Indian Institute of Technology (IIT) Madras intends to begin a variety of artificial intelligence initiatives for the Responsible AI Center with the recent funding of $1 million from Google. FE Education was informed by B Ravindran, director of the Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras. According to Ravindran, "We seek to increase the accuracy rate of any AI-based system and focus on excluded populations and women-specific industries." It's interesting to note that the Responsible AI Center at IIT Madras will work in collaboration with many stakeholders. "One of our stakeholders is Google. We intend to collaborate with other industry leaders from a variety of fields, such as law, finance, and education, in the upcoming fiscal year, Ravindran said. The AI Center seeks to advance the use of artificial intelligence in a variety of industries, including banking and finance, government policy, education, and health care, among others. To make AI accessible in all industries throughout India, Ravindran stated, "We will also do seminars and awareness campaigns.

In applications including chemoinformatics, bioinfor- matics, information retrieval, text classification, com- puter vision and others, a variety of common issues have been identified involving frequency of occurrence, variation and similarities of instances, and lack of pre- cise class labels. These issues continue to be important hurdles in machine intelligence and my doctoral thesis focuses on developing robust machine learning models that address the same.

It's often said that data is the oil of the twenty-first century, and artificial intelligence is the driving force. Companies in all sectors are combining the reasoning abilities of the human mind with the processing power of computers, developing algorithms that can trawl through colossal data sets to help businesses make more informed decisions. That means that tomorrow's future business leaders need more than a passing familiarity with AI. For this reason, several of the world's best business schools have launched specialist master's programs in AI. Canada's Smith School of Business, the University of Bologna in Italy, and Imperial College London are among the top-tier institutions running AI MSc courses that give students the technical, managerial and interpersonal skills they need to master machines.

Much of his work is directed toward understanding interactions and learning from them. Ravindran is also a co-organiser of several noted data science and AI-focused conferences, seminars and workshops in India. Bidyut Baran Chaudhuri is the founding head of Computer Vision and Pattern Recognition Unit at ISI, Calcutta.

It was his unique teaching style that got me and a bunch of my friends hooked to this topic and field – his enthusiasm towards the material, the intuitive examples that he gives…,

The Shapley value---probably the most important normative payoff division scheme in coalitional games---has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. For instance, in the case of unweighted networks our algorithms are able to return the exact solution about 1600 times faster than the Monte Carlo approximation, even if we allow for a generous 10% error margin for the latter method.

In reinforcement learning (RL), rewards have been considered the most important feedback in understanding the environment. However, recently there have been interesting forays into other modes such as using sporadic supervisory inputs. This brings into the learning process richer information about the world of interest. In this paper, we model these supervisory inputs as specific types of instructions that provide information in the form of an expert's control decision and certain structural regularities in the state space. We further provide a mathematical formulation for the same and propose a framework to incorporate them into the learning process.