Recommender systems are proving to be an invaluable tool for extracting user-relevant content helping users in their daily activities (e.g., finding relevant places to visit, content to consume, items to purchase). However, to be effective, these systems need to collect and analyze large volumes of personal data (e.g., location check-ins, movie ratings, click rates .. etc.), which exposes users to numerous privacy threats. In this context, recommender systems based on Federated Learning (FL) appear to be a promising solution for enforcing privacy as they compute accurate recommendations while keeping personal data on the users' devices. However, FL, and therefore FL-based recommender systems, rely on a central server that can experience scalability issues besides being vulnerable to attacks. To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles. In PEPPER, users gossip model updates and aggregate them asynchronously. At the heart of PEPPER reside two key components: a personalized peer-sampling protocol that keeps in the neighborhood of each node, a proportion of nodes that have similar interests to the former and a simple yet effective model aggregation function that builds a model that is better suited to each user. Through experiments on three real datasets implementing two use cases: a location check-in recommendation and a movie recommendation, we demonstrate that our solution converges up to 42% faster than with other decentralized solutions providing up to 9% improvement on average performance metric such as hit ratio and up to 21% improvement on long tail performance compared to decentralized competitors.

A recommender system is a system that helps users filter irrelevant information and create user interest models based on their historical records. With the continuous development of Internet information, recommendation systems have received widespread attention in the industry. In this era of ubiquitous data and information, how to obtain and analyze these data has become the research topic of many people. In view of this situation, this paper makes some brief overviews of machine learning-related recommendation systems. By analyzing some technologies and ideas used by machine learning in recommender systems, let more people understand what is Big data and what is machine learning. The most important point is to let everyone understand the profound impact of machine learning on our daily life.

Artificial Intelligence is becoming more and more popular as more and more software is being created for our convenience. AI technology will soon become more common, and more and more intelligent software solutions will be created to make our lives easier. In this article, we will be covering 10 of the best Artificial Intelligence Software and giving a brief review of the software. Artificial intelligence software is becoming more and more popular, with Microsoft's Cortana voice assistant reaching a user base of over 100 million users. Cortana is a virtual assistant that can help you navigate your day-to-day life.

AI is the future of business, and it's already here. It has a great business impact that every entrepreneur should embrace. AI is a major focus of digital marketing right now, which means that businesses need to be on top of their game when it comes to virtual assistants. They're an AI-powered tool that can help with tasks like scheduling meetings, managing your inbox, analyzing data and more. Think of them like a digital assistant in your pocket -- except this one can do so much more than schedule your events or manage your inbox.

Artificial intelligence (AI) is redefining the travel industry and assisting businesses in achieving their objectives. AI in the travel industry is continuing to provide cutting-edge services to travelers. Tour and travel companies are incorporating artificial intelligence to improve the traveler experience. AI-enabled apps assist users or travelers in automatically adjusting the prices of flights or rooms based on their needs. Travelers today rely on technology to keep them moving in their pursuit of preferences worldwide.

Deep learning recommendation models (DLRMs) have been widely applied in Internet companies. The embedding tables of DLRMs are too large to fit on GPU memory entirely. We propose a GPU-based software cache approaches to dynamically manage the embedding table in the CPU and GPU memory space by leveraging the id's frequency statistics of the target dataset. Our proposed software cache is efficient in training entire DLRMs on GPU in a synchronized update manner. It is also scaled to multiple GPUs in combination with the widely used hybrid parallel training approaches. Evaluating our prototype system shows that we can keep only 1.5% of the embedding parameters in the GPU to obtain a decent end-to-end training speed.

The thrust for increased rating capacity of wind turbines has resulted into larger generators, longer blades, and taller towers. Presently, up to 16 MW wind turbines are being offered by wind turbines manufacturers which is nearly a 60 percent increase in the design capacity over the last five years. Manufacturing of these turbines involves assembling of gigantic sized components. Due to the frequent design changes and the variety of tasks involved, conventional automation is not possible making it a labor-intensive activity. However the handling and assembling of large components are challenging the human capabilities. The article proposes the use of mobile robotic assistants for partial automation of wind turbines manufacturing. The robotic assistant can result into reducing production costs, and better work conditions. The article presents development of a robot assistant for human operators to effectively perform assembly of wind turbines. The case is from a leading wind turbines manufacturer. The developed system is also applicable to other cases of large component manufacturing involving intensive manual effort.

A few short years ago, personal digital assistants like Amazon's Alexa, Apple's Siri, and Google Assistant sounded futuristic. Now, the future is here and this future is embedded, augmented, and ubiquitous. Digital assistants can be found in your office, home, car, hotel, phone, and many other places. They have recently undergone a massive transformation and run on operating systems that are fueled by artificial intelligence (A.I.). They observe and collect data in real-time and have the capability to pull information from different sources such as smart devices and cloud services and put the information into context using A.I. to make sense of the situation.

"Google fires engineer who contended its AI technology was sentient." A new discovery (or debacle) is reported practically every week, sometimes exaggerated, sometimes not. Policymakers struggle to know what to make of AI and it's hard for the lay reader to sort through all the headlines, much less to know what to be believe. Here are four things every reader should know. First, AI is real and here to stay.

What can we do in 24 hours? What happens in our lives between sunrise and sunset? What happens in 24 hours around the world? On average, in 24 hours, I will experience 104,000 heartbeats, I'll take a breath about 23,000 times, I'll walk about 8,000 steps on average, and in the shower, I'll spend about 12 minutes. My body will shed and create up to 50 trillion new cells, and I usually spend 20 minutes in the bathroom. There will be a 0.35 mm growth in my hair, and I will also lose somewhere between 40 and 100 hairs at the same time, and on average, I'll speak for roughly 48,000 words.