But it is also symbolic of the chasm between good intentions and reality that many of us may have experienced recently. This high-achieving CEO says that, while working from home, he was "going to write a lot on that", but didn't. He turns to look at its blank expanse. It's comforting for those of us who also haven't used this change of pace for vast plans and self-improvement. Which is not to say that McLeod has had a quiet year – far from it. Isolating at home, without the usual options of meeting people, he saw a 63% rise in the number of people downloading Hinge, his dating app.

Technology is an essential part of the development and growth of humans. Artificial intelligence is one such technology that is gaining momentum and hype. With technology becoming a part of our everyday lives, AI has become a topic of debates and discussions where technocrats consider it as a blessing and for some, it is a disaster. Having said that, we are still unsure about the future of artificial intelligence. Is artificial intelligence a threat or a blessing?

Did you know that companies with omnichannel customer engagement strategies retain 89% of customers compared to just 33% for those with a weak omnichannel strategy? Furthermore, Google research claims that 42% of in-store shoppers search for information online while in-store. Another extensive study by the Harvard Business Review studied the shopping behavior of just over 46,000 customers and focused on which channels they used and why. The writing is on the wall: Fusing the shopping experience across channels – or in other words, omnichannel marketing – is the future, and the future is now. To that end, let's look at four examples of brands that are offering a seamless omnichannel experience and blurring the lines between physical and digital shopping, one channel at a time.

Artificial Intelligence solutions are slowly making a profound impact on our lives. It is soon becoming a mainstream technology. Whether it is automatic parking systems, smart sensors, and personal assistance by virtual assistants, Artificial intelligence is causing digital disruption in different industries. It is making its presence felt in the education industry changing traditional and conventional teaching methods. The academic world is getting more high tech with convenient and personalized teaching experience thanks to the numerous applications of Artificial Intelligence in education.

Tinder is looking to make its popular dating app even safer, rolling out a new feature designed to stop harassment before it starts. Known as'Are You Sure? (AYS),' the feature uses artificial intelligence to detect what it deems'harmful language,' giving users a prompt prior to sending a message that could be considered harmful. The AYS notification has supposedly reduced'inappropriate language' in messages by 10 percent in early testing. Additionally, the company said that members who saw the AYS? prompt were'less likely' to be reported for inappropriate messages over the next month. According to a source familiar with the situation, hate speech, overly sexual content and'all language that goes against community guidelines' are flagged by the AI.

Some smart home aficionados still eulogize Google's Chromecast Audio, but Belkin's new Soundform Connect aims to fulfill a similar role -- for iOS users, anyway. The $100 dongle can connect to any traditional home speaker and turn it into an AirPlay 2-compatible smart speaker you can cast audio to from iPhones and iPads running iOS 11.4 and iPadOS 11.4 or newer, plus Macs running Catalina and Apple TVs with tvOS 11.4. And when we "any" home speaker, we really mean it. The Soundform has at least one nice touch the Chromecast doesn't -- beyond still existing, that is. In addition to the classic 3.5mm jack, there's also a port for standard optical connections -- the Chromecast Audio required audiophiles to own or purchase a TOSLINK-to-3.5mm According to Belkin, users will able to ask Siri to play their music or podcasts on the speaker in question, as well as ask the virtual assistant what's playing in each room and remotely control the speaker's volume.

Machine learning is a facet of Artificial Intelligence (AI) and a data analysis method based on the idea that computer systems and machines can learn from data by identifying patterns and making decisions without excessive human intervention. The global machine learning market is estimated to grow from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022. Machine learning can be used in diverse industries and the finance sector is one of those. To precisely enable financial establishments to identify suspicious activity and prevent fraud is again the functionality of machine learning. The shopping recommendations you receive based on your online activity and purchase history result from machine learning.

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user's behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user's preferences. Further, although heterogeneous graphs have been applied in different areas, a proper way to construct a heterogeneous graph for interactive news data with an appropriate learning mechanism on it is still desired. To address the above concerns, we propose a graph-based behavior-aware network, which simultaneously considers six different types of behaviors as well as user's demand on the news diversity. We have three main steps. First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to learn behavior sequence representations. Third, we introduce core and coritivity features for the behavior graph, which measure the concentration degree of user's interests. These features affect the trade-off between accuracy and diversity of our personalized recommendation system. Taking these features into account, our system finally achieves recommending news to different users at their different levels of concentration degrees.

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness problems in recommendations. Just like users have personalized preferences on items, users' demands for fairness are also personalized in many scenarios. Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands. Besides, previous works on fair recommendation mainly focus on association-based fairness. However, it is important to advance from associative fairness notions to causal fairness notions for assessing fairness more properly in recommender systems. Based on the above considerations, this paper focuses on achieving personalized counterfactual fairness for users in recommender systems. To this end, we introduce a framework for achieving counterfactually fair recommendations through adversary learning by generating feature-independent user embeddings for recommendation. The framework allows recommender systems to achieve personalized fairness for users while also covering non-personalized situations. Experiments on two real-world datasets with shallow and deep recommendation algorithms show that our method can generate fairer recommendations for users with a desirable recommendation performance.

Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while negative examples are sampled from uninteracted ones. However, noisy examples are prevalent in real-world implicit feedback. A noisy positive example could be interacted but it actually leads to negative user preference. A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference. Conventional training methods overlook these noisy examples, leading to sub-optimal recommendation. In this work, we propose probabilistic and variational recommendation denoising for implicit feedback. Through an empirical study, we find that different models make relatively similar predictions on clean examples which denote the real user preference, while the predictions on noisy examples vary much more across different models. Motivated by this observation, we propose denoising with probabilistic inference (DPI) which aims to minimize the KL-divergence between the real user preference distributions parameterized by two recommendation models while maximize the likelihood of data observation. We then show that DPI recovers the evidence lower bound of an variational auto-encoder when the real user preference is considered as the latent variables. This leads to our second learning framework denoising with variational autoencoder (DVAE). We employ the proposed DPI and DVAE on four state-of-the-art recommendation models and conduct experiments on three datasets. Experimental results demonstrate that DPI and DVAE significantly improve recommendation performance compared with normal training and other denoising methods. Codes will be open-sourced.