It sounds like we can all take a breath and forget about robot attacks occurring -- at least anytime soon. Robots turning against their makers is a common theme in science fiction. However, there's "no cause for concern that AI poses an imminent threat to humanity," according to Fast Company, citing the first report from the One Hundred Year Study on Artificial Intelligence (AI100). The Stanford University-hosted project represents a standing committee of AI scientists. The AI100 project is ongoing but will not issue reports annually -- the next one will be published "in a few years."

To produce a regression analysis of inference that can be justified or trustworthy in the sense that helpful. The term in the statistical methods that generate a linear the best estimator is not bias (best linear unbiased estimator) abbreviated BLUE. Then there are some other things that are also important to note, in which the data to be processed, must meet certain requirements. Must meet the assumptions of single colinearity, meaning between independent variables with each independent variable others in the regression model no multicollinearity, is a condition where there is a linear relationship was perfect or near perfect between the independent variables. Must meet homoscedasticity assumptions, it means a state where the variance the existing data on every variable must be the same (constant).

Linear regression: Oldest type of regression, designed 250 years ago; computations (on small data) could easily be carried out by a human being, by design. Can be used for interpolation, but not suitable for predictive analytics; has many drawbacks when applied to modern data, e.g. A better solution is piecewise-linear regression, in particular for time series. Logistic regression: Used extensively in clinical trials, scoring and fraud detection, when the response is binary (chance of succeeding or failing, e.g. for a new tested drug or a credit card transaction). Suffers same drawbacks as linear regression (not robust, model-dependent), and computing regression coeffients involves using complex iterative, numerically unstable algorithm.

In today's blog post, I demonstrated how to train a simple neural network using Python and Keras. We then applied our neural network to the Kaggle Dogs vs. Cats dataset and obtained 67.376% accuracy utilizing only the raw pixel intensities of the images. Starting next week, I'll begin discussing optimization methods such as gradient descent and Stochastic Gradient Descent (SGD). I'll also include a tutorial on backpropagation to help you understand the inner-workings of this important algorithm.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

Royal Philips (NYSE: PHG, AEX: PHIA) today unveiled a series of intelligent and comprehensive connected radiology solutions at the 2016 Radiological Society of North America Annual Meeting (RSNA), beginning November 27 through December 1 at McCormick Place in Chicago. At the Philips booth (#6735), RSNA attendees will experience a new portfolio of digital imaging systems, intelligent software and services to enhance diagnostics, improve patient care and operational efficiencies. Radiology is at the center of the majority of healthcare decisions, driving the timely detection and accurate diagnosis and treatment of disease at its earliest stages. As health organizations continue to move toward value-based care, they need intelligent solutions to meet the challenges they face in improving outcomes, lowering cost of healthcare delivery and enhancing patient satisfaction. "Radiologists are playing a pivotal role in determining the right path to the right treatment," said Robert Cascella, CEO, Diagnosis and Treatment, Philips.

BMW is an iconic brand in the automobile industry, well-established and well-liked, but that hasn't translated to success for the German automaker when it comes to electric vehicles. Having sold only about 25,000 of the i3 hatchbacks in 2015, the company is planning to upgrade its small car next year. German weekly Welt am Sonntag (in German) reported Sunday the i3 will get a makeover that will see both the front and rear ends of the car reworked. Additionally, new battery technology will give the vehicle increased range as well, which BMW says is currently 114 miles on full charge. The range can be extended up to 180 miles using a "Range Extender."

Tech giant Intel has announced a new strategy focused on artificial intelligence (AI) and based on a new portfolio of technologies centered on its recent acquisition of AI company Nervana Systems. The new portfolio will include products and services for everything from network edge to data center use cases to help accelerate the growth of AI technologies. "Intel sees AI transforming the way businesses operate and how people engage with the world," the company said in a statement yesterday. "Intel is assembling the broadest set of technology options to drive AI capabilities in everything from smart factories and drones to sports, fraud detection and autonomous cars." Dubbed Intel Nervana, the new platform comes courtesy of the company's acquisition of the two-year-old Nervana Systems announced three months ago.

Svetlana Alexievich, winner of the 2015 Nobel Prize in literature, called the nuclear catastrophes at Chernobyl and Fukushima events that people cannot yet fully fathom and warned against the hubris that humans have the power to conquer nature. The 68-year-old Belarusian writer was in Tokyo at the invitation of researchers at the University of Tokyo, where she gave a lecture on Friday. More than 200 people attended. The Nobel laureate, who writes in Russian, is known for addressing dramatic and tragic events involving the former Soviet Union – World War II, the Soviet war in Afghanistan, the 1986 Chernobyl nuclear disaster and the 1991 collapse of the communist state. Her style is distinctive in that she presents the testimonies of ordinary people going through traumatic experiences as they speak, without intruding on their narratives.