Thank you all for the huge response to this emerging course! We are delighted to have over 300 students in over 145 different countries. I'm genuinely touched by the overwhelmingly positive and thoughtful reviews. It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)... I've got you covered.

Amazon yesterday announced its ONNX-MXNet package to import Open Neural Network Exchange (ONNX) deep learning models into Apache MXNet, signifying the company is on-board with Facebook and Microsoft in efforts to open-source AI. With the ONNX-MXNet Python package, developers running models based on open-source ONNX will be able to run them on Apache MXNet. Basically, this allows AI developers to keep models but switch networks, as opposed to starting from scratch. If you can imagine a thousand start ups and another thousand universities all creating at the bleeding edge of machine learning technology, but unable to share work due to'format' issues, you won't be very far off from the state of things without initiatives like ONNX. With Facebook and Microsoft all-in on the idea of open-source AI platforms, and now Amazon joining them, it's looking like ONNX is the path forward.

Our world is advancing at an extremely rapid rate. Technologies such as artificial intelligence, machine learning, drones, internet of things, augmented reality, and blockchain are growing in popularity every single day. Personally, I feel another industrial revolution is approaching quickly and the world we will in is going to drastically change. Blockchain is a difficult technology to understand but it has the potential to impact many organizations across the globe. If you're looking to get a head start on an innovative idea that will change our world then you're in the right place!

We aim to enable robot to learn object manipulation by imitation. Given external observations of demonstrations on object manipulations, we believe that two underlying problems to address in learning by imitation is 1) segment a given demonstration into skills that can be individually learned and reused, and 2) formulate the correct RL (Reinforcement Learning) problem that only considers the relevant aspects of each skill so that the policy for each skill can be effectively learned. Previous works made certain progress in this direction, but none has taken private information into account. The public information is the information that is available in the external observations of demonstration, and the private information is the information that are only available to the agent that executes the actions, such as tactile sensations. Our contribution is that we provide a method for the robot to automatically segment the demonstration of object manipulations into multiple skills, and formulate the correct RL problem for each skill, and automatically decide whether the private information is an important aspect of each skill based on interaction with the world. Our experiment shows that our robot learns to pick up a block, and stack it onto another block by imitating an observed demonstration. The evaluation is based on 1) whether the demonstration is reasonably segmented, 2) whether the correct RL problems are formulated, 3) and whether a good policy is learned.

If you want a talk on Python with the Theano library in under an hour, targeted towards beginners, then you can refer to this talk by Alec Radford. Unlike most other talks on this topic, this one compares the features of an'old' net versus a'modern' net, ie nets prior to 2000 versus nets post-2012.

Artificial intelligence (AI) is rapidly transforming industries, enabling new experiences, greater efficiencies and cost savings. As advances in AI continue, few areas of our lives will remain untouched, education included. With the explosion of new technologies, education is becoming more affordable and accessible and innovators are pushing the boundaries of what is possible. The student experience is changing, as high levels of customisation are made possible. However, this technology has a limit, beyond which the human element is an essential part of the learning experience.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. With a title like "What's new, Atlas?" for a video like this, you know that Boston Dynamics is just messing with us now: The game played out on a real set wherein Cozmo would roll through a series of trials, exploring rooms and solving puzzles which tested his ability to move, to place, stack, and turn blocks, and recognize faces and pets, testing Reddit's collective will to help him. Cozmo's quest: to gather three golden key cubes to be able to escape to Reddit's front page.

For as long as artificial intelligence and machine learning tools have been moving into the workforce, there have been rumblings of robots taking over the work of people, and the impact that could have on their career prospects. However, new studies undertaken by global professional services brand Genpact of 5,000 respondents in the United Kingdom, United States, and Australia, shows that the level of concern among the workers themselves is not very high. Roughly twenty percent of those surveyed in the UK felt that their jobs were threatened by AI, with only six percent feeling this strongly. But, although they did not feel overly cautious about their own prospects, they saw the potential disadvantages for the next generation of workers, with over fifty percent responding there was a threat to their children's careers, and over eighty percent stating that new skills will be needed for those workers in order to succeed in an AI advanced environment. The reason for this caution can be found in the training, or lack thereof, in the use of AI.

Building on the success of UiPath Academy launched in April 2017 which counts 30,000 students of the RPA Developer Foundation Diploma course, Academy 2 is the first free-of-charge online Advanced Diploma for RPA Developers. The curriculum offers developers practical RPA development exercises focused on proven use cases, enabling them to master the UiPath RPA platform in a controlled environment. This is delivered alongside a series of webinars preparing users for a certification exam. Utilising UiPath's methodology of automation best practices, Academy 2 also provides in-depth teaching on how to develop scalable enterprise-grade RPA deployment. Also at UiPath Forward Americas a powerful line-up of early RPA adopters will present to an attendance of more than 700.

We are living in a world of data overload. From behavioral analytics to customer preferences, businesses now have so much data at their fingertips that they're unable to process and consume all of it in a meaningful way. This is where the magic of machine learning comes in. When applied to massive internal company datasets, machine learning technology can derive important insights and provide actionable recommendations and predictions at superhuman scale. But as automation, machine learning, and artificial intelligence technologies continue to show up in our daily experiences, more and more users are asking questions.