One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Researchers from the Allen Institute for AI and University of California, Irvine, have released AllenNLP Interpret, a toolkit for explaining the results from natural-language processing (NLP) models. The extensible toolkit includes several built-in methods for interpretation and visualization components, as well as examples using AllenNLP Interpret to explain the results of state-of-the art NLP models including BERT and RoBERTa. In a paper published on arXiv, the research team described the toolkit in more detail. AllenNLP Interpret uses two gradient-based interpretation methods: saliency maps, which determine how much each word or "token" in the input sentence contributes to the model's prediction, and adversarial attacks, which try to remove or change words in the input while still maintaining the same prediction from the model. These techniques are implemented for a variety of NLP tasks and model architectures.
Fueled by improvements in speech recognition, machine learning, better algorithms, cloud processing, and more powerful computing devices, the quality of machine translations is improving. Learning another language has never been a simple proposition. It can take months of study to absorb the basics and years to become fluent. Of course, there's the added headache that learning a language doesn't help if a person encounters one of the world's other 7,000 or so languages. "There has always been a need for human translators and interpreters," says Andrew Ochoa, CEO of translation technology firm Waverly Labs.
We have all heard of "automated" support features, but as artificial intelligence (AI) has continued to advance, the support landscape is shifting to focus on the possibility of "autonomous" support. What exactly is the difference? Here, Jens Trotzky, head of Artificial Intelligence Technology for SAP Support, discusses the role of AI in the development of autonomous support, and how SAP innovation is helping to make it a reality. A: Most customer support resources available today – even some of the most sophisticated – are considered automated in some capacity. In a nutshell, automated support is predictive technology that is pre-scripted by support engineers based on a fixed set of standards and defining factors.
Google unveiled a new Pixel smartphone and other hardware devices Tuesday, all aimed at getting people even more dependent on its artificial-intelligence services. The Pixel 4 phone promises to respond to AI queries even faster than before, while a home Wi-Fi system is getting the AI features for the first time. The company also unveiled a new smart speaker and wireless earbuds, both invoking the AI-powered Google Assistant. The Assistant, akin to Apple's Siri and Amazon's Alexa, is now available on more than 1 billion devices, including ones made by other manufacturers. With Google's own products, though, the company can steer users to Assistant features even more.
This has been a repeated news item in the business media for a few years now. As automated systems and Artificial Intelligence (AI) gets better and better customer service agents will watch their jobs disappear – stolen by robots and software agents. The reality is far more complex because there are many reasons why a customer gets in touch with customer service. It could be a very simple requirement, like a password reset, or an application for a new mortgage that will require a detailed conversation with a great deal of personal information and documentation. There is no single type of customer service interaction and therefore one of the initial challenges that companies have found is deciding when an interaction can be automated and when a human should handle the interaction.
Teachers learn best from experience, and from each other. Communities of practice are an important opportunity for teachers to continue to refine their teaching, reflect, and learn valuable insights from colleagues and mentors -- whether through discussing their own practice, or hearing about the practice of others. Teachers in low-resource/disconnected environments who want to improve their content-specific pedagogy and general teaching practice, can frequently be cut off from these opportunities. In such environments, teachers may have limited numbers of peers with which to interact, especially for those who are subject specialists. Standard models of teacher development and continual professional development are best delivered through communities of practice, because they allow for engagement in discussions with other teachers, and learning from their experience, in order to improve the quality of lessons for their students.
We explored the need for automated curriculum alignment in crisis contexts, and the possible role of artificial intelligence (AI) in recognizing curricular mandates and patterns, and recommending pertinent educational content in return. This work is part of a broader collaboration working with refugees and partner organizations to explore utilizing digital education to support learning in these contexts. The experience of engaging our professional communities in such a challenging question was as valuable as the outputs themselves, so we've been sharing the discussions and debates we've had as they may be useful in other's work. Over the past month, the Design2Align blog post series has covered topics such as contextual display and creation of metadata, teacher-generated content annotations, technical considerations in OER for curriculum alignment facilitation, and open models for just-in-time learning pathway recommendations. Today, Learning Equality's UX Design Lead, Jessica Aceret talks about the specific curriculum needs for crisis contexts, and how it requires not only a human touch but also an alignment tool that provides intelligent content recommendations so that the relevant resources can be more easily found.
Google designers show their latest devices during a visit to its hardware studio where they designed the company's new Pixel 4 flagship phone, Pixel Go laptop, Pixel Buds wireless headphones, Nest Mini smart speaker and Nest Wifi system. Though the Pixel 4 and Pixel 4 XL smartphones may be the stars of the Made by Google media event in New York City, a new and impressive smart speaker garnered quite a bit of chatter, too. Nest Mini ($49), which is about the size of a doughnut, has some improved hardware and software to go up against its primary competitor, Amazon Echo Dot. This diminutive smart speaker offers twice the bass as the original Google Home Mini, the company says. Indeed, the audio sounded fuller and louder than its predecessor when we cranked it up in a post-event demo session.
Google's freshly unveiled Chrome OS clamshell, the Pixelbook Go, won't ship with much new software. That's because the team spent outsize time ensuring its existing features worked without issue, according to Google senior director of product management Matt Vokoun and senior product marketing manager Tom Kim. "We really focused this year on stability, quality, and performance," Vokoun told VentureBeat in an interview following this morning's Made by Google press briefing. "So there's less sizzle and fewer new features … but the amount of time [spent] internally testing and testing with actual users almost doubled." One of those features is Android app compatibility.
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