May 7, 2018 Written by: David H. Deans Early adopters are taking a strategic approach to enabling their enterprise with artificial intelligence (AI) technologies. They're solving complex problems, infusing intelligent system capabilities into business processes and investing to create a new AI-enhanced future. However, many other leaders seek a way to prioritize their initial investments as cognitive computing presents virtually endless possibilities across business processes and functions. For those who are just getting started, here are six key issues for your consideration. If you have limited or no experience with these technologies, then you'll need guidance – useful information about lessons learned and best practices from the pioneers.

Using artificial intelligence, scientists have created a tool that lets anyone visualize what the structures inside a cell look like -- even when you only have images of the outside. The Allen Integrated Cell, available for free online, creates 3D visualizations that could help researchers better understand disease. This cell tool focuses on human stem cells, or cells that haven't yet turned into, say, muscle cells or heart cells. If we better understand the inner workings of a healthy cell, then we can better understand what goes wrong when it turns into something like a cancer cell, says Greg Johnson, a scientist at the Allen Institute. "This can help us'roll back in time' from a cancer cell to observe the changes that are happening, and detect them as early as possible," he adds.

Linear regression is one of the essential tools in statistical analysis. In this course, we'll walk through step-by-step how to conduct many important analyses using SPSS. Although you will learn the basics of what these statistics are, we'll avoid complicated mathematical discussions and go right to what you need to know to conduct these analyses. Linear regression is basically a tool that allows you to test relationships between many variables at the same time, control for variables' effects, and create simple statistical models that allow you to make predictions. In this course, we'll cover the following key topics: You'll not only learn how to conduct these analyses, we'll also go over how to interpret the statistical results and how to graph the results using SPSS and a special Excel template I've created for you.

Whatever be your motivation to learn OpenCV, i can assure you that you've come to the right course. Hands on Computer Vision with OpenCV & Python is THE most comprehensive and cost-effective video course you will find on the web right now. This course is tailor made for an individual who wishes to transition quickly from an absolute beginner to an OpenCV expert in just three weeks. I ensure this by breaking down and articulating the most difficult concepts in plain and simple manner, replacing tough equations by examples and concepts by using small code snippets. This course covers topics using a methodical step-by-step approach with increasing difficulty, starting outright with the very basics and fundamentals.

Today, young people are in pole position to steer the best possible future of the development of artificial intelligence (AI). As Douglas Adams famously said: "Anything that's invented between when you're fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it. Anything invented after you're thirty-five is against the natural order of things." The next generation are coming of age as the most exciting chapter in the development of AI is written. And there is a huge opportunity for organisations to harness the power of this younger generation to play a guiding role in how this technology is used and develops.

About this course: We invite you to a fascinating journey into Graph Theory -- an area which connects the elegance of painting and the rigor of mathematics; is simple, but not unsophisticated. Graph Theory gives us, both an easy way to pictorially represent many major mathematical results, and insights into the deep theories behind them. In this course, among other intriguing applications, we will see how GPS systems find shortest routes, how engineers design integrated circuits, how biologists assemble genomes, why a political map can always be colored using a few colors. We will study Ramsey Theory which proves that in a large system, complete disorder is impossible! By the end of the course, we will implement an algorithm which finds an optimal assignment of students to schools.

Welcome to this course: TensorFlow Slim(TF-Slim) In Depth. TF-Slim is a lightweight library for defining, training and evaluating complex models in TensorFlow. It is a library that makes building, training and evaluation neural networks simple. The course helps you obtain in-depth knowledge of TensorFlow-Slim, making you the go-to person for solving artificial intelligence problems. At the end of this course, you will have mastered the offerings of TensorFlow, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.

Now that computer scientists at Lawrence Berkeley National Laboratory's National Energy Research Scientific Computing Center (NERSC) have demonstrated 15 petaflops deep-learning training performance on the Cray Cori supercomputer, the NERSC staff is working to address the data management issues that arise when running production deep-learning codes at such scale. The existing deep learning tools were not designed to efficiently ingest or manage the terabyte- to petabyte-sized deep-learning training sets that scientists can now use on this leadership class supercomputer. "Enabling the NERSC user community to perform deep learning at scale on Cori," Quincey Koziol (Staff, Berkeley Lab) observes, "means scientists can use deep learning as part of their leading-edge scientific efforts." Thus NERSC staff are working to break new ground in adapting existing deep-learning frameworks to run efficiently at scale on thousands of nodes while giving researchers the ability to create and manage training sets containing tens to hundreds of terabytes of data in a portable fashion. For these datasets, it is imperative that they are formatted so Cori can ingest them efficiently at runtime.

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist's toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.

Carnegie Mellon University's School of Computer Science will offer a new undergraduate degree in artificial intelligence beginning this fall, providing students with in-depth knowledge of how to transform large amounts of data into actionable decisions. SCS has created the new AI degree, the first offered by a U.S. university, in response to extraordinary technical breakthroughs in AI and the growing demand by students and employers for training that prepares people for careers in AI. "Specialists in artificial intelligence have never been more important, in shorter supply or in greater demand by employers," said Andrew Moore, dean of the School of Computer Science. "Carnegie Mellon has an unmatched depth of expertise in AI, making us uniquely qualified to address this need for graduates who understand how the power of AI can be leveraged to help people." This spring, U.S. News and World Report ranked SCS as the No. 1 graduate school for artificial intelligence. The bachelor's degree program in computer science teaches students to think broadly about methods that can accomplish a wide variety of tasks across many disciplines, said Reid Simmons, research professor of robotics and computer science and director of the new AI degree program.