Major disruptive technologies like AI are a boon for different kinds of industries as well as consumers worldwide. Almost all AI models are intended to help patients, senior citizens, children, youth, and other age groups across the world. We need to know what has AI promised to enhance the lives of handicapped people. AI for handicapped people is necessary to understand because there are millions of tech-savvy handicapped people who can reap the benefits of cutting-edge technologies. The Paralympics has implemented multiple AI models for disabled people who are professional players and spectators to represent their respective countries.

The world of AI and Machine Learning has many layers and can be quite complex to learn. Many terms are out there and unless you have a basic understanding of the landscape it can be quite confusing. In this article, expert Eric Enge will introduce the basic concepts and try to demystify it all for you. This is also the first of a four-part article series to cover many of the more interesting aspects of the AI landscape. There are so many different terms that it can be hard to sort out what they all mean.

"For more technology and data that one integrates into a software, in the end always experience and learning are the fundamental pillars. The important thing is to understand how to extract them intelligently ". With that phrase, Álvaro Echeverría, co-founder and CEO of SimpliRoute, recalls the need that shaped the idea of creating an AI virtual assistant to optimize its logistics platform. The startup is dedicated to optimizing routes for dispatch vehicles. The problem, according to Echeverría, was that despite the fact that logarithms and data science effectively optimize logistics a lot, "there are things that no default software can evaluate, such as whether a street is in poor condition, whether it is too narrow for a truck. This valuable information is held by the drivers ".

Recommender Systems are algorithms that predict a user's preference for an item. Reciprocal Recommenders are a subset of recommender systems, where the items in question are people, and the objective is therefore to predict a bidirectional preference relation. They are used in settings such as online dating services and social networks. In particular, images provided by users are a crucial part of user preference, and one that is not exploited much in the literature. We present a novel method of interpreting user image preference history and using this to make recommendations. We train a recurrent neural network to learn a user's preferences and make predictions of reciprocal preference relations that can be used to make recommendations that satisfy both users. We show that our proposed system achieves an F1 score of 0.87 when using only photographs to produce reciprocal recommendations on a large real world online dating dataset. Our system significantly outperforms on the state of the art in both content-based and collaborative filtering systems.

However, next they ask you to submit a selfie of yourself putting your right hand on your head, to prove that your picture really is of you. You don't know how to contact the man in the stock photo and you're not sure that he would send you a selfie. You do your best, but Bumble rejects your effort. There's no option to change your initially submitted profile photo until you've passed this verification so you abandon this account and start again. You don't want to compromise your privacy by submitting real photos of yourself, so you take a profile picture of Jenna the intern and then another picture of her with her right hand on her head. She is confused but she knows who pays her salary, or at least who might one day pay her salary if the next six months go well and a suitable full-time position is available. You take the same set of photos of Wilson in…marketing?

Artificial intelligence (AI) is the study and design of algorithms that perform tasks or behaviors that a person could reasonably deem to require intelligence if a human were to do it. Broadly construed, an intelligent system can take many forms: a system designed to be indistinguishable from humans; a speech assistant such as Alexa, Siri, Cortana, or Google Assistant; a self-driving car; a recommender in an online commerce site; or a nonplayer character in a video game. We refer to intelligent systems as agents when they are capable of making some decisions on their own based on given goals. Machine learning (ML) is a particular approach to the design of intelligent system in which the system adapts its behavior based on data. It is the success of ML algorithms in particular that have lead to recent growth in commercialization of AI.

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.