While we saw Amazon's new smart thermostat go on sale earlier this week, now you can get the more advanced Google Nest Thermostat for less, too. The smart home gadget is down to $99 at Amazon right now, which is 24 percent off its normal price and close to its all-time low. All colors have been discounted and you can get the device with a trim kit for only $114, or 21 percent less than usual. Normally $130, the Energy Star-certified Nest Thermostat came out in 2020 as an affordable alternative to Google's Nest Learning Thermostat. The standard model doesn't have the luxury materials or the hi-res display that the Learning model does, but the biggest selling points remain the same.

However, a model can have hundreds, if not thousands of features. Most feature statistics changes are benign. The problem is not how to detect these changes, but how to know which change actually requires your attention. Real-time machine learning is largely an infrastructure problem. Solving it will require the data science/ML team and the platform team to work together. Both online inference and continual learning require a mature streaming infrastructure.

Let's say there was a person who experienced every online meeting cliche. Even before the pandemic, I wondered if AI assistants could fundamentally change how we do meetings. When the world entered the era of remote work, it was a much needed switch. It can't be that easy to replace a human, right? The median estimate among leading computer scientists reported a 50% chance that high-level machine intelligence systems technology would arrive within 45 years.

More companies are relying on cloud technology than ever before. They are discovering the benefits of using the cloud to utilize data and facilitate communications between employees, customers, contractors and other stakeholders. One of the underappreciated benefits of cloud technology is that it makes it easier to work with virtual assistants. Savvy executives and small business owners realize that virtual assistants can perform many important tasks a lot more efficiently. Cloud technology has helped VAs perform their jobs more easily and effortlessly exchange documents with their employers.

From low-rise jeans to reruns of the sitcom Friends, generation Z has a seemingly endless appetite for 90s and early 00s nostalgia. Now that extends to their romantic lives, as Tinder has introduced a blind date feature to boost its popularity among young people – by enabling them to meet partners in a way that resembles the pre-smartphone era. The new feature on the dating app matches people based on preferences, and enables them to make conversation before they are allowed to view each other's photos. It will shortly be available in the US before being expanded globally. Tinder says the feature is intended to respond to demands from generation Z, usually defined as people born between 1997 and 2012, for more authentic connections as a backlash to online dating's earlier emphasis on superficial judgments based on preened Instagram-ready photos on dating profiles.

Tinder has launched a new feature that could bring back memories of dating in the pre-smartphone era. It's a new Fast Chat experience called Blind Date that pairs members before allowing them to view each other's profiles. Tinder says its purpose is to give users a "low-pressure way to put their personality first," since they'll have to rely on conversation to make a first impression. The mode, which was perhaps partly inspired by the popularity of Netflix dating show Love is Blind, pairs people up based on their answers to random icebreaker questions, such as "I put ketchup on ____." Participants then enter a timed chat with their only knowledge of each other being their answers to those questions.

Tens of thousands of smart people start their day with their personalized digest by Refind.

Financial services technology firm Refinitiv has launched a new artificial intelligence (AI) assistant for Microsoft Teams to provide financial professionals with stock market news and actionable insights. Refinitiv AI Alerts - which is powered by technology from AI specialist ModuleQ - uses permissions and Microsoft 365 interactions to automatically learn the user's individual priorities and recommends content based on email conversations and upcoming meetings. "Microsoft Teams has become an indispensable platform for professionals across financial services, with institutions accelerating their adoption, and increasingly integrating critical data and tools into the platform to simplify the workflow and user experience of professionals," said Andrea Remyn Stone, group head of data and analytics at London Stock Exchange Group, which owns Refinitiv. "Refinitiv AI Alerts brings critical content and insights to Refinitiv's customer base within this platform, allowing users to discover and act on timely information across Teams, Refinitiv solutions and Microsoft 365 seamlessly." The new solution is the latest result of Refinitiv's partnership with Microsoft, which has previously helped financial services organisations connect, collaborate and leverage data to make more informed decisions.

Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems. In this paper, we focus on the Review-based Non-overlapped Recommendation (RNCDR) problem. The problem is commonly-existed and challenging due to two main aspects, i.e, there are only positive user-item ratings on the target domain and there is no overlapped user across different domains. Most previous CDR approaches cannot solve the RNCDR problem well, since (1) they cannot effectively combine review with other information (e.g., ID or ratings) to obtain expressive user or item embedding, (2) they cannot reduce the domain discrepancy on users and items. To fill this gap, we propose Collaborative Filtering with Attribution Alignment model (CFAA), a cross-domain recommendation framework for the RNCDR problem. CFAA includes two main modules, i.e., rating prediction module and embedding attribution alignment module. The former aims to jointly mine review, one-hot ID, and multi-hot historical ratings to generate expressive user and item embeddings. The later includes vertical attribution alignment and horizontal attribution alignment, tending to reduce the discrepancy based on multiple perspectives. Our empirical study on Douban and Amazon datasets demonstrates that CFAA significantly outperforms the state-of-the-art models under the RNCDR setting.

The glitches we often see in conversational interfaces like Alexa and Siri reveal to us the unique human ability we have to deploy empathy in conversations and social life. But in order for artificially intelligent machines to learn, more and more we must express ourselves in a reduced language and must simplify the complex range of human expression into something AI can understand. As we learn to live within the narrow confines of the computer,we may increasingly begin to lose the creative expressive potential of our bodies and language,writes David Berry. The imitation game, better known as the Turing test, was developed by Alan Turing in 1950. As one of the early pioneers of computers, he argued that if a computer could imitate a human successfully, it might thereby be thought of as intelligent.