The developers of the dating app Tinder recently announced that new safety features would be added to its app throughout 2020. These updates include a means to connect users with emergency services when they feel unsafe and more safety information provided through the app. Given that many users, especially women, experience harassment, sexism and threatening behaviour on Tinder, these appear to be positive steps to addressing such issues. Tinder also mentioned app updates will incorporate artificial intelligence (AI) to validate profile photos. "The [AI] feature allows members to self-authenticate through a series of real-time posed selfies, which are compared to existing profile photos using human-assisted AI technology."

Artificial Intelligence (AI) is one of the few emerging technologies that promise to bring about some striking transformations in the blooming world of Android app development. When it comes to improving business relations, growth, and expectations, this technology has got the highest spotlight that cannot be overlooked by anyone looking to make a meaningful impact in the business world through technology. It is interesting to see how AI is growing rapidly to become the next big thing the world has ever known. Today, many app development companies around the world are not only interested in adopting AI but are also focused on putting the technology into the hands of people. Basically, they are looking to introduce it through apps in their mobile devices.

Artificial intelligence has the power of transforming anything remotely stupid into an intelligent object! Yes, AI has been doing this for quite some time now and with the rise of voice assistants, things have become more exciting. Businesses around the world, have now understood the importance of "Voice Commerce". It all began with speech-to-text technology developed by Google. 'Google Voice Search' has been launched for iPhones, this advance app utilized data centers so that it can easily compute data and can analyze data, this is actually a good example of human speech.

Your Amazon Echo can help you get some much-deserved rest. It can be tricky trying to fall asleep when you have a million things running through your head. Did I remember to send that email? Should I have done that differently? It's enough to keep anyone up at night, especially if you're not getting enough sleep.

The creator of the legendary'Konami Code' cheat, Kazuhisa Hashimoto, has died. The Japanese video game developer, who passed away on Tuesday at the age of 61, created the legendary cheat code that is still used by game developers today. The Konami Code – up, up, down, down, left, right, left, right, B, A, Start – gives gamers benefits such as extra lives or power-ups when entered on the keypad. Hashimoto's passing was confirmed by his former employer and gaming giant Konami on Wednesday night. The cause of his death was undisclosed.

What if we could speak with our devices, cars, and homes just as easily as we do with our friends? Conversation is the bedrock of human communication, a transformative tool that reveals what's inside our heads and hearts. Voice is our primary means of connecting with others--and, increasingly, it's how we want to engage with the machines around us, too. The art of human conversation can be maddeningly difficult for even very sophisticated machines, but we're on a path to creating solutions that are much closer to what we need. Thanks to advances in speech recognition, artificial intelligence, neural networks, and processing power, we can tap into the capabilities of our machines simply by speaking.

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold start. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives. On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. On the other hand, we introduce datasets used in these works. Finally, we propose several potential research directions in this field.

In this dissertation, we cover some recent advances in collaborative filtering and ranking. In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with graph information, and how our proposed new method can encode very deep graph information which helps four existing graph collaborative filtering algorithms; chapter 3 is on the pairwise approach for collaborative ranking and how we speed up the algorithm to near-linear time complexity; chapter 4 is on the new listwise approach for collaborative ranking and how the listwise approach is a better choice of loss for both explicit and implicit feedback over pointwise and pairwise loss; chapter 5 is about the new regularization technique Stochastic Shared Embeddings (SSE) we proposed for embedding layers and how it is both theoretically sound and empirically effectively for 6 different tasks across recommendation and natural language processing; chapter 6 is how we introduce personalization for the state-of-the-art sequential recommendation model with the help of SSE, which plays an important role in preventing our personalized model from overfitting to the training data; chapter 7, we summarize what we have achieved so far and predict what the future directions can be; chapter 8 is the appendix to all the chapters.

Recommender systems today have become an essential component of any commercial website. Collaborative filtering approaches, and Matrix Factorization (MF) techniques in particular, are widely used in recommender systems. However, the natural data sparsity problem limits their performance where users generally interact with very few items in the system. Consequently, multiple hybrid models were proposed recently to optimize MF performance by incorporating additional contextual information in its learning process. Although these models improve the recommendation quality, there are two primary aspects for further improvements: (1) multiple models focus only on some portion of the available contextual information and neglect other portions; (2) learning the feature space of the side contextual information needs to be further enhanced. In this paper, we propose a Collaborative Dual Attentive Autoencoder (CATA++) for recommending scientific articles. CATA++ utilizes an article's content and learns its latent space via two parallel autoencoders. We use attention mechanism to capture the most pertinent part of information in making more relevant recommendations. Comprehensive experiments on three real-world datasets have shown that our dual-way learning strategy has significantly improved the MF performance in comparison with other state-of-the-art MF-based models according to various experimental evaluations. The source code of our methods is available at: https://github.com/jianlin-cheng/CATA.

Gone are the days when you get up and turn the dial or press some buttons on your thermostat to get the airflow going in your home. Thanks to popular voice assistants like Amazon's Alexa and Google Assistant--and the invention of smart thermostats--controlling your Sensi smart thermostat is easier than ever when you follow these simple steps. Skip further down the article to see instructions for setting up with Google Assistant. Alexa is the most popular smart assistant out there, and she can help you control your Sensi smart thermostat. Create a Sensi account using a valid email address and a strong password.