Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing recommendations to users. In reality, however, various important and interesting phenomena only emerge or become visible over time, e.g., when a recommender system continuously reinforces the popularity of already successful artists on a music streaming site or when recommendations that aim at profit maximization lead to a loss of consumer trust in the long run. In this paper, we discuss how Agent-Based Modeling and Simulation (ABM) techniques can be used to study such important longitudinal dynamics of recommender systems. To that purpose, we provide an overview of the ABM principles, outline a simulation framework for recommender systems based on the literature, and discuss various practical research questions that can be addressed with such an ABM-based simulation framework.

Artificial Intelligence (AI) has already found its applications across sectors, and nowadays, it has become a hot-selling potato in the automotive industry, especially in cars. The global automotive industry is projected to reach $9 trillion by 2030, as per the Statista report. As a result, carmakers are embedding AI-based solutions for a better customer experience, thereby ensuring good market share for themselves. In this article, we list recent AI features embedded in cars by auto firms in 2021. The British automotive company MG featured a personal AI assistant and first-in-segment Autonomous Level 2 technology in their soon to be launched mid-size SUV – Astor.

Self-emptying robot vacuums are all the rage, and Yeedi has jumped on the trend with its Yeedi Vac station. This robot vacuums, mops, and self-empties its dustbin, providing about 30 days of automated cleaning before you must swap out its dust bag. It also maps room layouts to enable customized cleaning, and it supports voice control through Amazon Alexa and Google Assistant, making it an attractive option for people who don't want to break the bank for the luxury of fully automated floor cleaning. Both the robot and its auto-empty station are finished in matte white. There's a 3D visual SLAM camera on top that the robot uses to map your space for more efficient cleaning routes. The auto-empty station stores a 2.5-liter disposable dust bag that can hold around 30 days of collected dirt and debris.

The Siri voice assistant is a favorite tool of both young and old as even youngsters who are only starting to talk know about it and ask Siri for help! Yes, it does talk with the voice of Susan Bennett, yet it provides a seamless way of human-like interaction.

Artificial Intelligence is a brand-new term. The majority of us are unsure what it is. However, we all employ Artificial Intelligence in some manner in our daily lives. We begin our day with AI when we wake up to the alarm set by Google Assistant. When you use the Google search engine to find a local restaurant, you use AI once again.

If you know Siri, Cortana from Microsoft, Denise from Nextos, Alexa from Amazon or those handy voice GPS directions on smartphones, then congrats! This course will just help you bridge the gap through an ocean of knowledge with the power of Artificial intelligence based TTS tools. Text to speech, abbreviated as TTS, is a synthesis of speech that transforms text into voice output. Text to speech systems was first developed to help the visually impaired by providing the user with a spoken voice created by a machine that would "read" text. Text to speech enables content owners to adapt in terms of how they communicate with the content to the specific needs and desires of each user.

By this summer, Martine had her dating ritual figured out. It was a series of small actions that changed the mood in her Queens bedroom, so that she could contemplate making flirtatious chit-chat from the same space that she'd been using to perform her sales job, take online classes, exercise, and sleep. "I'd turn off everything," she told me. She'd sit on her floor, cross-legged, and meditate for twenty minutes. Next, she'd pull out her essential oils, open a bottle of something nice, lavender or peppermint, and take a sniff.

Simply put across, AI is described as as any task performed by a program or a machine that requires application of human like intelligence to accomplish the task. It's technical simulation i.e., technology which uses complex algorithmic techniques to simulate the way neurons works in human brain. Neurons are the basic unit of our nervous system. AI is superset of Machine learning, Cognitive learning and deep learning, Reinforcement Learning. ML is algorithmic & statistical approach to approximate conclusions, predictions without direct human input.

Understanding and comprehending video content is crucial for many real-world applications such as search and recommendation systems. While recent progress of deep learning has boosted performance on various tasks using visual cues, deep cognition to reason intentions, motivation, or causality remains challenging. Existing datasets that aim to examine video reasoning capability focus on visual signals such as actions, objects, relations, or could be answered utilizing text bias. Observing this, we propose a novel task, along with a new dataset: Trope Understanding in Movies and Animations (TrUMAn), with 2423 videos associated with 132 tropes, intending to evaluate and develop learning systems beyond visual signals. Tropes are frequently used storytelling devices for creative works. By coping with the trope understanding task and enabling the deep cognition skills of machines, data mining applications and algorithms could be taken to the next level. To tackle the challenging TrUMAn dataset, we present a Trope Understanding and Storytelling (TrUSt) with a new Conceptual Storyteller module, which guides the video encoder by performing video storytelling on a latent space. Experimental results demonstrate that state-of-the-art learning systems on existing tasks reach only 12.01% of accuracy with raw input signals. Also, even in the oracle case with human-annotated descriptions, BERT contextual embedding achieves at most 28% of accuracy. Our proposed TrUSt boosts the model performance and reaches 13.94% performance. We also provide detailed analysis to pave the way for future research. TrUMAn is publicly available at:https://www.cmlab.csie.ntu.edu.tw/project/trope

From automating the creation of personalized videos to enabling new virtual customer experiences, these companies are deploying AI to help brands and retailers create engaging digital content. Brands and retailers are relying more and more on digital content -- which can range from product images for e-commerce sites to virtual try-on features to online videos -- to increase brand awareness, convert online shoppers, and boost loyalty. Video content is gaining particular momentum, with the number of times execs have mentioned the term during earnings calls shooting up in Q2'21. But given these ever-growing digital content needs, a trend accelerated by the Covid-19 pandemic, conventional production approaches may not be sufficient for brands and retailers to deliver personalized and engaging content at scale. Enter synthetic media -- images, videos, sounds, or any other form of content that has been generated, edited, or enabled by artificial intelligence.