It's almost impossible to escape the impact frontier technologies are having on everyday life. At the core of this impact are the advancements of artificial intelligence, machine learning, and deep learning. These change agents are ushering in a revolution that will fundamentally alter the way we live, work, and communicate akin to the industrial revolution – more specifically, AI is the new industrial revolution. The most exciting and promising of these frontier technologies is the advancements happening in the deep learning space. While still nascent, it's deep learning percolating into your smartphone, driving advancements in healthcare, creating efficiencies in the power grid, improving agricultural yields, and helping us find solutions to climate change.

Humanity's relentless march towards a future where machines rule everything gained some ground today, as Google revealed that one of its fancy artificial brains is now better at diagnosing some medical conditions than human doctors are. This should prove extremely valuable for whenever the inevitable robot uprising results in a swift demise for the human race. Google's announced its findings in the Journal of the American Medical Association in a paper detailing the company's work with deep learning algorithms and how they can be utilized for medical purposes. In this particular case, Google set its sights on diagnosing diabetic retinopathy via retina photographs. The neural network Google used for the project was fed over 128,000 images to train it in detecting the condition.

Is Artificial Intelligence the Future of Airline Customer Service? Google's Featured Snippets on Desktop Now Written By Artificial Intelligence Here are the top 20 AI investors ... and their biggest 2016 deals AI writes Christmas songs... and it sounds awful Fujitsu Offers Deep Learning Platform with World-Class Speed, AI Services that Support Industries ... Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

As a smart technology entrepreneur with a machine intelligence research background and passionate about advancing the state-of-the-art in machine or artificial intelligence (AI) to help solve real-world problems, it is very encouraging and exciting to see the AI buzz in the tech industry right now, the progress made in the field to create an even stronger intelligence toolbox, and the ever increasing practical applications in all industries and enterprise functions. Machine intelligence is not only changing the way we use our computers and smartphones but the way we interact with the real world. It is also one of the key exponential technologies in the Fourth Industrial Revolution. Given how all the major technology companies are embracing machine intelligence as a core part of their business and the multitude of startups building their business on this technology, AI is clearly not a passing fad, but being pushed across the rest of the tech world too. In this post I'm not only addressing some key topics about the current and future state of machine intelligence, but also practical steps we are taking here in Africa to not only use smart technology to solve problems, but also make a contribution towards advancing the-state-of-the-art in machine intelligence.

Is Artificial Intelligence the Future of Airline Customer Service? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Google researchers got an eye-scanning algorithm to figure out on its own how to detect a common form of blindness, showing the potential for artificial intelligence to transform medicine remarkably soon. The algorithm can look at retinal images and detect diabetic retinopathy--which affects almost a third of diabetes patients--as well as a highly trained ophthalmologist can. It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual deterioration of vision. If caught early it can be treated, but a sufferer may experience no symptoms early on, making screening vital.

Last week I read Abadi and Andersen's recent paper [1], Learning to Protect Communications with Adversarial Neural Cryptography. I thought the idea seemed pretty cool and that it wouldn't be too tricky to implement, and would also serve as an ideal project to learn a bit more Theano. This post describes the paper, my implementation, and the results. The authors set up their experiment as follows. We have three neural networks, named Alice, Bob, and Eve.

We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledgebase. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et al. 2015) for learning skills. Skill distillation enables the H-DRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et al. 2015) in sub-domains of Minecraft.

Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionary learning is proposed for such applications. The proposed algorithm selects training samples that maximize two selection criteria, namely representative and uncertainty. This algorithm trains a deep network efficiently by actively selecting training samples at each iteration. The proposed algorithm is applied for the classification of hyperspectral images, and compared with other classification algorithms employing active learning. It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.

Understanding the nature of dark energy, the mysterious force driving the accelerated expansion of the Universe, is a major challenge of modern cosmology. The next generation of cosmological surveys, specifically designed to address this issue, rely on accurate measurements of the apparent shapes of distant galaxies. However, shape measurement methods suffer from various unavoidable biases and therefore will rely on a precise calibration to meet the accuracy requirements of the science analysis. This calibration process remains an open challenge as it requires large sets of high quality galaxy images. To this end, we study the application of deep conditional generative models in generating realistic galaxy images. In particular we consider variations on conditional variational autoencoder and introduce a new adversarial objective for training of conditional generative networks. Our results suggest a reliable alternative to the acquisition of expensive high quality observations for generating the calibration data needed by the next generation of cosmological surveys.