Everyday activities like using a GPS to map out the best driving route or translating a document consume energy, water and mineral resources -- lots of it. These applications run in the cloud, a nebulous term for the millions of powerful computers in vast data centers worldwide. Mobile applications depend on legions of computers to store trillions of data and perform split-second operations (e.g. Estimates of the energy consumption of data centers range between 1-2% of total global consumption. All signs indicate that data center energy consumption is about to skyrocket.

SensOre Ltd (ASX:S3N) is pleased to announce that SensOre and Gold Road (ASX: GOR) have reached agreement to restructure arrangements surrounding the Yilgarn Exploration Ventures (YEV) portfolio. SensOre has agreed to acquire Gold Road Resources' 40% minority interest in YEV for 800,000 SensOre shares. Yilgarn Exploration Ventures holds a portfolio of prospective gold assets in the Eastern Goldfields of Western Australia. SensOre aims to become the top performing minerals targeting company in the world through the deployment of AI and machine learning (ML) technologies, specifically its Discriminant Predictive Targeting (DPT) workflow. SensOre collects all available geological information in a terrane and places it in a multidimensional hypercube or data cube.

The increasing importance of carbon capture technologies for deployment in remediating CO2 emissions, and thus the necessity to improve capture materials to allow scalability and efficiency, faces the challenge of materials development, which can require substantial costs and time. Machine learning offers a promising method for reducing the time and resource burdens of materials development through efficient correlation of structure-property relationships to allow down-selection and focusing on promising candidates. Towards demonstrating this, we have developed an end-to-end "discovery cycle" to select new aqueous amines compatible with the commercially viable acid gas scrubbing carbon capture. We combine a simple, rapid laboratory assay for CO2 absorption with a machine learning based molecular fingerprinting model approach. The prediction process shows 60% accuracy against experiment for both material parameters and 80% for a single parameter on an external test set. The discovery cycle determined several promising amines that were verified experimentally, and which had not been applied to carbon capture previously. In the process we have compiled a large, single-source data set for carbon capture amines and produced an open source machine learning tool for the identification of amine molecule candidates (https://github.com/IBM/Carbon-capture-fingerprint-generation).

In many areas of the observational and experimental sciences data is scarce. Data observation in high-energy astrophysics is disrupted by celestial occlusions and limited telescope time while data derived from laboratory experiments in synthetic chemistry and materials science is time and cost-intensive to collect. On the other hand, knowledge about the data-generation mechanism is often available in the sciences, such as the measurement error of a piece of laboratory apparatus. Both characteristics, small data and knowledge of the underlying physics, make Gaussian processes (GPs) ideal candidates for fitting such datasets. GPs can make predictions with consideration of uncertainty, for example in the virtual screening of molecules and materials, and can also make inferences about incomplete data such as the latent emission signature from a black hole accretion disc. Furthermore, GPs are currently the workhorse model for Bayesian optimisation, a methodology foreseen to be a guide for laboratory experiments in scientific discovery campaigns. The first contribution of this thesis is to use GP modelling to reason about the latent emission signature from the Seyfert galaxy Markarian 335, and by extension, to reason about the applicability of various theoretical models of black hole accretion discs. The second contribution is to extend the GP framework to molecular and chemical reaction representations and to provide an open-source software library to enable the framework to be used by scientists. The third contribution is to leverage GPs to discover novel and performant photoswitch molecules. The fourth contribution is to introduce a Bayesian optimisation scheme capable of modelling aleatoric uncertainty to facilitate the identification of material compositions that possess intrinsic robustness to large scale fabrication processes.

Energetic materials (EM) such as propellants, explosives, and pyrotechnics are key components in many military and civilian applications. EMs are composites of organic crystals, plasticizers, metals, and other inclusions, forming complex microstructural morphologies, which strongly influence the properties and performance characteristics of these materials (1). For instance, the sensitivity to impact and shock loading--one of the key performance parameters for the design of safe and reliable EMs--is strongly influenced by their microstructures (2-4). Voids, cracks, and interfaces in EM microstructures are potential sites for energy localization, i.e., the formation of hightemperature regions called "hotspots" (5-8). Such hotspots are considered to be critical if they grow and produce steady deflagration fronts (9). If a sufficient number of such critical hotspots are generated in the microstructure, chemical energy release can be rapid enough to couple with the incident shock wave, initiating a detonation. Therefore, microstructural features localize energy release at hotspots and shock-microstructure interactions can lead to a shock-to-detonation transition in EMs. 1

Generating low-level robot task plans from high-level natural language instructions remains a challenging problem. Although large language models have shown promising results in generating plans, the accuracy of the output remains unverified. Furthermore, the lack of domain-specific language data poses a limitation on the applicability of these models. In this paper, we propose CLAIRIFY, a novel approach that combines automatic iterative prompting with program verification to ensure programs written in data-scarce domain-specific language are syntactically valid and incorporate environment constraints. Our approach provides effective guidance to the language model on generating structured-like task plans by incorporating any errors as feedback, while the verifier ensures the syntactic accuracy of the generated plans. We demonstrate the effectiveness of CLAIRIFY in planning chemistry experiments by achieving state-of-the-art results. We also show that the generated plans can be executed on a real robot by integrating them with a task and motion planner.

While Silicon Valley has transformed every industry from health care to banking, agriculture has remained largely untouched -- until now. Ever since OpenAI's breakthrough with ChatGPT, the term AI has been thrown around so many times it's starting to lose its meaning. Nevertheless, artificial intelligence has seeped into every industry from enterprise software to autonomous vehicles, taking around 10% of global venture dollars in 2022. Grow your revenue with all-in-one prospecting solutions powered by the leader in private-company data. Agriculture has not been immune to the AI revolution that has gripped the tech world.

MIT researchers have developed resilient artificial muscles that can enable insect-scale aerial robots to effectively recover flight performance after suffering severe damage. It is estimated that a foraging bee bumps into a flower about once per second, which damages its wings over time. Yet despite having many tiny rips or holes in their wings, bumblebees can still fly. Aerial robots, on the other hand, are not so resilient. Poke holes in the robot's wing motors or chop off part of its propellor, and odds are pretty good it will be grounded.

As a world market leader in crop protection, we help farmers to counter threats and ensure enough safe, nutritious, affordable food for all – while minimizing the use of land and other agricultural inputs. Syngenta Crop Protection keeps plants safe from planting to harvesting. From the moment a seed is planted through to harvest, crops need to be protected from weeds, insects, and diseases as well as droughts and floods, heat, and cold. Syngenta Crop Protection is headquartered in Switzerland. This role is based in Basel, Switzerland or Jealott's Hill, UK.

With robots being deployed in increasingly complex environments like underground mines and planetary surfaces, the multi-sensor fusion method has gained more and more attention which is a promising solution to state estimation in the such scene. The fusion scheme is a central component of these methods. In this paper, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is detected. Both the Cramer-Rao Lower Bound (CRLB) theory and simulation test are used to demonstrate the higher accuracy of our method compared to methods using a single observation. Furthermore, the proposed system is evaluated in perceptually challenging datasets against various state-of-the-art sensor-fusion methods. The results show that the proposed system achieves real-time and high estimation accuracy performance despite the challenging environment and poor observations.