In the Create a Question and Answer Bot with Amazon Lex and Amazon Alexa blog post, we showed you how you could create a QnABot (pronounced "Q and A Bot") for a situation in which your users have questions and you have answers. Now, what if this situation were reversed? What if you could ask users questions using quizzes, polls, surveys, and tests? These are valuable ways to drive user engagement and collect actionable feedback. Our most recent QnABot update includes the new Questionnaire Bot feature, which allows content designers to rapidly create quizzes for users and integrate them with existing QnABot content.

Deciding which sub-tool to use for a given proof state requires expertise specific to each ITP. To mitigate this problem, we present PaMpeR, a Proof Method Recommendation system for Isabelle/HOL. Given a proof state, PaMpeR recommends proof methods to discharge the proof goal and provides qualitative explanations as to why it suggests these methods. PaMpeR generates these recommendations based on existing hand-written proof corpora, thus transferring experienced users' expertise to new users. Our evaluation shows that PaMpeR correctly predicts experienced users' proof methods invocation especially when it comes to special purpose proof methods.

For consumers, the continuing growth of voice UX is undoubtedly a welcome development – as evidenced by the proliferation of platforms and devices integrating the tech, and the increasing number of people getting acclimated to using voice. But for web and application designers, voice interaction represents, perhaps, the biggest UX challenge since the dawn of the touchscreen age. By now, everyone is familiar with all the quirky names of voice assistants--Siri, Cortana, Bixby, and Alexa--a testament to the new level voice UI has reached in the last few years. The technology only looks to soar higher, with its capabilities penetrating more and more digital ecosystems. Voice User Interfaces (VUI), essentially, is the ability to speak to devices and its capability, in turn, to understand and act upon users' commands.

As our society's technological progress marches forward, we've become ever more fascinated with the concept of artificial general intelligence (AGI). From IBM's Jeopardy-playing computer, Watson to television programs like Westworld, we've collectively begun exploring and philosophizing about the potential of AGI. Of course, most discussions about AGI in our popular culture are focused on the future, and not the current realities of the present when it comes to artificial general intelligence. Below, we'll discuss the current realities of AGI and what breakthroughs we're on the cusp of in 2018. How Close Are We To True Artificial General Intelligence?

Siri, do you believe in God? Siri, do you believe in God? Siri, I insist, do you believe in God? "I would ask that you address your spiritual questions to someone more qualified to comment. She – is it a she? – has a point: artificial intelligences (AI) like Siri are less situated than humans to answer questions about religion and spirituality. Existential angst, ethical inquiries, theological considerations: these belong exclusively to the domain of Homo Sapiens. But some futurists and tech experts predict a not-so-distant future in which AI, having achieved a certain indistinguishability from humans, will be truly intelligent. At that point, they claim, AI will experience the world in ways not too unlike the ways that we experience it – emotionally, intelligently, and spiritually. When that day comes, I'll have a new question for her. "Siri, do you have a soul?" A consideration of AI's religious status can be found in some of the earliest discussions of modern computing.

Early adopters commonly used smart speakers such as Amazon Echo and Google Assistant as timers, home speakers and news readers. Today, one in six Americans own a smart speaker such as Amazon Echo (aka Alexa) or Google Home, making intelligent speakers the fastest growing consumer technology when compared with virtual reality, augmented reality and wearables. As a parent and a technologist who founded Visionarus, a company that develops advanced access control systems for gated communities based on speech recognition, I'm always looking for ways I can introduce and experience disruptive technology with my child. After doing my due diligence and researching on the most common concerns parents, educators, security and privacy communities have related to this new technology, I experimentally gave my kindergarten-age son a smart speaker for Christmas. Six-plus months later, I believe this experiment positively expanded his knowledge, developed his logical thinking, entertained him, improved his writing and even helped him let go of his fear of the dark.

Benevolent hackers found a smart way around Windows 10's Cortana voice assistant. Microsoft last week issued a patch to Windows 10 machines for a vulnerability that allowed hackers to use Cortana voice commands to sneak past the operating system's lock screen protections. But just what could hackers have done, and what could they still do to unpatched machines? In videos put together for Forbes, Israeli researchers from the Technion Israeli Institute of Technology showed what was possible, whether it was executing a program or viewing a private document (such as a list of passwords) from behind the lockscreen, proving just how problematic Microsoft's Cortana can be. The weakness was found separately by McAfee researchers, and Yuval Ron and Ron Marcovich, software engineering students at the Technion Israeli Institute of Technology, as part of a project overseen by independent security researchers Amichai Shulman and Tal Be'ery.

Technologically, the 20-year jump from 2015 to 2035 will be huge. During that time some elements of our world will change beyond recognition while others will stay reassuringly (or disappointingly) familiar. Consider the 20 years to 2015. Back in 1995 we were in the early days of the internet, we worked in cubicles and our computers were chunky and powered by Windows 95. There were no touch screen phones or flat screen TVs; people laughed at the idea of reading electronic books, and watching a home movie meant loading a clunky cassette into your VCR.

Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations. Motivated by the success of this approach, we introduce two different models of reviews and study their effect on collaborative filtering performance. While the previous state-of-the-art approach is based on a latent Dirichlet allocation (LDA) model of reviews, the models we explore are neural network based: a bag-of-words product-of-experts model and a recurrent neural network. We demonstrate that the increased flexibility offered by the product-of-experts model allowed it to achieve state-of-the-art performance on the Amazon review dataset, outperforming the LDA-based approach. However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.

Whether consumers know it or not, three next-generation technologies are playing a major role in shaping their experience with brands -- and the future of consumer goods marketing: artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). To keep pace and effectively compete in an increasingly connected marketplace, brands are investing in these three technologies to continually fine-tune their customer strategies, using hyper-personalized information across touchpoints. Have you ever wondered how Netflix makes movie and TV show recommendations, how Facebook prompts friends to be tagged in photos, and how Alexa, Siri and Google Now assist in our day-to-day activities? These are real-life examples of machine learning -- a subset of AI. ML uses a customer's historic data and behavioral patterns to create high-quality predictions of their future behavior.