One-day research workshops on the application of AI approaches to a dedicated area of research (e.g. Workshops may be held in any Canadian city, but must include participants from multiple research institutions (universities, research institutes, research hospitals). The goal of the workshop should be to identify opportunities for the application of AI to the specific domain of interest, identify emerging research opportunities and foster the development of new collaborations. Up to $20,000 of funding is available and applicants will be asked to provide a complete budget. CIFAR will provide some logistical support to workshop organizers (e.g.

Now approaching its fourth successful year, the next edition of Future Steel Forum will take place in Prague, the capital of the Czech Republic on 2 – 3 June 2020. Future Steel Forum is all about the application of industry 4.0 to the steelmaking process.. Delegates can expect to hear from the world's leading experts on high-tech steelmaking n a whole range of topics including: For 2020, we have more steelmakers involved than ever before including: ArcelorMittal, Tata Steel India, Emirates Steel, POSCO, Mtinvest Digital, Big River Steel, Liberty Steel Group, Kobe Steel, Buderus Edelstahl, Badische Stahl Engineering and TMK. Delegates can expect lively conversation, animated discussion panels and plenty of networking opportunities. Take advantage of the early-bird rate and register for your pass today. "It's an exceptional opportunity to assess the evolution of the steel industry and to meet relevant people."

Particulate matter pollution is one of the deadliest types of air pollution worldwide due to its significant impacts on the global environment and human health. Particulate Matter (PM2.5) is one of the important particulate pollutants to measure the Air Quality Index (AQI). The conventional instruments used by the air quality monitoring stations to monitor PM2.5 are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, these stations cannot provide high spatial and temporal resolution in real-time. To overcome the disadvantages of existing methodology this article presents analytical equations based prediction approach for PM2.5 using an Artificial Neural Network (ANN). Since the derived analytical equations for the prediction can be computed using a Wireless Sensor Node (WSN) or low-cost processing tool, it demonstrates the usefulness of the proposed approach. Moreover, the study related to correlation among the PM2.5 and other pollutants is performed to select the appropriate predictors. The large authenticate data set of Central Pollution Control Board (CPCB) online station, India is used for the proposed approach. The RMSE and coefficient of determination (R2) obtained for the proposed prediction approach using eight predictors are 1.7973 ug/m3 and 0.9986 respectively. While the proposed approach results show RMSE of 7.5372 ug/m3 and R2 of 0.9708 using three predictors. Therefore, the results demonstrate that the proposed approach is one of the promising approaches for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.

Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly assess their contribution. However, the calculation of SV is time-consuming and computationally costly. In this paper, we propose FedCoin, a blockchain-based peer-to-peer payment system for FL to enable a feasible SV based profit distribution. In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol. It is in contrast to the popular BitCoin network where consensus entities "mine" new blocks by solving meaningless puzzles. Based on the computed SVs, a scheme for dividing the incentive payoffs among FL clients with nonrepudiation and tamper-resistance properties is proposed. Experimental results based on real-world data show that FedCoin can promote high-quality data from FL clients through accurately computing SVs with an upper bound on the computational resources required for reaching consensus. It opens opportunities for non-data owners to play a role in FL.

In drug discovery, knowledge of the graph structure of chemical compounds is essential. Many thousands of scientific articles in chemistry and pharmaceutical sciences have investigated chemical compounds, but in cases the details of the structure of these chemical compounds is published only as an images. A tool to analyze these images automatically and convert them into a chemical graph structure would be useful for many applications, such drug discovery. A few such tools are available and they are mostly derived from optical character recognition. However, our evaluation of the performance of those tools reveals that they make often mistakes in detecting the correct bond multiplicity and stereochemical information. In addition, errors sometimes even lead to missing atoms in the resulting graph. In our work, we address these issues by developing a compound recognition method based on machine learning. More specifically, we develop a deep neural network model for optical compound recognition. The deep learning solution presented here consists of a segmentation model, followed by three classification models that predict atom locations, bonds and charges. Furthermore, this model not only predicts the graph structure of the molecule but also produces all information necessary to relate each component of the resulting graph to the source image. This solution is scalable and could rapidly process thousands of images. Finally, we compare empirically the proposed method to a well-established tool and observe significant error reductions.

Peggy and Chris Vandersluis, president and founder, HMS Software, talks about how voice is leading the way in how enterprises function. He explains that using technology has become natural now and will be the default in business. They also discuss privacy and security considerations.

The inference of chemical reaction networks is an important task in understanding the chemical processes in life sciences and environment. Yet, only a few reaction systems are well-understood due to a large number of important reaction pathways involved but still unknown. Revealing unknown reaction pathways is an important task for scientific discovery that takes decades and requires lots of expert knowledge. This work presents a neural network approach for discovering unknown reaction pathways from concentration time series data. The neural network denoted as Chemical Reaction Neural Network (CRNN), is designed to be equivalent to chemical reaction networks by following the fundamental physics laws of the Law of Mass Action and Arrhenius Law. The CRNN is physically interpretable, and its weights correspond to the reaction pathways and rate constants of the chemical reaction network. Then, inferencing the reaction pathways and the rate constants are accomplished by training the equivalent CRNN via stochastic gradient descent. The approach precludes the need for expert knowledge in proposing candidate reactions, such that the inference is autonomous and applicable to new systems for which there is no existing empirical knowledge to propose reaction pathways. The physical interpretability also makes the CRNN not only capable of fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems. Finally, the approach is applied to several chemical systems in chemical engineering and biochemistry to demonstrate its robustness and generality.

While artificial intelligence offers opportunities to automate and innovate, just 30% of workplaces are actually using it. Combined with a lack of understanding of the technology, employers don't have the internal structure and personnel needed to launch the power of AI into their business model, says Augustine Walker, senior director of product management for Veritone, an AI solutions provider. "There isn't a lot of focus on what tools are out there so that I can make my business better with AI," Walker says. "The ubiquity of the talent pool and the capabilities are not out there yet -- it's still maturing." Walker spoke with Employee Benefit News on how AI can actually be a catalyst for creativity and why data scientists are a critical piece to the puzzle.

Automating molecular design using deep reinforcement learning (RL) holds the promise of accelerating the discovery of new chemical compounds. A limitation of existing approaches is that they work with molecular graphs and thus ignore the location of atoms in space, which restricts them to 1) generating single organic molecules and 2) heuristic reward functions. To address this, we present a novel RL formulation for molecular design in Cartesian coordinates, thereby extending the class of molecules that can be built. Our reward function is directly based on fundamental physical properties such as the energy, which we approximate via fast quantum-chemical methods. To enable progress towards de-novo molecular design, we introduce MolGym, an RL environment comprising several challenging molecular design tasks along with baselines. In our experiments, we show that our agent can efficiently learn to solve these tasks from scratch by working in a translation and rotation invariant state-action space.

The European Patent Office recently turned down an application for a patent that described a food container. This was not because the invention was not novel or useful, but because it was created by artificial intelligence. By law, inventors need to be actual people. This isn't the first invention by AI – machines have produced innovations ranging from scientific papers and books to new materials and music. That said, being creative is clearly one of the most remarkable human traits.