Retinitis pigmentosa (RP) is a rare genetic disorder that sabotages the retina's ability to respond to light by disabling rod photoreceptors. The National Eye Institute estimates that one in 4,000 people around the world suffer from the disorder. In the UK however, people who have lost their sight to RP may soon have hope as the National Health Service (NHS) funds the implantation of a device among ten patients to help treat this form of blindness. The Argus II Bionic Eye implant works in tandem with a small camera mounted on a pair of glasses that is worn by the patient. Images from the camera are converted into wireless signals via electrodes attached to the retina.

From Elgar to Adele, and the Beatles or Pink Floyd to Kanye West, London's Abbey Road Studios has hosted a storied list of musical stars since opening in 1931. The man sitting at the keyboard where John Lennon may have finessed A Day in the Life is Siavash Mahdavi, CEO of AI Music, a British tech startup exploring the intersection of artificial intelligence and music. His company is one of two AI firms currently taking part in Abbey Road Red, a startup incubator run by the studios that aims to forge links between new tech companies and the music industry. It's not alone: Los Angeles-based startup accelerator Techstars Music, part-funded by major labels Sony Music and Warner Music Group, included two AI startups in its programme earlier this year: Amper Music and Popgun. This is definitely a burgeoning sector.

Summary: Sensors that know how you feel? Sensors that want to change the way you feel? When did that happen and better yet how? We're getting used to sensors finding out what we're doing. Apparently they are now sufficiently sophisticated that they can even tell if I'm sitting up straight (yes Mom – BTW using a camera is almost cheating, you should be able to do this with just an accelerometer and a gyro).

Artificial intelligence and increasing automation is going to decimate middle class jobs, worsening inequality and risking significant political upheaval, Stephen Hawking has warned. In a column in The Guardian, the world-famous physicist wrote that "the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining." He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years and decades. The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines. Technology has already gutted many traditional manufacturing and working class jobs -- but now it may be poised to wreak similar havoc with the middle classes.

This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before. Deep Learning is becoming the method of choice for its exceptional accuracy and capturing capacity for unstructured data.

Google parent Alphabet Inc. GOOGL 0.58% is pitching an idea to store power from renewable energy in tanks of molten salt and cold liquid, an example of the tech giant trying to marry its far-reaching ambitions with business demand. Alphabet's research lab, dubbed X, said Monday that it has developed plans to store electricity generated from solar panels or wind turbines as thermal energy in hot salt and cold liquids, such as antifreeze. The lab is seeking partners in the energy industry, including power-plant developers and utilities, to build a prototype to plug into the electrical grid. Whether the project, called Malta, ever comes to market depends as much on a sound business model as it does on science. Academics said the technology is likely years away from market, if it ever makes it.

The following essay is reprinted with permission from The Conversation, an online publication covering the latest research. As an Artificial Intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb?

The vanishing gradient problem was a major obstacle for the success of deep learning. In recent years it was gradually alleviated through multiple different techniques. However the problem was not really overcome in a fundamental way, since it is inherent to neural networks with activation functions based on dot products. In a series of papers, we are going to analyze alternative neural network structures which are not based on dot products. In this first paper, we revisit neural networks built up of layers based on distance measures and Gaussian activation functions. These kinds of networks were only sparsely used in the past since they are hard to train when using plain stochastic gradient descent methods. We show that by using Root Mean Square Propagation (RMSProp) it is possible to efficiently learn multi-layer neural networks. Furthermore we show that when appropriately initialized these kinds of neural networks suffer much less from the vanishing and exploding gradient problem than traditional neural networks even for deep networks.

We introduce the Probabilistic Generative Adversarial Network (PGAN), a new GAN variant based on a new kind of objective function. The central idea is to integrate a probabilistic model (a Gaussian Mixture Model, in our case) into the GAN framework which supports a new kind of loss function (based on likelihood rather than classification loss), and at the same time gives a meaningful measure of the quality of the outputs generated by the network. Experiments with MNIST show that the model learns to generate realistic images, and at the same time computes likelihoods that are correlated with the quality of the generated images. We show that PGAN is better able to cope with instability problems that are usually observed in the GAN training procedure. We investigate this from three aspects: the probability landscape of the discriminator, gradients of the generator, and the perfect discriminator problem.

Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure. Our approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep neural network that detects landmarks consistently with such visual effects. Furthermore, we show that the learned landmarks establish meaningful correspondences between different object instances in a category without having to impose this requirement explicitly. We assess the method qualitatively on a variety of object types, natural and man-made. We also show that our unsupervised landmarks are highly predictive of manually-annotated landmarks in face benchmark datasets, and can be used to regress these with a high degree of accuracy.