Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale or multiscale systems. One of the long-standing problems in plasma physics is the integration of kinetic physics into fluid models, which is often achieved through sophisticated analytical closure terms. In this paper, we successfully construct a multi-moment fluid model with an implicit fluid closure included in the neural network using machine learning. The multi-moment fluid model is trained with a small fraction of sparsely sampled data from kinetic simulations of Landau damping, using the physics-informed neural network (PINN) and the gradient-enhanced physics-informed neural network (gPINN). The multi-moment fluid model constructed using either PINN or gPINN reproduces the time evolution of the electric field energy, including its damping rate, and the plasma dynamics from the kinetic simulations. In addition, we introduce a variant of the gPINN architecture, namely, gPINN$p$ to capture the Landau damping process. Instead of including the gradients of all the equation residuals, gPINN$p$ only adds the gradient of the pressure equation residual as one additional constraint. Among the three approaches, the gPINN$p$-constructed multi-moment fluid model offers the most accurate results. This work sheds light on the accurate and efficient modeling of large-scale systems, which can be extended to complex multiscale laboratory, space, and astrophysical plasma physics problems.

Solving fluid dynamics equations often requires the use of closure relations that account for missing microphysics. For example, when solving equations related to fluid dynamics for systems with a large Reynolds number, sub-grid effects become important and a turbulence closure is required, and in systems with a large Knudsen number, kinetic effects become important and a kinetic closure is required. By adding an equation governing the growth and transport of the quantity requiring the closure relation, it becomes possible to capture microphysics through the introduction of ``hidden variables'' that are non-local in space and time. The behavior of the ``hidden variables'' in response to the fluid conditions can be learned from a higher fidelity or ab-initio model that contains all the microphysics. In our study, a partial differential equation simulator that is end-to-end differentiable is used to train judiciously placed neural networks against ground-truth simulations. We show that this method enables an Euler equation based approach to reproduce non-linear, large Knudsen number plasma physics that can otherwise only be modeled using Boltzmann-like equation simulators such as Vlasov or Particle-In-Cell modeling.

Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in the systems. In this work, we apply a deep learning architecture to learn fluid partial differential equations (PDEs) of a plasma system based on the data acquired from a fully kinetic model. The learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effects such as Landau damping. Based on the learned fluid closure, the data-driven, multi-moment fluid modeling can well reproduce all the physical quantities derived from the fully kinetic model. The calculated damping rate of Landau damping is consistent with both the fully kinetic simulation and the linear theory. The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve fluid closure and reduce the computational cost of multi-scale modeling of global systems.

Before you can even think about building an algorithm to read an X-ray or interpret a blood smear, the machine has to know what's what in an image. All of the promise of AI in healthcare -- an area that has attracted $11.3 billion in private investment in 2021, can't be realized without carefully labeled data sets that tell machines what exactly they're looking for. Creating those labeled data sets is becoming an industry itself, boasting companies well north of unicorn status. Today, Encord, a small startup just out of Y Combinator, is looking to take a piece of the action. Aiming to generate labeled data sets for computer vision projects, Encord launched its own beta version of an AI-assisted labeling program called CordVision.

After meeting at an entrepreneur matchmaking event, Ulrik Hansen and Eric Landau teamed up to parlay their experience in financial trading systems into a platform for faster data labeling. In 2020, the pair of finance industry veterans founded Encord to adapt micromodels typical in finance to automated data annotation. Micromodels are neural networks that require less time to deploy because they're trained on less data and used for specific tasks. Encord's NVIDIA GPU-driven service promises to automate as much as 99 percent of businesses' manual data labeling with its micromodels. "Instead of building one big model that does everything, we're just combining a lot of smaller models together, and that's very similar to how a lot of these trading systems work," said Landau.

A car park opposite the infamous New York City housing estate where rapper Jay-Z grew up seems an unlikely place for an agricultural revolution. Ten shipping containers dominate a corner of the Brooklyn parking area, each full of climate control tech, growing herbs that are distributed to local stores on bicycles. This is urban farming at its most literal. The containers are owned by Square Roots, part of America's fast-expanding vertical farming industry, a sector run by many tech entrepreneurs who believe food production is ripe for disruption. The world's best basil reputedly comes from Genoa, Italy. Square Roots grows Genovese seeds in a container that recreates the city's daylight hours, humidity, Co2 levels - and all fed hydroponically in nutrient-rich water.

In early 2015, a University of California, San Diego team successfully used micro-motor powered nanobots inside live mice -- without causing damage to their stomach linings, changing healthcare forever. In mid-2015, this concept was quickly advanced by mechanical engineers at Drexel University working in partnership with Daegu Gyeongbuk Institute of Science and Technology (DGIST) in South Korea. What they created were more efficient'micro-swimmers' capable of breaking through clogged arteries and leaving anticoagulant medication to prevent future blockage. Indeed, artificial intelligence (AI), from big data and machine learning to caretaker robots and medical nanobots, can help humans live longer. It's a primary reason scientists have predicted human lifespan to increase to 125 years by 2070.

SAN FRANCISCO--Cryptographers said at the RSA Conference Tuesday they're skeptical that advances in quantum computing and artificial intelligence will profoundly transform computer security. "I'm skeptical there will be much of an impact," Ron Rivest, a MIT professor and inventor of several symmetric key encryption algorithms, said early at the annual Cryptographers' Panel here. Susan Landau, a professor who specializes in cybersecurity policy and computer science at Worcester Polytechnic Institute, said that while artificial intelligence can be helpful when it comes to processing lots of data effectively, she doesn't think it will be useful in fingering out series attacks or anomalous situations. Adi Shamir, Borman Professor of Computer Science at the Weizmann Institute, said he was optimistic about AI's potential when it comes to defense – anything that involves finding deviations in behavior – but said he doubts it can ever be used in offensive sense, such as in identifying zero days, something he said requires more ingenuity and originality. The discussion was steered by a report recently released by the Global Risk Institute on the emergence of quantum computing technologies.

It was written not by a human being, but by my computer program EWI (an acronym for "experiments in writing intelligence"). EWI was fed the texts of two of Hofstadter's books--namely, Gödel, Escher, Bach (winner of the Pulitzer Prize for General Nonfiction in 1980) and Metamagical Themas--and then, following its code, EWI carefully analyzed these two books for their uniquely Hofstadterian stylistic elements and features, after which it recombined these stylistic elements in new fashions. EWI thereby came up with some 25 new and highly diverse "Hofstadter articles," one of which is given below, and the article is followed by a brief commentary about EWI and its output by Hofstadter himself. Actually, I should state up front that the wonderful sparkling dialogues of GEB, which are a substantial part of that book, were not used by EWI in generating any of the articles, because EWI is unfortunately not yet able to work with inputs that belong to different genres, such as chapters and dialogues. To combine stylistic aspects of two or more different genres of writing represents a very thorny challenge indeed. Endowing EWI with that extra level of flexibility is one of my next major goals.