But bahgawd, the technology Apple is pulling off really is falling squarely into the realm of magical. Anyone else just wowed by the amount of technology embedded into this new iPhone? Our phones are learning more about us then we ever knew Before.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA "seed region" (nt 2 to 8) is required for functional targeting, but typically only identify 80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed. We present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3'UTR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process. We collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain 20,000 validated miRNA:gene exact target sites.

"Accessibility by design" is an important concept for Microsoft, and one that underpins many of its artificial intelligence-powered products, including Seeing AI. Announced on Wednesday among a series of other AI tools, Seeing AI is a free mobile application designed to support people with visual impairments by narrating the world around them. The app -- which is an ongoing research project bringing together deep learning and Microsoft Cognitive Services -- can read documents, making sense of structural elements such as headings, paragraphs, and lists, as well as identify a product using its barcode. It can additionally recognise and describe images in other apps, and even pinpoint people's faces and provide a description of their appearance, though camera quality and lighting might influence its description. At the Microsoft Future of Artificial Intelligence event in Sydney, Kenny Johar Singh, a Melbourne-based cloud solutions architect at Microsoft, demonstrated Seeing AI, which he uses to help navigate the physical world.

The deep-learning models need to be shipped as part of the operating system, taking up valuable NAND storage space. They also need to be loaded into RAM and require significant computational time on the GPU and/or CPU. Unlike cloud-based services, whose resources can be dedicated solely to a vision problem, on-device computation must take place while sharing these system resources with other running applications. Finally, the computation must be efficient enough to process a large Photos library in a reasonably short amount of time, but without significant power usage or thermal increase.

You should be able to see that with this definition our neural net shouldn't be as easily fooled by our misshapen Kardashian. This new architecture also achieves significantly better accuracy on the following data set. This data set was carefully designed to be a pure shape recognition task that shows the ability to recognize the objects even from different points of view. It beat out the state-of-the-art CNN, reducing the number of errors by 45%. Further more, in their most recent paper, they found that Capsules show far more resistance to white box adversarial attack than a baseline convolutional neural network. I have pieced together a repo that is an implementation of Hinton's paper (many thanks to naturomics). In order to use the Capsule Network model you first need to train it. The following guide will get you a model trained on the MNIST data set. For those of you who don't know, MNIST is a data set of handwritten digits and is a good baseline for testing out machine learning algorithms.

Face ID has been a bit of a thorn in Apple's side for its iPhone X, no thanks to claims the AI-powered login mechanism can be tricked by cheapish masks or relatives of handset owners. Now, this week, Apple has published a blog post describing in a fair amount of detail how its algorithms behind the Face ID authentication system work. The approach is based on OverFeat, a model developed by researchers at New York University, that teaches a deep convolutional neural network (DCN) to classify, locate and detect objects in images. It works by using a binary classifier to detect whether or not a face is present in images taken from the front-facing camera, and a feature extractor and bounding box regression network to perform the actual identification. Pictures from the camera are run through the feature extractor to break down the images into shapes and portions, and these are passed to the binary classifier and the regressor.

– Any one working within industries like the mobility, fintech, mobile money, payments, banking or InsureTech with little knowledge of data science is actually sitting on gold mine to explore and show what Data Science / AI can do for that company. Today every company on this planet collect vast quantities of data on a daily basis or even per second. For example credit card issuers with every credit card swipe and completed transaction capture critical customer information, In case of mobile payments/money the same thing happen or even in banks same scenarios. However, the raw data alone does not generate the insights needed to drive business decisions or simply not good enough at all. It's the proper analysis of this data that unlocks its true value.

We introduce MinimalRNN, a new recurrent neural network architecture that achieves comparable performance as the popular gated RNNs with a simplified structure. It employs minimal updates within RNN, which not only leads to efficient learning and testing but more importantly better interpretability and trainability. We demonstrate that by endorsing the more restrictive update rule, MinimalRNN learns disentangled RNN states. We further examine the learning dynamics of different RNN structures using input-output Jacobians, and show that MinimalRNN is able to capture longer range dependencies than existing RNN architectures.

Error backpropagation is a highly effective mechanism for learning high-quality hierarchical features in deep networks. Updating the features or weights in one layer, however, requires waiting for the propagation of error signals from higher layers. Learning using delayed and non-local errors makes it hard to reconcile backpropagation with the learning mechanisms observed in biological neural networks as it requires the neurons to maintain a memory of the input long enough until the higher-layer errors arrive. In this paper, we propose an alternative learning mechanism where errors are generated locally in each layer using fixed, random auxiliary classifiers. Lower layers could thus be trained independently of higher layers and training could either proceed layer by layer, or simultaneously in all layers using local error information. We address biological plausibility concerns such as weight symmetry requirements and show that the proposed learning mechanism based on fixed, broad, and random tuning of each neuron to the classification categories outperforms the biologically-motivated feedback alignment learning technique on the MNIST, CIFAR10, and SVHN datasets, approaching the performance of standard backpropagation. Our approach highlights a potential biological mechanism for the supervised, or task-dependent, learning of feature hierarchies. In addition, we show that it is well suited for learning deep networks in custom hardware where it can drastically reduce memory traffic and data communication overheads.

We use deep feedforward artificial neural networks to approximate solutions of partial differential equations of advection and diffusion type in complex geometries. We derive analytical expressions of the gradients of the cost function with respect to the network parameters, as well as the gradient of the network itself with respect to the input, for arbitrarily deep networks. The method is based on an ansatz for the solution, which requires nothing but feedforward neural networks, and an unconstrained gradient based optimization method such as gradient descent or quasi-Newton methods. We provide detailed examples on how to use deep feedforward neural networks as a basis for further work on deep neural network approximations to partial differential equations. We highlight the benefits of deep compared to shallow neural networks and other convergence enhancing techniques.