You might think that artificial intelligence is only something the tech giants are focused on and that it doesn't have any impact on your household or everyday life. But the reality is different. Whether you realize it or not, Artificial Intelligence is everywhere. The application of AI is not only for big sectors or finance or manufacturing, it is also impacting our daily lives. So, let's find out about the applications of AI in your daily life.

Artificial intelligence (AI) is transforming the landscape of 21st century marketing. Long gone are the days of throwing spaghetti on the wall and shooting in the dark to acquire new customers and to regain their business. With the amount of data growing exponentially on a daily basis, AI can help businesses scale their marketing efforts and leverage the data for actionable insights leading to greater ROI. Look up the term "marketing" and you'll find something that mentions actions or activities involving a business or company, promoting or selling products or services. Is that something that you or your company does?

Vida Health, a virtual chronic care platform, announced that it is rolling out a new value-based price structure, which puts Vida's fees at-risk for behavioral and physical health. This news comes just months after the company landed $110 million in Series funding. The service includes a number of health conditions, including obesity, diabetes, hypertension, depression and anxiety.

In a content-based approach to recommendation, a lot of information is available for both items and users which is useful to create profiles. We used a graph model to represent these profiles, connecting each item to its features and each user to features of interest. Even the nearest neighbor network was built using only this information. The collaborative filtering approach, on the other hand, relies on data related to the different kinds of interactions between users and items. Such information is generally referred to as a user–item dataset.

With the development of information technology, human beings are constantly producing a large amount of information at all times. How to obtain the information that users are interested in from the large amount of information has become an issue of great concern to users and even business managers. In order to solve this problem, from traditional machine learning to deep learning recommendation systems, researchers continue to improve optimization models and explore solutions. Because researchers have optimized more on the recommendation model network structure, they have less research on enriching recommendation model features, and there is still room for in-depth recommendation model optimization. Based on the DIN\cite{Authors01} model, this paper adds multi-head and multi-modal modules, which enriches the feature sets that the model can use, and at the same time strengthens the cross-combination and fitting capabilities of the model. Experiments show that the multi-head multi-modal DIN improves the recommendation prediction effect, and outperforms current state-of-the-art methods on various comprehensive indicators.

The travel marketing platform of Alibaba serves an indispensable role for hundreds of different travel scenarios from Fliggy, Taobao, Alipay apps, etc. To provide personalized recommendation service for users visiting different scenarios, there are two critical issues to be carefully addressed. First, since the traffic characteristics of different scenarios, it is very challenging to train a unified model to serve all. Second, during the promotion period, the exposure of some specific items will be re-weighted due to manual intervention, resulting in biased logs, which will degrade the ranking model trained using these biased data. In this paper, we propose a novel Scenario-Aware Ranking Network (SAR-Net) to address these issues. SAR-Net harvests the abundant data from different scenarios by learning users' cross-scenario interests via two specific attention modules, which leverage the scenario features and item features to modulate the user behavior features, respectively. Then, taking the encoded features of previous module as input, a scenario-specific linear transformation layer is adopted to further extract scenario-specific features, followed by two groups of debias expert networks, i.e., scenario-specific experts and scenario-shared experts. They output intermediate results independently, which are further fused into the final result by a multi-scenario gating module. In addition, to mitigate the data fairness issue caused by manual intervention, we propose the concept of Fairness Coefficient (FC) to measures the importance of individual sample and use it to reweigh the prediction in the debias expert networks. Experiments on an offline dataset covering over 80 million users and 1.55 million travel items and an online A/B test demonstrate the effectiveness of our SAR-Net and its superiority over state-of-the-art methods.

Apple will soon offer the HomePod mini in three new colors. Announced during the company's Unleased event on Monday, the new yellow, orange and blue colorways will join the existing white and space gray models in November. The price will remain unchanged at $99 in the US. Apple first announced the HomePod mini last fall. As you'd expect, the speaker comes with deep integration with the company's other products. In addition to Siri support, its built-in U1 ultra-wideband chip allows you to quickly and easily hand off audio from your iPhone to the speaker.

Apple thinks it has a simple way to boost Apple Music adoption: limit control in return for a lower fee. The company has introduced an Apple Music Voice Plan that offers access to the full song catalog for just $5 per month, so long as you're willing to rely solely on Siri control. It's pitched as ideal for HomePod and AirPod owners and others who are more likely to use a voice assistant than tap their phone. The new tier will be available later in the fall in 17 countries, including the US, UK and Canada. You can start a trial by asking Siri to "start my Apple Music Voice trial."

Most people associate artificial intelligence and machine learning with futuristic applications like Terminator, Hal or Samantha, but applications using AI and ML are more common than you think. We have Siri, Alexa and Google Assistant. The ML algorithm recommends movies on Netflix, and we shouldn't forget about the Tesla self-driving car.

When people think about artificial intelligence (AI) today, they might think of computers that can speak to us like Alexa or Siri, or grand projects like self-driving cars. These are very exciting and attention-grabbing, but the reality of AI is actually thousands of tools and apps running quietly behind the scenes, making our lives more straightforward by automating simple tasks or making predictions. This is true across every industry and business function, and particularly true in marketing, where leveraging AI to put products and services in front of potential customers has been standard practice for some time, even though we may not always realize it! In business today, the term AI is used to describe software that is capable of learning and getting better at doing its job without input from humans. This means that while we've become used to using machines to help us with the heavy lifting, now they can start to help us with jobs that require thinking and decision-making, too.