Artificial Intelligence (AI) is one of the most booming topics in every industry. Based on the demand, Artificial Intelligence Courses are offered by a number of massive open online courses (MOOCs) providers like Udemy, Coursera, and edX. Some of this popular MOOC providers offer some in-depth artificial intelligence programs. Majority of these artificial intelligence tutorials are often taught by industry top AI researchers or experts. However, these courses are cheaper compared to the university courses.

Imagine if something not designed with you or anyone like you in mind was the driving force of how regular interactions permeate your life. Imagine it controls what products are marketed to you, how you can use certain consumer products (or not), influences your interactions with law enforcement, and even determines your health care diagnoses and medical decisions. There are problems brewing at the core of artificial intelligence and machine learning (ML). AI algorithms are essentially opinions embedded in code. AI can create, formalize, or exacerbate biases by not including diverse perspectives during ideation, testing, and implementation.

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator's actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD's performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.

An artificial intelligence enabled robot has passed the written test of China's national medical licensing examination for the first time, marking another milestone in the quest for AI technology to match or surpass human intelligence. Named Xiaoyi, the robot developed by Tsinghua University and Chinese information technology firm iFlytek, achieved a score of 456, 96 points higher than the required mark of 360 points, according to a company announcement. To pass the text, Xiaoyi was required to memorize and understand the contents of one million medical images, 53 medical books, two million medical records, and 400,000 pieces of medical literature and medical reports, a task which normally takes five years of study by a medical student. The robot reportedly failed an earlier attempt to pass the test. "Xiaoyi's successful pass in the written exam represent a significant development in the field of cognitive intelligence," said iFlytek in the company announcement.

It isn't just the tech entrepreneurs and Hollywood directors who dream about the role that artificial intelligence can play, or will play, in everyday human life--educators have begin to join them. However, those dreams aren't always pleasant and may, in fact, sometimes turn into nightmares. If computer systems are able to perform tasks that humans have performed for thousands of years, will it render teachers and administrators a thing of the past? Or is artificial intelligence the secret to freeing up educators' time for other, non-routine tasks, like mentoring and spending more one-on-one time with students? To find out, I went straight to the source--eight educators, including superintendents, coaches and teachers--to find out whether AI tickles their fancy or scares them straight.

If your computer doesn't have a GPU or has a non-Nvidia GPU, you have several great options: Use Crestle, through your browser: Crestle is a service (developed by fast.ai

There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... There's also a great appetite for learning about Data Science too. Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list.

The old paradigms of Data Architecture are evolving at an ever-increasing rate. Past enterprise architectures are undergoing significant technological changes in the face of new trends, including Big Data, non-relational data stores, Blockchain, Internet of Things, Machine Learning and Artificial Intelligence, Data Lakes, and many others. Enterprises must still contend with their foundational data assets and legacy systems though -- Data Governance, Master Data Management, Data Quality, Data Modeling, and other traditional Data Management concepts and practices are more important than ever. What are the emerging trends in Data Architecture? How can next-generation architectures help an enterprise to become data-driven?

Natural gradient descent is an optimization method traditionally motivated from the perspective of information geometry, and works well for many applications as an alternative to stochastic gradient descent. In this paper we critically analyze this method and its properties, and show how it can be viewed as a type of approximate 2nd-order optimization method, where the Fisher information matrix can be viewed as an approximation of the Hessian. This perspective turns out to have significant implications for how to design a practical and robust version of the method. Additionally, we make the following contributions to the understanding of natural gradient and 2nd-order methods: a thorough analysis of the convergence speed of stochastic natural gradient descent (and more general stochastic 2nd-order methods) as applied to convex quadratics, a critical examination of the oft-used "empirical" approximation of the Fisher matrix, and an analysis of the (approximate) parameterization invariance property possessed by natural gradient methods, which we show still holds for certain choices of the curvature matrix other than the Fisher, but notably not the Hessian.

This is the third year we've run a STEM-focused gift guide -- and if you're intending to buy a child something fun and quasi-educational in holiday season 2017 it's fair to say there has never been so many programmable bots and kits to choose from, all pledging to spark or sustain an interest in coding and electronics. This year's guide reflects this boom in'educational' techie toys -- featuring programmable and controllable robots of all stripes and shapes, as well as some fully fledged learn-to-code computers and a few lower tech alternatives for variety (and those not wanting to give yet another gadget). Gadget makers are piling into this space because of the ability to charge top dollar for toys that can claim a few STEM smarts. And it's clear the line between connected devices and learn to code tools is being increasingly blurred -- although the jury's still out on how much lasting educational value any of these gizmos can offer vs more structured learning and guidance. The impact of a STEM toy will obviously vary from child to child.