<aside> 📌 Key Enabling Technologies (KETs) refer to a group of advanced and cross-cutting technologies that have a significant impact on multiple industrial sectors and play a crucial role in driving innovation, competitiveness, and sustainable development. These technologies provide a foundation for creating new products, processes, and services across various industries.
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Digital twin technology has extended beyond manufacturing into the realms of IoT, AI, and data analytics, enabling data professionals to optimize operations and simulate scenarios. Essentially, a digital twin is a virtual representation of physical objects or systems, including buildings, factories, cities, and potentially even people and processes. But what else are they for? Find out here…
What is a digital twin and why is it important to IoT?
Taking inspiration from ecological systems, an improved AI could incorporate feedback loops, redundant pathways, and adaptable decision-making frameworks, potentially leading to a more versatile 'general intelligence.' Furthermore, the field of ecology may offer insights into the reasons behind emergent behaviors like 'hallucinations' in large language models such as ChatGPT, which are absent in smaller models. Ecology's holistic approach to complex systems enables a deeper exploration of the mechanisms underlying these AI behaviors.
Ecology and artificial intelligence: Stronger together
A synergistic future for AI and ecology | Proceedings of the National Academy of Sciences
Inflatable soft robots, known for their safety and adaptability, have faced challenges in integrating sensing and control systems without compromising their softness and capabilities. A research team from the Republic of Korea has introduced "soft valve" technology that integrates sensors and control valves into the soft body, addressing this challenge. These all-soft valves, operating without electricity, serve the dual purpose of detecting stimuli and controlling motion using air pressure, enabling safer operation in environments like underwater or spark-sensitive settings while reducing weight on robotic systems.
Groundbreaking Soft Valve Technology Enabling Sensing and Control Integration in Soft Robots
Los Alamos National Laboratory developed a machine-learning algorithm that set a world record for processing massive data sets by dividing them into manageable segments. This highly scalable algorithm works efficiently on both laptops and supercomputers, addressing hardware limitations and benefiting fields such as cancer research, satellite imagery analysis, and national security science. Ismael Boureima, a computational physicist at Los Alamos National Laboratory, explained that their "out-of-memory" implementation of non-negative matrix factorization simplifies processing exponentially growing data sets.
Not too big: Machine learning tames huge data sets
Distributed out-of-memory NMF on CPU/GPU architectures