The platform explores each topic alongside commentary and perspectives from 3M experts, scientists, engineers and designers at the forefront of their fields. Recommended AI News: H2O.ai Democratizes Deep Learning with H2O Hydrogen Torch "Every day, 3Mers around the world are unlocking the next phase of what's possible and exploring the latest trends in science and technology," says Kevin Gilboe, head of 3M Design, International. "3M Futures is a great opportunity for us to showcase the broad ways 3M is helping to shape the future -- developing, designing and engineering cutting-edge solutions within the most relevant global trends, while profiling the incredible talent across our company who are experts in their fields." The expert contributors chosen to participate include an array of 3M scientists, engineers, designers and other leaders at the forefront of their industries. From artificial intelligence to equity, each is passionate about their field and has a personal interest in broad applications of the relevant technology and science.

It is only a matter of time before artificial intelligence (AI) replaces even the most elite consultants. The only way to survive this transition is to upgrade yourself to become an AI consultant. Logical handling and machine learning (ML) capabilities form the basis of artificial intelligence (AI) consulting services. These capabilities assist enterprises with improving their business activities; and thus, many organizations rely on AI consultants and information science solutions. Lockdowns have forced businesses to conduct business off-site.

The fluctuation effect of gradient expectation and variance caused by parameter update between consecutive iterations is neglected or confusing by current mainstream gradient optimization algorithms.Using this fluctuation effect, combined with the stratified sampling strategy, this paper designs a novel \underline{M}emory \underline{S}tochastic s\underline{T}ratified Gradient Descend(\underline{MST}GD) algorithm with an exponential convergence rate. Specifically, MSTGD uses two strategies for variance reduction: the first strategy is to perform variance reduction according to the proportion p of used historical gradient, which is estimated from the mean and variance of sample gradients before and after iteration, and the other strategy is stratified sampling by category. The statistic \ $\bar{G}_{mst}$\ designed under these two strategies can be adaptively unbiased, and its variance decays at a geometric rate. This enables MSTGD based on $\bar{G}_{mst}$ to obtain an exponential convergence rate of the form $\lambda^{2(k-k_0)}$($\lambda\in (0,1)$,k is the number of iteration steps,$\lambda$ is a variable related to proportion p).Unlike most other algorithms that claim to achieve an exponential convergence rate, the convergence rate is independent of parameters such as dataset size N, batch size n, etc., and can be achieved at a constant step size.Theoretical and experimental results show the effectiveness of MSTGD

You can read this post on our blog. Now let's dive into the activity at and around rOpenSci! Consult our Events page to find your local time and how to join. Find out about more events. Maëlle Salmon (Research Software Engineer with rOpenSci) and Karthik Ram (rOpenSci executive director) authored a commentary "The R Developer Community Does Have a Strong Software Engineering Culture" in the latest issue of The R Journal edited by Di Cook, as a response to the discussion paper "Software Engineering and R Programming: A Call for Research" by Melina Vidoni (who's an Associate editor of rOpenSci Software Peer Review).

Suresh Sethi, associate professor of natural resources and the environment in CALS; Carla Gomes, professor of computer science at Cornell Bowers CIS; and Alex Flecker, professor of ecology and evolutionary biology in CALS, are pictured on a field trip to the Marañón River, in Peru.

A new research project has found that the discretionary decisions made by human bank managers can be replicated by machine learning systems to an accuracy of more than 95%. Using the same data available to bank managers in a privileged dataset, the best-performing algorithm in the test was a Random Forest implementation – a fairly simple approach that's twenty years old, but which still outperformed a neural network when attempting to mimic the behavior of human bank managers formulating final decisions about loans. The Random Forest algorithm, one of four put through their paces for the project, achieves high human-equivalent scoring vs. performance of bank managers, despite the relative simplicity of the algorithm. The researchers, who had access to a proprietary dataset of 37,449 loan ratings across 4,414 unique customers at'a large commercial bank', suggest at various points in the preprint paper that the automated data analysis that managers are given to make their decision has now become so accurate that bank managers rarely deviate from it, potentially signifying that bank managers' part in the loan approval process chiefly consists of retaining someone to fire in the event of a loan default. 'From a practical perspective it is worth noting that our results may indicate that the bank could process loans faster and cheaper in the absence of human loan managers with very comparable results.

Fishermen gaffing catfish at the Madeira River rapids in the state of Rondônia, Brazil. These rapids are now completely submerged, and a nearby …

Donald Trump has won the United States presidency and Brexit has promised to take the UK out of the European Union. Both campaigns employ Cambridge Analytica, which harvested the data of millions of Facebook users to personalise electoral messaging to them and sway their voting intentions. Millions of people begin to ask themselves whether, in the digital era, they have lost something deeply valuable: their privacy. Two years later, countless European email inboxes would be filling up with messages from companies, asking people for permission to continue processing their data – the aim was compliance with the new General Data Protection Regulation (GDPR). Despite its imperfections, this law has served as a point of reference for laws in Brazil and Japan, and inaugurated the modern era of data protection.

Kernel design for Multi-output Gaussian Processes (MOGP) has received increased attention recently. In particular, the Multi-Output Spectral Mixture kernel (MOSM) arXiv:1709.01298 approach has been praised as a general model in the sense that it extends other approaches such as Linear Model of Corregionalization, Intrinsic Corregionalization Model and Cross-Spectral Mixture. MOSM relies on Cram\'er's theorem to parametrise the power spectral densities (PSD) as a Gaussian mixture, thus, having a structural restriction: by assuming the existence of a PSD, the method is only suited for multi-output stationary applications. We develop a nonstationary extension of MOSM by proposing the family of harmonizable kernels for MOGPs, a class of kernels that contains both stationary and a vast majority of non-stationary processes. A main contribution of the proposed harmonizable kernels is that they automatically identify a possible nonstationary behaviour meaning that practitioners do not need to choose between stationary or non-stationary kernels. The proposed method is first validated on synthetic data with the purpose of illustrating the key properties of our approach, and then compared to existing MOGP methods on two real-world settings from finance and electroencephalography.

The Latent Dirichlet Allocation (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates. In this model, a negative binomial regression is embedded within LDA, enabling straight-forward interpretation of the regression coefficients and the analysis of the quantity of cluster-specific elements in each sampling units (instead of the analysis being focused on modeling the proportion of each cluster, as in Structural Topic Models). We use slice sampling within a Gibbs sampling algorithm to estimate model parameters. We rely on simulations to show how our algorithm is able to successfully retrieve the true parameter values and the ability to make predictions for the abundance matrix using the information given by the covariates. The model is illustrated using real data sets from three different areas: text-mining of Coronavirus articles, analysis of grocery shopping baskets, and ecology of tree species on Barro Colorado Island (Panama). This model allows the identification of mixed-membership clusters in discrete data and provides inference on the relationship between covariates and the abundance of these clusters.