Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents" – any device that perceives its environment and takes actions that maximize its chance of success at some goal. Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futurism. Machine learning is a type of AI that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. Deep learning is a specific machine learning technique. Most deep learning methods involve artificial neural networks, modeling how our brain works. At the moment deep learning forms the basis for most of the incredible advances in machine learning (and in turn AI). Big data is a term for extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
The paper submissions for ICLR 2017 in Toulon France deadline has arrived and instead of a trickle of new knowledge about Deep Learning we get a massive deluge. This is a gold mine of research that's hot off the presses. Many papers are incremental improvements of algorithms of the state of the art. I had hoped to find more fundamental theoretical and experimental results of the nature of Deep Learning, unfortunately there were just a few. There was however 2 developments that were mind boggling and one paper that is something I've been suspecting for a while now and has finally been confirm to shocking results. It really is a good news, bad news story.
As medical imaging technology continues to take advantage of every new deep learning breakthrough, the challenge is that the computing technology on which it relies must evolve just as quickly. A company called Nvidia is leading that charge under the guidance of Kimberley Powell, who is confident that Nvidia's processors are not only meeting the deep learning standards of medical imagining, but also pushing the industry forward as a whole.
This online course is designed to introduce you to the Spark platform and train you to use all the APIs it offers. This course is split into 8 interactive modules which cover over 7 Apache Spark topics with a Project at the end of the course – receive a certificate of completion at the end of the course.
Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.
One good way to frame the question of the limits of Deep Learning is in the context of the Principle of Computational Equivalence by Stephen Wolfram. Wolfram showed that simple cellular automation are able to exhibit complex behaviour that cannot be predicted from initial conditions or the simple rules that specify its incremental behaviour. Certain kinds of cellular automata can exhibit complex behaviour that cannot be reduced to a mathematical model that capture its behaviour in closed form. Wolfram examples of an'irreducible' system that exhibits this complex behaviour are the brain and weather systems. Wolfram classifies these kinds of systems as exhibiting "Universality".