We smell different as we age. An odor-producing molecule 2-nonenal is only part of the story. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. There's a scientific explanation behind an aging body's distinct scent, but the chemistry only tells half the story. Breakthroughs, discoveries, and DIY tips sent six days a week.

Google TV's new Gemini features range from useful to unnecessary I checked out a preview of the new features during CES 2026. I met up with a few people from Google at the Encore Villas during CES (which is just 2,500 feet from my hotel but took 28 minutes to walk to, thanks to Vegas's pedestrian-averse design [also I got lost]). Once there, I saw what " more Gemini " will mean for people with a Google TV. The AI integration ranged from useful to probably unnecessary. The most useful bit, for me at least, came at the end.

The use of machine learning methods for predicting the properties of crystalline materials encounters significant challenges, primarily related to input encoding, output versatility, and interpretability. Here, we introduce CrystalBERT, an adaptable transformer-based framework with novel structure that integrates space group, elemental, and unit cell information. The method's adaptability lies not only in its ability to seamlessly combine diverse features but also in its capability to accurately predict a wide range of physically important properties, including topological properties, superconducting transition temperatures, dielectric constants, and more. CrystalBERT also provides insightful physical interpretations regarding the features that most significantly influence the target properties. Our findings indicate that space group and elemental information are more important for predicting topological and superconducting properties, in contrast to some properties that primarily depend on the unit cell information. This underscores the intricate nature of topological and superconducting properties. By incorporating all these features, we achieve a high accuracy of 91% in topological classification, surpassing prior studies and identifying previously misclassified topological materials, further demonstrating the effectiveness of our model.

This paper elucidates the challenges and opportunities inherent in integrating data-driven methodologies into geotechnics, drawing inspiration from the success of materials informatics. Highlighting the intricacies of soil complexity, heterogeneity, and the lack of comprehensive data, the discussion underscores the pressing need for community-driven database initiatives and open science movements. By leveraging the transformative power of deep learning, particularly in feature extraction from high-dimensional data and the potential of transfer learning, we envision a paradigm shift towards a more collaborative and innovative geotechnics field. The paper concludes with a forward-looking stance, emphasizing the revolutionary potential brought about by advanced computational tools like large language models in reshaping geotechnics informatics.

The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used tool for predicting material performances. While confidence intervals are commonly reported for such ML models, prediction intervals, i.e., the uncertainty on each prediction, are not as frequently available. Here, we investigate three easy-to-implement approaches to determine such individual uncertainty, comparing them across ten ML quantities spanning energetics, mechanical, electronic, optical, and spectral properties. Specifically, we focused on the Quantile approach, the direct machine learning of the prediction intervals and Ensemble methods.

When Tony Stark needs to travel to space in the original Iron Man movie, he asks his artificial intelligent (AI) assistant J.A.R.V.I.S. to make a suit that can survive harsh conditions. As AI specialist Kamal Choudhary explains: "The way I see it, what J.A.R.V.I.S. did is, it had a database of materials, scanned the database, found a suitable material, tested it, then synthesized an alloy that could survive space conditions. "That's what we want our system to do, and that's why we called it JARVIS." Choudhary, a researcher at the National Institute of Standards and Technology (NIST), is the founder and developer of JARVIS (Joint Automated Repository for Various Integrated Simulations)--an open dataset designed to automate materials discovery and optimization. Writing in npj Computational Materials in December 2021, Choudhary and Brian DeCost (NIST) described the latest enhancements to JARVIS that apply AI to speed discovery. Combining graph neural networks with chemical and structural knowledge about materials, their Atomistic Line Graph Neural Network (ALIGNN) outperforms previously reported models on atomistic prediction tasks with very high accuracy and better or comparable model training speed. "ALIGNN can predict characteristics in seconds instead of months," Choudhary said. Beyond the inspiration from Iron Man, there was the Materials Genome Initiative. Originated in 2011 under President Obama, the initiative is a multi-federal agency effort to discover, manufacture, and deploy advanced materials twice as fast and at a fraction of the cost of traditional methods. NIST's original contribution to the initiative was the creation of a database of materials and their characteristics, obtained rigorously, using standardized, cutting-edge computing methods. Several such databases have been established, but "what's particular about the JARVIS database is that it contains modules for various kinds of computational approaches," according to David Vanderbilt, professor of physics at Rutgers University, member of the National Academy of Sciences, and a contributor to the project. "There are many different theoretical levels on which you can approach the field.

Uncertainty quantification in Artificial Intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in material science. While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i.e., the evaluation of the uncertainty on each prediction, are seldomly available. In this work we compare 3 different approaches to obtain such individual uncertainty, testing them on 12 ML-physical properties. Specifically, we investigated using the Quantile loss function, machine learning the prediction intervals directly and using Gaussian Processes. We identify each approachs advantages and disadvantages and end up slightly favoring the modeling of the individual uncertainties directly, as it is the easiest to fit and, in most cases, minimizes over-and under-estimation of the predicted errors. All data for training and testing were taken from the publicly available JARVIS-DFT database, and the codes developed for computing the prediction intervals are available through JARVIS-Tools.

It's not just state and federal regulators' safety concerns that are preventing automotive manufacturers from producing self-driving cars for the masses. First they have to figure out how to make them more energy efficient. Opening the cargo area of a typical self-driving test vehicle usually reveals a trunk full of computers and wires needed to process petabytes of sensor data in real-time. That doesn't leave a lot of room for luggage, groceries, or anything else you usually transport in a car, not to mention the huge amounts of energy these systems suck from the batteries as they process all this information. That's why BMW I Ventures and Toyota AI Ventures has invested in Recogni, a San Jose, Calif.-based startup that is developing an artificial intelligence platform optimized for autonomous vehicles that can process information quickly while consuming very little energy.

Artificial intelligence (AI) is being counted (pdf) among the hottest startup sectors in India this year, but the highly specialised space is struggling to grow due to the lack of a primary input: engineers. "Forget getting people of our choice, we don't even get applications when we advertise for positions for our AI team," said 25-year-old Tushar Chhabra, co-founder of Cron Systems, which builds internet of things (IOT)-related solutions for the defence sector. "It's as if people are scared of the words'artificial intelligence.' They start freaking out when we ask them questions about AI." India has over 170 startups focused purely on AI, which have together raised over $36 million. The sector has received validation from marquee investors like Sequoia Capital, Kalaari Capital, and business icon Ratan Tata.

Incuspaze, a Gurgaon-based co-working and incubation centre, will launch an incubation programme for startups in artificial intelligence, machine learning, IoT and big data, the company said in a statement on Monday. The 6,000 sq ft centre was launched by UK-based angel investor Sanjay Choudhary last week. Incuspaze will invest between Rs 1 -5 lakh in five to six startups in its batch, and will also offer parallel services such as marketing, accounting and legal. "We are building a global self-sustainable ecosystem for startups and entrepreneurs. We are not only providing a co-working space, but also investor support, mentoring, allied services, global partnerships and to ensure peak performance for all startup founders," said Choudhary in the statement. Choudhary said Incuspaze will enable startups to accelerate product/service development by implementing lean startup methodologies through calculated risk-taking.