Grindr is tightening privacy controls for the Olympic Village in Beijing. Bloomberg has learned the gay dating app is blocking people outside the Village from using the location-based Explore feature to find athletes in or near the area. The move is meant to protect athletes from harassment or persecution so they can "feel confident" connecting with each other during the Winter Olympic Games, Grindr for Equality director Jack Harrison-Quintana said. Anyone who uses Grindr inside the Village will see a pop-up telling them people outside the area can't browse the locale using Explore. "Your privacy is important to us," Grindr says in the alert.

One of the stars of the working-out-from-home boom is struggling. Peloton won't go quietly though and is making some big changes. The company will replace the CEO and co-founder, John Foley, who will become executive chairman, with former Spotify COO Barry McCarthy reportedly set to step into his shoes. While Foley is sticking around, the company is cutting around 2,800 corporate positions -- these won't include Peloton's instructors who lead its live classes. The company said in a press release about the lay-offs that its "monthly membership will be complimentary for impacted team members for an additional 12 months."

In a busy hospital, a radiologist uses an artificial intelligence system to help her diagnose medical conditions based on patients' X-ray images. Using the AI system can help her make faster diagnoses, but how does she know when to trust the AI's predictions? Instead, she may rely on her expertise, a confidence level provided by the system itself, or an explanation of how the algorithm made its prediction -- which may look convincing but still be wrong -- to make an estimation. To help people better understand when to trust an AI "teammate," Massachusetts Institute of Technology researchers created a technique that guides humans to a more accurate understanding of when a machine makes correct predictions and when it makes incorrect ones. The research is supported by the U.S. National Science Foundation.

Consumers have incredibly high expectations for companies to deliver faster and more personalized experiences, which is fueling the demand for Artificial Intelligence (AI) solutions. In turn, the increase in this utilization of AI is driving growth for many startups. Here are some AI tactics you can use today to scale and grow your business. Around 90 percent of consumers consider an immediate response important, and they demand a connected experience whenever they interact with a brand. Therefore, more organizations are investing in smarter solutions for better customer support. One of the simplest ways to do this is to embrace AI-powered applications, such as chatbots, voice search, and virtual assistants to improve customer experience.

Below are some AI applications that you may not realise are AI-powered: Online shopping and advertising Artificial intelligence is widely used to provide personalised recommendations to people, based for example on their previous searches and purchases or other online behaviour. AI is hugely important in commerce: optimising products, planning inventory, logistics etc. Web search Search engines learn from the vast input of data, provided by their users to provide relevant search results. Digital personal assistants Smartphones use AI to provide services that are as relevant and personalised as possible. Virtual assistants answering questions, providing recommendations and helping organise daily routines have become ubiquitous. Machine translations Language translation software, either based on written or spoken text, relies on artificial intelligence to provide and improve translations.

On May 8, 2018, Google I/O was held at Shoreline Amphitheatre in Mountain View, California. If you are wondering what Google I/O is, don't worry, I've got your back. "Google I/O brings together developers from around the globe annually for talks, hands-on learning with Google experts, and the first look at Google's latest developer products." In the Keynote, Sundar Pichai, the CEO of Alphabet Inc. (Google's parent company), shared the then-latest developments that Google had been working on. One of the projects that he spoke about was something that maybe no one saw coming; an application of Artificial Intelligence (AI), soon to be on our own smartphones, that left the world in awe.

The creation of novel recommendation algorithms for social networks is currently struggling with the volume of available data originating in such environments. Given that social networks can be modeled as graphs, a distributed graph-oriented support to exploit the computing capabilities of clusters arises as a necessity. In this thesis, a platform for graph storage and processing named Graphly is proposed along with GraphRec, an API for easy specification of recommendation algorithms. Graphly and GraphRec hide distributed programming concerns from the user while still allowing fine-tuning of the remote execution.

Recommender systems have become essential tools in many application areas as they help alleviate information overload by tailoring their recommendations to users' personal preferences. Users' interests in items, however, may change over time depending on their current situation. Without considering the current circumstances of a user, recommendations may match the general preferences of the user, but they may have small utility for the user in his/her current situation.We focus on designing systems that interact with the user over a number of iterations and at each step receive feedback from the user in the form of a reward or utility value for the recommended items. The goal of the system is to maximize the sum of obtained utilities over each interaction session. We use a multi-armed bandit strategy to model this online learning problem and we propose techniques for detecting changes in user preferences. The recommendations are then generated based on the most recent preferences of a user. Our evaluation results indicate that our method can improve the existing bandit algorithms by considering the sudden variations in the user's feedback behavior.

The user ratings in recommendation systems are usually in the form of ordinal discrete values. To give more accurate prediction of such rating data, maximum margin matrix factorization (M3F) was proposed. Existing M3F algorithms, however, either have massive computational cost or require expensive model selection procedures to determine the number of latent factors (i.e. the rank of the matrix to be recovered), making them less practical for large scale data sets. To address these two challenges, in this paper, we formulate M3F with a known number of latent factors as the Riemannian optimization problem on a fixed-rank matrix manifold and present a block-wise nonlinear Riemannian conjugate gradient method to solve it efficiently. We then apply a simple and efficient active subspace search scheme to automatically detect the number of latent factors. Empirical studies on both synthetic data sets and large real-world data sets demonstrate the superior efficiency and effectiveness of the proposed method.

Recommender system has become an indispensable component in many e-commerce sites. One major challenge that largely remains open is the cold-start problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RaPare) to break this ice barrier. The center-piece of our RaPare is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RaPare strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering.