I spend a lot of time in the air, travelling around the globe spreading the word about On-Demand Education Marketplace, or ODEM.IO. One of the questions I'm asked most often on my business trips is "How will blockchain technology help ODEM to improve education?" My answer: Blockchain gives us the opportunity to go big. Blockchain, automated smart contracts and artificial intelligence enable us to provide solutions to global problems in education such as accessibility, cost and the removing the inefficiencies caused by intermediaries. For students the result is improved access to a learning that better prepares them for the changing demands of the job market.

Artificial intelligence and machine learning are making their way into all aspects of our lives and businesses. Every time you ask Amazon's Alexa for the weather forecast or book a car through Lyft, you're benefitting from the power of AI. Entrepreneurs, in particular, are seeing their companies transformed by these technologies, and that trend will only continue in the coming years. One obvious opportunity for leveraging AI and machine learning in your business lies in teaching new employees about their responsibilities and the company. Many businesses already use online training programs and simulators when onboarding new employees.

In the wake of the Parkland school shooting, President Trump is meeting with video game executives and members of congress to discuss the role of simulated violence and the impact on America's youth. They called it the "Filthy 15." Fifteen songs from 15 bands or artists that the Parents Music Resource Center found offensive due to explicit content. The PMRC's leaders were Susan Baker, wife of then-Treasury Secretary James Baker, and Tipper Gore. Gore was wife of then-Sen. The acts in question were Prince, AC/DC, Cyndi Lauper, Madonna, Def Leppard, Motley Crue, Black Sabbath, Sheena Easton and Vanity.

Industrial robots are primarily known from the automotive industry's production lines. The goal of this class is to present robots instead as multifunctional and flexible interfaces between the digital and the physical world that can be used for anything from innovative, large-scale fabrication to immersive virtual reality (VR) simulators. This extension beyond the robots' initial scope is enabled by new software developments that facilitate a seamless workflow from design to machine through Dynamo software and KUKA prc. Utilizing parametric design tools lets us use robots for mass customization and small lot sizes, rather than mass fabrication. The class will provide an overview on how to utilize industrial robots through Dynamo and Fusion 360 software, and present realized projects by both small to medium-size enterprises as well as international corporations.

Concepts in the book are laid out clearly, often with diagrams, but the book moves quickly. The book expects you to keep up or you will fall behind. That being said, each section has an overview of the concepts to be covered and ends with worked examples and quiz questions, the answers to which are available on the book's website. Take my free 7-day email crash course now (with sample code). Click to sign-up and also get a free PDF Ebook version of the course.

Based on the summary, it's evident that Python is a better choice over other languages mainly because it being generic enough that it not just good for statistical/machine-learning related tasks but other generic tasks and having better support for all DNN frameworks like Tensorflow, Caffe. But, R can be very handy for a quick prototype that doesn't need to use DNN frameworks. So, to summarize, R is the choice of language for a quick prototype but for long term python is the choice of language.

It's okay for me, since I did not put that much effort in it.The I feel bad for the other students though. So there are two electives, semantic segmentation, and functional safety. Functional safety is interesting but I chose semantic segmentation, because it is a coding project, the functional safety project is to write a document. I learned about the concept of functional safety, and functional safety frameworks to ensure that vehicles is safe, both at the system and component levels.

For this year's report, I took a stab at learning all the CSS/JS that I've always wanted to know, and then went after the task of making a fully responsive report. I've succeeded in doing so, and so this PDF version isn't as good as the real thing. In the next few days I will be sharing a link to the real digital experience. But for now -- enjoy this static version of the report which has a few parts that couldn't render to static form. Because ... this year's report is truly computationally designed and therefore needs to be expressed appropriately (smile). Expect a video version on my new YouTube channel "John Maeda is Learning." What can I do about it? As the marginal return on computing power (a la Moore's law) diminishes and technology is less of a differentiating factor, the value of design has entered the foreground. Five (20%) of the top cumulative-funded VC- backed ventures that have raised additional capital since 2013 are noted to have designer co-founders.

Penn will now build robots for the army. The United States Army Laboratory has awarded a five-year, $27 million grant to the School of Engineering and Applied Science to create autonomous, intelligent robots designed to learn from and adapt to challenging environments. The robots will be charged with assisting humans in tasks like hostage rescue, gathering information in the wake of terrorist attacks or natural disasters, and humanitarian missions. Penn will lead the Army Research Laboratory's Distributed and Collaborative Intelligent Systems and Technology (DCIST) Collaborative Research Alliance, working alongside MIT, the Georgia Institute of Technology, and faculty members from the University of California–San Diego, the University of California–Berkeley, and the University of Southern California. The researchers will focus on advancing the field of distributed intelligence and learning, building a cohesive team of robots, sensors, and humans.

Whether you you need guidance on learning to code or you are a seasoned machine learning practitioner, Google is here to help. The search engine giant has come up with a new course -- 'Learn with Google AI' -- that acts as a practical introduction to machine learning to all users for free. Machine learning (ML) is a branch of artificial intelligence (AI) and pertains to a computer's ability to execute certain tasks on its own without being programmed by a human being. Some examples of ML include self-driving cars, speech recognition, language translators, etc. Google's new machine learning crash course is designed to provide a fast-paced self-study guide for aspiring machine learning practitioners using high-level TensorFlow (TF) APIs. It features a series of video lessons with lectures from ML experts, real-world case studies and hands-on practice exercises to help users learning about key ML algorithms and frameworks.