The recent deep learning revolution has created an enormous opportunity for accelerating compute capabilities in the context of physics-based simulations. Here, we propose EikoNet, a deep learning approach to solving the Eikonal equation, which characterizes the first-arrival-time field in heterogeneous 3D velocity structures. Our grid-free approach allows for rapid determination of the travel time between any two points within a continuous 3D domain. These travel time solutions are allowed to violate the differential equation - which casts the problem as one of optimization - with the goal of finding network parameters that minimize the degree to which the equation is violated. In doing so, the method exploits the differentiability of neural networks to calculate the spatial gradients analytically, meaning the network can be trained on its own without ever needing solutions from a finite difference algorithm. EikoNet is rigorously tested on several velocity models and sampling methods to demonstrate robustness and versatility. Training and inference are highly parallelized, making the approach well-suited for GPUs. EikoNet has low memory overhead, and further avoids the need for travel-time lookup tables. The developed approach has important applications to earthquake hypocenter inversion, ray multi-pathing, and tomographic modeling, as well as to other fields beyond seismology where ray tracing is essential.

In order to take preventive steps to maintain air quality, forecasting the evolution of pollution levels becomes a useful tool for decision makers: detecting pollution peaks beforehand could give cities enough time to take and communicate effective measures. Multiple research papers have focused on this issue and have dealt with the prediction of air quality. Bai et al. [1] describes the state of the art in this exercise and collects a range of diverse solutions applied to this problem. However, the prediction of the expected value of pollution concentrations through point-forecasting does not provide enough information about the likelihood of the pollutant levels reaching a certain threshold. Indeed, we have an estimate but we usually do not have a description of the confidence of the model nor the uncertainty in the predictions. Therefore, it is difficult to estimate the probability of the pollutant reaching above a certain threshold. The reason this probability estimation is so important is because the measures taken by cities to limit pollution (for example, limiting traffic) impact the daily routines of citizens and prove themselves to be quite unpopular. Therefore, local governments need to have an estimation of the confidence in the prediction to safely engage in those preventive measures.

IBM Research Castor, a cloud-native system for managing and deploying large numbers of AI time-series models in IoT applications, is described. Modelling code templates, in Python and R, following a typical machine-learning workflow are supported. A knowledge-based approach to managing model and time-series data allows the use of general semantic concepts for expressing feature engineering tasks. Model templates can be programmatically deployed against specific instances of semantic concepts, thus supporting model reuse and automated replication as the IoT application grows. Deployed models are automatically executed in parallel leveraging a serverless cloud computing framework. The complete history of trained model versions and rolling-horizon predictions is persisted, thus enabling full model lineage and traceability. Results from deployments in real-world smart-grid live forecasting applications are reported. Scalability of executing up to tens of thousands of AI modelling tasks is also evaluated.

Neural networks can be used to solve classification problems (predict classes) and regression problems (predict continuous values). Today we will be doing both at the same time. We start with a simplified task: detect and classify one single object in an image instead of several objects. How does an object detection dataset look like? Well, the inputs to our model are of course images and the labels are typically four values that describe a ground truth bounding box plus a category the object in this box belongs two.

The US Department of Energy (DOE) has decided to provide funds up to $40 million over a three-year period for new research in data, artificial intelligence (AI), and machine learning to address the challenges associated with issues related to data production and management at DOE scientific user facilities. Dr. Chris Fall, Director of DOE's Office of Science, said: "Major scientific facilities at our DOE national laboratories are generating vast and growing amounts of data for researchers every day. Proposals are likely to cover a wide variety of different challenges, including extracting information from complex data sets, managing facility operations in real-time, and optimising experiments through the creation of virtual laboratory environments, among other topics. The funding opportunity focuses on 18 DOE Office of Science user facilities, comprising of particle accelerators, accelerator test facilities, x-ray light sources, neutron scattering sources, and nanoscale science research centres, overseen by three major programme offices: basic energy sciences, high energy physics, and nuclear physics. According to the latest Ericsson Mobility Report, data volumes in mobile networks are increasing at an exceptional rate and mobile data traffic is expected to grow fourfold by 2025, reaching up to 160 exabytes per month.

More and more, across the globe, the effects of global warming are being felt. Global movements like Extinction Rebellion have repeatedly caused disruption by protesting in cities around the world and are symbolic of the growing attention being paid to this important issue. Despite the boom in public awareness, methods to combat the issue have been slow to develop – separating rubbish into recycling and general waste is as far as the majority of households go. More advanced technology, such as solar panels and wind turbines remain out of reach to many due to their high cost and space required for installation. However, another technology may have a far bigger impact in the fight against climate change.

A detailed analysis is given of a family of critical points determining spurious minima for a model student-teacher 2-layer neural network, with ReLU activation function, and a natural $\Gamma = S_k \times S_k$-symmetry. For a $k$-neuron shallow network of this type, analytic equations are given which, for example, determine the critical points of the spurious minima described by Safran and Shamir (2018) for $6 \le k \le 20$. These critical points have isotropy (conjugate to) the diagonal subgroup $\Delta S_{k-1}\subset \Delta S_k$ of $\Gamma$. It is shown that critical points of this family can be expressed as an infinite series in $1/\sqrt{k}$ (for large enough $k$) and, as an application, the critical values decay like $a k^{-1}$, where $a \approx 0.3$. Other non-trivial families of critical points are also described with isotropy conjugate to $\Delta S_{k-1}, \Delta S_k$ and $\Delta (S_2\times S_{k-2})$ (the latter giving spurious minima for $k\ge 9$). The methods used depend on symmetry breaking, bifurcation, and algebraic geometry, notably Artin's implicit function theorem, and are applicable to other families of critical points that occur in this network.

Dynamical systems comprised of autonomous agents arise in many relevant problems such as multi-agent robotics, smart grids, or smart cities. Controlling these systems is of paramount importance to guarantee a successful deployment. Optimal centralized controllers are readily available but face limitations in terms of scalability and practical implementation. Optimal decentralized controllers, on the other hand, are difficult to find. In this paper, we use graph neural networks (GNNs) to learn decentralized controllers from data. GNNs are well-suited for the task since they are naturally distributed architectures. Furthermore, they are equivariant and stable, leading to good scalability and transferability properties. The problem of flocking is explored to illustrate the power of GNNs in learning decentralized controllers.

There are plenty of tools and point solutions that address bits and pieces of the challenge of delivering artificial intelligence (AI) and Internet of things (IoT) applications. C3.ai's focus is on delivering an end-to-end platform for developing, deploying and running these applications in production at scale. Whether customers use every aspect of the C3.ai platform or not, big enterprise-scale companies seem to be attracted by that promise of quickly developing and running innovative, data-driven applications at scale. There was plenty of evidence of that fact at C3.ai's February 25-27 Transform conference in San Francisco, where customers including Bank of America, Shell, 3M and Engie detailed their deployments. C3.ai's cloud-first platform is comprehensive, addressing the needs of developers, data engineers and data scientists, and the operational teams challenged with bringing applications into production at scale.

AI startups are an area that has been growing for the past several years. This tech has applications in dozens of professions and niches the world over. As reported by Statista, the market research firm Tractica stated that in 2019, the global AI software market was expected to increase 154 per cent, with a forecast worth approximately 14.7 billion US dollars. This is just one of many stats indicating that an AI startup would be a smart enterprise in which you might invest. If you're wondering about the benefits of AI companies, there are many.