We introduce a new, high-throughput, synchronous, distributed, data-parallel, stochastic-gradient-descent learning algorithm. This algorithm uses amortized inference in a compute-cluster-specific, deep, generative, dynamical model to perform joint posterior predictive inference of the mini-batch gradient computation times of all worker-nodes in a parallel computing cluster. We show that a synchronous parameter server can, by utilizing such a model, choose an optimal cutoff time beyond which mini-batch gradient messages from slow workers are ignored that maximizes overall mini-batch gradient computations per second. In keeping with earlier findings we observe that, under realistic conditions, eagerly discarding the mini-batch gradient computations of stragglers not only increases throughput but actually increases the overall rate of convergence as a function of wall-clock time by virtue of eliminating idleness. The principal novel contribution and finding of this work goes beyond this by demonstrating that using the predicted run-times from a generative model of cluster worker performance to dynamically adjust the cutoff improves substantially over the static-cutoff prior art, leading to, among other things, significantly reduced deep neural net training times on large computer clusters.

We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adversarial network (GAN) setting, while modeling image data. We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models. We show that CapsuleGAN outperforms convolutional-GAN at modeling image data distribution on MNIST and CIFAR-10 datasets, evaluated on the generative adversarial metric and at semi-supervised image classification.

It is a phrase that more and more executives and developers alike are hearing in stand-up meetings and the board room. Companies from Google to IBM to Microsoft, are investing millions of dollars and man-hours into the development of deep learning platforms and products and spending largely on advertising and marketing to show their efforts. While such developments promise very exciting ventures in the months and years to come, organizations and individuals need to remember that deep learning is still a field that is expanding and going through its growing pains. True success with deep learning depends on the how, the what and the why of its application across business lines. Dr. Sid J. Reddy reflects these exact sentiments in his article, "Deep Learning is only as good as its data."

This year will finally deliver the benefits of Deep Learning to mobile platforms. We expect significant improvements in privacy, personalization, offline functionality and cost of services across all mobile application segments. Alexa, Cortana and Siri will soon live on our phones, answering questions, translating and being helpful even when we're traveling abroad or hiking off-the-grid. Video games will become more entertaining, challenging and engaging even when we play against the computer. Video streaming will take less of our bandwidth and mobile data, while the image quality will improve.

These days we hear a lot of companies using the buzz words Like Machine Learning, Deep Learning and AI in their products and services, however the real world problem is that when asked about the impact of the services or the product they have, then most of the times it comes to on this single statement of "overall increase in business efficiency", thus it creates confusion and the statement is pretty ambiguous in nature, which is sometimes baseless and without experimental driven. Hence in this article, I want to uncover this above statement and make it easier for both companies and the clients to calculate the actual impact of the product/services. Being a technology consultant and working in the domain of AI, I personally used the exact statement whenever I wanted to pitch my services to the client, but when realizing the quantitative impact of the services I was unable to justify my piece of work because the services offered didn't provide me the numbers and the figures. Reviewing the archives of the 19th century on the industrial revolution, I summarized that it was a way of automation in those days, and there was growth in the overall production of the products just by getting new machinery in the supply chain. The whole change of system can be called AI of that era in a simpler way.

Artificial intelligence is quickly becoming an integral part of every tech company's strategy, so it's no surprise that big firms are ramping up their acquisition of AI startups. M&A activity has already seen a fivefold increase in the number of AI startup acquisitions -- from 22 in 2013 to 115 in 2017. While the race is far from over, Google and Apple have acquired the most AI startups since 2012. With close to 14 acquisitions, Google is currently leading the charge to buy AI startups. The company's most recent acquisition involves the conversational commerce platform Banter, which helps businesses connect with their customers over popular messaging platforms like Facebook Messenger, Twitter, and Snapchat.

Application of Artificial Intelligence is experiencing rapid growth. Understanding the evolution of key technologies and trends and emerging opportunities is critical for business success. Key AI technologies include Machine Learning, Deep Learning, expert systems. The AI application market segments include Healthcare, automotive, self driving cars, image classification, natural language processing, voice recognition, smart robots, bioinformatics, life sciences, transportation, data mining and wave computing. By leveraging the big data driven cloud based pintels intelligence analytics you have the opportunity to continuously monitor AI technologies, identify emerging trends and opportunities in machine learning, deep learning, natural language processing markets and computer vision sectors.

The networks fight each other and improve together, like two humans playing against each other in a game.

Today is International Women's Day, and we're celebrating by highlighting some of the leading ladies in AI, machine learning and deep learning who have spoken at RE•WORK Summits and dinners over the past 12 months. Whilst the technology industry is seeing more and more women in top roles, the gender imbalance is still clear. At RE•WORK, we're passionate about encouraging women and girls into STEM and are proud to host our series of dinners and our Podcast celebrating women in AI. Earlier this year we hosted the AI Assistants Summit in San Francisco, where 50% of our speakers were women. This was a fantastic showcase of diversity, and we are striving to have more and more female experts presenting at our Summits.

Predictive Analytics World for Financial is heading to Las Vegas, NV on June 3-7, 2018 and we're excited to announce the speaker line-up for the conference program. Meet this diverse array of industry leading professionals -- from Data Scientists to C-Level Executives and so much more, we're excited to introduce to you these stellar industry leaders who are shaping the financial services industry: In 2018, there will be only ONE PAW in the U.S. – Mega-PAW – with five (5) parallel events amounting to seven (7) tracks: PAW Business, PAW Financial, PAW Healthcare, PAW Manufacturing, and Deep Learning World.