Simulation has emerged as a popular method to study the long-term societal consequences of recommender systems. This approach allows researchers to specify their theoretical model explicitly and observe the evolution of system-level outcomes over time. However, performing simulation-based studies often requires researchers to build their own simulation environments from the ground up, which creates a high barrier to entry, introduces room for implementation error, and makes it difficult to disentangle whether observed outcomes are due to the model or the implementation. We introduce T-RECS, an open-sourced Python package designed for researchers to simulate recommendation systems and other types of sociotechnical systems in which an algorithm mediates the interactions between multiple stakeholders, such as users and content creators. To demonstrate the flexibility of T-RECS, we perform a replication of two prior simulation-based research on sociotechnical systems. We additionally show how T-RECS can be used to generate novel insights with minimal overhead. Our tool promotes reproducibility in this area of research, provides a unified language for simulating sociotechnical systems, and removes the friction of implementing simulations from scratch.

Federated Learning (FL) McMahan et al. [2017], a privacy-by-design machine learning approach, has introduced new ways to build recommender systems (RS). Unlike traditional approaches, the FL approach means that there is no longer a need to collect and store the users' private data on central servers, while making it possible to train robust recommendation models. In practice, FL distributes the model training process to the users' devices (i.e., the client or edge devices), thus allowing a global model to be trained using the user-specific local models. Each user updates the global model locally using their personal data and sends the local model updates to a server that aggregates them according to a pre-defined scheme. This is in order to update the global model. A prominent direction of research in this domain is based on Federated Collaborative Filtering (FCF) Ammad-Ud-Din et al. [2019], Chai et al. [2020], Dolui et al. [2019] that extends the standard Collaborative Filtering (CF) Hu et al. [2008] model to the federated mode. CF is one of the most frequently used matrix factorization models used to generate personalized recommendations either independently or in combination with other types of model Koren et al. [2009].

Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy. Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models. However, both big deep neural and language models are trained with huge amounts of data which often lies on the server side. Since text data is widely originated from end users, in this work, we look into recent NLP models and techniques which use federated learning as the learning framework. Our survey discusses major challenges in federated natural language processing, including the algorithm challenges, system challenges as well as the privacy issues. We also provide a critical review of the existing Federated NLP evaluation methods and tools. Finally, we highlight the current research gaps and future directions.

Amazon has rolled out some new ways to interact with its digital voice assistant, Alexa. The assistant available on Amazon devices such as Fire tablets and Echo speakers will now answer to a new name: Ziggy. Also, users can ask to Alexa to "change your voice" to someone with a more masculine or deeper voice. Although the assistant is always known as Alexa, users can update the wake words they use to get Alexa to answer, including "Amazon," "Echo" and "Computer." The new voice and wake words launched on July 15.

Financial institutions are using AI-powered solutions to unlock revenue growth opportunities, minimise operating expenses, and automate manually intensive processes. Many in the financial services industry believe strongly in the potential of AI. A recent survey by NVIDIA of financial services professionals showed 83% of respondents agreeing that AI is important to their company's future success. The survey, titled'State of AI in Financial Services', also showed a substantial financial impact of AI for enterprises with 34% of those who replied agreeing that AI will increase their company's annual revenue by at least 20%. The approach to using AI differed based on the type of financial firm.

Fox News congressional correspondent Jacqui Heinrich has the latest from Capitol Hill on'America Reports' The FBI was tipped off to a Texas man arrested Friday for allegedly assaulting police officers during the Capitol riot after messaging with a woman he met on the dating app Bumble in January, the Justice Department announced. Andrew Quentin Taake, 32, was charged with assaulting an officer, obstructing an official proceeding, and other offenses for his actions during the riot, which allegedly included pepper-spraying several officers and assaulting others with a whip-like weapon. The FBI received a tip from a woman he met on the online dating app, Bumble, on Jan. 9. Screenshots of their messages show that Taake sent the woman a selfie that was taken "about 30 minutes after being sprayed," allegedly telling the potential suitor that he was at the riot "from the very beginning." A woman who Andrew Quentin Taake matched with on Bumble tipped off the FBI about his alleged Capitol riot involvement. Taake allegedly flew to Washington, D.C., from Houston the day before the riot and returned home a few days later.

The latest Voice AI trend has been evolving day by day by adding value to third-party platforms such as Alexa or Google for implementing omnichannel voice AI experiences with the help of voice assistants to enhance customer experiences. According to the latest survey, 46% of the businesses across various industries are not sure about the change voice technology could bring into their organizations. Though this is the same case with the consumers, over the adoption of voice user interfaces emerges to increase at an expanding rate. According to Statista, the number of digital voice assistants is likely to reach 8.4billion units by 2024, which is more than the world's population. This is due to the increasing demand for Voice AI assistants for better convenience and accessibility.

The artificial intelligence (AI) and machine learning is getting stronger than ever. Many applications and projects have been developed based on AI already. Take the example of Apple Siri or the advertising algorithms that pushes products and services based on our Google search. The question though is, can AI take the place of a human and replace him or her?! Some believe we will be able to teach a robot or artificial material to perform tasks quickly and efficiently than a human. The idea falls on the line of a screwdriver where we use it because we cannot unscrew just by using our bare hands.

With the increasing importance of e-commerce and the immense variety of products, users need help to decide which ones are the most interesting to them. This is one of the main goals of recommender systems. However, users' trust may be compromised if they do not understand how or why the recommendation was achieved. Here, explanations are essential to improve user confidence in recommender systems and to make the recommendation useful. Providing explanation capabilities into recommender systems is not an easy task as their success depends on several aspects such as the explanation's goal, the user's expectation, the knowledge available, or the presentation method. Therefore, this work proposes a conceptual model to alleviate this problem by defining the requirements of explanations for recommender systems. Our goal is to provide a model that guides the development of effective explanations for recommender systems as they are correctly designed and suited to the user's needs. Although earlier explanation taxonomies sustain this work, our model includes new concepts not considered in previous works. Moreover, we make a novel contribution regarding the formalization of this model as an ontology that can be integrated into the development of proper explanations for recommender systems.

With the rapid development of mobile devices, speech-related technology is booming like never before. Many service providers like Google offer the ability to search through the voice on the android platform. For android mobile phones, 'Ok Google' uses this functionality to search a particular keyword to initiate the voice-based commands. Keyword recognition refers to speech technology that recognizes the existence of a word or short phrase within a given stream of audio. It is synonymously referred to as keyword spotting.