Imagine being paralysed and having an implanted microchip that could action a message from your brain to move your prosthetic arm. Or a diagnostic system that could pick up Alzheimer's a decade before you develop any symptoms. Or a 3D printing machine that could print a pill with a combination of drugs tailored just for you. The faculty chair for medicine and founder of Exponential Medicine at the Silicon Valley-based Singularity University, no one could be more serious – or ambitious – about the revolutionary impact that technology will have on the future of healthcare. The internet of things, constant connectivity, ever cheaper hardware, big data, machine learning: Kraft's list of converging "meta-trends" goes on.

Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many, many more applications we consider decidedly "futuristic." One of the central foundations of deep learning is linear regression; using probability theory to gain deeper insight into the "line of best fit." This is the first step to building machines that, in effect, act like neurons in a neural network as they learn while they're fed more information. In this course, you'll start with the basics of building a linear regression module in Python, and progress into practical machine learning issues that will provide the foundations for an exploration of Deep Learning. Access 20 lectures & 2 hours of content 24/7 Use a 1-D linear regression to prove Moore's Law Learn how to create a machine learning model that can learn from multiple inputs Apply multi-dimensional linear regression to predict a patient's systolic blood pressure given their age & weight Discuss generalization, overfitting, train-test splits, & other issues that may arise while performing data analysis The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer.

Decades ago, artificial intelligence was a distant concept meant for future generations. We're seeing advancements every day that will make our lives easier and more efficient. And as a way to make big data useful, AI might be a game changer. Data collection has always been an important facet of business, but never before have we been able to access it in such a responsive and useful manner. The collection is easy, and with digital storage, we can mine that data for real value.

It is a fact that healthcare is unsustainable. American health spending will reach nearly $5 trillion, or 20 percent of gross domestic product by 2021. The World Health Organization (WHO) estimates that there is a worldwide shortage of around 4.3 million physicians, nurses, and allied health workers. So how could we change it? The most likely solution is technology.

Under President Obama's leadership, America continues to be the world's most innovative country, with the greatest potential to develop the industries of the future and harness science and technology to help address important challenges. Over the past 8 years, President Obama has relentlessly focused on building U.S. capacity in science and technology. This Thursday, President Obama will host the White House Frontiers Conference in Pittsburgh to imagine the Nation and the world in 50 years and beyond, and to explore America's potential to advance towards the frontiers that will make the world healthier, more prosperous, more equitable, and more secure. Today, to ready the United States for a future in which Artificial Intelligence (AI) plays a growing role, the White House is releasing a report on future directions and considerations for AI called Preparing for the Future of Artificial Intelligence. This report surveys the current state of AI, its existing and potential applications, and the questions that progress in AI raise for society and public policy.

When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. New York, meet the world's tech scene This is your chance to join them. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination.

The answer to the question "What machine learning algorithm should I use?" is always "It depends." It depends on the size, quality, and nature of the data. It depends what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Even the most experienced data scientists can't tell which algorithm will perform best before trying them. The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Microsoft Azure Machine Learning library of algorithms.

Stephanie Yee and Tony Chu created a step-by-step visual explanation of what actually happens as a machine "learns." It's part one of an interesting exercise to explain statistical concepts through interaction design.

Succeeding in today's fast-paced business economy requires companies to harness data quickly and at scale. As the volume, velocity, and variety of data increase, it's becoming necessary to use machine learning and artificial intelligence (AI) to sift through all the incoming information, make sense of it, and accurately predict future business direction. It takes the right expertise, the right tools, and the right data to achieve the promise of machine learning. Even with all of those factors in place, it's still easy to get it wrong. "Machine learning gives us a very powerful set of techniques for making predictions, but it can also lead to disastrous results if you don't understand what your machine learning algorithm is doing," said Spencer Greenberg, a mathematician and founder of decision-making website ClearerThinking.org, in an interview.

Three years ago, Jeff Bezos announced that drones are eventually going to deliver Amazon orders. In the past year, he brought out Amazon's Alexa artificial intelligence service, which understands speech well enough that you can say, "Alexa, I really need a waffle cone maker," and she'll put one in your Amazon online shopping cart, even though nobody needs a waffle cone maker. Both of these technologies--drones and cloud AI--are exciting today, yet still wobbly works in progress. But in coming years, Amazon or some other company is going to put them together. And that, finally, will evolve into a technology that could become as significant to humans as domesticated dogs.