The e-learning or digital content ecosystem is quickly evolving, and mobile learning, multi-device content, mobile responsive content (call it what you will) is here to stay. #1: Define What "Mobile" Means to You--and Your Clients Mobile learning can be defined as many different types of learning experiences. #2: Decide on a Minimum Device Set Once you are clear on what mobile learning and mobile responsive mean to you, you are in a better position to decide on a minimum device set. #3: Understand Your New Design Parameters The good news: It's finally time to say goodbye to fixed screen, click-next, sleep-inducing e-learning content! #4: Adjust Your Storyboarding Process If you've been an instructional designer as long as I have, you'll have experimented with different types of storyboarding. For me, Microsoft PowerPoint works the best. Example #5: Integrate Scrolling into the Design Process If you are not careful, your instructional designers will simply transfer their fixed screen design ideas into long scrolling pages.
Somewhere, buried in one of tens of millions of cell samples, could lie the next great breakthrough in disease prevention or cure. But one of the great barriers to finding it could be the need for human eyes to evaluate a corresponding mountain of cell images, one by one. In an era when terabytes of data can be analyzed in just a few days, the opportunity to enhance automation of biomedical analysis could help researchers achieve breakthroughs faster in the treatment of almost every disease--from cancer, diabetes and rare disorders to the common cold. To spur this automation, Booz Allen Hamilton (NYSE: BAH) and Kaggle today launched the 2018 Data Science Bowl, a 90-day competition that calls on thousands of participants globally to train deep learning models to examine images of cells and identify nuclei, regardless of the experimental setup--and without human intervention. Creators of the top algorithms will split $170,000 in cash and prizes, including an NVIDIA DGX Station, a personal AI supercomputer that delivers the computing capacity of 400 CPUs in a desktop workstation.
The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? What is the best order in which to use selected resources? It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won't be necessary; some extra time spent on the earlier steps should help compensate.
Excited about using AI to improve your organization's operations? I want to warn you about bias and how it can appear in those types of projects, share some illustrative examples, and translate the latest academic research on "algorithmic bias." What we call things shapes our understanding of them. That's why I try to avoid the hype-driven term "artificial intelligence." Most projects called that are more usefully described as "machine learning."
This blog post is based on a conference talk I gave at the PyTexas and North Bay Python conferences. The blog post is a little more detailed, but if you prefer watching video to reading text you can watch the talk on YouTube. Machine learning seems to be everywhere these days, but a lot of the information about what it is and how it works can be somewhat opaque. On one end of the spectrum there's the "just run this code" approach, which is great if you're learning a new library for a familiar task, but can seem a bit like magic when it's demonstrating something you've not done before. On the other end of the spectrum is the mathematical explanation.
Restricting the number of potential readers is unfortunate because an interdisciplinary view of the world around us must be developed. This book should have been written to show a scientist with a good mathematics background how to do modeling and simulation. Scientific research needs more people trained in system concepts, people trained to understand and apply the Weltanschauung of system theory. Indeed, the recent recommendation for science education that came out of the Science for All Americans study, sponsored by the American Association for the Advancement of Science, emphasized an interdisciplinary approach to scientific concepts. By limiting the technical accessibility of this book, the author has not helped us address the need for training scientists in the use of interdisciplinary tools in scientific research.
The three competitions--(1) AAAI Mobile Robot, (2) AUVS Unmanned Ground Robotics, and (3) IJCAI RoboCup--were used in different years for an introductory undergraduate robotics course, an advanced graduate robotics course, and an undergraduate practicum course. Based on these experiences, a strategy is presented for incorporating competitions into courses in such a way as to foster intellectual maturation as well as learn lessons in organizing courses and fielding teams. The article also provides a classification of the major robot competitions and discusses the relative merits of each for educational projects, including the expected course level of computer science students, equipment needed, and costs. The sponsorship of such competitions ranges from local clubs of enthusiasts to large professional organizations, such as the American Association for Artificial Intelligence (AAAI), which sponsors the annual AAAI Mobile Robot Competition and Exhibition as part of its annual ...
It included an invited talk, paper presentations, model AI assignments, a teaching and mentoring workshop, a best educational video award, and a robotics track. The symposium was established in response to growing community interest in sharing best practices for (1) how to teach AI and (2) how AI can serve as a motivating problem for teaching concepts in other areas of computer science, especially in introductory computer science courses. EAAI encourages the sharing of innovative educational approaches that convey or leverage AI and its many subfields, including robotics, machine learning, natural language, and computer vision. EAAI follows the successful 2008 Spring Symposium on "Using AI to Motivate Greater Participation in Computer Science" and the 2008 AAAI AI Education Colloquium. Fifty-five attendees formally registered for the event, but many other AAAI attendees also visited one or more EAAI events.
Nearly two decades ago, when Amazon began offering predictive book recommendations based off previous orders, no one batted an eye when new types of automated convenience emerged. However, when that same type of convenience enters the workplace, an inherent uneasiness arises. Automation is the next frontier for the enterprise, and artificial intelligence (AI) is more mainstream than ever. As algorithms' ability to learn expands, so does viable applications in the enterprise. By 2021, AI is forecast to recover about 6.2 billion hours of productivity and add $2.9 trillion in business value.