Ever wondered how Google assistant and Siri can speak with us exactly like humans. This is the magic of Deep Learning. So without wasting time let's jump directly to the topic. The above diagram will help you to get an overview of how the process happens inside the voice assistant. First I will explain each process in-depth and in the end, I will summarise the entire process with the help of an example.

What is powering the onslaught of Artificial Intelligence in every industry across the world? In very simple words, you teach the machine how to derive results. The results purely depend on algorithms used and the data that is poured to train/teach the machine. Machine learning is being used to power recommendation systems, audio/video classification software, autonomous driving, and many more industrial processes. There are 97 million songs in the world, now these are just the songs that are documented.

Almost half of dating app users would trust AI to find them a match, according to new research from cybersecurity firm Kaspersky. Their trust could put them in danger, however. Kaspersky also warned that many dating apps have major privacy risks. Up front: The mass adoption of dating apps means people now find potential partners through algorithmic recommendations. Here's how your business can benefit from free citizen data To investigate the tech's effect on relationships, Kaspersky commissioned Sapio to survey more than 18,000 dating app users from six continents.

All the sessions from Transform 2021 are available on-demand now. Device42, a cloud discovery platform, this month launched a multicloud migration and recommendation engine the company claims is the first to support all major cloud providers. Using machine learning to drive its suggestions, Device42 says the service can perform real-time discovery of IT resources to create an inventory, leveraging dependency mapping to show the relationship and impact of resources on business units. Organizations often face risks of business outages and disruptions when attempting to migrate to the cloud. And according to IDG research, only 25% achieve their initial goals.

Nothing, the hardware startup from OnePlus co-founder Carl Pei, has officially unveiled its first product: the Ear 1 wireless earbuds. If you've been following the teasers, you're probably familiar with the buds by now. Nothing already revealed the $99 price tag, pitting them against affordable rivals like the Amazon Echo Buds, OnePlus Buds and Google's Pixel Buds A; showed off the transparent design; and detailed the active-noise cancellation (ANC), which relies on a three-microphone setup. All that was really left was the release date and some gaps around specs, which are getting filled in today. The Ear 1 will initially be available online at nothing.tech as part of a limited drop on July 31st starting at 9AM ET.

Amazon's latest Echo Buds have dropped to below $100 for the first time since Prime Day. Best Buy is offering the active noise-canceling earbuds for $80, down from their normal price of $120. The Alexa-powered buds were already affordable, but the latest deal should help sway the neutrals. Especially considering that they were only released in April. Amazon improved its Echo Buds in all the right places with the second-gen model.

This post is sponsored by NVIDIA. AI is enabling digital transformation across the financial services industry, from fintech and investment firms to commercial and retail banks. With AI, banks can better protect their customers' accounts, secure payments, improve return on investments, and personalize content, investments, and next-action recommendations for their customers. These AI-enabled services were also the top use cases for AI found in the NVIDIA "State of AI in Financial Services" survey of C-suite leaders, managers, developers and IT architects in the global financial industry: fraud detection, portfolio optimization, and sales and marketing enablement. The growing capabilities of AI and increase in available data mean that financial firms need to execute an AI strategy, or risk being left behind their competitors.

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.