In line with last year's forecasts, the AI market continues to grow steadily, and in addition to qualitative improvement in technologies, there is a further expansion of the areas in which Artificial Intelligence is being implemented, including such traditional industries as engineering, mining, and agriculture. The spread of AI is due to the fact that the technology has matured enough while continuing to evolve. Above all, we can expect a significant increase in the production of specialized computer chips. Market leaders like NVIDIA, AMD, ARM, and Qualcomm have already begun manufacturing processors optimized for speech recognition and computer vision. According to the experts, the AI chip market will grow by 30-40% this year, while research company Allied Market Research forecasts that the global market could grow to $91.185 billion by 2025.

The field of artificial intelligence (AI) is growing at a rapid pace, developing algorithms and automated machines that show promise in making the workplace more efficient and less biased. Many of us already interact with artificial intelligence in our daily lives, often without even realizing it--it's responsible for everything from credit score calculators to search engine results to what we see on social media.1 Likewise, organizations have introduced AI into many work processes, especially recruiting and talent-management functions. In many cases, algorithms sort through numerous factors to profile people and make predictions about them. AI hiring and talent-management systems have the potential to move the needle on gender equality in workplaces by using more objective criteria in recruiting and promoting talent.2 But what happens if the algorithm is actually relying on biased input to make predictions?

The energy industry is undergoing a rapid transformation in recent past owing to the enhanced role of renewables and enhanced data-driven models making the value chain smarter. In the context of the primary constituents of this sector comprising of coal, power, renewables, solar energy, oil, and gas, there is a huge role AI can play. The biggest disruption in power in recent times is in the smart grid which is quite flexible in comparison to the traditional grid. AI can be a huge enabler in the form of providing optimal configurations etc to create a really smart and efficient grid. By thorough analysis of data related to losses AI can help prevent transmission and distribution losses.

We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems. We apply these networks to molecular systems with two goals: learning atomic potential energy surfaces for use in Molecular Dynamics simulations, and learning ground state properties of molecules calculated by Density Functional Theory. Some of the key features of our network are that (a) each neuron explicitly corresponds to a subset of atoms; (b) the activation of each neuron is covariant to rotations, ensuring that overall the network is fully rotationally invariant. Furthermore, the non-linearity in our network is based upon tensor products and the Clebsch-Gordan decomposition, allowing the network to operate entirely in Fourier space. Cormorant significantly outperforms competing algorithms in learning molecular Potential Energy Surfaces from conformational geometries in the MD-17 dataset, and is competitive with other methods at learning geometric, energetic, electronic, and thermodynamic properties of molecules on the GDB-9 dataset.

An attribution method was developed to interpret a recurrent neural network (RNN) trained to predict a child's risk of ICU mortality using multi-modal, time series data in the Electronic Medical Records. By learning a sparse, binary mask that highlights salient features of the input data, critical features determining an individual patient's severity of illness could be identified. The method, called Learned Binary Masks (LBM), demonstrated that the RNN used different feature sets specific to each patient's illness; and further, the features highlighted aligned with clinical intuition of the patient's disease trajectories. LBM was also used to identify the most salient features across the model, analogous to "feature importance" computed in the Random Forest. This measure of the RNN's feature importance was further used to select the 25% most used features for training a second RNN model. Interestingly, but not surprisingly, the second model maintained similar performance to the model trained on all features. LBM is data-agnostic and can be used to interpret the predictions of any differentiable model.

In this paper we propose a new method to assist in labeling data arriving from fast running processes using anomaly detection. A result is the possibility to manually classify data arriving at a high rates to train machine learning models. To circumvent the problem of not having a real ground truth we propose specific metrics for model selection and validation of the results. The use case is taken from the food packaging industry, where processes are affected by regular but short breakdowns causing interruptions in the production process. Fast production rates make it hard for machine operators to identify the source and thus the cause of the breakdown. Self learning assistance systems can help them finding the root cause of the problem and assist the machine operator in applying lasting solutions. These learning systems need to be trained to identify reoccurring problems using data analytics. Training is not easy as the process is too fast to be manually monitored to add specific classifications on the single data points.

In this paper, we develop a convolutional neural network model to predict the mechanical properties of a two-dimensional checkerboard composite quantitatively. The checkerboard composite possesses two phases, one phase is soft and ductile while the other is stiff and brittle. The ground-truth data used in the training process are obtained from finite element analyses under the assumption of plane stress. Monte Carlo simulations and central limit theorem are used to find the size of the dataset needed. Once the training process is completed, the developed model is validated using data unseen during training. The developed neural network model captures the stiffness, strength, and toughness of checkerboard composites with high accuracy. Also, we integrate the developed model with a genetic algorithm (GA) optimizer to identify the optimal microstructural designs. The genetic algorithm optimizer adopted here has several operators, selection, crossover, mutation, and elitism. The optimizer converges to configurations with highly enhanced properties. For the case of the modulus and starting from randomly-initialized generation, the GA optimizer converges to the global maximum which involves no soft elements. Also, the GA optimizers, when used to maximize strength and toughness, tend towards having soft elements in the region next to the crack tip.

Graphs are ideal representations of complex, relational information. Their applications span diverse areas of science and engineering, such as Feynman diagrams in fundamental physics, the structures of molecules in chemistry or transport systems in urban planning. Recently, many of these examples turned into the spotlight as applications of machine learning (ML). There, common challenges to the successful deployment of ML are domain-specific constraints, which lead to semantically constrained graphs. While much progress has been achieved in the generation of valid graphs for domain- and model-specific applications, a general approach has not been demonstrated yet. Here, we present a general-purpose, sequence-based, robust representation of semantically constrained graphs, which we call SELFIES (SELF-referencIng Embedded Strings). SELFIES are based on a Chomsky type-2 grammar, augmented with two self-referencing functions. We demonstrate their applicability to represent chemical compound structures and compare them to perhaps the most popular 2D representation, SMILES, and other important baselines. We find stronger robustness against character mutations while still maintaining similar chemical properties. Even entirely random SELFIES produce semantically valid graphs in most of the cases. As feature representation in variational autoencoders, SELFIES provide a substantial improvement in the task of in reconstruction, validity, and diversity. We anticipate that SELFIES allow for direct applications in ML, without the need for domain-specific adaptation of model architectures. SELFIES are not limited to the structures of small molecules, and we show how to apply them to two other examples from the sciences: representations of DNA and interaction graphs for quantum mechanical experiments.

Check out part one and two of this series for the first five and fifty years in AI. In part three we push the very limits of reality and look 500 years into the swirling depths of tomorrow. We've spread out towards the stars and colonized the solar system, from settlements orbiting the glittering rings of Saturn, to sprawling cities on the red hills of Mars built by nano insects invisible to the eyes. When their big bellies are filled to bursting, they rocket along invisible superhighways, delivering He3 to energy hungry fusion micro-reactors that power the interplanetary economy. Beyond the rings, deep space mining ships release clouds of drones like baby spiders into the wind and they digest asteroids hurtling in the endless void. The drones fuel an unprecedented building boom on nearly every planet circling the sun, as city after city goes up on barren rocks long hostile to organic life.

Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimensions)--such as in the case of clinical patient data. To address these challenges, we propose (1) a continuous-time version of the Gated Recurrent Unit, building upon the recent Neural Ordinary Differential Equations (Chen et al., 2018), and (2) a Bayesian update network that processes the sporadic observations. We bring these two ideas together in our GRU-ODE-Bayes method. We then demonstrate that the proposed method encodes a continuity prior for the latent process and that it can exactly represent the Fokker-Planck dynamics of complex processes driven by a multidimensional stochastic differential equation. Additionally, empirical evaluation shows that our method outperforms the state of the art on both synthetic data and real-world data with applications in healthcare and climate forecast. What is more, the continuity prior is shown to be well suited for low number of samples settings.