Today Dell EMC announced new Ready Solutions for AI. With specialized designs for Machine Learning with Hadoop and Deep Learning with NVIDIA, the Dell EMC Ready Solutions simplify AI environments, deliver faster, deeper insights than the competition1, and leverage Dell EMC's proven expertise to help organizations realize the full potential of AI. There's no doubt that AI is the future, and our customers are preparing for it now," said Tom Burns, senior vice president, Networking & Solutions, Dell EMC. "Our goal is to lead the industry with the most powerful and fully-integrated AI solutions. What we're announcing today allows customers at any scale to start seeing better business outcomes and positions them for AI's increasingly important role in the future." Emerging technologies such as AI will transform lives and how people work and conduct business over the next decade. According to Dell Technologies' research with 3,800 business leaders around the globe, conducted in partnership with VansonBourne, nearly 80% of organizations will be investing in advanced AI technologies within the next five years. AI is increasingly a strategic priority for most organizations. However, deploying and managing AI workloads is complex, costly, and requires extensive integration and testing of the hardware and software. The new Dell EMC Ready Solutions for AI were built to simplify AI, deliver faster, deeper insights, and leverage Dell EMC's proven AI expertise. Organizations no longer have to individually source and piece together their own solutions. Instead, they can rely on a Dell EMC-designed and validated set of best-of-breed technologies for software – including AI frameworks and libraries – with compute, networking and storage. Dell EMC's portfolio of services from consulting to deployment, support and education helps customers drive the rapid adoption and optimization of their AI environments. In this video from the Dell EMC HPC Community Meeting, Jay Boisseau from Dell EMC describes how the company is moving forward as a thought leader in Artificial Intelligence. Dell EMC and NVIDIA engineered this deep learning design to be built around Dell EMC PowerEdge servers with NVIDIA Tesla V100 Tensor Core GPUs. AI is being driven by leaps in GPU computing power that defy the slowdown in Moore's Law," said Ian Buck, vice president and general manager, Accelerated Computing Group, NVIDIA.

I just created this timeline based on several papers and other timelines with the purpose of everyone seeing that Deep Learning is much more than just Neural Networks. There has been really theoretical advances, software and hardware improvements that were necessary for us to get to this day. If you want it just ping me and I'll send it to you. Deep Learning has been around for quite a while now. So why it became so relevant so fast the last 5–7 years?

Download the free ebook, "Artificial Intelligence: Teaching Machines to Think Like People." This interview is one in a series of interviews featured in the report. A better understanding of the reasons why neurons spike could lead to smart AI systems that can store more information more efficiently, according to Geoff Hinton, who is often referred to as the "godfather" of deep learning. Geoff Hinton is an emeritus distinguished professor at the University of Toronto and an engineering fellow at Google. He is one of the pioneers of neural networks, and was part of the small group of academics that nursed the technology through a period of tepid interest, funding, and development.

Amazon SageMaker now provides the latest version 1.2 of Apache MXNet. The built-in MXNet container within Amazon SageMaker makes it easy to run your deep learning scripts by taking advantage of the capabilities of SageMaker including distributed & managed training and real-time deployment of machine learning models.

The market for machine learning in medical imaging will continue to grow, reaching about $2 billion in annual expenditures worldwide by 2023. That's the estimate of Signify Research, a research organization based in the United Kingdom. Signify Research sees particularly rapid expansion of the market for algorithms using deep learning to support software used in image analysis. It estimates the market at less than $200 million this year, but growing to nearly $1.4 billion by 2023. The consultancy defines machine learning applications as encompassing software for automated detection, quantification, decision support and diagnosis.

Stanford University's Machine Learning Course Andrew Ng is the man. The founder of Google Brain and former chief scientist at Baidu, Andrew Ng's course is the clear winner in terms of ratings, reviews, and syllabus fit. Seeing how this course was what practically founded Coursera, that doesn't seem unbelievable. Although it has a smaller scope than the original Stanford class, it covers a large number of algorithms and techniques. The estimated timeline is eleven weeks, which includes two weeks of neural networks and deep learnings.

Despite the hype surrounding artificial intelligence, we're still in the early stages of adopting machine learning in the enterprise, according to a new survey released today by O'Reilly Media. The survey also found that large-scale production deep learning rarely happens on the cloud, and that companies pursuing machine learning are actively embracing privacy, security, and fairness. Nearly half (49%) of the 11,400 people who took O'Reilly's survey this June indicated they were in the exploration phase of machine learning and have not deployed any machine learning models into production. That compares to 36% of who said they were an early adopter (models in production from two to five years), while 15% considered themselves sophisticated users (models in production for more than five years). "A lot of people are very interested in machine learning, but a lot of them are in the getting-started phase in terms of actually putting these things into productions in products and services," O'Reilly's Chief Data Scientist Ben Lorica tells Datanami.

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are some of the hottest topics right now. The term "AI" is thrown around every day. Actually you can read many articles about the benefits of AI to retail or how AI is transforming the fashion industry; and you also hear executives saying they want to implement AI in the customer journey to improve the customer experience or internal processes. While we're all familiar with the term "Artificial Intelligence", quite often, many people don't understand what AI is and what's the difference between Machine Learning and Deep Learning. If you are one of them, don't worry.

The back-propagation algorithm is the cornerstone of deep learning. Despite its importance, few variations of the algorithm have been attempted. This work presents an approach to discover new variations of the back-propagation equation. We use a domain specific lan- guage to describe update equations as a list of primitive functions. An evolution-based method is used to discover new propagation rules that maximize the generalization per- formance after a few epochs of training. We find several update equations that can train faster with short training times than standard back-propagation, and perform similar as standard back-propagation at convergence.

This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the "Adam-type", includes the popular algorithms such as the Adam, AMSGrad and AdaGrad. Despite their popularity in training deep neural networks, the convergence of these algorithms for solving nonconvex problems remains an open question. This paper provides a set of mild sufficient conditions that guarantee the convergence for the Adam-type methods. We prove that under our derived conditions, these methods can achieve the convergence rate of order $O(\log{T}/\sqrt{T})$ for nonconvex stochastic optimization. We show the conditions are essential in the sense that violating them may make the algorithm diverge. Moreover, we propose and analyze a class of (deterministic) incremental adaptive gradient algorithms, which has the same $O(\log{T}/\sqrt{T})$ convergence rate. Our study could also be extended to a broader class of adaptive gradient methods in machine learning and optimization.