We were very excited to chat recently with Trip O'Dell who, among his other impressive roles, has been a product design lead at Amazon where he worked on the future vision for their virtual assistant, Alexa. Our discussion with Trip was full of juicy insights about Voice UI and the ethical considerations behind Alexa's design, and how we as designers need to be leaders in making those ethical choices. Below is an article we wrote and edited in collaboration with Trip using speech-to-text tools and a linear audio editor. All the words are his, we just helped put them on your screen. When it comes to Voice UI there's a lot of conversation around voices, but very little around personality--whether or not we want to describe it as a personality--that we're creating. How trustworthy should that personality be? Where should there be a natural boundary?

No one likes it when a stranger butts into their conversation. Especially when they interrupt with some astonishing non-sequitur. Create a free Amazon Business account and get a 30-day free trial to Business Prime. You're watching TV and chatting about the painfully demanding couple on House Hunters International when a distant voice pipes up: "The circumference of the Earth is 24,901 miles." Or whatever you call your Google Home person.

Voice technology in education is taking over the academic sphere for both developers and learners. Marissa, from Alexa Education sheds more light on how the phenomenon is revolutionizing experiences in the education sector. Marissa has gathered massive passion for voice technology in education during her early days when she was producing CD-ROM educational content for'edutainment'. Her interest grew as she migrated to Microsoft where she worked on several projects that impacted the education industry such as Xbox and Encarta. Now, during her tenure in Amazon's Alexa Education, Marissa explains how her team is developing a solid connection between institutions and their stakeholders by providing efficient ways in which learners access educational content powered by technology.

"It's machine learning's job to find patterns based on the data you give it to help you focus on the data points most likely to lead to conversion." Elizabeth Gallagher, chief revenue officer at Lineate talks about how machine learning (ML) and artificial intelligence (AI) are changing the game for ecommerce brands. With the use of predictive analytics, marketers can create personalized marketing campaigns. In this edition of MarTalk Connect, Gallagher shares the key data points marketers should use to provide personalized recommendations. She stresses how data-driven automation and machine learning are strategic assets to enhance the customer journey.

Implementation of the "Internet of Things" in the modern world is gaining pace at breakneck speed. Society is moving away from standalone devices and entering the realm of inter-connectivity. With uses in different facets of life, such as personal gadgets, retail, electricity distribution and financial services, IoT is making its mark. One such application field of IoT is in Smart Homes, or more specifically in the Heating, Ventilation, and Air Conditioning industry (HVAC). According to a report by Zion Market Research, the global smart HVAC control market is expected to reach almost USD 28.3 billion by 2025 as compared to USD 8.3 billion in 2018.

Artificial Intelligence (AI) is a computer or electronic device performing actions as if it were a human – it would apply some sort of intelligence factor or representation to accomplish the task. Some of these human services these electronic devices are performing include different planning methods and actions that include learning, problem solving, motion, thought manipulation, social response and intelligence, creativity, knowledge representation, and imitation. These electronic manipulations happening occur simultaneously with our daily lives, most of the time without us even realizing. Different examples of frequently used AI programming include virtual assistants (like Amazon's Alexa or Apple's Siri) photo recognition (like on social platforms and personal devices), and spam and credit card fraud testing; as well as more in-depth projects, like self-driving cars, check-out kiosks, and recommendation engines that frequent your past purchases to create their own ads. As consumers and participants in a fast-paced electronically changing world, we have not only let these AI infiltrations become a part of our daily lives, but we also have not educated ourselves on their pros and cons.

Facing a fridge full of ingredients but still don't know what to cook? Tired of following the same recipes and eager to try something new and creative? Thanks to AI technologies such as image recognition and machine learning, people can now save time, food and money in the kitchen while discovering creative and tasty recipes and even generating their own new and personalized flavours. Facebook has developed an image-to-recipe generation system which enables users to reverse engineer a recipe by simply inputting an image of the dish they want to prepare. First, ingredients and ingredient co-occurrence are generated by exploiting visual features extracted from the food image.

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate "ties" due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to $\sqrt{M/N}$, where $M$ and $N$ are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.

Intelligent assistants like Cortana, Siri, Alexa, and Google Assistant are trained to parse information when the conversation is synchronous and short; however, for email-based conversational agents, the communication is asynchronous, and often contains information irrelevant to the assistant. This makes it harder for the system to accurately detect intents, extract entities relevant to those intents and thereby perform the desired action. We present a neural model for scoping relevant information for the agent from a large query. We show that when used as a preprocessing step, the model improves performance of both intent detection and entity extraction tasks. We demonstrate the model's impact on Scheduler (Cortana is the persona of the agent, while Scheduler is the name of the service. We use them interchangeably in the context of this paper.) - a virtual conversational meeting scheduling assistant that interacts asynchronously with users through email. The model helps the entity extraction and intent detection tasks requisite by Scheduler achieve an average gain of 35% in precision without any drop in recall. Additionally, we demonstrate that the same approach can be used for component level analysis in large documents, such as signature block identification.

Matrix factorization (MF) has been widely applied to collaborative filtering in recommendation systems. Its Bayesian variants can derive posterior distributions of user and item embeddings, and are more robust to sparse ratings. However, the Bayesian methods are restricted by their update rules for the posterior parameters due to the conjugacy of the priors and the likelihood. Neural networks can potentially address this issue by capturing complex mappings between the posterior parameters and the data. In this paper, we propose a variational auto-encoder based Bayesian MF framework. It leverages not only the data but also the information from the embeddings to approximate their joint posterior distribution. The approximation is an iterative procedure with cross feedback of user and item embeddings to the others' encoders. More specifically, user embeddings sampled in the previous iteration, alongside their ratings, are fed back into the item-side encoders to compute the posterior parameters for the item embeddings in the current iteration, and vice versa. The decoder network then reconstructs the data using the MF with the currently re-sampled user and item embeddings. We show the effectiveness of our framework in terms of reconstruction errors across five real-world datasets. We also perform ablation studies to illustrate the importance of the cross feedback component of our framework in lowering the reconstruction errors and accelerating the convergence.