"Produce better-quality products but at less operational cost and with efficiency" is a timeless goal for the manufacturing industry. The role and future of AI and ML in the manufacturing industry are promising. AI and ML can enable the manufacturing industry to scale their businesses and help them grow. The "Smart Manufacturing" revolution is already making it easier for businesses to attain this objective than ever before. According to many experts, artificial intelligence and machine learning are expected to affect factories and the manufacturing sector in the future significantly.

Epidemiology Modeling an emerging infectious disease is an inexact science. At an early stage of an epidemic, we only have sparse data, little knowledge of the mechanisms driving emergence, and an urgent need to devise control measures that will be effective. Using epidemiological incidence reports, Brett and Rohani have developed a detection algorithm for disease (re)emergence that is agnostic to the mechanisms involved. This supervised statistical learning algorithm was trained on data collected for mumps outbreaks in England and resurgent pertussis in the United States. The algorithm successfully anticipated reemergence of mumps 4 years in advance, which would have given plenty of time for mitigation efforts to be implemented. The algorithm also performed well for vector-borne diseases, including dengue in Puerto Rico, and predicted the rapid emergence of plague in Madagascar. The success of this approach stems from the common statistical properties of incidence data across disease emergence contexts and has obvious application for monitoring waves of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reemergence. PLOS BIOL. 18 , e3000697 (2020).

Threat hunters are able to see related behaviors and entities, and search and filter without having to master a query language. Because the platform connects users, devices, and IP addresses, hunters always know the "who" behind every indicator. Threat hunters can seamlessly explore data from disparate sources, pivot on behaviors and interesting data facets, and visualize data and relationships in multiple ways. E8's platform enables behavior hunting, allowing threat hunters to key in on the abnormal behaviors of internal resources that are typically the early warning signs that a threat is present. E8 Security's Fusion Behavioral Intelligence Platform enables security analysts to detect and hunt for unknown threat indicators, and respond before a breach occurs.

DeepMind, the London-based artificial intelligence lab acquired by Google in 2014, has accomplished more than a few spectacular stunts of machine learning. Its neural networks bested a human champion at the notoriously tough game of Go, inculcated the basic rules of soccer on a digital ant-like creature, and teased out winning strategies for more than 49 Atari 2600 games. But now, the outfit's robots are being tasked with a more humanistic pursuit: eye disease research. On Tuesday, DeepMind announced a long-term project that will see the company's machine-learning algorithms parse "millions" of eye scans to tease out early warning signs that human doctors might otherwise miss. The new project, which is based out of the U.K.'s Moorfields Eye Hospital in east London, is the fruit of DeepMind's ongoing partnership -- dubbed DeepMind Health -- with the country's National Health Service.