MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS. Recent machine learning approaches provide promising data-driven approaches for learning representations for system behaviors from experimental data and high fidelity simulations. The package faciliates learning and using data-driven models for (i) dynamics of the system at larger spatial-temporal scales (ii) interactions between system components, (iii) features yielding coarser degrees of freedom, and (iv) features for new quantities of interest characterizing system behaviors. MLMOD provides hooks in LAMMPS for (i) modeling dynamics and time-step integration, (ii) modeling interactions, and (iii) computing quantities of interest characterizing system states. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. Here we discuss our prototype C++/Python package, aims, and example usage. The package is integrated currently with the mesocale and molecular dynamics simulation package LAMMPS and PyTorch. For related papers, examples, updates, and additional information see https://github.com/atzberg/mlmod and http://atzberger.org/.

We introduce adversarial learning methods for data-driven generative modeling of the dynamics of $n^{th}$-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable $m$-step stochastic numerical integrators. We introduce different formulations and training methods for learning models of stochastic dynamics based on observation of trajectory samples. We develop approaches using discriminators based on Maximum Mean Discrepancy (MMD), training protocols using conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. The adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

We don't know how the massive shifts in consumer behavior brought on by the COVID-19 pandemic will evolve or endure. But we do know that as our lives change, marketers' data change. Both the current impact and the future implications may be significant. I asked Alex Atzberger, CEO of Episerver, a digital experience company, to put the issues in perspective. Paul Talbot: How is AI holding up?

Droughts, with their increasing frequency of occurrence, continue to negatively affect livelihoods and elements at risk. For example, the 2011 in drought in east Africa has caused massive losses document to have cost the Kenyan economy over $12bn. With the foregoing, the demand for ex-ante drought monitoring systems is ever-increasing. The study uses 10 precipitation and vegetation variables that are lagged over 1, 2 and 3-month time-steps to predict drought situations. In the model space search for the most predictive artificial neural network (ANN) model, as opposed to the traditional greedy search for the most predictive variables, we use the General Additive Model (GAM) approach. Together with a set of assumptions, we thereby reduce the cardinality of the space of models. Even though we build a total of 102 GAM models, only 21 have R2 greater than 0.7 and are thus subjected to the ANN process. The ANN process itself uses the brute-force approach that automatically partitions the training data into 10 sub-samples, builds the ANN models in these samples and evaluates their performance using multiple metrics. The results show the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months. The champion ANN model recorded an R2 of 0.78 in model testing using the out-of-sample data. This illustrates its ability to be a good predictor of drought situations 1-month ahead. Investigated as a classifier, the champion has a modest accuracy of 66% and a multi-class area under the ROC curve (AUROC) of 89.99%

Increasingly available data and rising computational power have combined to usher in a new age of information. We seldom go a day without using some service powered by sophisticated techniques from the data sciences. Machine learning is a set of techniques that have revolutionized the modern world. These approaches involve computer programs that analyze features in input data and develop their own ways of identifying relevant patterns and information. Its applications range from voice recognition in our cell phones and cars to internet searches and recommendation systems.

On September 11 and 12, 2018, I attended the 22nd edition of the SAP Forum in São Paulo, Brazil, the largest technology and business event in Latin America, with superlative numbers: more than 15,600 participants, more than 9,600 unique participants, 81 sponsors and 423 presentations. It was two intense days, with a lot of technology and innovation, which showed the importance of digital transformation for business. At the opening session of the event, SAP Brazil president Cristina Palmaka set the tone for what would be the event, talking about the interaction between man and machine, being aligned with SAP's purpose of making companies better, impacting the society. And she asked the following questions for reflection: What is the purpose of the company you work for? What would happen in society if tomorrow your company ceased to exist, or if your employment ceased to exist?

Many of these people are being exploited in ways that have existed throughout history: About 22% are victims of "forced sexual exploitation," with others made to work in agriculture, manufacturing, construction, or domestic labor, according to the report from the U.N.'s International Labor Organization. Researchers and activists say part of the solution to this ancient problem may be surprisingly modern: Machine learning and similar statistical tools can identify suppliers of goods and services that are more likely to involve forced labor, whether they're electronics manufacturers in developing countries or escort services in the United States. In the U.S., where sex work is frequently advertised online, leaving a digital trail, these techniques can also help guide law enforcement to sex trafficking gangs and their victims. In international trade, that kind of information can help buyers work with their vendors to ensure ethical practices throughout the supply chain or, failing that, switch to new vendors to stay in compliance with regulatory requirements and their own customers' ethics. "A lot of companies are becoming a lot more purpose-driven, and I think there's a lot more importance even to end consumers today about the type of companies they're buying [from]," says Alex Atzberger, president of SAP Ariba, a massive business-to-business procurement network.

SAP acquired procurement software vendor Ariba in 2011, but the company's history dates back two decades. This week, SAP Ariba executives briefed analysts in Boston, giving an overview of recent roadmap milestones as well as a look ahead at what's yet to come. Growth markers: There are now 2.4 million suppliers on Ariba's business network, with more than $1 trillion in commerce transactions each year. In addition, Ariba has a presence in 190 countries. Yet Ariba has set some lofty goals for additional growth.