Reliance has announced purchasing 72.69 percent of shares on a fully diluted basis of Bengaluru-based Embibe, an artificial intelligence-based education platform. Reliance has agreed to invest up to $180 million over three years into Embibe, which uses data analytics to deliver personalised learning to students. The ed-tech company will use the capital for business growth, geographical expansion, and to deepen its R&D on AI in education. It will also focus on catering to students across K-12, higher education, professional skilling, vernacular languages, and all curriculum categories across India and internationally, Reliance said. "The investment in Embibe underlines Reliance's commitment to growing the education sector in India and the world, and making education accessible to the widest possible group of students by deploying technology," said Reliance Jio director Akash Ambani.
Web-based social networks typically use public trust systems to facilitate interactions between strangers. These systems can be corrupted by misleading information spread under the cover of anonymity, or exhibit a strong bias towards positive feedback, originating from the fear of reciprocity. Trust propagation algorithms seek to overcome these shortcomings by inferring trust ratings between strangers from trust ratings between acquaintances and the structure of the network that connects them. We investigate a trust propagation algorithm that is based on user triads where the trust one user has in another is predicted based on an intermediary user. The propagation function can be applied iteratively to propagate trust along paths between a source user and a target user. We evaluate this approach using the trust network of the CouchSurfing community, which consists of 7.6M trust-valued edges between 1.1M users. We show that our model out-performs one that relies only on the trustworthiness of the target user (a kind of public trust system). In addition, we show that performance is significantly improved by bringing in user-level variability using mixed-effects regression models.
A few weeks ago, as COVID-19 wreaked havoc across the world, Facebook put down $5.7 billion for an almost 10% stake in Reliance's Jio Platform -- a company that has so many arms that you're surprised when they're not involved in something. The most important of Reliance's arms is Jio, the telecom company, which began operations in 2016 when Reliance chairman Mukesh Ambani leveraged his oil and gas business to scrape together over $30 billion in debt to jumpstart the company. So far, it's been a brilliant bet. The Facebook-Reliance deal has set off a whole tsunami of speculation on what this pact is really about and whom it will benefit. Many have lauded Facebook for inking what they believe is a pretty savvy deal by getting access, through Reliance Jio's consumers, to what is going to be a humongous market in India that a predator like Jio will most likely dominate.
The infrastructure of modern society is controlled by software systems. These systems are vulnerable to attacks; several such attacks, launched by "recreation hackers," have already led to severe disruption. However, a concerted and planned attack whose goal is to reap harm could lead to catastrophic results (for example, by disabling the computers that control the electrical power grid for a sustained period of time). The survivability of such information systems in the face of attacks is therefore an area of extreme importance to society. This article is set in the context of self-adaptive survivable systems: software that judges the trustworthiness of the computational resources in its environment and that chooses how to achieve its goals in light of this trust model.