Chemist Omar Yaghi invented materials called MOFs, a few grams of which have the surface area of a football field. In school, we learn about the Stone Age, the Bronze Age - and we are currently in a silicon age characterised by computers and phones. What might define the next age? Omar Yaghi at the University of California, Berkeley, thinks a family of materials he helped pioneer in the 1990s has a good shot. They are metal-organic frameworks (MOFs), and working out how to make them earned him a share of the 2025 Nobel prize in chemistry .

Carbon Nanotube has long been seen as a promising candidate for high-performance electronic material, yet its unique 1D structure leads to challenges in device fabrication. Many processing approaches have been proposed to produce better performing CNTFETs and this explosion of data needs an efficient way to explore.

We propose tabular two-dimensional correlation analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This method visualizes similarities and phase lags in structural parameter changes through heatmaps, combining hierarchical clustering and asynchronous correlations. We applied the proposed method to datasets of carbon nanotube (CNTs) films annealed at various temperatures and revealed the complexity of their hierarchical structures, which include elements like voids, bundles, and amorphous carbon. Our analysis addresses the challenge of attempting to understand the sequence of structural changes, especially in multifaceted characterization data where 11 structural parameters derived from 8 characterization methods interact with complex behavior. The results show how phase lags (asynchronous changes from stimuli) and parameter similarities can illuminate the sequence of structural changes in materials, providing insights into phenomena like the removal of amorphous carbon and graphitization in annealed CNTs. This approach is beneficial even with limited data and holds promise for a wide range of material analyses, demonstrating its potential in elucidating complex material behaviors and properties.

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.

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.

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. Inspired by the hardiness of bumblebees, MIT researchers have developed repair techniques that enable a bug-sized aerial robot to sustain severe damage to the actuators, or artificial muscles, that power its wings -- but to still fly effectively.

Today's digital world is an expanding frontier of emerging technologies. There are endless innovations, inspired by data, informed by data, enabled by data, and that create value from data. One thing we've seen more and more enterprises do to keep up with this digital revolution is the adoption of cloud services for a variety of IT functions, to an extent that modern approaches to building and running programs are often described as "cloud-native." According to Gartner, while only about 10 percent of enterprise-generated data is created and processed outside a traditional data center or cloud, this figure is expected to soar to 75 percent by 2025. The cloud alone simply isn't efficient enough to keep up with the volume and velocity of data that enterprises will be faced with as time goes on. So what is the missing piece to keeping up?

Why are we on the verge of creating a technology that will combine the computer with the human nervous system into a single complex? Can a computer system handle the flood of data from billions of living neurons? I will try to answer these questions in this article. In the previous article "Individual artificial intelligence: A new technology that will change our world", we talked about the fact that a new type of artificial intelligence will become a bioelectronic hybrid in which a living human brain and a computer will work together. Thus, a new type of AI will be born – individual artificial intelligence.

Why are we on the verge of creating a technology that will combine the computer with the human nervous system into a single complex? Can a computer system handle the flood of data from billions of living neurons? I will try to answer these questions in this article. In the previous article "Individual artificial intelligence: A new technology that will change our world", we talked about the fact that a new type of artificial intelligence will become a bioelectronic hybrid in which a living human brain and a computer will work together. Thus, a new type of AI will be born – individual artificial intelligence.

Model monitoring for predictive analytics at the edge begins with input, i.e. the data and how it is collected. I like to say, the Internet used to be a thing, but now, things are the Internet. In the Internet of Things, "things" are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems. The data that is collected at the edge often needs to be processed in real-time in order to fuel predictive modeling or to reveal novel patterns in the data that may inspire questions we didn't think to ask about the things that we are monitoring. Some examples of edge applications are technologies like drones or self-driving cars, which operate autonomously through software controlled plans and onboard edge sensors, including GPS.