I read a pretty heartbreaking story this week about a young girl named Alexa who has reportedly been bullied at school. According to NBC San Diego, Alexa's classmates have been treating her just like an Amazon Echo, asking "Alexa" the weather and treating her like a "servant." Unfortunately, at least for that young girl, the name "Alexa" has become pretty much synonymous with the Amazon Echo and is often used interchangeably with other smart home devices. My guess is that Amazon won't change the name to something else. However, there is a way for you to change the name.

In this article we are going to show how we built this simple experiment using various cloud based services. In order for the virtual assistant to interpret what a user wants to do, we must define user intents. One example of an intent is opening an account. Once we have created the intent, we need to define how the user will express his intent. In this case, we need to input utterances, i.e. variations of possible user statements for the intent.

Digital banking is all about making customer experiences faster, easier and more engaging. Voice response marketing through smart speakers and digital assistants is increasingly the go-to bank marketing channel for "on-the-go-go-go" consumers. Make it frictionless To access financial and account information, respond to calls to action and offers, register reviews, handle transactions or reach out for help, voice response marketing is gaining in popularity. Instead of having to type in a website URL, focus on a QR code, or click on a text link, all of which can cause respondents to procrastinate, the frictionless availability of smart speakers and digital assistants encourages in-the-moment action. That can make the difference between closing a sale and losing the prospect altogether.

The coronavirus pandemic is changing the way we shop, especially for groceries. From donning masks to wearing gloves, many Americans are taking extra precautions when stocking up on pantry staples and other essential items. Grocery shopping online has become a great option for many during quarantine, and if you're shopping at Walmart, Google Assistant can help. With a few simple voice commands, you can order everything you need from Walmart using a Google-Assistant-enabled speaker or smartphone. While this may not be ideal for massive shopping lists, when you're standing in your kitchen looking at a cookbook or recipe site, it's convenient to just read off what you need to your smart assistant.

The impact of Artificial Intelligence in Healthcare is life-changing. The ability of AI to mimic human cognitive functions is bringing a paradigm shift in the industry. Artificial intelligence, when applied to healthcare, includes a collection of technologies that helps to sense, act, learn, and interpret. AI implementations are relatively simpler when they are focused on highly specialized diagnostics, personal patient engagement, workflows of multiple highly complicated organizations. It is vastly complex when working with big data sets.

Building smart conversational virtual assistants that can bring omni-channel contextual conversations to life. Virtual assistants from Skil.AI can be leveraged for accelerated deployments and capitalize business benefits.

Homeschooling is set to be revolutionized by artificial intelligence, as US homeschoolers are offered use of the world's leading AI learning engine for the first time. Families can now access a low-cost AI teaching assistant that automatically grades, plans work and personalizes learning for children. The world's largest buying club for homeschoolers, Homeschool Buyers Co-op, has announced a new premium curriculum collection, Virtua. Teaming up with British AI pioneers CENTURY Tech, they have developed the program's cornerstone curriculum Virtua School – a bespoke AI-powered learning platform for US homeschoolers. Having worked with thousands of families and schools across the world, CENTURY has used its educational expertise to develop a new AI platform tailored specifically to the needs of homeschooling families.

Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods. In this paper, given the user-item interaction graph, we propose RNE, a data-efficient Recommendation-based Network Embedding method, to give personalized and diverse items to users. Specifically, we propose a diversity- and dynamics-aware neighbor sampling method for network embedding. On the one hand, the method is able to preserve the local structure between the users and items while modeling the diversity and dynamic property of the user interest to boost the recommendation quality. On the other hand the sampling method can reduce the complexity of the whole method theoretically to make it possible for billion-scale recommendation. We also implement the designed algorithm in a distributed way to further improves its scalability. Experimentally, we deploy RNE on a recommendation scenario of Taobao, the largest E-commerce platform in China, and train it on a billion-scale user-item graph. As is shown on several online metrics on A/B testing, RNE is able to achieve both high-quality and diverse results compared with CF-based methods. We also conduct the offline experiments on Pinterest dataset comparing with several state-of-the-art recommendation methods and network embedding methods. The results demonstrate that our method is able to produce a good result while runs much faster than the baseline methods.

We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources. Our method is able to learn the multi-view model without transferring the user's personal data to a central server. As far as we are aware this is the first federated model to provide recommendations using multi-view matrix factorization. The model is rigorously evaluated on three datasets on production settings. Empirical validation confirms that federated multi-view matrix factorization outperforms simpler methods that do not take into account the multi-view structure of the data, in addition, it demonstrates the usefulness of the proposed method for the challenging prediction tasks of cold-start federated recommendations.