In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimizes electrolyte conductivity over a single-salt, ternary solvent design space. Using this workflow, we identify 6 fast-charging electrolytes in 2 work-days and 42 experiments (compared with 60 days using exhaustive search of the 1000 possible candidates, or 6 days assuming only 10% of candidates are evaluated). Our method finds the highest reported conductivity electrolyte in a design space heavily explored by previous literature, converging on a high-conductivity mixture that demonstrates subtle electrolyte chemical physics.

Data is what powers machine learning solutions. Quality datasets enable training models with the needed detection and classification accuracy, though sometimes the accumulation of sufficient and applicable training data that should be fed into the model is a complex challenge. For instance, to create data-intensive apps human annotators are required to label a huge number of samples, which results in complexity of management and high costs for businesses. In addition to that, there is the difficulty associated with data acquisition related to safety regulations, privacy, or ethical concerns. When we have a limited dataset including only a finite number of samples per class, few-shot learning may be useful.

The need for construction is more significant than ever. The projected 70% increase in urban population over the next 15 years will require many new buildings. Although the European Union anticipates that such a need will arise, builders still do not see this opportunity. So if you want to enter the construction industry or any other profession in this field, I have good news for you -- you are living in a hellish boom time! Unfortunately, this boom will lead to a climate catastrophe on a hitherto unknown scale.

We demonstrate that the space of intuitionistic fuzzy values (IFVs) with the linear order based on a score function and an accuracy function has the same algebraic structure as the one induced by the linear order based on a similarity function and an accuracy function. By introducing a new operator for IFVs via the linear order based on a score function and an accuracy function, we present that such an operator is a strong negation on IFVs. Moreover, we propose that the space of IFVs is a complete lattice and a Kleene algebra with the new operator. We also observe that the topological space of IFVs with the order topology induced by the above two linear orders is not separable and metrizable but compact and connected. From exactly new perspectives, our results partially answer three open problems posed by Atanassov [Intuitionistic Fuzzy Sets: Theory and Applications, Springer, 1999] and [On Intuitionistic Fuzzy Sets Theory, Springer, 2012]. Furthermore, we construct an isomorphism between the spaces of IFVs and q-rung orthopedic fuzzy values (q-ROFVs) under the corresponding linear orders. Meanwhile, we introduce the concept of the admissible similarity measures with particular orders for IFSs, extending the previous definition of the similarity measure for IFSs, and construct an admissible similarity measure with the linear order based on a score function and an accuracy function, which is effectively applied to a pattern recognition problem about the classification of building materials.

The future of computer vision and machine learning can be seen trundling at about 1 mile per hour at a lettuce field in the Salinas Valley of California. In certain fields, a tractor is pulling a highly specialized robot called the "lettuce bot." The robot, made by Blue River Technology, contains enough smarts to differentiate the weeds from the budding lettuce plants and then kill those weeds with an injection of fertilizer. The result is a weed-free field without the use of expensive and harmful pesticides -- making Blue River's robot a threat to the $31-billion pesticide business and a friend of organic farmers. The startup, founded in 2011, on Monday said it has raised $3.1 million in a Series A round led by Khosla Ventures.

Artificial intelligence (AI), when matured, will revolutionise the way that heavy industry operates. Within the mining industry, it is applicable in a range of services, from geo-mapping in the planning stage to monitoring and evaluating the mine site during operations.

The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment interactions needed to solve the complex ObtainDiamond task with sparse rewards. To address the challenge, in this paper, we present SEIHAI, a Sample-efficient Hierarchical AI, that fully takes advantage of the human demonstrations and the task structure. Specifically, we split the task into several sequentially dependent subtasks, and train a suitable agent for each subtask using reinforcement learning and imitation learning. We further design a scheduler to select different agents for different subtasks automatically. SEIHAI takes the first place in the preliminary and final of the NeurIPS-2020 MineRL competition.

Neural networks (NNs) are increasingly being used to predict new materials, the rate and yield of chemical reactions, and drug-target interactions, among others. For these applications, they are orders of magnitude faster than traditional methods such as quantum mechanical simulations. The price for this agility, however, is reliability. Because machine learning models only interpolate, they may fail when used outside the domain of training data. But the part that worried Rafael Gómez-Bombarelli, the Jeffrey Cheah Career Development Professor in the MIT Department of Materials Science and Engineering, and graduate students Daniel Schwalbe-Koda and Aik Rui Tan was that establishing the limits of these machine learning (ML) models is tedious and labor-intensive.

The nutrient content of our vegetables is down 40% over the last two decades and our soil health is suffering due to increasingly harsh herbicide use, according to Carbon Robotics founder Paul Mikesell. And farmers are increasingly concerned about the long-term health impacts of continually spraying chemicals on their fields. But not weeding will cost half your crop, killing profitability. A self-driving farm robot that kills 100,000 weeds an hour ... by laser. "We wanted [to] figure out if there's a better way we could do this."

NIR Calibration-Model Services NIR Machine Learning as a Service, a Game Changer for Productivity and Accuracy/Precision! ( NIRS Spectroscopy AI MLaaS) LINK Spectroscopy and Chemometrics News Weekly 43, 2021 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK This week's NIR news Weekly is sponsored by Your-Company-Name-Here – NIR-spectrometers. Check out their product page … link Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "Monitoring pilot-scale polyhydroxyalkanoate production from fruit pulp waste using near-infrared spectroscopy" LINK "Toward automated non-destructive diagnosis of chloride attack on concrete structures by near infrared spectroscopy" LINK "Spectra-structure correlations in NIR region of polymers from quantum chemical calculations.