AI and data science is transforming business. The Accelerate AI Business Summit will give you the knowledge and connections to be at the forefront.

Google and online learning hub Udacity have launched a free course designed to make it simpler for software developers to grasp the fundamentals of machine learning. The "Intro to TensorFlow for Deep Learning" course is designed to be more accessible to developers than previous machine-learning courses offered by Udacity. "Our goal is to get you building state-of-the-art AI applications as fast as possible, without requiring a background in math," says Mat Leonard, head of the School of AI at Udacity. "If you can code, you can build AI with TensorFlow. You'll get hands-on experience using TensorFlow to implement state-of-the-art image classifiers and other deep learning models. You'll also learn how to deploy your models to various environments including browsers, phones, and the cloud."

Artificial intelligence (AI) offers unprecedented opportunities to improve the way underwriters select, target and price risk. Those who integrate emerging technologies and new sources of data will gain a competitive edge by way of improved loss ratios, innovative products, and a more efficient workforce. What practical steps can insurers take to prepare for the age of bionic underwriting? Join Intelligent Insurer as they talk to Zurich, ERGO, and Tapoly about leveraging artificial intelligence to power the bionic underwriter of the future. In this webinar, you'll get exclusive insights from: Don't worry if you can't join live, the recordings will be sent to you if you register.

Some of the nation's top schools, including the Massachusetts Institute of Technology and Georgetown University, are offering educational programs for nontechnical senior managers looking to learn more about artificial intelligence and its business applications. These programs are designed to be crash courses in AI, covering machine learning, robotics and other topics. They can vary in duration and delivery--some are multiweek online offerings, others are multiday boot camps--and typically land in the $3,000-a-student range.

To me, the answer is simple. Companies like NVIDIA represent the future state of Artificial Intelligence, not the current state of AI. The reason for that divide is simple – the most important word in AI technology is: "democratization." GPUs are expensive, especially compared to their mainstream counterpart, the Central Processing Unit. GPUs are not mainstream, whereas the CPU is found in nearly every computer on the market today.

The Laboratory for Advanced Visualization and Applications (LAVA), the Hawaii Data Science Institute and the Academy for Creative Media System jointly hosted Old Ways New's 1st workshop in the world bringing Indigenous researchers into discussions surrounding the future of Artificial Intelligence. The workshop focused on how to progress the conceptual theory and practice of next level of A.I. The key question the workshop will address is this: from an Indigenous perspective, what should our relationship with A.I. be? Related questions include: how can Indigenous epistemologies and ontologies contribute to the global conversation regarding society and A.I.? How do we broaden discussions regarding the role of technology in society beyond the largely culturally homogeneous research labs and Silicon Valley startup culture? How do we imagine a future with A.I. that contributes to the flourishing of all humans and non-humans?

News: Straight Dope is growing up. Much of this content has been incorporated into the new Dive into Deep Learning Book available at https://d2l.ai/. This repo contains an incremental sequence of notebooks designed to teach deep learning, Apache MXNet (incubating), and the gluon interface. Our goal is to leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place. If we're successful, the result will be a resource that could be simultaneously a book, course material, a prop for live tutorials, and a resource for plagiarising (with our blessing) useful code.

In this tutorial, you will learn how to apply Holistically-Nested Edge Detection (HED) with OpenCV and Deep Learning. We'll apply Holistically-Nested Edge Detection to both images and video streams, followed by comparing the results to OpenCV's standard Canny edge detector. Edge detection enables us to find the boundaries of objects in images and was one of the first applied use cases of image processing and computer vision. When it comes to edge detection with OpenCV you'll most likely utilize the Canny edge detector; however, there are a few problems with the Canny edge detector, namely: Holistically-Nested Edge Detection (HED) attempts to address the limitations of the Canny edge detector through an end-to-end deep neural network. This network accepts an RGB image as an input and then produces an edge map as an output.

Along with the proliferation of personalized learning platforms also comes the rise of the presence of artificial intelligence in educational technology. Before going further, we must first acknowledge the truth about the term artificial intelligence -- it is a buzzword that connotes many different applications. It holds different meaning in different contexts and, much of the time, is used indiscriminately without clear boundaries or specifications. To try and avoid just adding to the noise, we will focus on three main topics: (1) deconstructing the buzzword into its component parts, (2) characterizing current use cases and predicting future ones, and (3) exploring risks and trade-offs. First, let us define, as clearly as possible, what all is under consideration when we mention the term.

This post is authored by Kristin M. Tolle, Director of Program Management for Advanced Analytics Ecosystem Development and Training at Microsoft. Cortana Intelligence, Microsoft's end-to-end platform for Advanced Analytics, offers a suite of services to solve real world customer problems. The suite has many moving parts – Data Lake, HDInsight (Hadoop), Event Hub, Machine Learning and R – just to name a few, and we realize it may be challenging for some of you to experience first-hand how all these services work together in concert. My team, which is tasked with training our partners to use these services to address their customers' needs, is keenly aware of the breadth of that knowledge surface area. In this blog post, I outline some of the best ways for you to learn about all things Big Data and Advanced Analytics from Microsoft, including many hands-on training options, and also how to stay in the loop on our future offerings.