Tackling carbon emissions is one of the biggest challenges faced by the world today. For big business, this means making a strategic and managed move towards increasing the use of renewable energy sources, as well as creating efficiencies across all aspects of their operations. It's a difficult task to manage alone, even for an enterprise on the scale of tech giant Microsoft or energy titan Shell. But working together creates new possibilities that go further than what it is likely they could accomplish individually. Beyond meeting their own zero-carbon commitments, there's the opportunity to help other companies within their vast ecosystems of customers and suppliers to meet their environmental and safety goals, too.

Chemists seeking to understand the origins of life have published a wide range of reactions that may have yielded the building blocks of proteins, nucleic acids, and lipids from simple precursors. Wołos et al. scoured the literature to document each such reaction class and then wrote software that applied the reactions first to the simplest compounds such as cyanide, water, and ammonia, and then iteratively to each successive generation of products. The resulting network predicted a variety of previously unappreciated routes to biochemically relevant compounds, several of which the authors validated experimentally. Science , this issue p. [eaaw1955][1] ### INTRODUCTION Although hundreds of organic reactions have been validated under consensus prebiotic conditions, we still have only a fragmentary understanding of how these individual steps combined into complete synthetic pathways to generate life’s building blocks, which other abiotic molecules might have also formed, how independent reactions gave rise to chemical systems, and how membranes encapsulating these systems came into being. Answering such questions requires consideration of very large numbers of possible synthetic pathways. Starting with even a few primordial substrates—e.g., H2O, N2, HCN, NH3, CH4, and H2S—the number of prebiotically synthesizable molecules grows rapidly into the tens of thousands. Detailed analysis of this space and its synthetic connectivity may be beyond the cognition of individual chemists but can be performed by smart computer algorithms. ### RATIONALE We harnessed the power of computer-assisted organic synthesis to map the network of molecules that are synthesizable from basic prebiotic feedstocks. This was done by encoding currently known prebiotic reactions in a machine-readable format, augmenting these reaction transforms with information about incompatible groups and reaction conditions, and then applying them iteratively to a set of basic prebiotic substrates. The reaction network thus created was queried by algorithms to identify complete synthetic routes as well as those tracing reaction systems—notably, reaction cycles. All calculations were supported by a software application that is freely available to the scientific community. ### RESULTS We demonstrate that this network comprises more abiotic molecules than biotic molecules. The biotic compounds differ from the abiotic compounds in several ways: They are more hydrophilic, more thermodynamically stable, and more balanced in terms of the hydrogen bond donors and acceptors they contain and are synthesizable along routes with fewer changes of conditions. The network contains not only all known syntheses of biotic compounds but also previously unidentified routes, several of which (e.g., prebiotic syntheses of acetaldehyde and diglycine, as well as malic, fumaric, citric, and uric acids) we validated by experiment. We also demonstrate three notable forms of chemical emergence: (i) that the molecules created within the network can themselves enable new types of prebiotic reactions; (ii) that within just a few synthetic generations, simple chemical systems (including self-regenerating cycles) begin to emerge; and (iii) that the network contains prebiotic routes to surfactant species, thus outlining a path to biological compartmentalization. We support these conclusions with experimental results, establishing previously undescribed prebiotic reactions and entire reaction systems—notably, a self-regenerating cycle of iminodiacetic acid. ### CONCLUSION Computer-generated reaction networks are useful in identifying synthetic routes to prebiotically relevant targets and are indispensable for the discovery of prebiotic chemical systems that are otherwise challenging to discern. As our network continues to grow by means of crowd-sourcing of newly validated prebiotic reactions, it will allow continued simulation of chemical genesis, beginning with molecules as simple as water, ammonia, and methane and leading to increasingly complex targets, including those of current interest in the chemical and pharmaceutical industries. ![Figure][2] Network of prebiotic chemistry. Computer simulation of plausible prebiotic reactions creates a network of molecules that are synthesizable from prebiotic feedstocks and establishes multiple unreported—but now experimentally validated—syntheses of prebiotic targets as well as self-regenerating cycles. In this schematic illustration, light blue nodes represent abiotic molecules, dark blue nodes represent molecules along newly discovered prebiotic syntheses of uric and citric acids, and red nodes represent other biotic molecules. The challenge of prebiotic chemistry is to trace the syntheses of life’s key building blocks from a handful of primordial substrates. Here we report a forward-synthesis algorithm that generates a full network of prebiotic chemical reactions accessible from these substrates under generally accepted conditions. This network contains both reported and previously unidentified routes to biotic targets, as well as plausible syntheses of abiotic molecules. It also exhibits three forms of nontrivial chemical emergence, as the molecules within the network can act as catalysts of downstream reaction types; form functional chemical systems, including self-regenerating cycles; and produce surfactants relevant to primitive forms of biological compartmentalization. To support these claims, computer-predicted, prebiotic syntheses of several biotic molecules as well as a multistep, self-regenerative cycle of iminodiacetic acid were validated by experiment. [1]: /lookup/doi/10.1126/science.aaw1955 [2]: pending:yes

The open-endedness of the challenge means that AIs need to master multiple objectives. To win, they must impress eight human judges from a range of backgrounds, including architects, archaeologists, and game designers. These judges score the AI city planners in four areas: how well they adapt their designs to specific locations; how well the layouts work, according to criteria such as whether there are bridges and roads between different areas; how appealing they are aesthetically; and how much the designs evoke a narrative--are there details that tell a story about how a town came to be, such as a ruin or a pit from which building materials might have been mined? "Making a Minecraft village for an unseen map is something a 10-year-old human could do," says Salge. "But it is really difficult for an AI." For example, one entrant started by identifying the type of environment--desert or forest, say--and then generated buildings that looked as if they had been built out of common local materials.

Digital transformation is one of the top priorities for industrial companies. The largest players are already moving in this direction, for many years continuously working to improve production efficiency and launching large-scale optimisation programs. They're called advanced analytics or digital innovation, and at their core, the technology could be summarised under artificial intelligence. In all cases, the efforts to utilise AI models or data analytics systems are part of a bigger digital transformation effort of the progressing companies. In an industrial context, such strategies for cost-saving and process optimisation often start from pilot projects, or top management directives for digital change guide them. In general, changes in processes or investments in capital-intensive and competitive industries require large sums of money. Traditional capital expenditures usually stretch over a long period, so a current financial standing may not allow for a complete physical overhaul of the plants or facilities. These high costs lead to the search for cheaper alternatives.

New books are available for subscription. Deep Diamond() is a new Deep Learning library written in Clojure. Its goal is to be simple, superfast, and to support both CPU and GPU computing. But it's Clojure, you might say. Python is supported by Google and Facebook.

In this paper, we consider data acquired by multimodal sensors capturing complementary aspects and features of a measured phenomenon. We focus on a scenario in which the measurements share mutual sources of variability but might also be contaminated by other measurement-specific sources such as interferences or noise. Our approach combines manifold learning, which is a class of nonlinear data-driven dimension reduction methods, with the well-known Riemannian geometry of symmetric and positive-definite (SPD) matrices. Manifold learning typically includes the spectral analysis of a kernel built from the measurements. Here, we take a different approach, utilizing the Riemannian geometry of the kernels. In particular, we study the way the spectrum of the kernels changes along geodesic paths on the manifold of SPD matrices. We show that this change enables us, in a purely unsupervised manner, to derive a compact, yet informative, description of the relations between the measurements, in terms of their underlying components. Based on this result, we present new algorithms for extracting the common latent components and for identifying common and measurement-specific components.

Toronto, Ontario--(Newsfile Corp. - September 16, 2020) - GoldSpot Discoveries Corp. (TSXV: SPOT) (the "Company" or "GoldSpot") has been engaged by Cerrado Gold Inc. ("Cerrado") to apply machine learning and its proprietary data science expertise to identify new exploration targets on Cerrado's Minera Don Nicolas (MDN) project, located in Santa Cruz, Argentina. In its analysis, GoldSpot will work with Cerrado's technical team to integrate and analyze geological and remote sensing data available in the area. The process will explore the potential for gold mineralization within the MDN properties, to produce GoldSpot Smart Targets which fuse geoscience knowledge with data science insights. "Minera Don Nicolas is in the mineral and data rich Deseado Massif, an area where GoldSpot is having significant success, particularly at Yamana Gold's Cerro Moro project. MDN has robust property-wide datasets and we look forward to supporting Cerrado's technical team and advancing exploration efforts. The project has significant potential with a land package of more than 273,000 hectares," stated Denis Laviolette, Executive Chairman and President of GoldSpot Discoveries.

Researchers have used a combination of AI and quantum mechanics to reveal how hydrogen gradually turns into a metal in giant planets. Dense metallic hydrogen – a phase of hydrogen which behaves like an electrical conductor – makes up the interior of giant planets, but it is difficult to study and poorly understood. By combining artificial intelligence and quantum mechanics, researchers have found how hydrogen becomes a metal under the extreme pressure conditions of these planets. The researchers, from the University of Cambridge, IBM Research and EPFL, used machine learning to mimic the interactions between hydrogen atoms in order to overcome the size and timescale limitations of even the most powerful supercomputers. They found that instead of happening as a sudden, or first-order, transition, the hydrogen changes in a smooth and gradual way. The results are reported in the journal Nature.

Metamaterials are emerging as a new paradigmatic material system to render unprecedented and tailorable properties for a wide variety of engineering applications. However, the inverse design of metamaterial and its multiscale system is challenging due to high-dimensional topological design space, multiple local optima, and high computational cost. To address these hurdles, we propose a novel data-driven metamaterial design framework based on deep generative modeling. A variational autoencoder (VAE) and a regressor for property prediction are simultaneously trained on a large metamaterial database to map complex microstructures into a low-dimensional, continuous, and organized latent space. We show in this study that the latent space of VAE provides a distance metric to measure shape similarity, enable interpolation between microstructures and encode meaningful patterns of variation in geometries and properties. Based on these insights, systematic data-driven methods are proposed for the design of microstructure, graded family, and multiscale system. For microstructure design, the tuning of mechanical properties and complex manipulations of microstructures are easily achieved by simple vector operations in the latent space. The vector operation is further extended to generate metamaterial families with a controlled gradation of mechanical properties by searching on a constructed graph model. For multiscale metamaterial systems design, a diverse set of microstructures can be rapidly generated using VAE for target properties at different locations and then assembled by an efficient graph-based optimization method to ensure compatibility between adjacent microstructures. We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems that achieve desired distortion behaviors.

Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores. These pores give MOFs record-breaking internal surface areas, which can measure up to 7,800 m2 in a single gram of material. As a result, MOFs are extremely versatile and find multiple uses: separating petrochemicals and gases, mimicking DNA, producing hydrogen, and removing heavy metals, fluoride anions, and even gold from water are just a few examples. Because of their popularity, material scientists have been rapidly developing, synthesizing, studying, and cataloguing MOFs. Currently, there are over 90,000 MOFs published, and the number grows every day.