An artificial intelligence-powered platform designed to enhance traditional mobile banking with seamless crypto integration is about to launch v2 of its mobile application, describing it as an all-in-one crypto-financial solution. BlockBank, which describes itself as a platform designed for professional traders and new retail market participants, reported that the new version of its application is a significant advancement compared to what has been offered in the space so far. The application is said to consist of four main components: a centralized custodial wallet, a non-custodial Web 3.0 wallet, banking and an AI-powered robo-advisor. The team emphasized that users won't have to sacrifice security, privacy or decentralization when using its application. One of the measures taken to ensure a high level of security is BlockBank's partnership with Shield Finance, a multichain decentralized finance insurance aggregator, and Bridge Mutual, a platform that integrates a DeFi risk coverage application into its system.

Alexa is the natural language processing based system by Amazon. Alexa is the virtual assistant in products like Amazon Echo, Dot, Tap, FireTV and other third party products (there are 100 of these). The technology was first launched in 2012 and has now become an integral part of all of our lives. From kids have found someone who can help with their homework, to elderly who lean on Alexa as a reliable partner for reminding them of their medication to daily chores. For young professionals and busy moms it gives freedom to make lists, control smart homes etc with simply the use of voice commands.

Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset of thousands of items. We propose a Global-Local Kernel-based matrix completion framework, named GLocal-K, that aims to generalise and represent a high-dimensional sparse user-item matrix entry into a low dimensional space with a small number of important features. Our GLocal-K can be divided into two major stages. First, we pre-train an auto encoder with the local kernelised weight matrix, which transforms the data from one space into the feature space by using a 2d-RBF kernel. Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item. We apply our GLocal-K model under the extreme low-resource setting, which includes only a user-item rating matrix, with no side information. Our model outperforms the state-of-the-art baselines on three collaborative filtering benchmarks: ML-100K, ML-1M, and Douban.

In this paper we study the personalized book recommender system in a child-robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children's fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance and it can operate on embedded consumer devices with limited computational resources.

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.