Single-particle traces of the diffusive motion of molecules, cells, or animals are by-now routinely measured, similar to stochastic records of stock prices or weather data. Deciphering the stochastic mechanism behind the recorded dynamics is vital in understanding the observed systems. Typically, the task is to decipher the exact type of diffusion and/or to determine system parameters. The tools used in this endeavor are currently revolutionized by modern machine-learning techniques. In this Perspective we provide an overview over recently introduced methods in machine-learning for diffusive time series, most notably, those successfully competing in the Anomalous-Diffusion-Challenge. As such methods are often criticized for their lack of interpretability, we focus on means to include uncertainty estimates and feature-based approaches, both improving interpretability and providing concrete insight into the learning process of the machine. We expand the discussion by examining predictions on different out-of-distribution data. We also comment on expected future developments.

Deploying machine learning is a multi-step process. Deploying machine learning is a multi-step process. It involves selecting a model, training it for a specific task, validating it with test data, and then deploying and monitoring the model in production. Here, we'll discuss these steps and break them down to introduce you to machine learning. Machine learning refers to systems that, without explicit instruction, are capable of learning and improving.

Social media influencers have become a common tool in a marketeer's toolbox to reach current and potential customers, building awareness, third-party credibility and purchases. But the rapid growth of this channel (in little over 10 years) has brought with it significant debate on its effectiveness and ethics. And those criticisms warrant the introduction of machine learning. Criticisms levelled at the industry and individual influencers have included: a lack of transparency of the effectiveness of influencers, unfilled promises made by those representing influencers, and a lack of knowledge among in-house marketing teams on how to use influencers for their brand effectively. The industry has certainly matured over this time, with steps being taken to address these challenges.

The March issue of Health Affairs demonstrates the potential of health care delivery system innovation to improve value for both patients and clinicians. Technology innovations such as machine learning and artificial intelligence systems are promising breakthroughs to improve diagnostic accuracy, tailor treatments, and even eventually replace work performed by clinicians, especially that of radiologists and pathologists. Machine-learning systems infer patterns, relationships, and rules directly from large volumes of data in ways that can far exceed human cognitive capacities. As the computational underpinning of tools such as e-mail spam filters, product and content recommendations, targeted advertisements, and, more recently, autonomous vehicles, machine learning is already ubiquitous in many economic sectors. Yet, machine-learning applications are still used sparingly today in the delivery of care.

With machine-learning technology, retailers can address the common--and costly--problem of having too much or too little fresh food in stock. Fresh food, already a fiercely competitive arena in grocery retail, is becoming an even more crowded battleground. Discounters, convenience-store chains, and online players are recognizing the power of fresh-food categories to drive store visits, basket size, and customer loyalty. With fresh products accounting for up to 40 percent of grocers' revenue and one-third of cost of goods sold, getting fresh-food retailing right is more important than ever.1 1.Raphael Buck and Arnaud Minvielle, "A fresh take on food retailing," Perspectives on retail and consumer goods, Winter 2013/14. Fresh food is perishable, demand is highly variable, and lead times are often uncertain.

With machine-learning technology, retailers can address the common--and costly--problem of having too much or too little fresh food in stock. Fresh food, already a fiercely competitive arena in grocery retail, is becoming an even more crowded battleground. Discounters, convenience-store chains, and online players are recognizing the power of fresh-food categories to drive store visits, basket size, and customer loyalty. With fresh products accounting for up to 40 percent of grocers' revenue and one-third of cost of goods sold, getting fresh-food retailing right is more important than ever.1 1.Raphael Buck and Arnaud Minvielle, "A fresh take on food retailing," Perspectives on retail and consumer goods, Winter 2013/14. Fresh food is perishable, demand is highly variable, and lead times are often uncertain.

Government agencies are tasked with the challenge of providing citizens with more efficient, effective, and transparent services with strict and often decreasing budgets. Government agencies can use machine learning to increase operational efficiencies by analyzing datasets, finding patterns and anomalies, and making predictions about future events. Skytree's state-of-the art machine learning software can analyze both structured and unstructured data sets in real-time to produce fast, accurate and scalable results that are up to 10,000 times faster than previous approaches. Skytree comes with a breadth of advanced machine learning methods that utilize the research available to you to make predictions with the highest accuracy available, far surpassing what's possible with basic analytics. Detect and prevent fraudulent transactions, accounts and vendors.