Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots. Although contextual reinforcement learning provides a principled framework for learning and generalization of behaviors across related tasks, it generally relies on uninformed sampling of environments from an unknown, uncontrolled context distribution, thus missing the benefits of structured, sequential learning. We introduce a novel relative entropy reinforcement learning algorithm that gives the agent the freedom to control the intermediate task distribution, allowing for its gradual progression towards the target context distribution. Empirical evaluation shows that the proposed curriculum learning scheme drastically improves sample efficiency and enables learning in scenarios with both broad and sharp target context distributions in which classical approaches perform sub-optimally.

Graph neural networks (GNN) such as GCN, GAT, MoNet have achieved state-of-the-art results on semi-supervised learning on graphs. However, when the number of labeled nodes is very small, the performances of GNNs downgrade dramatically. Self-training has proved to be effective for resolving this issue, however, the performance of self-trained GCN is still inferior to that of G2G and DGI for many settings. Moreover, additional model complexity make it more difficult to tune the hyper-parameters and do model selection. We argue that the power of self-training is still not fully explored for the node classification task. In this paper, we propose a unified end-to-end self-training framework called \emph{Dynamic Self-traning}, which generalizes and simplifies prior work. A simple instantiation of the framework based on GCN is provided and empirical results show that our framework outperforms all previous methods including GNNs, embedding based method and self-trained GCNs by a noticeable margin. Moreover, compared with standard self-training, hyper-parameter tuning for our framework is easier.

In this paper, we give a formalization of this shared pattern, which we call G IMLI, prove its general requirements, and derive a general-purpose algorithm for implementing similar approaches. Based on this analysis and algorithm, we describe a library of our design, higher, which we share with the community to assist and enable future research into these kinds of meta-learning approaches. We end the paper by showcasing the practical applications of this framework and library through illustrative experiments and ablation studies which they facilitate. 1 I NTRODUCTION Although it is by no means a new subfield of machine learning research (see e.g. Schmidhuber, 1987; Bengio, 2000; Hochreiter et al., 2001), there has recently been a surge of interest in meta-learning (e.g. This is due to the methods meta-learning provides, amongst other things, for producing models that perform well beyond the confines of a single task, outside the constraints of a static dataset, or simply with greater data efficiency or sample complexity. Due to the wealth of options in what could be considered "meta-" to a learning problem, the term itself may have been used with some degree of underspecification. However, it turns out that many meta-learning approaches, in particular in the recent literature, follow the pattern of optimizing the "meta-parameters" of the training process by nesting one or more inner loops in an outer training loop. Such nesting enables training a model for several steps, evaluating it, calculating or approximating the gradients of that evaluation with respect to the meta-parameters, and subsequently updating these meta-parameters.

Leaders - These are the likes of Google, Facebook, Microsoft, Yandex, Amazon etc. they develop ML libraries in languages like Python/C and export it to the world. They also read the latest research papers, extend new research, and push the frontier of ML. Working here is quite challenging because you need to be well versed with the theory of ML and also have good programming and software engineering skills. Followers - These are companies that use analytics to solve real life problems for e.g. in finance, loan prediction, or self driving cars, or retail analytics, or insurance propensity modelling, music recommendation etc. for these kind of roles, the emphasis is more on integrating the ML part with the product. You don't need to write a classifier code from scratch, but use existing libraries that the Leader companies create.

An AI-based eLearning platform is a machine/system that possesses the ability to perform different tasks requiring human intelligence. It maintains the ability to create solutions to human-related problems, like speech recognition, translations involving different languages, decision making, and much more. Even in our mobile devices, an Artificial Intelligence engine is incorporated to help with studying our patterns in order to create likely suggestions during texting. Even though the AI-based eLearning platform hasn't become a standard learning approach amidst most learning organizations, there's a need for it. Although Artificial Intelligence is not of much use, it's on the way to making a positive contribution toward the effectiveness of eLearning training.

The World Health Organization says that compulsively playing video games now qualifies as a new mental health condition, in a move that some critics warn may risk stigmatizing too many young players. Time in front of screens – TV, video games, smartphones – hurts kids' performance at school, right? Some screen time is worse than others when it comes to kids and academic performance, according to a new analysis published in JAMA Pediatrics, a respected medical journal. Television viewing, followed by video games, were the two activities most tied to poor school performance, researchers showed in a review of 58 studies published over the decades. That kind of screen time affected both children and teens – though overall, teens' performance seemed to suffer the most as screen time increased.

A picture of British mathematician Alan Turing hangs behind one of his notebooks during an auction preview in 2015. Turing argued that the ultimate test of a computer's intelligence was whether it could communicate with a human in a way indistinguishable from another human mind. Increasingly, AI-generated writing is making researchers think again about what the test really means. Jacob Berkowitz is a writer in Almonte, Ont., the founder of Quantum Writing and a writer-in-virtual-residence at University of Ottawa's Institute for Science, Society and Policy. I remember, clearly, my son's first word.

There is a general debate going on how ethical or unethical the use of AI is, however not many people are talking about the challenges in adoption of AI by Small and Medium-sized enterprises. So, before we go one pondering about how people will lose their jobs due to AI, or before we actually start looking for new careers without actually knowing what AI is about, let me take you through a few challenges we are facing in the implementation of Machine learning and Deep learning programs and apps developed on AI platforms, in the real world especially by the majority of businesses around the globe. AI phobia is not a new kind of fear, it is a fear which we have been living with all our lives due to the irrational works of fiction writers and movies. This fear has been around long before the technology was even developed if you have watched movies like Terminator, you know exactly what I am talking about. This phobia is so rampant that even great minds like Stephen Hawkings and Elon Musk have been very vocal about their irrational fear of AI.

There has never been a better time to indulge in the science and technology of Artificial Intelligence. We are a group of people who love what we do. Our founders have a wealth of experience working on various ground-breaking products including self driving cars, AWS AI services, GMail, Google Docs and flash storage systems. Backgrounds include key roles at Google, AWS, Uber, founding team of a startup which had a billion dollar IPO, and degrees from IIT, Stanford and Dartmouth. We are looking for talented backend software engineers, machine learning software engineers and research scientists to be part of the founding team.

With the unemployment rate at a low 3.7% and the skills shortage severe, corporations need to get creative about finding talented job candidates. IBM is among the technology giants testing new methods involving artificial intelligence to overcome the labor market challenges. AI has been applied to the job application process directly as a method to prevent human bias in hiring decisions. Now more companies are using AI assessment tools to reverse-engineer job roles and find candidates often overlooked by recruiters. IBM introduced its SkillsBuild platform in France in May 2019 with the goal of identifying job skills and employment opportunities for members of disadvantaged communities.