Things aren't great, but there are a few positive signs, we promise. Climate news hasn't been great in 2025. Global greenhouse-gas emissions hit record highs (again). This year is set to be either the second or third warmest on record. Climate-fueled disasters like wildfires in California and flooding in Indonesia and Pakistan devastated communities and caused billions in damage. In addition to these worrying indicators of our continued contributions to climate change and their obvious effects, the world's largest economy has made a sharp U-turn on climate policy this year.

Earth structural heterogeneities have a remarkable role in the petroleum economy for both exploration and production projects. Automatic detection of detailed structural heterogeneities is challenging when considering modern machine learning techniques like deep neural networks. Typically, these techniques can be an excellent tool for assisted interpretation of such heterogeneities, but it heavily depends on the amount of data to be trained. We propose an efficient and cost-effective architecture for detecting seismic structural heterogeneities using Convolutional Neural Networks (CNNs) combined with Attention layers. The attention mechanism reduces costs and enhances accuracy, even in cases with relatively noisy data. Our model has half the parameters compared to the state-of-the-art, and it outperforms previous methods in terms of Intersection over Union (IoU) by 0.6% and precision by 0.4%. By leveraging synthetic data, we apply transfer learning to train and fine-tune the model, addressing the challenge of limited annotated data availability.

We present a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery. Our approach incorporates an innovative data augmentation technique that involves the use of multi-layer synthetic (MLS) or quasi-2.5D images which are generated by blending 2D synthetic images. The MLS images more closely resemble 3D synthetic and real scanning electron microscopy (SEM) images of CNTs but without the computational cost of performing expensive 3D simulations or experiments. Mechanical properties such as stiffness and buckling load for the MLS images are estimated using a physics-based model. The proposed deep learning architecture, CNTNeXt, builds upon our previous CNTNet neural network, using a ResNeXt feature representation followed by random forest regression estimator. Our machine learning approach for predicting CNT physical properties by utilizing a blended set of synthetic images is expected to outperform single synthetic image-based learning when it comes to predicting mechanical properties of real scanning electron microscopy images. This has the potential to accelerate understanding and control of CNT forest self-assembly for diverse applications.

Machine learning software developer and data scientist skills top the list of high-demand software development talent and are two of the hottest …

These conclusions are drawn from ETR's most recent October survey, the demographics of which are highlighted below. Bradley highlights the critical aspects of the October survey demographics. The fourth calendar quarter is often the most productive for vendors as buyers tend to spend later in the year both to lock in year-over-year budget comparisons and to get ready for the following January's project push. Having said that, it's not uncommon for ETR's October survey data to show softness relative to first-half expectations. Nonetheless, as the graphic below shows, aggregate Net Score projections for Q4 2020 are the lowest in the multi-year history of ETR's survey.

Ever since the Dawn spacecraft picked up images of what look to be a vast network of bright spots in the Occator crater on Ceres--a dwarf planet in the asteroid belt--there's been conjecture over whether the whiteish spots are made up of ice, or some kind of volcanic salt deposits. Meanwhile, another controversy has been brewing over them: What exactly are those shapes seen in the bright spots, called Vinalia Faculae? Are they squares or triangles? Because the strange patterns are so strikingly geometric, researchers from the University of Cadiz in Spain have taken a closer look at the bright spots to figure out whether humans and machines look at planetary images differently. The overall goal was to figure out if artificial intelligence can help us discover and make sense of technosignatures, or potentially detectable signals from distant, advanced civilizations, according to NASA.

Scientists may need to think twice when using artificial intelligence to help in the search for extraterrestrial life, a new study suggests. A Spanish team used an AI system that interpreted the shape of a triangle outside a square from a NASA image of a crater on the dwarf planet Ceres. Researchers then brought together 163 volunteers with no training in astronomy to describe what the saw on the image of the crater. While the AI detected a both a square and a triangle, the majority of humans only interpreted a square. But once the triangle was pointed out to the humans, the amount of people who said they could see it rose from 7 per cent to 56 per cent. This suggests the influence of AI could be strong on the human brain when interpreting alien characteristics on other planets, even when it's questionable they even exist.

Currently co-founder and CTO at Nudge.ai, which uses artificial intelligence to help salespeople find useful trigger events at their target accounts. Letting AI do the heavy burden of research allows sales professionals to focus on selling, while never missing a chance to turn latent demand into active demand. Prior to that, co-founder and CTO of Eloqua, a company I helped guide to a market-leading position in marketing automation, while growing it to a $100 million revenue run rate, through its IPO on the NASDAQ, and to ultimate acquisition by Oracle. Tell me about your early career. It may seem strange considering that Nudge.ai is not my first software startup, but I'm not even originally a software guy.

The true promise of technology is to amplify the best traits in humanity. It doesn't always seem like that, and I'll admit there are legitimate concerns about the possible negative impact of the fourth industrial revolution. However, being the hallucinogenic optimist that I am, I like to focus attention on the bright spots that show us a beautiful future. I've recently invested in one of those bright spots. Visit is a startup in India that amplifies humanity -- while solving a very big and serious problem.

We all want more time, more open space, more capacity to do our High-Value Activities. But creating more capacity requires us to remove Low-Value Activities from our world. That's easy with the things we know are time-suckers. But what about the things we don't know? How do we find and remove time-suckers that aren't so obvious?