For financial institutions that have yet to adopt conversational banking, the potential user base already exists. Nearly two-in-five U.S. adults are now users of smart speakers, such as Amazon Alexa or Google Home. In November 2020, eMarketer estimated that 128.0 million people in the US used a voice assistant – 44.2% of internet users and 38.5% of the total population. Consumers are programming themselves to find things using their voices. They're doing less button-pressing and more speaking to control their entertainment experiences, for example--from Amazon Fire TV to Apple TV.

I live in the curious intersection of art, design, and code. For the past two years, I've worked with a small group of artists to develop Alexa, Call Mom!, an immersive storytelling installation using Amazon's Alexa platform. Our project is far from the type of third-party apps you typically see for Amazon's voice assistant -- "Alexa, Play Jeopardy!" and "Alexa, Ask Pikachu to Talk" are two popular examples -- as it invites users to engage with Alexa in a way that's just a bit… off. Alexa, Call Mom! leads participants through an immersive séance experience. It is a parodic reimaging of the classic horror séance and an exploration of the tense relationships we share with conversational devices in our home.

The brushless motor that almost silently spins its 10.1-inch HD display around a 350-degree arc is the feature that will grab your attention when you take it out of the box, but you'll quickly discover many more things to get jazzed over when you set about exploiting its capabilities to the fullest. This is a fantastic feature whether you're following a recipe, engaging in a video call, or watching a movie on Netflix. And Amazon gives you full control over how motion occurs: You can disable it entirely, enable it only for some activities--such as when making video calls, watching a video, or following a recipe--or you can activate/deactivate it on demand by saying things like "Alexa, follow me," "Alexa, turn right," or "Alexa, turn off motion." If you place the Echo Show next to a wall or in a corner, you can adjust how far it will rotate so that it doesn't bump into anything as it spins. The display apparently has a clutch or a similar mechanism that automatically disengages the motor while at rest, allowing you to manually turn the display left or right even if motion is enabled.

Let's create a movie recommender based on ratings. In this example we have a collection of movies, a bunch of users, and movie ratings from users that range from 1 to 5. These ratings are sparse because each user rates only a small percentage of the total movies, and they are biased because users' ratings are distributed differently. Our goal is to take any user ID and search for recommended movies for that user. We will use Pinecone to tie everything together and expose the recommender as a real-time service that will take any user ID and return relevant movie recommendations.

Researchers at the University of Washington have figured out a way to use machine-learning algorithms to turn smart speakers into sensitive medical devices that can detect irregular heartbeats. The scientists use smart speakers like Amazon Echo or Google Home to send out an inaudible sound that bounces off a person's chest and returns to the device, reshaped in a way that reveals the heartbeat. An uneven cardiac rhythm can be associated with ailments including strokes or sleep apnea. The researchers employed a machine-learning algorithm to tease out the heartbeats from other sounds and signals such as breathing, which is easier to detect because it involves a much larger motion. The algorithm was also needed to zero in on erratic heart rhythms -- which from a health perspective are generally more important to identify than a steady "lub-dub."

You might soon have a new use for your Amazon Echo, Google Home or other smart speaker: checking your heart for irregular rhythms. Researchers at the University of Washington have developed an artificial intelligence system using smart speakers to monitor your heartbeat without requiring physical contact. Their findings were published in the peer-reviewed journal Communications Biology. The study had people sit 1 to 2 feet from a smart speaker, which starts playing an inaudible continuous sound. The sound then bounces off the person and back to the speaker, where the AI system is able to detect individual heartbeats.

The use of technology in daily life is nothing to be surprised about. But oftentimes, we await a future where AI-products fill our space, forgetting how much it has already invaded our routine. AI-powered products are already taking over our lives for good. Even though we anticipate the importance of technology in daily life at maximum is a few years away, they are already causing considerable effects and have big impacts on us. The phrase'technology in daily life' may sound intimidating to some, but it has been in use for decades and its applications are more common than you might imagine.

The implementation of AI in ecommerce should come as no surprise. Online businesses have always been quick to adopt new technologies, and this is how the industry thrives; enhancing the customer experience, discovering new markets, and driving further sales. And with the continued development of AI technology like chatbots, visual search, and personalized recommendations, the world of ecommerce is transforming again. But just how effective and useful is AI-powered tech? Where is it being used?

Amazon's Echo and other smart speakers like the Google Home could be used to monitor the rhythm of a person's heart. Academics created an AI-powered device which monitors regular, and irregular, heartbeats using the same tools found in smart speakers. The prototype, which was built in a lab but could be incorporated into speakers in the future, was found to be almost as good as medical devices in hospitals. The search for heartbeats begins when a person sits within one to two feet of the smart speaker. Then the system plays an inaudible continuous sound, which bounces off the person and then returns to the speaker.

Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail, less popular, items. The effectiveness of these approaches is often assessed using different metrics to evaluate the extent to which over-concentration on popular items is reduced. However, not much attention has been given to the user-centered evaluation of this bias; how different users with different levels of interest towards popular items are affected by such algorithms. In this paper, we show the limitations of the existing metrics to evaluate popularity bias mitigation when we want to assess these algorithms from the users' perspective and we propose a new metric that can address these limitations. In addition, we present an effective approach that mitigates popularity bias from the user-centered point of view. Finally, we investigate several state-of-the-art approaches proposed in recent years to mitigate popularity bias and evaluate their performances using the existing metrics and also from the users' perspective. Our experimental results using two publicly-available datasets show that existing popularity bias mitigation techniques ignore the users' tolerance towards popular items. Our proposed user-centered method can tackle popularity bias effectively for different users while also improving the existing metrics.