Despite their great success in practical applications, there is still a lack of theoretical and systematic methods to analyze deep neural networks. In this paper, we illustrate an advanced information theoretic methodology to understand the dynamics of learning and the design of autoencoders, a special type of deep learning architectures that resembles a communication channel. By generalizing the information plane to any cost function, and inspecting the roles and dynamics of different layers using layer-wise information quantities, we emphasize the role that mutual information plays in quantifying learning from data. We further propose and also experimentally validate, for mean square error training, two hypotheses regarding the layer-wise flow of information and intrinsic dimensionality of the bottleneck layer, using respectively the data processing inequality and the identification of a bifurcation point in the information plane that is controlled by the given data. Our observations have direct impact on the optimal design of autoencoders, the design of alternative feedforward training methods, and even in the problem of generalization.

I am sure you have heard about the discussion that's going on where machines will replace or alter approximately 35 million jobs worldwide. We don't know how true is that but a report states that robot automation will take over 800 million jobs by 2030. This isn't limited to education space alone but includes every industry. Intelligent machines are the future and it is coming soon to take over the world. Well, let's see how true is this in the higher education space.

The past two decades have seen the workplace transformed by digital advances. Gone are many traditional structures and practices, replaced with new ways of doing business, designed to support collaboration and digitally-enabled remote and flexible working. As the technology behind AI and robotics becomes more sophisticated, the number of jobs that remain untouched by automation will decrease. "To keep pace, businesses must rethink how they organise work, reinvent jobs, redeploy staff and implement robust plans for the future," says Lynda Gratton, professor of management practice at London Business School (LBS). There are also emerging social trends and shifting demographics to consider.

Live call-in discussion: As artificial intelligence continues to develop, concerns grow about its invasive nature and reach. How much are we willing to cede to the machines, and what effect will that have on our lives? The Vermont House recently passed a bill that would create an AI commission to address these subjects. John Quinn, the state's digital services secretary, and Burlington Rep. Brian Cina discuss these issues and what the proposed commission would address. We also hear from Milo Cress, a Champlain Valley Union High School student, who played an important role in the House passage of the bill which would create the commission.

Employees at all stages of their careers are challenged by the technological and socio-economical changes that are limiting the suitability of these employee's current skills and learning. Widening gap between the skills available and skills in demand is certainly alarming and you should not overlook a timely career advice. To brace yourself for a future-ready career you will require advanced technical training or specialized education. Dynamic re-skilling and learning on-the-go are keys to be successful in the competitive job market. Everybody is talking about Artificial Intelligence.

I've found the review on the first three courses by Arvind N very useful in taking the decision to enroll in the first course, so I hope, maybe this can also be useful for someone else. Taking the five courses is very instructive. The content is well structured and good to follow for everyone with at least a bit of an understanding on matrix algebra. Some experience in writing Python code is a requirement. The programming assignments are well designed in general.

We love Festo because every year they invest an entirely appropriate amount of time and money into bio-inspired robots that are totally cool and very functional but have limited usefulness. More often than not, it seems like Festo is able to take some of what it learns from designing and constructing these things and create practical new revenue-generating products. Which is good for them, and means they'll keep making cool stuff. Over the last few years, we've met ants, butterflies, flying jellyfish and penguins, kangaroos, seagulls, and much more. Festo has just announced its two newest bionic learning network robots--one is a very convincing flying fox, and the other is a walking, tumbling robot inspired by a Saharan spider.

Artificial intelligence is set to reshape business and society. For AI to yield economic value, however, designing algorithms compatible with human thought processes is critical. The ability of artificial intelligence (AI) applications to automate tasks associated with human knowledge is rapidly progressing. Examples include recognizing faces, sensing emotions, driving cars, interpreting spoken language, reading text, writing reports, grading student papers, and even setting people up on dates. Yet at a business level, AI projects often fail to deliver desired outcomes because they are not designed to promote smart adoption by human users.

At age 25, Anjney Midha has a stronger resume than some people twice his age. Before graduating from Stanford, he joined the venture capital firm Kleiner Perkins Caufield & Byers. He led the firm's investment in Magic Leap, the mysterious and much-hyped augmented reality company. Then he ditched venture capital to pursue a dream that had followed him from a technology-free young adulthood on a bird sanctuary in India, to the hyper-connected streets of Singapore, to his days at Stanford. That dream was to share his world--more than he could show in a photo, better than what he could convey with words--with the family and friends he'd left in India.

GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite(TM), today announced the immediate availability of GridGain Professional Edition 2.4, a fully supported version of Apache Ignite 2.4. GridGain Professional Edition 2.4 now includes a Continuous Learning Framework, which includes machine learning and a multilayer perceptron (MLP) neural network that enable companies to run machine and deep learning algorithms against their petabyte-scale operational datasets in real-time. Companies can now build and continuously update models at in-memory speeds and with massive horizontal scalability. GridGain Professional Edition 2.4 also enhances the performance of Apache Spark(TM) by introducing an API for Apache Spark DataFrames, adding to the existing support for Spark RDDs. GridGain Continuous Learning Framework GridGain Professional Edition 2.4 now includes the first fully supported release of the Apache Ignite integrated machine learning and multilayer perceptron features, making continuous learning using machine learning and deep learning available directly in GridGain.