With technological advancement and digitization, education industry is increasing at a fast pace. Education is changing dynamically with more and more privatization of this sector specially in India. From a data Fifty percent of India's population is the youth. This means that the Indian education sector is huge with a population of 1.13 billion. India has around 367 universities, 18,000 colleges, about half a million teachers, and 11 million pupils.

Despite what you hear about artificial intelligence, machines still can't think like a human, but in the last few years they have become capable of learning. And suddenly, our devices have opened their eyes and ears and cars have taken the wheel. Today, artificial intelligence is not as good as you hope and not as bad as you fear, but humanity is accelerating into a future that few can predict. That's why so many people are desperate to meet Kai-Fu Lee, the oracle of AI. Kai-Fu Lee is in there, somewhere, in a selfie scrum at a Beijing Internet Conference.

We provide a brief tutorial on the use of concentration inequalities as they apply to system identification of state-space parameters of linear time invariant systems, with a focus on the fully observed setting. We draw upon tools from the theories of large-deviations and self-normalized martingales, and provide both data-dependent and independent bounds on the learning rate. I. INTRODUCTION A key feature in modern reinforcement learning is the ability to provide high-probability guarantees on the finite-data/time behavior of an algorithm acting on a system. The enabling technical tools used in providing such guarantees are concentration of measure results, which should be interpreted as quantitative versions of the strong law of large numbers. This paper provides a brief introduction to such tools, as motivated by the identification of linear-time-invariant (LTI) systems.

Nonlinear embedding manifold learning methods provide invaluable visual insights into a structure of high-dimensional data. However, due to a complicated nonconvex objective function, these methods can easily get stuck in local minima and their embedding quality can be poor. We propose a natural extension to several manifold learning methods aimed at identifying pressured points, i.e. points stuck in the poor local minima and have poor embedding quality. We show that the objective function can be decreased by temporarily allowing these points to make use of an extra dimension in the embedding space. Our method is able to improve the objective function value of existing methods even after they get stuck in a poor local minimum.

With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is extremely challenging. In this paper, we present a general platform for personalized predictive modeling of behavioural states like students' level of stress. Through the use of Auto-encoders and Multitask learning we extend the prediction of stress to both sequences of passive sensor data and high-level covariates. Our model outperforms the state-of-the-art in the prediction of stress level from mobile sensor data, obtaining a 45.6 % improvement in F1 score on the StudentLife dataset.

This paper introduces a novel deep learning based method, named bridge neural network (BNN) to dig the potential relationship between two given data sources task by task. The proposed approach employs two convolutional neural networks that project the two data sources into a feature space to learn the desired common representation required by the specific task. The training objective with artificial negative samples is introduced with the ability of mini-batch training and it's asymptotically equivalent to maximizing the total correlation of the two data sources, which is verified by the theoretical analysis. The experiments on the tasks, including pair matching, canonical correlation analysis, transfer learning, and reconstruction demonstrate the state-of-the-art performance of BNN, which may provide new insights into the aspect of common representation learning.

This paper aims to question the suitability of the Turing Test, for testing machine intelligence, in the light of advances made in the last 60 years in science, medicine, and philosophy of mind. While the main concept of the test may seem sound and valid, a detailed analysis of what is required to pass the test highlights a significant flow. Once the analysis of the test is presented, a systematic approach is followed in analysing what is needed to devise a test or tests for intelligent machines. The paper presents a plausible generic framework based on categories of factors implied by subjective perception of intelligence. An evaluative discussion concludes the paper highlighting some of the unaddressed issues within this generic framework.

Some of the greatest scientists and inventors of the future are sitting in high school classrooms right now, breezing through calculus and eagerly awaiting freshman year at the world's top universities. They may have already won Math Olympiads or invented clever, new internet applications. We know these students are smart, but are they prepared to responsibly guide the future of technology? Developing safe and beneficial technology requires more than technical expertise -- it requires a well-rounded education and the ability to understand other perspectives. But since math and science students must spend so much time doing technical work, they often lack the skills and experience necessary to understand how their inventions will impact society.

The fight against videos altered by the use of artificial intelligence just got a new ally. According to researchers at UC Berkeley and the University of Southern California, a new algorithm can help spot whether a video has been manipulated via a process known as'deepfaking.' Counter-intuitively, the tool that scientists say will aid them in their crusade against faked videos happens to be the very same tool that helps make the videos in the first place: artificial intelligence. The fight against videos altered by the use of artificial intelligence just got a new ally. Pictured is a grab from a deep fake video where Steve Buscemi's face is superimposed over Jennifer Lawrence's body Deepfakes are so named because they utilize deep learning, a form of artificial intelligence, to create fake videos.

Japan aims to make a computer terminal available to every school student by around fiscal 2025, the education ministry said Tuesday. The target is included in the ministry's new plan to improve the educational environment through the use of technology. A ministry survey in March 2018 found that computers were distributed on average at a rate of 1 terminal per 5.6 students at public elementary and high schools across the country. By prefecture, Saga performed best with a rate of 1 terminal per 1.8 students, while Saitama saw the worst rate of 1 terminal per 7.9 students. To further increase the number of computers at schools, the ministry's plan showed examples of how computers can be procured at lower costs.