AI refers to a computer system that is trained to perform tasks with human-like abilities, especially cognitive abilities. The training process includes repetitive exposure to the same elements until the computer can recognize and recall those elements in future. As the computer learns, it also acquires the ability to self-correct and reason. The AI market is still growing. Predictions show that by 2026, demand for AI will reach over $300 billion.

Recommender system has been proven to be significantly crucial in many fields and is widely used by various domains. Most of the conventional recommender systems rely on the numeric rating given by a user to reflect his opinion about a consumed item; however, these ratings are not available in many domains. As a result, a new source of information represented by the user-generated reviews is incorporated in the recommendation process to compensate for the lack of these ratings. The reviews contain prosperous and numerous information related to the whole item or a specific feature that can be extracted using the sentiment analysis field. This paper gives a comprehensive overview to help researchers who aim to work with recommender system and sentiment analysis. It includes a background of the recommender system concept, including phases, approaches, and performance metrics used in recommender systems. Then, it discusses the sentiment analysis concept and highlights the main points in the sentiment analysis, including level, approaches, and focuses on aspect-based sentiment analysis.

One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users' commands, a task trivial for humans due to their common sense. In this paper, we propose a zero-shot commonsense reasoning system for conversational agents in an attempt to achieve this. Our reasoner uncovers unstated presumptions from user commands satisfying a general template of if-(state), then-(action), because-(goal). Our reasoner uses a state-of-the-art transformer-based generative commonsense knowledge base (KB) as its source of background knowledge for reasoning. We propose a novel and iterative knowledge query mechanism to extract multi-hop reasoning chains from the neural KB which uses symbolic logic rules to significantly reduce the search space. Similar to any KBs gathered to date, our commonsense KB is prone to missing knowledge. Therefore, we propose to conversationally elicit the missing knowledge from human users with our novel dynamic question generation strategy, which generates and presents contextualized queries to human users. We evaluate the model with a user study with human users that achieves a 35% higher success rate compared to SOTA.

Artificial Intelligence is no new concept. The phrase was first coined by John McCarthy in 1956[1], when he invited a group of researchers to discuss the notion of'thinking machines' during a conference at Dartmouth College. Since then, it has been a point of fascination for scientists, academics, software developers, and moviemakers alike. Fast-forward to today where you'll find lots of examples hiding in plain sight. From digital assistants like Amazon's Alexa or Apple's Siri, who use AI to learn from user interactions, to automated email responses and search engines predicting what you're looking for.

Artificial intelligence (AI) has virtually unlimited applications that are part of our everyday life. It offers countless solutions across all industries. Artificial intelligence is a major market player in the business world. AI plays a key role in data analysis, marketing, finance, business, advertising, medicine, technology, science and engineering where machines are learning from stimuli and reacting in ways more human than ever before. Artificial intelligence has several advantages and disadvantages, so it's important to know how to use it to maximize its potential within your organization.

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.