This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i.e., previous turns of question-answer pairs). CQG is a crucial task for developing intelligent agents that can drive question-answering style conversations or test user understanding of a given passage. Towards that end, we propose a new approach named Reinforced Dynamic Reasoning (ReDR) network, which is based on the general encoder-decoder framework but incorporates a reasoning procedure in a dynamic manner to better understand what has been asked and what to ask next about the passage. To encourage producing meaningful questions, we leverage a popular question answering (QA) model to provide feedback and fine-tune the question generator using a reinforcement learning mechanism. Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method in comparison with various baselines and model variants. Moreover, to show the applicability of our method, we also apply it to create multi-turn question-answering conversations for passages in SQuAD.

Hierarchical Reinforcement Learning algorithms have successfully been applied to temporal credit assignment problems with sparse reward signals. However, state-of- the-art algorithms require manual specification of sub-task structures, a sample inefficient exploration phase and lack semantic interpretability. Human infants, on the other hand, efficiently detect hierarchical substructures induced by their surroundings. In this work we propose a cognitive-inspired Reinforcement Learning architecture which uses grammar induction to identify sub-goal policies. More specifically, by treating an on-policy trajectory as a sentence sampled from the policy-conditioned language of the environment, we identify hierarchical constituents with the help of unsupervised grammatical inference. The resulting set of temporal abstractions is called action grammars (Pastra & Aloimonos, 2012) and can be used to enable efficient imitation, transfer and online learning.

The current edition of this books is the 3rd Edition and I strongly suggest that every programmer should have this in their bookshelf, but only for short reading and references. It's not possible to finish this book in one sitting and some of you may find it difficult to read as well, but don't worry, you can combine your learning with an online course like Data Structures and Algorithms: Deep Dive Using Java along with this book. This is like the best of both world, you learn basic Algrotihsm quickly in an online course and then you further cement that knowledge by going through the book, which would make more sense to you now that you have gone through a course already.

Machine Learning's ability to instantly detect anomalies more efficiently is enabling enterprises to make a smooth transition from traditional ruled-based processes to intelligent solutions by using unstructured data sets. We help our clients build custom anomaly detection and self-optimizing machine learning models to prevent, detect, and manage frauds.We develop our ML-driven fraud detection and prevention models based upon our clients' risk profile and specific pain points. Our advanced fraud prevention models constantly learn and prevent traditional and trending tactics. Be it reducing application fraud, retail and eCommerce fraud or open-account fraud-we will help build the best model for your business.

VI's vary greatly depending on how they are deployed. Virtual intelligence (VI) programs that make intelligent decisions based on the virtual environments built around them, or merely interact with their environments in some manner. Below are the Critical Components to Creating a VI Platform. Artificial Intelligence is a technological term which deals with machines demonstrating intelligence like humans. Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.Most common day to day examples of AI is voice assistants (Siri, Alexa), self-driving cars, text and other predictions, smart email filtering.

Like any developing technology, there has always remained an atmosphere of intrigue encircling the idea of Artificial Intelligence (AI), as well as its applicability in various industries. The discovery of AI has been discussed over the years. Some see this technology as the beginning step towards a life where human professions are no longer needed. Whereas, others see it as a cost-efficient method of being more productive. The technology has its benefits and risks however, the actuality may fall somewhere among these limits, especially when it comes to the industry of education.

Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library! Welcome to the best online course for learning about Deep Learning with Python and PyTorch! PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python.

The prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities and backgrounds. There is thus a growing need to accommodate for individual differences in e-learning systems. This paper presents an algorithm called EduRank for personalizing educational content to students that combines a collaborative filtering algorithm with voting methods. EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. These aspects include grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for each student, rather than ordering them according to the student's predicted score. The EduRank algorithm was tested on two data sets containing thousands of students and a million records. It was able to outperform the state-of-the-art ranking approaches as well as a domain expert. EduRank was used by students in a classroom activity, where a prior model was incorporated to predict the difficulty rankings of students with no prior history in the system. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance.

"The unprecedented explosion in the amount of information we are generating and collecting, thanks to the arrival of the internet and the always-online society, powers all the incredible advances we see today in the field of artificial intelligence (AI) and Big Data." Banks can better predict loan defaults, retailers can improve customer experience, and much more.

Experts think artificial intelligence could help people do all sorts of things over the next couple of decades: power self-driving cars, cure cancer, and yes, transform K-12 education. Artificial Intelligence has always been part of our collective imagination. There is, of course, a ton of hype. Experts think this new type of "machine learning" could help people do all sorts of things over the next couple of decades: power self-driving cars, cure cancer, cope with global warming, and yes, transform K-12 education and the jobs students are preparing for. It's too early to say how much of that promise will end up bearing out. But it's a good idea for educators to get familiar with AI, whether they are the chief technology officer of a large urban district or a 1st grade teacher in a rural community.