Continuous-process industries are at an inflection point regarding what they can do with automation, making now an ideal time to address inefficiencies and hazards. This feature was originally published in the December 2021 issue of InTech magazine. The era of the remote workforce has brought to light a cross-industry need for resilient, futureproof industrial control systems supporting more efficient, sustainable, and safe manufacturing plants. Through the integration of autonomous software and technologies along the production line, and the innovation of process control systems, plant operators can achieve long-term operational benefits. Ongoing advancements in process automation have enabled users to create steady-state and dynamic models for plant and control design, assess equipment performance and troubleshoot issues, evaluate process design, and resolve operating problems.

A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modelling applications. Two different types of generative models are considered here. The first is a physics-based model based on the stochastic finite element (SFE) method, which is widely used when modelling structures that have material and loading uncertainties imposed. Such models can be calibrated according to data from the structure and would be expected to outperform any other model if the modelling accurately captures the true underlying physics of the structure. The potential use of SFE models as digital mirrors is illustrated via application to a linear structure with stochastic material properties. For situations where the physical formulation of such models does not suffice, a data-driven framework is proposed, using machine learning and conditional generative adversarial networks (cGANs). The latter algorithm is used to learn the distribution of the quantity of interest in a structure with material nonlinearities and uncertainties. For the examples considered in this work, the data-driven cGANs model outperform the physics-based approach. Finally, an example is shown where the two methods are coupled such that a hybrid model approach is demonstrated.

Algae are a varied category of aquatic plant-like creatures. Phytoplankton is a term used to describe oceanic algae. These basic creatures generate energy from sunlight through photosynthesis, which allows them to manufacture carbohydrates, oils, and proteins. These can then be processed to produce a third-generation biofuel. Biofuel is any fuel derived from living things or living things' waste products (like fecal matter or urine).

Researchers at Texas A&M AgriLife are using artificial intelligence (AI) to set a new world record for creating algae as a dependable, economic source for biofuel that can be employed as an alternative fuel source for jet aircraft and other transportation requirements. Joshua Yuan, Ph.D., AgriLife Research scientist, professor and chair of Synthetic Biology and Renewable Products in the Texas A&M College of Agriculture and Life Sciences Department of Plant Pathology and Microbiology, is directing the study. The team's findings have been reported in the January issue of Nature Communications. Ongoing research is sponsored by the U.S. Department of Energy Fossil Energy Office. The study is also being financially supported by a gift from Dr. John '90 and Sally '92 Hood, who recently met with Yuan to talk about his biofuels research program.

The start of the Democratizing AI Newsletter which focuses in the first edition on "Artificial Intelligence a Key Exponential Technology in the Smart Technology Era" coincides with the launch of BiCstreet's "AI World Series" Live event, which kicks off both virtually and in-person (limited) from 10 March 2022, where this theme, amongst others, will be discussed in more detail over a 10-week AI World Series programme. The event is an excellent opportunity for companies, startups, governments, organisations and white collar professionals all over the world, to understand why Artificial Intelligence is critical towards strategic growth for any department or genre. See the 10 Weekly Program here: https://www.BiCstreet.com)). We live in tremendously exciting times where we already experience the disruptive and far-reaching impact of a smart technology revolution that seems to be on track to comprehensively change how we live, work, play, interact, and relate to one another.

Inventing what the world needs- that is now Edison described the crux of innovation in technology. Big problems represent even bigger opportunities. To quote famous Canadian ice hockey player Wayne Gretzky, who scored many hits in his time, the trick is not to "skate where the puck is," but to "skate where the puck is going." It has come up with the most scalable solutions which can impact business across the world. Whether it is clean energy, robotics, quantum computing, synthetic biology, telemedicine, AI, or cloud education and NUI software, it can solve all the biggest problems confronting mankind. Creating value means coming up with something people will pay for in the real world. Virtual technologies can open up a window of possibilities, given their widespread application.

Can it help in judicial processes to reduce the pendency of cases? The spread of smart technologies like AI across organizations is being driven by the seeking of competitive advantage, but are senior leaders prepared for the impact? The green energy revolution promised by nuclear fusion is now a step closer, thanks to the first successful use of a cutting-edge artificial intelligence system to shape the superheated hydrogen plasmas inside a fusion reactor. How can AI support diversity, equity and inclusion? There is a history of distrust between communities of color and emerging technologies such as AI, particularly in the justice system.

The total quantity of carbon dioxide (CO2) and other greenhouse gases (GHG) emitted in the lifecycle of the product or service, or in any specific financial year, is referred to as a carbon footprint. The measurement is commonly represented in kilos of CO2 equivalents, accounting for the impacts of various greenhouse gases on global warming. A carbon credit is a marketable permit or certification that entitles the holder to emit one tonne of carbon dioxide or the equivalent of some other greenhouse gas -- it is effectively a carbon offset for greenhouse gas producers. The primary purpose of carbon credits is to help reduce greenhouse gas emissions from industrial activity in order to mitigate the impacts of global warming. They can also sell excess carbon credits.

In today's changing energy landscape, business leaders recognize that innovation, new technology, and automation are fundamental to remain competitive. The electric power industry is continuing to move towards a cleaner, more reliable, and resilient grid. Computer Vision is one of the most mature AI technologies with a highly disruptive impact on the power and utilities industry. This article explores how the next-generation AI vision technology can help pave the way to increase operational efficiency, safety, and reliability in the electric power industry. The most popular applications include AI vision inspection and monitoring, foreign object detection, abnormal situation detection, and intelligent control of field personnel and operation behavior.

We present a simple and effective approach for posterior uncertainty quantification in deep operator networks (DeepONets); an emerging paradigm for supervised learning in function spaces. We adopt a frequentist approach based on randomized prior ensembles, and put forth an efficient vectorized implementation for fast parallel inference on accelerated hardware. Through a collection of representative examples in computational mechanics and climate modeling, we show that the merits of the proposed approach are fourfold. (1) It can provide more robust and accurate predictions when compared against deterministic DeepONets. (2) It shows great capability in providing reliable uncertainty estimates on scarce data-sets with multi-scale function pairs. (3) It can effectively detect out-of-distribution and adversarial examples. (4) It can seamlessly quantify uncertainty due to model bias, as well as noise corruption in the data. Finally, we provide an optimized JAX library called {\em UQDeepONet} that can accommodate large model architectures, large ensemble sizes, as well as large data-sets with excellent parallel performance on accelerated hardware, thereby enabling uncertainty quantification for DeepONets in realistic large-scale applications.