Raghu Ravinutala is CEO and co-founder of enterprise-grade conversational AI platform Yellow.ai. After 27 years since the commercialization of the internet, 5 billion people have an online presence today. The first two phases of the Web--Web 1.0 and 2.0--were relatively staid compared to what's coming. Today, we're on the threshold of Web3 and the metaverse--internet environments where transactions are logged on the blockchain and powered by AI. By 2026, Web3 technologies--such as digital twins, smart spaces, virtual and augmented reality, and advanced virtual assistants--will transform how people interact with the world.

Recommendation systems have been widely used in various domains such as music, films, e-shopping, etc. After mostly avoiding digitization, the art world has recently reached a technological turning point due to the pandemic, making online sales grow significantly as well as providing quantitative online data about artists and artworks. In this work, we present a content-based recommendation system on contemporary art relying on images of artworks and contextual metadata of artists. We gathered and annotated artworks with advanced and art-specific information to create a completely unique database that was used to train our models. With this information, we built a proximity graph between artworks. Similarly, we used NLP techniques to characterize the practices of the artists and we extracted information from exhibitions and other event history to create a proximity graph between artists. The power of graph analysis enables us to provide an artwork recommendation system based on a combination of visual and contextual information from artworks and artists. After an assessment by a team of art specialists, we get an average final rating of 75% of meaningful artworks when compared to their professional evaluations.

In the late summer of 2020, when much of normal social life was suspended, a relationship that I had been in for several years abruptly collapsed. I was thirty-nine and scared by the idea that I would not be reproducing the kind of heteronormative nuclear family I had grown up in. I wandered the sidewalks of my Brooklyn neighborhood, where discarded masks littered the gutters, with a sense of having been exiled from my own life. My apartment, with its cat and its plants, still existed but was no longer my home; I could get a glass of cold prosecco at my favorite bar, but the people I used to see there seemed to have vanished. In Haruki Murakami's novel "1Q84," a character climbs down a ladder into a parallel existence in which things appear to be the same but nothing really is.

Aside from Black Friday, Amazon Prime Day is the best time of year to pick up an Echo device since most of them are deeply discounted. Amazon didn't disappoint this year -- all of its Echo Show smart displays are on sale for Prime Day, key among them being the Echo Show 8 for $75. Also on sale are the Echo Show 5 for only $35, the swiveling Echo Show 10 for $180 and the Echo Show 15 for $180 as well. It's the second-generation Show 8 that you can get for $75, and we gave it a score of 87 when it came out last year. We like its 8-inch, 1,280 x 800-resolution touchscreen, minimalist design and solid sound quality.

The'intelligence' of AI is growing all the time. And AI has many forms, from Spotify's recommendation system to self-drive cars. AI utilises natural language processing (NLP) to deliver natural and human-like language. It mimics humans and generates human-like messages by analysing commands. That said, it is still challenging to create an AI tool that understands the nuances of natural human languages is hard.

In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs). Leveraging these data, various doctor recommendation systems have been proposed. Typically, such studies process the EHR data in a flat-structured manner, where each encounter was treated as an unordered set of features. Nevertheless, the heterogeneous structured information such as service sequence stored in claims shall not be ignored. This paper presents a doctor recommendation system with time embedding to reconstruct the potential connections between patients and doctors using heterogeneous graph attention network. Besides, to address the privacy issue of patient data sharing crossing hospitals, a federated decentralized learning method based on a minimization optimization model is also proposed. The graph-based recommendation system has been validated on a EHR dataset. Compared to baseline models, the proposed method improves the AUC by up to 6.2%. And our proposed federated-based algorithm not only yields the fictitious fusion center's performance but also enjoys a convergence rate of O(1/T).

With the advent of big data era and the development of artificial intelligence and other technologies, data security and privacy protection have become more important. Recommendation systems have many applications in our society, but the model construction of recommendation systems is often inseparable from users' data. Especially for deep learning-based recommendation systems, due to the complexity of the model and the characteristics of deep learning itself, its training process not only requires long training time and abundant computational resources but also needs to use a large amount of user data, which poses a considerable challenge in terms of data security and privacy protection. How to train a distributed recommendation system while ensuring data security has become an urgent problem to be solved. In this paper, we implement two schemes, Horizontal Federated Learning and Secure Distributed Training, based on Intel SGX(Software Guard Extensions), an implementation of a trusted execution environment, and TensorFlow framework, to achieve secure, distributed recommendation system-based learning schemes in different scenarios. We experiment on the classical Deep Learning Recommendation Model (DLRM), which is a neural network-based machine learning model designed for personalization and recommendation, and the results show that our implementation introduces approximately no loss in model performance. The training speed is within acceptable limits.

Today, we have more information readily available at our fingertips (or by simple voice command) than any other time in history. Whenever you pose a question to Amazon's Alexa or the Google Assistant, you're effectively asking an artificial intelligence (AI) search algorithm to cull the Internet for a brief sentence or two that will answer your question. Increasingly, architects are using AI-leaning software tools in a similar way, calling on algorithms to cull the world of architectural possibilities quickly and efficiently for design approaches that help to meet the growing demand for sustainable architecture and green technology. "In architecture, AI is generally synonymous with generative design -- or, as I like to call it, 'optioneering,'" explains Dan Stine, director of design technology at Lake Flato Architects, San Antonio, Texas. "Our software tools use algorithms that generate a large number of design options based on parameters we define, then rank those options according to how well they meet our criteria. Ultimately, we select the option that works best for a given project."

It's not just that one uncle who's not allowed at Thanksgiving anymore who's been spreading misinformation online. The practice began long before the rise of social media -- governments around the world have been doing it for centuries. But it wasn't until the modern era, one fueled by algorithmic recommendation engines built to infinitely increase engagement, that nation-states have managed to weaponize disinformation to such a high degree. In his new book Tyrants on Twitter: Protecting Democracies from Information Warfare, David Sloss, Professor of Law at Santa Clara University, explores how social media sites like Facebook, Instagram, and TikTok have become platforms for political operations that have very real, and very dire, consequences for democracy while arguing for governments to unite in creating a global framework to regulate and protect these networks from information warfare. Excerpted from Tyrants on Twitter: Protecting Democracies from Information Warfare, by David L. Sloss, published by Stanford University Press, 2022 by the Board of Trustees of the Leland Stanford Junior University.

In an online wild west populated by scammers and hackers, dating apps pose challenges beyond just finding a partner. It's getting harder to tell if your date is who they say they are, and that's before you consider the data security and privacy implications of using the apps on your smartphone. It's difficult to maintain privacy when apps such as Hinge, Tinder and Bumble need to collect data to match you with potential dates. Then there's the data you share with other users – including your sexual orientation, age and social media information – that could put you at risk if it gets into the wrong hands. Here's what you need to know about using dating apps safely and privately, while still getting the most out of them.