Siri means a secret in Swahili and in Norse, a beautiful woman who leads you to victory. To those of us in Appleland using the voice-controlled personal assistant, it stands for "Speech Interpretation and Recognition Intelligence." And when you put Siri to work, she can move productivity mountains. I use Siri to give me directions while driving with Apple CarPlay. I ask her to remind me to stop by the grocery store on my Apple Watch.

Artificial intelligence (AI) is one of the hottest topics in today's headlines. It powers natural language recognition for voice-powered assistants like Siri and Alexa, beats world-class Google Go players, and enables hyper-targeted e-commerce and content recommendations across the web on industry-giant websites that include Target and Netflix. But recently, AI has begun actively expanding its footprint in the enterprise. Executives are trying to more fully comprehend what AI is and how they can use it to capitalize on business opportunities by gaining insight into the data they collect and engaging with customers more productively to hone their competitive edge. AI is the frontier of enterprise technology, but there remain many misperceptions about what it is and how it works.

A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users. Two models with disjoint and hybrid payoffs are considered to characterize the phenomenon that users' preferences towards different items vary differently over time. In the disjoint payoff model, the reward of playing an arm is determined by an arm-specific preference vector, which is piecewise-stationary with asynchronous and distinct changes across different arms. An efficient learning algorithm that is adaptive to abrupt reward changes is proposed and theoretical regret analysis is provided to show that a sublinear scaling of regret in the time length $T$ is achieved. The algorithm is further extended to a more general setting with hybrid payoffs where the reward of playing an arm is determined by both an arm-specific preference vector and a joint coefficient vector shared by all arms. Empirical experiments are conducted on real-world datasets to verify the advantages of the proposed learning algorithms against baseline ones in both settings.

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.