It's an exciting time to be working on recommender systems. Not only are they more relevant than ever before, with Facebook recently investing in a 12 trillion parameter model and Amazon estimating that 35% of their purchases come from recommendations, but there is a wealth of powerful, cutting edge techniques with code available for anyone to try. So the tools are at hand to build something neat to deliver personalized recommendations to your users! The problem is, knowing if it's any good. When John Harrison was developing the marine chronometer, which revolutionized long-distance sea travel by allowing ships to accurately determine their longitude, he had a problem with evaluation: to measure the device's accuracy in practice required a long sea voyage. Similarly, the gold standard for evaluating a recommender system is expensive and time consuming: an A/B test, in which real users selected at random see the new model, and their behavior is compared to users who saw the old model. In both cases if this was the only way to evaluate, it would be impossible to try out new ideas with agility, or to quickly iron out flaws. Instead, it's necessary to have a quick, cheap way to evaluate a model.

Global pandemic limitations have had a direct influence on traditional real estate processes – and for the better, unexpectedly. Thousands of businesses, realtors, appraisers, mortgage lenders, and others have been forced to manage the crisis by incorporating rapidly emerging PropTech, and with good cause. Real estate AI apps can manage predetermined data flows, learn user behavior, streamline and speed operations, and allow more accurate assessments and market forecasts in the short term. Real estate AI apps are being embraced by homeowners, potential renters, and purchasers, and investors are aware that real estate is the world's greatest asset class. These top 10 AI apps help real estate professionals interact with prospects more rapidly, boost sales, manage renters and properties, and more.

Apple didn't even touch on the HomePod line during its iPhone 13 event, but that doesn't mean the smart speakers will go untouched this fall. To start, you can set two or more HomePod minis as your default speakers for an Apple TV 4K. You won't have to specify them when it's time to sit down for a movie. They won't exactly produce thunderous audio, but they could save you from buying separate smart speakers or a pricier soundbar. The update enables Siri voice control through supporting HomeKit accessories.

Artificial intelligence (AI) helps improve predictive analytics, sales forecasting, customer needs, process automation and security systems. The concept of artificial intelligence or machines that aim to emulate human thinking is undergoing vigorous research and is a topic that is increasingly being associated with the Internet of things. An AI enabled IoT system extends the functionality and value of an organization's offering, without the need for committing additional resources to achieve the increased value. This is exemplified by under Armour(UA) and IBM's collaboration on the UA Record app, which is an AI-based personal fitness coaching system, that uses a variety of sensor data to suggest highly personalized, context-relevant fitness activities to users. Such applications of AI are going to be more commonplace in the future as they are already having a significant impact on many industries.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Constructor, a San Francisco, California-based ecommerce personalization startup, today announced that it raised $55 million in a series A round led by Silversmith Capital Partners. The funding, which brings the company's total raised to $61.5 million, will be put toward product development, hiring, and go-to-market efforts, according to CEO Eli Finkelshteyn. Online commerce conversions are well behind in-store -- the average online shop sees less than 3% in conversions. But even though $4.2 trillion was spent on ecommerce platforms in 2020 alone, few ecommerce retailers have invested in a digital merchandising strategy.

Machine Learning (ML) is NOT the same as Artificial Intelligence. That said, the connection between the two is undoubtedly undeniable. Machine learning is part of AI in which the algorithms allow the system to locate patterns and learn the trends in the data and try to make decisions without human intervention. ML technology is evolving so rapidly that every generation is entirely different from the last. The first types of ML were just programmed to perform certain tasks in case of a specific event.

IoT and artificial intelligence are two of the major disruptive technologies that have penetrated into the health and fitness industry in recent years. People are now more concerned with their health and fitness to keep their bodies fit from many potential diseases such as diabetes, thyroid, and many more. To keep up with the huge demand, the fitness industry has started transforming traditional gyms into smart gyms by implementing AI models in gyms. People are getting attracted to smart gyms to become more fit and healthy to raise the standard of living. The fitness industry is booming in the 21st century with the help of these user-friendly disruptive technologies.

When you sit back and think about the changes that have been made in technology over the past few years, it is quite amazing. It was not that long ago that home phones were more common than mobile phones. However, now almost everyone has a mobile phone and many people do not have a landline. The same can be said for newer technologies such as Google Nest Audio or Amazon Alexa. Households that own a smart speaker state that using it has become an essential part of their day.

Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g., "It doesn't look good for a date"), requiring some degree of common sense for a preference to be inferred. In this work, we present a method for transforming a user critique into a positive preference (e.g., "I prefer more romantic") in order to retrieve reviews pertaining to potentially better recommendations (e.g., "Perfect for a romantic dinner"). We leverage a large neural language model (LM) in a few-shot setting to perform critique-to-preference transformation, and we test two methods for retrieving recommendations: one that matches embeddings, and another that fine-tunes an LM for the task. We instantiate this approach in the restaurant domain and evaluate it using a new dataset of restaurant critiques. In an ablation study, we show that utilizing critique-to-preference transformation improves recommendations, and that there are at least three general cases that explain this improved performance.

Deb Norton spends her days helping teachers in Wisconsin's Oshkosh Area school district get more comfortable with technology tools they're using to engage students. A few years ago, she started seeing increasing mentions of artificial intelligence. Around then, the International Society for Technology in Education asked her to lead a course on the uses of artificial intelligence in the K-12 classroom. She was initially intrigued when she saw students light up at the mention of artificial intelligence. It soon became clear to her that they were already experiencing AI in their daily lives, with tools like Instagram filters or chatbots on websites.