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."

To celebrate the first UK Robotics Week (25 June - 1 July 2016) Plymouth University organises an afternoon of academic presentations, followed by a public exhibition and debate on robotics and artificial intelligence. From 15:00 onwards you are welcome to a series of quick-fire academic presentations on cutting-edge robotics research by the University's research team. At 17:30 there is the opportunity to visit the robotics labs at the University and see robots in action, all while having a drink and chatting to our research team. This will be followed at 19:00 by a public debate on'Robots and Artificial Intelligence: bright future or impending gloom?'. As part of the UK Robotics week, Plymouth University invites you to attend a debate on robotics, artificial intelligence and its impact on society.

I have been invited to write a book chapter on lexical choice for translators (contact me if you want to see a preprint). To get acquainted on this audience different from my usual computer science I read a few papers on professional translators use of technology. Two of them are quite interesting and I recommend them not only because they make for a good read and they have implications outside translation: Translation Skill-sets in a Machine-translation Age by Anthony Pym (2013) and Is Machine Translation Post-editing Worth the Effort?: A Survey of Research into Post-editing and Effort by Maarit Koponen (2016). This search finished by reading a short ebook by researchers at the MIT Center for Digital Business titled Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. In that book plus the papers there's this call for humans, if we want to remain employed, to hybridize our work and to seek out ways to work with the computer as some sort of partnership.

Last week, the New York Times reported the first successful "limb reanimation" in a person with quadriplegia. Ian Burkhart, 24, had broken his neck as a teen in a diving accident. His spine was damaged at the fifth cervical vertebra, leaving him paralyzed from the shoulders down. Using nerve bypass technology that transmits his thoughts directly to his hand muscles, he has regained control over his right hand and fingers. This is the first time a brain-computer interface has been used to help an individual move his own hands. The research was published in in the journal Nature, and explains how machine learning and MATLAB were used in this project.