AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.

A recommendation engine is a system that suggests products, services, information to users based on analysis of data. Notwithstanding, the recommendation can derive from a variety of factors such as the history of the user and the behaviour of similar users. Recommendation systems are quickly becoming the primary way for users to expose to the whole digital world through the lens of their experiences, behaviours, preferences and interests. And in a world of information density and product overload, a recommendation engine provides an efficient way for companies to provide consumers with personalised information and solutions. A recommendation engine can significantly boost revenues, Click-Through Rates (CTRs), conversions, and other essential metrics.

Recommender Systems are becoming more and more relevant as the amount of information on "The Internets" is exponentially increasing: Finding what you might enjoy from books, movies, games to who to follow on Instagram becomes increasingly difficult. Moreover, we (the users) require faster interactions with the products we use on a daily basis, so we don't feel like we waste time (even though we do it more than ever before in human history). As the amounts of data increase, your computational resources might have a hard time to produce fast enough results. Here, we'll have a look at a succinct implementation of a Recommender System that is both the basis of many real-world implementations and is easy to understand. Traditionally, recommender systems are built around user ratings given for a set of items, e.g.

Artificial Intelligence (AI) and Machine Learning (ML) are two trendy buzzwords in the market right now, and often appear to be utilized interchangeably. They are not fairly the same thing, but the observation is that they many times direct to a little confusion. So I had deliberation to write this piece of a blog to clarify the difference. Both terminologies come into picture when the subject is data analytics, insights, Big Data and the wider ways how technological changes are driving the entire world. Artificial Intelligence (AI) is the wider concept of machines being able to execute tasks in a way that we would regard it as "smart".

Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsatisfying user experiences. In this paper, we propose a novel diversified recommendation model, named Diversified Contextual Combinatorial Bandit (DC$^2$B), for interactive recommendation with users' implicit feedback. Specifically, DC$^2$B employs determinantal point process in the recommendation procedure to promote diversity of the recommendation results. To learn the model parameters, a Thompson sampling-type algorithm based on variational Bayesian inference is proposed. In addition, theoretical regret analysis is also provided to guarantee the performance of DC$^2$B. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed method.

An Echo Dot owner claims that Amazon's Alexa assistant has started calling him a's*******' whenever he asks the personal assistant to play him music. Micheal Slade, 29, was reportedly shocked when his Echo Dot speaker began to swear at him following his cancellation of his Amazon Prime subscription. The incident has reportedly left Amazon engineers puzzled -- with the tech firm offering Mr Slade gift cards and a year of free Prime membership in compensation. Software is available for the Echo Dot speaker that can make Alexa curse -- but it is unclear whether someone might have deliberately uploaded this to the device. An Echo Dot owner claims that Amazon's Alexa assistant has started calling him a's*******' whenever he asks the personal assistant to play him music Online business owner and Cwmbran, South Wales resident Michael Slade, 29, said that the trouble began the day after called Amazon to cancel his subscription to their Prime subscription service.

Manulife has announced a new digitally immersive conversational AI solution for its app that aims to serve the needs of millennial clients. The company says that the conversational AI, called MAI, is a virtual assistant designed to help Canadians develop better financial habits and enhance their financial wellbeing. MAI can help users keep track of their balances, and can give them insight into their spending habits. It can also help by answering personal finance questions. The feature is part of the bank's new digital'Manulife All-In Banking Package.' The AI technology is powered by Kasisto, a conversational AI platform.

Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. In this paper, we propose an adaptive deep learning strategy for cross-domain recommendation, referred to as ADC. We design a neural architecture and formulate a cross-domain loss function, to compute the non-linearity in user preferences across domains and transfer the knowledge of users' multiple behaviors, accordingly. In addition, we introduce an efficient algorithm for cross-domain loss balancing which directly tunes gradient magnitudes and adapts the learning rates based on the domains' complexities/scales when training the model via backpropagation. In doing so, ADC controls and adjusts the contribution of each domain when optimizing the model parameters. Our experiments on six publicly available cross-domain recommendation tasks demonstrate the effectiveness of the proposed ADC model over other state-of-the-art methods. Furthermore, we study the effect of the proposed adaptive deep learning strategy and show that ADC can well balance the impact of the domains with different complexities.

Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge is to capture and weigh friends' preferences, as in practice they do necessarily match. In this paper, we propose a Neural Attention mechanism for Social collaborative filtering, namely NAS. We design a neural architecture, to carefully compute the non-linearity in friends' preferences by taking into account the social latent effects of friends on user behavior. In addition, we introduce a social behavioral attention mechanism to adaptively weigh the influence of friends on user preferences and consequently generate accurate recommendations. Our experiments on publicly available datasets demonstrate the effectiveness of the proposed NAS model over other state-of-the-art methods. Furthermore, we study the effect of the proposed social behavioral attention mechanism and show that it is a key factor to our model's performance.

If you've taken the leap and added this cute little button of a machine to your smart home arsenal, congratulations! The Google Home Mini is about to revolutionize your life in ways you probably haven't realized it was even capable of yet. While you won't ever get the same sound quality out of the Google Home Mini as you could expect from it's OG sibling (the Google Home), it is comparable in pretty much every other way. Fun fact: If you've got multiple Google devices within earshot (your phone, the Google Home, a few Google Home Minis…) and they all hear you call out an "OK, Google," or "Hey, Google," at once, they'll sync up to ensure only one replies. And that's just the beginning of what this mini smart speaker is capable of.