The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We encourage everyone to take a look at the QUT Robot Academy by visiting our website. In this video, students learn how we make robot joints move to the angles or positions that are required to achieve the desired end-effector motion.
This week's Featured Blog Friday comes from our Reykjavik University student intern, Guðbjörn Einarsson aka Mannsi, who has been working closely with our Data Scientist, Agnes Jóhannsdóttir, to implement Machine Learning technology into our AGR software. As always, if you have any questions or comments regarding this blog post, feel free to comment on this blog post, tweet us @AGRDynamics, or contact us here. AGR Dynamics is certain Machine Learning will play a big role in the future of our business. If you haven't already read through the other Machine Learning blog posts on Recommender Systems and Introduction To Machine Learning you really should, as they are great. Another area where Machine Learning can be applied is sales forecasting. Here we would like to briefly explain how that works and go through the pros and cons. The most common approach is to use a method called Neural Network. Neural Networks are designed to mimic how the human brain operates and learns and is one of ...
"I thought it was crazy that Anheuser-Busch needed a sophomore to help them with hiring mechanical engineering students for their full time jobs," she says. Wessel is the CEO and cofounder of WayUp, an online platform connecting college students and recent graduates with potential employers. To join, a student or recent graduate starts by filling out a profile with personal information, work experience, hobbies, and fun facts about themselves. "Students are starting to define their own career identities thematically rather than along the more rigid lines that previous generations might have," says Pulin Sanghvi, executive director of Princeton University's Office of Career Services.
More recently, lethal autonomous weapon systems (LAWS) powered by artificial intelligence (AI) have begun to surface, raising ethical issues about the use of AI and causing disagreement on whether such weapons should be banned in line with international humanitarian laws under the Geneva Convention. The campaign defines three types of robotic weapons: human-in-the-loop weapons, robots that can select targets and deliver force only with a human command; human-on-the-loop weapons, robots that can select targets and deliver force under the oversight of a human operator who can override the robots' actions; and human-out-of-the-loop weapons, robots that are capable of selecting targets and delivering force without any human input or interaction. Reporting on a February 2016 round-table discussion on autonomous weapons, civilian safety, and regulation versus prohibition among AI and robotics developers, Heather Roff, a research scientist in the Global Security Initiative at Arizona State University with research interests in the ethics of emerging military technologies, international humanitarian law, humanitarian intervention, and the responsibility to protect, distinguishes automatic weapons from autonomous weapons. Roff describes initial autonomous weapons as limited learning weapons that are capable both of learning and of changing their sub-goals while deployed, saying, "Where sophisticated automatic weapons are concerned, governments must think carefully about whether these weapons should be deployed in complex environments.
This obviously works well for dogs (all of whom are good) but it does present a significant shortcoming when training neural networks: the AI will only pursue high reward actions no matter what, even to the detriment of its overall efficiency. The UC Berkeley team's AI, however, has been imbued with the ability to make decisions and take action even when there isn't an immediate payoff. To train the AI, the researchers taught it to play Super Mario Bros. and VizDoom. We've already got Google training neural networks to design and generate baby neural nets, researchers at Brigham Young University teaching them to cooperate, and now this advancement enabling AI to teach itself.
Deep Learning For Coders is a new online course that, for the first time, promises to teach coders how to create state of the art deep learning models. Jeremy says that this is First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet!
In this introductory course, the "Backyard Data Scientist" will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the "techno sphere around us", why it's important now, and how it will dramatically change our world today and for days to come. We'll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science: To make sense of the Machine part of Machine Learning, we'll explore the Machine Learning process: Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete. So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know - everyday people... like you!
Summary: A great story about an AI-powered massive on-line open learning platform focused on STEM education. The private sector is utilizing the specialized cognitive engine developed by Value Spring Technology (VST) along with IBM Watson to build a universal tutor and the initial focus is on STEM. While this spacecraft and the experiments are the largest and most publically tangible elements of the program, the real goal is to motivate students in STEM studies and most importantly to provide a massive open on-line education platform powered by AI. Training Watson is done with thousands and thousands of question and answer pairs and training and the creation of the knowledge base is both human intensive and time consuming.
The plan was the beginning of a national effort to prepare Americans for a future with AI--a future some computer scientist believe our nation is ill-equipped to handle. Using research and concepts from several AI experts including Mark Stehlik of Carnegie Mellon and Rand Hindi of Snips, EdSurge put together the following three-step list educators can use to start implementing AI education in schools. Dr. Rand Hindi, CEO of Snips (a machine learning device company), is part of a research group working with the French government to prepare their country for AI. For Stehlik, the onus is on technology companies and higher education institutions to prepare K-12 teachers for AI instruction by providing them with curriculums, capacity and continuing education opportunities.
Automation, robotics, algorithms and artificial intelligence (AI) in recent times have shown they can do equal or sometimes even better work than humans who are dermatologists, insurance claims adjusters, lawyers, seismic testers in oil fields, sports journalists and financial reporters, crew members on guided-missile destroyers, hiring managers, psychological testers, retail salespeople, and border patrol agents. A recent study by labor economists found that "one more robot per thousand workers reduces the employment to population ratio by about 0.18-0.34 When Pew Research Center and Elon University's Imagining the Internet Center asked experts in 2014 whether AI and robotics would create more jobs than they would destroy, the verdict was evenly split: 48% of the respondents envisioned a future where more jobs are lost than created, while 52% said more jobs would be created than lost. This survey noted that employment is much higher among jobs that require an average or above-average level of preparation (including education, experience and job training); average or above-average interpersonal, management and communication skills; and higher levels of analytical skills, such as critical thinking and computer skills. A focus on nurturing unique human skills that artificial intelligence (AI) and machines seem unable to replicate: Many of these experts discussed in their responses the human talents they believe machines and automation may not be able to duplicate, noting that these should be the skills developed and nurtured by education and training programs to prepare people to work successfully alongside AI.