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Columbia University linguist John McWhorter opines on the impact AI translation might have on second language learning. We already know that Americans and UK folx are the most monolingual populations in the world; fewer than one in 100 American students currently become proficient in a language they learned in school. McWhorter argues that AI might offer utilitarian practicality for casual users, e.g. translating useful phrases on the fly while traveling: "With an iPhone handy and an appropriate app downloaded, foreign languages will no longer present most people with the barrier or challenge they once did." Yet McWhorter also says, "I don't think these tools will ever render learning foreign languages completely obsolete. Real conversation in the flowing nuances of casual speech cannot be rendered by a program, at least not in a way that would convey full humanity." He also suggests that genuinely acquiring a language will still beckon a few select people, e.g. those relocating to a new country, those who'd like to engage with literature or media in the original language, as well as those of us who find pleasure in mastering these new codes: that language learning will become "an artisanal pursuit" of sorts.

Teachers worried about students turning in essays written by a popular artificial intelligence chatbot now have a new tool of their own. Edward Tian, a 22-year-old senior at Princeton University, has built an app to detect whether text is written by ChatGPT, the viral chatbot that's sparked fears over its potential for unethical uses in academia. Tian, a computer science major who is minoring in journalism, spent part of his winter break creating GPTZero, which he said can "quickly and efficiently" decipher whether a human or ChatGPT authored an essay.

This article discusses the use of AI in learning languages. It discusses the benefits such as corrective feedback and being able to talk about your topic of choice. However, it also mentions some drawbacks such as potential biases and errors.

This article writes about the complexities of animal communication and how elephants are able to use low frequency sounds to stay in touch amongst each other. Generative artificial intelligence is able to help humans generate an algorithm that is capable of detecting animal calls, grumbles, grunts, squeaks etc and translate it into a language that humans are comfortable interpreting.

This article talks about how the Google Translate tool recently started using a neural network to translate between some of its most popular languages. The system is now so clever it can do this for language pairs on which it has not been explicitly trained.

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."

Facebook shut down an Artificial Intelligence experiment shortly after developers discovered that the machines were talking in a language the machines made up that humans couldn't understand. The developers first programmed the chatbots so they could talk in English to each other when trading items back and forth. When the chatbots got into a long negotiation, they started talking to each other in their own language. Even though these chatbots were not highly intelligent, it is concerning that they went off on their own so quickly. In 2014, scientist Stephen Hawking warned that Artificial Intelligence could be the end of the human race. Hopefully the Matrix is science fiction, not science prediction.

Summary: A new AI-based system called a semantic decoder can translate a person's brain activity -- while listening to a story or silently imagining telling a story -- into a continuous stream of text. Unlike other thought decoding systems in development, this system does not require subjects to have surgical implants, making the process noninvasive.

Believe it or not, sperm whales communicate with clicking noises-particularly patterns of different clicks referred to as codas. Scientists have found that animal language can be, in fact, complex and structured like our own language. Though it is debated whether or not animals actually have language, researchers continue to explore whether methods such as AI can help garner meanings-if exists-behind animal communication habits.

Artificial intelligence can make adults nervous, but experts say exploring it as a family is the best way to understand its pros and cons. It's important to understand how a chatbot works, employing a "neural network": a mathematical system that learns skills by analyzing large amounts of data. The chatbot works by scraping the internet for digital text or images. It gathers information from a variety of places, including websites, social media platforms and databases, but it does not necessarily choose the most reliable sources. In other words, even though chatbots may appear authoritative, rigorous and trustworthy, they are not always reliable and can produce content that is offensive, racist, biased, outdated, incorrect or simply inappropriate.

This article is about how scientists are discussing an A.I. that could translate thoughts into speech through MRI scans. A study was done on three people and analyzed their brain patterns as they listened to words and phrases in order to understand what part of the brain lights up when that word/phrase is said. They also used an A.I to translate MRI scans into words and phrases. However, the A.I.'s translation is doesn't make sense. While the result wasn't perfect, it discovered that the A.I can also decode meaning and imagination. While this is beginning of this kind of A.I. technology, it's possible that in the future, they will be able to decode our thoughts.

Interesting article about AI's influence and potential influence in business practices.

From the Zippys.com website: Did you know that Hawaiʻi is the only US state to have two official state languages - English and Hawaiian ('Ōlelo Hawaiʻi)? The month of February is designated as "Mahina 'Ōlelo Hawaiʻi" (Hawaiian language month) by the Hawaiʻi State legislature, various county councils around Hawaiʻi, and by other organizations. The purpose for the month is to celebrate and normalize the use of 'Ōlelo Hawaiʻi in everyday life, as well as to perpetuate the Hawaiian culture. Zippy's is proud to celebrate all of the cultures in Hawai'i that make our home a special place in the world. For the month of February, Zippy's is supporting Mahina 'Ōlelo Hawaiʻi Month by giving you an opportunity to learn a little bit of the language and to see what the Zippy's menu looks like in 'Ōlelo Hawaiʻi! Check out the Papa Kuhikuhi Mea`ai o ka Hale `Aina `o Zippy's in PDF form here. (Makemake `o Kumu Cowell i ka Pā Zip!)

This comprehensive talks about what AI is and where it came from, along with the basics of why it has gained so much success lately. It explores how we can go about trusting this new technology, both in terms of tailored online services like ChatGPT and in terms of bigger machinery such as smart homes, self-driving cars, and more.

Bradley Hayes, a post-doc student at the Massachusetts Institute of Technology, has invented @DeepDrumpf, an amusing bit of artificial intelligence. DeepDrumpf is a bot trained on the publicly available speech transcripts, tweets, and debate remarks of Donald Trump. Using a machine learning model known as a Recurrent Neural Network, the bot generates sequences of words based on priming text and the statistical structure found within its training data. Created to highlight the absurdity of this election cycle, it has amassed over 20,000 followers and has been viewed over 12 million times -- showcasing the consequences of training a machine learning model on a dataset that embodies fearmongering, bigotry, xenophobia, and hypernationalism. Here's a sample tweet: "We have to end education. What they do is unbelievable, how bad. Nobody can do that like me. Believe me."

Just in case you were under the misimpression that artificial intelligence will be taking over the world shortly, this article suggests that digital assistants really can't even handle the sort of everyday linguistic interaction that humans take for granted. Still, it is interesting to find out how product engineers are designing the assistants to become "smarter" at comprehending your words and requests. Machine learning algorithms can help devices deal with turn-by-turn exchanges. But each verbal exchange is limited to a simple, three- or four-turn conversation.

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 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."

Interesting article re: designing an efficient and responsive bot: leveraging key human linguistic features in order to have the machine "converse" with humans, comprehend their needs, and respond appropriately. Programming a machine to exhibit the conversational nuances and sophisticated comprehension of a normal human=hard.