IBM Research and Rensselaer Polytechnic Institute (RPI) are collaborating on a new approach to help students learn Mandarin. The strategy pairs an AI-powered assistant with an immersive classroom environment that has not been used previously for language instruction. The classroom, called the Cognitive Immersive Room (CIR), makes students feel as though they are in restaurant in China, a garden, or a Tai Chi class, where they can practice speaking Mandarin with an AI chat agent. The CIR was developed by the Cognitive and Immersive Systems Lab (CISL), a research collaboration between IBM Research and RPI. When learning a new language, especially one as difficult as Mandarin, it's important that students have many opportunities to speak and practice their conversational skills.

Tyson Navarro, 10, of Fremont, Calif., learns to build code using an iPad at a youth workshop at the Apple store in 2013. Code.org said a record number of female and under-represented minority students took AP computer science classes in 2018. SAN FRANCISCO -- Female, black and Latino students took Advanced Placement computer science courses in record numbers, and rural student participation surged this year, as the College Board attracted more students to an introductory course designed to expand who has access to sought-after tech skills. This year, 135,992 students took advanced placement (AP) computer science exams, a 31 percent increase from last year, according to data from the College Board, the organization that administers standardized tests that help determine college entrances as well as AP courses. Females and under-represented minorities were among the fastest growing groups.

With the tremendous generation of data, there are more and more situations where observations are described by more than one view. This is for example the case with multilingual documents that convey the same information in different languages or images that are naturally described according to different set of features (for example SIFT, HOG, CNN etc). In this paper, we study the related machine learning problem that consists in finding an efficient classification model from different information sources that describe the observations. This topic, called multiview learning Atrey et al. [2010], Sun [2013], has been expanding over the past decade, spurred by the seminal work of Blum and Mitchell on co-training Blum and Mitchell [1998] (with only two views). The aim is to learn a classifier which performs better than classifiers trained over each view separately (called view-specific classifier).

Cloze tests are widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH, containing questions used in middle-school and high-school language exams. With missing blanks carefully created by teachers and candidate choices purposely designed to be nuanced, CLOTH requires a deeper language understanding and a wider attention span than previously automatically-generated cloze datasets. We test the performance of dedicatedly designed baseline models including a language model trained on the One Billion Word Corpus and show humans outperform them by a significant margin. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending the long-term context to be the key bottleneck.

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.

As machine learning algorithms are increasing used in security-critical applications, there is a growing need to design them with active adversaries in mind. A class of adversarial attacks on machine learning that have received much attention is data poisoning attacks [21, 20, 19, 4, 7, 11]. Here, an adversary is aware of the learner's training data and algorithm, and has the power to alter a small fraction of the training data in order to make the trained classifier satisfy certain objectives. For example, a sabotage adversary may try to degrade the overall accuracy of the trained classifier as part of an industrial sabotage campaign, or a profit-oriented adversary may try to poison the training data so that the resulting model favors it - say, by recommending the its products over others. While there has been a long line of prior work on data poisoning [21, 20, 19, 4, 7, 11, 3, 8], most of it has focussed in the offline setting, where a classifier or some other model is trained on a fixed input.

As news trickeld out about the mass shooting at a video game tournament in Jacksonville, Florida on Sunday, some quickly recalled that right in that same spot there was a pro-gun control event a month ago. Survivors of the February school shooting in Parkland, Florida, which killed 17 people, held an event at the Jacksonville Landing in late July. The Jacksonville Landing is a waterfront commercial district in downtown Jacksonville with lots of bars and restaurants. Our hearts are with you Jacksonville Landing. It's crazy to think that March for Our Lives Road to Change had an event there just about a month ago.

An attendee looks at a Tifana.com Co. AI service character displayed on a screen at the Artificial Intelligence Exhibition & Conference in Tokyo, Japan, on Wednesday, April 4, 2018. The AI Expo will run through April 6. (Kiyoshi Ota/Bloomberg) It's generally accepted that as technology moves into classrooms, teachers will move, as the saying goes, "from a sage on the stage to a guide on side." That shift has rightly troubled teachers and teaching advocates who fear that educators who instruct, analyze and provide vital context will be diminished or co-opted outright by soulless, algorithm-driven tech. Generally, it's been easy to dismiss those fears in favor of some to-be-determined technology/teacher partnership.

In the last few years, machine learning applications have quietly entered every aspect of life: social media to speech recognition, radiology to retail, warfare to writing articles, coding to customer service, robotics to route optimization. During the 40 year information age, we told computers what to do. With advances in artificial intelligence, particularly machine learning, and faster processing chips we can feed computers giant data sets and they can (in narrow slivers) draw some inferences on their own. As we reported in Ask About AI, the rise of code that learns marks the beginning of a new era of augmented intelligence. It's a great opportunity for us to expand access to a great education and for young people to make a big contribution.

The 51-year-old artificial intelligence and robotics scientist is responsible for co-developing Google Street View, pioneering self-driving cars, founding Google X – the internet giant's secretive research lab – and revolutionising education by kickstarting massive open online courses (Moocs). His most recent project is developing flying cars. You launched your flying car company, Kitty Hawk, in 2015 backed by Google co-founder Larry Page and you have two projects in development – a personal aircraft called Flyer and an autonomous air taxi called Cora. Why do we need flying cars? The ground is getting more and more congested – we are all stuck in traffic all the time.