It was an accident waiting to happen. Up until then, I had been rather bored. So it was Wednesday, and the third "Press Talk" at the 66th Lindau Nobel Laureate Meeting. And the topic was artificial intelligence (A.I.). Müller-Jung, who's the head of science and nature at the German daily newspaper "Frankfurter Allgemeine Zeitung," had repeatedly said during his long and winding introduction, "We're not going to talk about self-driving cars or'rogue A.I.' here!"

Tesla announced today that the National Highway Traffic Safety Administration has opened an investigation into a recent fatal crash of a Model S with the company's Autopilot feature activated. The accident took place on May 7th in a small West Florida town called Williston. The Florida Highway Patrol is also conducting its own investigation of the accident, according to a public affairs officer there. The same officer reported that Tesla has, since the fatal accident in May, sent engineers down to Ocala, Florida to assist investigators in accessing data they needed to evaluate the causes of the crash. Tesla offered an account of the event in a blog post titled "A Tragic Loss" that went up today, detailing the crash, an "extremely rare circumstance," which occurred on a divided highway.

Our second model, called very deep convolutional neural net (or CNN), has its origins in image classification [4]. Speech can be viewed as an image if we consider the spectral representation of the audio signal with the two dimensions being time and frequency. As opposed to the classic CNN architectures employed in our previous system [5] that have only one or two convolutional layers with large (typically 9-by-9) kernels, our very deep CNN [6] has up to ten convolutional layers with small 3-by-3 kernels which preserve the dimensionality of the input. By stacking many of these convolutional layers with Rectified Linear Units nonlinearities before pooling layers, the same receptive field is created with less parameters and more nonlinearity. These two models which differ radically in architecture and input representation show good complementarity and their combination leads to additional gains over the best individual model.

Despite some predictions that artificial intelligence will one day take over the world, Microsoft CEO Satya Nadella says AI should be embraced and not feared, as he outlined design principles and goals that should be considered when creating the technology. In his essay published in Slate Tuesday, Nadella discussed the great promise of AI, or advanced machine learning, and how in an AI world, "productivity and communication tools will be written for an entirely new platform, one that doesn't just manage information but also learns from information and interacts with the physical world." Nadella added that "there are'musts' for humans too -- particularly when it comes to thinking clearly about the skills future generations must prioritize and cultivate." Those "musts" include empathy, education, creativity, judgment, and accountability. "Ultimately, humans and machines will work together -- not against one another. Computers may win at games, but imagine what's possible when human and machine work together to solve society's greatest challenges like beating disease, ignorance, and poverty," Nadella said in his essay.

The Stanford campus has been buzzing this week over the 2016 Global Entrepreneurship Summit, which kicked off here yesterday. This three-day event unites an estimated 1,500 entrepreneurs, academics and investors from around the world in a series of talks and panels designed to spark new ideas and partnerships. As part of the summit, Stanford and the White House Office of Science and Technology Policy are presenting a panel discussion tonight to explore the rapidly evolving field of artificial intelligence. Among the featured experts at "The Future of Artificial Intelligence: Emerging Topics and Societal Benefit" will be bioengineer Russ Altman, MD, PhD, faculty director of the One Hundred Year Study on Artificial Intelligence, and Fei-Fei Li, PhD, director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. Earlier this week Altman, who is also a professor of genetics and medicine, provided a sneak peek of some of the things we'll likely hear about tonight: This is a great opportunity for AI to help advance our understanding of health and disease.

NeuroFlow is a lightweight library to construct, train and evaluate Artificial Neural Networks. It is written in Scala, matrix operations are performed with Breeze ( NetLib for near-native performance). Type-safety, when needed, comes from Shapeless. To use Neuroflow within your project, add these dependencies (Scala Version 2.11.x): Usually the Sonatype repository resolvers are provided by default.

In a keynote talk, Dean outlined the history of machine learning (ML) and neural networks and various ways to programme models to take advantage of raw data coming through in the form of images or audio. He also detailed how ML has taken shape at Google, which recently announced that it will open a machine learning center in Europe. The company developed its own accelerator chips for artificial intelligence it calls tensor processing units (TPUs) after the open source TensorFlow algorithms it released last year.

Feature selection is also called variable selection or attribute selection. It is the automatic selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive modeling problem you are working on.

Before training the network, we need to shuffle these images then load batch of images to feed the neural network. Before we describe how we solve it, let's do some calculation first: A very naive approach is loading from a list by random seeking. If use this approach, we will spend 677 hours with HDD or 6.7 hours with SSD respectively. This is only about read. Although SSD looks not bad, but 1TB SSD is not affordable for everyone.

Every company is now a data company, capable of using machine learning in the cloud to deploy intelligent apps at scale, thanks to three machine learning trends: data flywheels, the algorithm economy, and cloud-hosted intelligence. That was the takeaway from the inaugural Machine Learning / Artificial Intelligence Summit, hosted by Madrona Venture Group* last month in Seattle, where more than 100 experts, researchers, and journalists converged to discuss the future of artificial intelligence, trends in machine learning, and how to build smarter applications. With hosted machine learning models, companies can now quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining machine learning systems. "Every successful new application built today will be an intelligent application," Soma Somasegar said, venture partner at Madrona Venture Group. "Intelligent building blocks and learning services will be the brains behind apps."