From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery

Recently, AAAI coordinated and The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) cosigned a statement with CRA, and the Thirty-First Conference on Innovative Applications of Artificial expressing concern about the proposed Intelligence (IAAI-19), will be held in Honolulu, Hawaii, USA, January tax bill and its ramifications for graduate 27 - February 1, 2019. The technical conference will continue its student stipends. Other organizational 3.5-day schedule, preceded by the workshop and tutorial programs.

A method to control results of gradient descent unsupervised learning in a deep neural network by using evolutionary algorithm is proposed. To process crossover of unsupervisedly trained models, the algorithm evaluates pointwise fitness of individual nodes in neural network. Labeled training data is randomly sampled and breeding process selects nodes by calculating degree of their consistency on different sets of sampled data. This method supervises unsupervised training by evolutionary process. We also introduce modified Restricted Boltzmann Machine which contains repulsive force among nodes in a neural network and it contributes to isolate network nodes each other to avoid accidental degeneration of nodes by evolutionary process. These new methods are applied to document classification problem and it results better accuracy than a traditional fully supervised classifier implemented with linear regression algorithm.

Goals for reinforcement learning problems are typically defined through hand-specified rewards. To design such problems, developers of learning algorithms must inherently be aware of what the task goals are, yet we often require agents to discover them on their own without any supervision beyond these sparse rewards. While much of the power of reinforcement learning derives from the concept that agents can learn with little guidance, this requirement greatly burdens the training process. If we relax this one restriction and endow the agent with knowledge of the reward function, and in particular of the goal, we can leverage backwards induction to accelerate training. To achieve this, we propose training a model to learn to take imagined reversal steps from known goal states. Rather than training an agent exclusively to determine how to reach a goal while moving forwards in time, our approach travels backwards to jointly predict how we got there. We evaluate our work in Gridworld and Towers of Hanoi and empirically demonstrate that it yields better performance than standard DDQN.

This paper describes a novel energy-based probabilistic distribution that represents complex-valued data and explains how to apply it to direct feature extraction from complex-valued spectra. The proposed model, the complex-valued restricted Boltzmann machine (CRBM), is designed to deal with complex-valued visible units as an extension of the well-known restricted Boltzmann machine (RBM). Like the RBM, the CRBM learns the relationships between visible and hidden units without having connections between units in the same layer, which dramatically improves training efficiency by using Gibbs sampling or contrastive divergence (CD). Another important characteristic is that the CRBM also has connections between real and imaginary parts of each of the complex-valued visible units that help represent the data distribution in the complex domain. In speech signal processing, classification and generation features are often based on amplitude spectra (e.g., MFCC, cepstra, and mel-cepstra) even if they are calculated from complex spectra, and they ignore phase information. In contrast, the proposed feature extractor using the CRBM directly encodes the complex spectra (or another complex-valued representation of the complex spectra) into binary-valued latent features (hidden units). Since the visible-hidden connections are undirected, we can also recover (decode) the complex spectra from the latent features directly. Our speech coding experiments demonstrated that the CRBM outperformed other speech coding methods, such as methods using the conventional RBM, the mel-log spectrum approximate (MLSA) decoder, etc.

This is literally a robotic intestine puking rocket fuel. It's being developed in Japan, by roboticists from Chuo University and JAXA, the Japan Aerospace Exploration Agency. You'll be relieved to learn that it's a robotic intestine puking rocket fuel with a purpose: It's designed to replicate the peristaltic motion of a real intestine in order to gently mix ingredients to make solid rocket fuel. The researchers say their machine is safer than conventional mixers because the fuel doesn't experience high shear stress inside the undulating rubber tubing and is never in contact with metal, avoiding the risk of fire and explosions. The idea is to turn the solid rocket fuel manufacturing process into a continuous operation rather than a discrete one, replacing rocket fuel mixing bowls that give you fuel in batches with a system that can just continuously pump out fuel instead.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The network models in our study were from existing references. All the underlying data set for our study could be available from the eleven sources listed below. Superfamilies of designed and evolved networks. Funding: This research is supported by the National Science Foundation of China with granting No.61773032.

Two years ago, one of India's major private lenders ICICI Bank became the first in the country to adopt software robotics, also known as robotic process automation (RPA) on a large scale. The operations department deployed around 200 robotics software programs, which helped in processing close to 10 lakh transactions daily. It's been two years and the bank's love for automation has picked up and how. According to research firm IDC, RPA is a software code that automates standardized, rules-based repetitive tasks, which were traditionally done by humans. Anita Pai, senior general manager and head of operations at ICICI Bank India, said the bank has now scaled up to 750 software robots and also doubled the number of transactions (close to 20 lakh) handled daily. "These robots are being used across different operations and LoBs including retail, wholesale banking, forex, treasury, agro and international operations.

Russia may soon use robots to carry out AI-powered assassinations as part of a new cold war, a computer expert has claimed. Future attacks on exiles and dissidents could be untraceable thanks to the use of automatons, warns Dr Jeremy Straub at Dakota State University. Intelligent machines could be programmed to hack into food ordering systems, like those used at fast food restaurants, to poison their victims. This would have the benefit of killing a target, while appearing to the outside world to be an accidental allergic reaction. The claims come in the wake of the high profile attack on former Russian spy Sergei Skripal and his daughter Yulia, which involved the use of a deadly nerve agent. Prime Minister Theresa May has laid the blame at Moscow's door, expelling a number of prominent diplomats from Britain in the process.

Ride-hailing giant Uber has sold its south-east Asia business to its leading rival Grab marking a further retreat from international operations. It comes after Uber also pulled out of China and Russia in recent years and as new chief executive Dara Khosrowshahi attempts to turn the firm#s fortunes around. Under the terms of the deal, Uber will take a 27.5 per cent stake in Grab, which offers ride-sharing, food delivery, bicycle hire and financial services. US firm Uber will also have a seat on Grab's board. Ride-hailing giant Uber is selling its Southeast Asia operation to its leading rival Grab marking a further retreat from international operations.