You trained a machine learning model, validated its performance across several metrics which are looking good, you put it in production, and then something unforeseen happened (a pandemic like COVID-19 arrived) and the model predictions have gone crazy. You fell victim to a phenomenon called concept drift. But don't feel bad as it happens to all of us all the time. Heraclitus, the Greek philosopher said, "Change is the only constant in life." In the dynamic world, nothing is constant.

We know that the range of AI-loaded smart products is constantly expanding. But what's less obvious is how AI is also transforming the world of services – enabling service-based businesses to improve their offering, and even develop entirely new services and revenue streams that are underpinned by AI. Just as in product-based businesses, AI has become a driving factor for success in the service sector. Here are three ways businesses are delivering a better service through AI. AI provides incredible opportunities to get to know your customers – what they like and don't like, what they actually do (as opposed to what they say they do), how they engage with your service, what factors would encourage them to engage more deeply, and do on.

We live in the ages where the world's information is only one click away. This influences us to share our thoughts online and spend time talking to our friends and families on social media. It is now a part of our daily life, and we can't ignore the fact. While it has both positive and negative impacts, disruptive technologies like AI and Machine Learning continue to prompt industries to introduce a range of tools to grab users' attention on social media. AI is seeping its push into the user interface on social media, all while creating a better path for users to interact with brands.

Alexa users can always count on Amazon's voice assistant for a hand, and with the $129.99 Echo Loop smart ring, they can have Alexa wrapped around their finger. Currently available on an invitation-only basis, the Loop is an Amazon Day 1 Edition product (essentially a beta device you pay for) that gives you access to Alexa with the tap of a button. As usual, you can ask Alexa to answer questions, set reminders, create to-do and shopping lists, get directions and the weather, control connected smart home devices, make calls, and more. If you're a virtual assistant power user, it offers a new level of convenience, letting you summon not only Alexa but Google Assistant or Siri wherever you are without having to reach for your phone.

Quaternion space has brought several benefits over the traditional Euclidean space: Quaternions (i) consist of a real and three imaginary components, encouraging richer representations; (ii) utilize Hamilton product which better encodes the inter-latent interactions across multiple Quaternion components; and (iii) result in a model with smaller degrees of freedom and less prone to overfitting. Unfortunately, most of the current recommender systems rely on real-valued representations in Euclidean space to model either user's long-term or short-term interests. In this paper, we fully utilize Quaternion space to model both user's long-term and short-term preferences. We first propose a QUaternion-based self-Attentive Long term user Encoding (QUALE) to study the user's long-term intents. Then, we propose a QUaternion-based self-Attentive Short term user Encoding (QUASE) to learn the user's short-term interests. To enhance our models' capability, we propose to fuse QUALE and QUASE into one model, namely QUALSE, by using a Quaternion-based gating mechanism. We further develop Quaternion-based Adversarial learning along with the Bayesian Personalized Ranking (QABPR) to improve our model's robustness. Extensive experiments on six real-world datasets show that our fused QUALSE model outperformed 11 state-of-the-art baselines, improving 8.43% at HIT@1 and 10.27% at NDCG@1 on average compared with the best baseline.

In this paper, we propose a two-stage ranking approach for recommending linear TV programs. The proposed approach first leverages user viewing patterns regarding time and TV channels to identify potential candidates for recommendation and then further leverages user preferences to rank these candidates given textual information about programs. To evaluate the method, we conduct empirical studies on a real-world TV dataset, the results of which demonstrate the superior performance of our model in terms of both recommendation accuracy and time efficiency.

Artificial Intelligence (AI) seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining and functioning when it is trying to solve problems. These kinds of problems are present in all fields, the most emerging ones in 2020 and even beyond. The aim of Artificial Intelligence is to enhance machine functions relating to human knowledge, such as reasoning, learning, and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice.

Google is making it easier for Assistant users to donate to an organization working to address racial bias in policing. Using your phone, ask Assistant to "donate to racial justice," and the digital helper will then take you through contributing to the Center for Policing Equity. You'll also find a dedicated card in Google Assistant's Snapshot. Whatever way you go about donating, 100 percent of every contribution goes to the organization. The Center for Policy Equity employs a data-driven approach to help communities and police organizations create a more equitable public safety model.

It might sound like hyperbole, but I don't believe it's an exaggeration to say that the Sonos One speaker has improved my quality of life. Prior to my experience with the Sonos One, I primarily listened to music on small smart devices like Echo Dot, my phone, or at best through my car speakers. The touch controls on top of the One can turn the mic on/off, play/pause music, and adjust the volume. It wasn't until my work at Reviewed allowed me to try out a pair of second-gen Sonos One speakers that I realized the extent to which I had been depriving myself of a quality music experience, and how the right music can change the way life feels. In the hellscape that has been the year 2020, it's been more important than ever to find ways to decompress and allow my mind to escape to a happier place.

Automated Speech Recognition (ASR) has created quite a stir from chatbots to virtual assistants, translating to a useful tech of the current generation. Natural Language Processing (NLP) and Natural Language Generation (NLG) have led to great developments in speech technology. However, the newest wave of speech technology is not restricted to simple understanding your voice, it goes a step further and recreates it! Fuelled by the speech to text and text to speech boom, ASR has captured everyone's attention towards voice search and voice assistant technology using synthetic artificial intelligence. The voice a business selects for automated customer interaction becomes the voice of the brand having an irrevocable impact on how they would earn the customer's trust.