Mathematicians at Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a radical new approach to machine learning: a new type of highly efficient "deep convolutional neural network" that can automatically analyze complex experimental scientific images from limited data.* As experimental facilities generate higher-resolution images at higher speeds, scientists struggle to manage and analyze the resulting data, which is often done painstakingly by hand. For example, biologists record cell images and painstakingly outline the borders and structure by hand. One person may spend weeks coming up with a single fully three-dimensional image of a cellular structure. Or materials scientists use tomographic reconstruction to peer inside rocks and materials, and then manually label different regions, identifying cracks, fractures, and voids by hand.

CEO of Google DeepMind Demis Hassabis speaks during a press conference after finishing the third match of the Google DeepMind Challenge Match between South Korean professional Go player Lee Sedol and Google's artificial intelligence program, AlphaGo in March 2016 (AP Photo/Lee Jin-man) Artificial intelligence is destined to power some of our most important services, but there's growing concern that it could repeat much of the prejudice that humans have about race, gender and more because of the way it's built. Simply put, when artificial intelligence is trained with biased data, it can make biased decisions. By way of example, facial recognition systems from IBM and Microsoft were recently shown to have struggled to properly recognize black women, while software used to help courts predict criminality has skewed towards black men. An experiment by Carnegie Mellon university also showed back in 2015 that "significantly fewer" women were being shown online ads for jobs paying more than $200,000. All of these systems were powered by machine-learning, which to most people would appear to be at the cutting edge of technology.

Within the field of image recognition and analysis, recent advances have been made thanks to deep learning techniques and the development of Convolutional Neural Networks (CNN). These are inspired by how our own brains work so efficiently to recognize objects – by filtering the lines, then the shapes and finally the objects themselves in a hierarchical approach. Of course the advantage for computers carrying out human tasks is that they can process many more images and quicker, plus they are as "focused" on the 372,487th image just as much as on the first. And with video analysis there's incredible potential when you think of the ability to quickly go through thousands of hours of material and still pick up the tiniest clues, something that's almost inconceivable even if you used teams of people working around the clock The democratization of machine learning and the newfound access to these techniques is well illustrated by a recent story. Washington County Sheriff's Office in Oregon, USA needed a quicker and more accurate way to identify suspects from images. This used to involve sending e-mails to law enforcement officers to ask if they recognized the person or people in the image.

Disclaimer: All the information in this article including the algorithm was provided and published for educational purpose only, not a solicitation for investment nor investment advice. Any reliance you place on such information is therefore strictly at your own risk. Bitcoin is the first decentralized digital currency. This means it is not governed by any central bank or some other authority. This cryptocurrency was created in 2009 but it became extremely popular in 2017.

In the US alone, tens of millions of mammograms are performed each year. Analyzing these images takes up a lot of doctors' time. The use of computer assistance to help read mammograms is becoming widespread, but doubts persist about whether the practice is helpful enough to justify its steep price tag. Lower-cost deep learning systems, which train themselves to recognize cancer, could help. Thanks to deep-learning methods like those more commonly used to spot everyday objects in photographs, a new system identified cancer's precise location more than 90 percent of the time in tests.

This paper addresses the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that the outputs of the neural network will always behave in a certain way for a given class of inputs. Most previous work on this topic was limited in its applicability by the size of the network, network architecture and the complexity of properties to be verified. In contrast, our framework applies to much more general class of activation functions and specifications on neural network inputs and outputs. We formulate verification as an optimization problem and solve a Lagrangian relaxation of the optimization problem to obtain an upper bound on the verification objective. Our approach is anytime, i.e. it can be stopped at any time and a valid bound on the objective can be obtained. We develop specialized verification algorithms with provable tightness guarantees under special assumptions and demonstrate the practical significance of our general verification approach on a variety of verification tasks.

It is a known fact that training recurrent neural networks for tasks that have long term dependencies is challenging. One of the main reasons is the vanishing or exploding gradient problem, which prevents gradient information from propagating to early layers. In this paper we propose a simple recurrent architecture, the Fourier Recurrent Unit (FRU), that stabilizes the gradients that arise in its training while giving us stronger expressive power. Specifically, FRU summarizes the hidden states $h^{(t)}$ along the temporal dimension with Fourier basis functions. This allows gradients to easily reach any layer due to FRU's residual learning structure and the global support of trigonometric functions. We show that FRU has gradient lower and upper bounds independent of temporal dimension. We also show the strong expressivity of sparse Fourier basis, from which FRU obtains its strong expressive power. Our experimental study also demonstrates that with fewer parameters the proposed architecture outperforms other recurrent architectures on many tasks.

Deep neural networks (DNNs) form the backbone of almost every state-of-the-art technique in the fields such as computer vision, speech processing, and text analysis. The recent advances in computational technology have made the use of DNNs more practical. Despite the overwhelming performances by DNN and the advances in computational technology, it is seen that very few researchers try to train their models from the scratch. Training of DNNs still remains a difficult and tedious job. The main challenges that researchers face during training of DNNs are the vanishing/exploding gradient problem and the highly non-convex nature of the objective function which has up to million variables. The approaches suggested in He and Xavier solve the vanishing gradient problem by providing a sophisticated initialization technique. These approaches have been quite effective and have achieved good results on standard datasets, but these same approaches do not work very well on more practical datasets. We think the reason for this is not making use of data statistics for initializing the network weights. Optimizing such a high dimensional loss function requires careful initialization of network weights. In this work, we propose a data dependent initialization and analyze its performance against the standard initialization techniques such as He and Xavier. We performed our experiments on some practical datasets and the results show our algorithm's superior classification accuracy.

While AI systems can now learn a game and beat champions within hours, they are hard to apply to business applications. MIT Sloan Management Review and Boston Consulting Group surveyed 3,000 business executives and found that while 85 percent of them thought that AI would provide their companies with a competitive advantage, only 1 in 20 had "extensively" incorporated it into their offerings or processes. The challenge is that implementing AI isn't as easy as installing software. It requires expertise, vision and information that isn't easily accessible. When you look at well-known applications of AI, such as Google's AlphaGo Zero, you get the impression it is like magic: AI learned the world's most difficult board game in just three days and beat champions; Nvidia's AI can generate photorealistic images of people who look like celebrities just by looking at pictures of real ones.

In order to alleviate data sparsity and over-fitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network). Unlike MLE directly maximizing the conditional likelihood, the bridge extends the point-wise ground truth to a bridge distribution conditioned on it, and the generator is optimized to minimize their KL-divergence. Three different GBNs, namely uniform GBN, language-model GBN and coaching GBN, are proposed to penalize confidence, enhance language smoothness and relieve learning burden. Experiments conducted on two recognized sequence prediction tasks (machine translation and abstractive text summarization) show that our proposed GBNs can yield significant improvements over strong baselines. Furthermore, by analyzing samples drawn from different bridges, expected influences on the generator are verified.