I have been on a 30-day challenge to improve my knowledge of Artificial Intelligence (AI), to understand how it works and how it impacts our lives, and this section talks about how not only have we already integrated it in our everyday lives, but in some cases already love it and depend on it. In this fifth section, we tackle "AI in Application." Exploring where AI is prevalent and the data that is being collected already is not surprising but it is humbling how much it has already penetrated our lives and how much we depend on it. Recently, a friend of mine named her baby Sirius. For those that love the Harry Potter books, the immediate connection is to Sirius Black, so of course being a Harry Potter fan I instantly loved it.

In this episode of the Data Exchange I speak with Denise Gosnell, Chief Data Officer at DataStax1. Denise is also the co-author of the new book, The Practitioner's Guide to Graph Data, which covers foundational tools and techniques needed to utilize graph technologies in production applications. This conversation is a great introduction to what has become an important class of technologies and tools. Graph technologies are used to power a wide array of applications, including recommendation engines, fraud detection systems, identity and access management, search, and many other use cases. Denise provides a set of practical recommendations and advice for developers who are interested in unlocking the power of large graphs.

Smart watches and fitness trackers are only the first signs of a world that will enfold us in a subtle but ubiquitous web of connectivity. We have digital assistants that can make a restaurant reservation, lock the garage door, or check whether our laundry is dry when asked. We have tablets with cameras that automatically track us as we move around to make our video calls feel more like being there. We even have home automation hubs that use subsonic sound waves to locate us in a room and make their displays more readable from a distance. The next logical step will be not needing to interact deliberately with our devices at all.

In the early 2000s, Songza implemented a manual music recommendation system for its listeners, where a team of music experts and curators would create playlists. But these recommendations were not objective, as they were dependent on the personal taste of the curators. It was an average experience for listeners, with a fair share of hits and misses, because it was impossible to make a playlist which catered to the varied tastes of a diverse set of people. The technology and the data did not exist back then to build a playlist that would be personalised to the taste of each individual listener. Along came Spotify a few years later, offering a highly personalised weekly playlist called Discover Weekly that quickly became one of their flagship offerings.

Simulating online recommender system performance is notoriously difficult and the discrepancy between the online and offline behaviors is typically not accounted for in offline evaluations. Recommender systems research tends to evaluate model performance on randomly sampled targets, yet the same systems are later used to predict user behavior sequentially from a fixed point in time. This disparity permits weaknesses to go unnoticed until the model is deployed in a production setting. We first demonstrate how omitting temporal context when evaluating recommender system performance leads to false confidence. To overcome this, we propose an offline evaluation protocol modeling the real-life use-case that simultaneously accounts for temporal context. Next, we propose a training procedure to further embed the temporal context in existing models: we introduce it in a multi-objective approach to traditionally time-unaware recommender systems. We confirm the advantage of adding a temporal objective via the proposed evaluation protocol. Finally, we validate that the Pareto Fronts obtained with the added objective dominate those produced by state-of-the-art models that are only optimized for accuracy on three real-world publicly available datasets. The results show that including our temporal objective can improve recall@20 by up to 20%.

This week was jam-packed with new product announcements but we also got a couple of good deals, too. Apple's AirPods Pro remain at their lowest price ever -- only $199 -- and the latest MacBook Air is $100 off. Those in need of a new smartphone can grab the latest from Samsung for $200 less and the solid Beats Solo Pro headphones are down to $199. Here are the best deals from this week that you can still buy today. You can get AirPods Pro for their lowest price ever, only $199, on Amazon, Walmart or Staples.

Tinder is a mobile dating app that can help you find singles in the local area. "Swipe right if you like her, Swipe left if you don't" is a linchpin to the company's success, and the format has been duplicated by numerous contemporaries. Tinder was first launched as a location-based dating app in 2012 within incubator Hatch Labs and join a venture between IAC and Xtreme Labs and now it's one of the most popular dating apps in the US with about 1.7 Billion swipes per day. Tinder has employed the freemium business model to earn revenue. It went from a "location-based" dating app to a global dating app that is present in 190 countries in less than 8 years.

Your financial health has improved since last month. Would you like to know by how much?" Blue, BBVA's new virtual assistant (VA), is the one doing the talking. Blue is integrated in the bank's mobile banking app in Spain (for Android and iOS) and can respond to over 100 user requests from the more than 800 features available in the application. Blue is pleasant, patient, and able to put itself in the shoes of others. It loves talking to humans and is always willing and ready to help customers. And it wants to learn lots of things in order to become even more insightful. These are just some of the personality traits that characterize BBVA's new voice assistant, which came to life out of a complex process that factored even the most minor details. The IT consulting firm, Gartner, defines virtual assistants as tools that help users perform a series of tasks that previously required human assistance. VAs use predictive models, natural language processing tools, recommendation engines, personalization systems based on artificial intelligence and advanced data analytics to do their job: assisting users and automating tasks. "VAs listen to and observe behaviors, build and maintain data models, and predict and recommend actions," the consulting firm explains. Blue's artificial intelligence capabilities are the result of a hybrid development: made up mostly of parts created by BBVA and others based on technologies that were readily available on the market and benefit from an advanced level of maturity, such as natural language processing (NLP) techniques. Specifically, the core of the system's artificial intelligence functionality is a 100 percent BBVA in-house development called Lenny. It is based on a set of cloud-based microservices and is responsible for orchestrating all the pieces that go into making Blue work. Thanks to this BBVA-developed'artificial brain', Blue is connected to the application functionalities that are powered by advanced data analytics. Examples include predicted banking transactions and financial health features, which the BBVA virtual assistant makes readily available to the customers by using natural, human-like dialog. "During the development of Blue, one of the most significant challenges was ensuring that we could cover the full range of functionality that BBVA, as a major player, offers in its app -- recognized as the most complete on the market -- in a voice and text-based virtual assistant," Eliseo Catalán, Head of BBVA Spain's Smart Assistants explains. Achieving this required that all the features in the app -- offered thanks to BBVA's digital capabilities -- were correctly set up and that each use case was thoroughly trained so that the user is given the appropriate response at every juncture of the customer journey. "We do all of this for the various platforms that might have different capabilities.

Alexa Conversations, a new deep learning-based approach that developers can use for creating natural voice experiences on Alexa with less effort, fewer lines of code, and less training data than before. Alexa Conversations let's you create skills with more configuration and less coding. Come along to this workshop to learn how to build an Alexa Conversations skill and take skills to the next level with less effort. Attendees will receive an introduction to Alexa Conversations and create a simple skill to get started on your journey. You will also need to create an Amazon Alexa Developer account and an AWS account if you want to follow along.

Pets need companions too, and now there's a website to help them. Pinder, a pet website styled after human dating app Tinder, allows owners to find pals for their pets, the New York Post reported. "We're just taking the effective format of Tinder and applying it to the pet community," Kevin Botero, the founder of Pinder, told the Post. The website shows only pet profiles – the profile setup page says "no humans allowed" – and currently all pets have to be in costume for the website's Halloween costume contest. According to the Post, the contest is a way to kick off the website's launch.