wordle, the process of intuiting a target word from color-coded clues provides a window into our subconscious understanding of how language works, according to UChicago linguist Jason Riggle. In effect, it turns everyone into a linguist, forcing us to wrestle with sound fragments and stitch them together according to probability distributions.

Recent studies suggest that metaphors are more than just fancy literary devices and that there is a psychological basis for linking cold with feelings of social isolation. Psychologists Chen-Bo Zhong and Geoffrey Leonardelli from the University of Toronto's Rotman School of Management wanted to test the idea that social isolation might generate a physical feeling of coldness. In their first experiment, they divided a group of volunteers into two groups. One group recalled a personal experience in which they had been socially excluded-rejection from a club, for example. This was meant to tap into their feelings of isolation and loneliness. The other group recalled an experience in which they had been accepted into a group. Afterwards, volunteers were asked to estimate the temperature in the room. Those who recalled memories of being ostracized experienced the ambient temperature of the room as colder. In a second experiment, researchers triggered feelings of exclusion by having volunteers play a computer-simulated ball tossing game. The game was designed so that some of the volunteers had the ball tossed to them many times, but others were left out. Afterwards, all the volunteers rated the desirability of certain foods and beverages: hot coffee, crackers, an ice-cold Coke, an apple, and hot soup. The findings were striking. As reported in the September issue of Psychological Science, a journal of the Association for Psychological Science, the "unpopular" volunteers who had been ostracized during the computer game were much more likely than the others to want either hot soup or hot coffee. Their preference for warm food and drinks presumably resulted from physically feeling cold as a result of being excluded. "It's striking that people preferred hot coffee and soup more when socially excluded," Leonardelli said. "Our research suggests that warm chicken soup may be a literal coping mechanism for social isolation."

Two University of Washington undergraduates have won a $10,000 Lemelson-MIT Student Prize for gloves that can translate sign language into text or speech. The Lemelson-MIT Student Prize is a nationwide search for the most inventive undergraduate and graduate students. This year, UW sophomores Navid Azodi and Thomas Pryor - who are studying business administration and aeronautics and astronautics engineering, respectively - won the "Use It" undergraduate category that recognizes technology-based inventions to improve consumer devices. Their invention, "SignAloud," is a pair of gloves that can recognize hand gestures that correspond to words and phrases in American Sign Language. Each glove contains sensors that record hand position and movement and send data wirelessly via Bluetooth to a central computer. The computer looks at the gesture data through various sequential statistical regressions, similar to a neural network. If the data match a gesture, then the associated word or phrase is spoken through a speaker.