Scientists have developed a novel tool designed to protect and conserve coral reefs by providing them with an abundance of feeding opportunities.
The device, dubbed the Underwater Zooplankton Enhancement Light Array (UZELA), is an autonomous, programmable underwater light that works to draw in nearby zooplankton, microscopic organisms that coral feed on.
After testing the submersible on two species of coral native to Hawaii over six months, researchers found that UZELA could greatly enhance local zooplankton density and increase the feeding rates of both healthy and bleached coral. Importantly, providing coral with greater amounts of food makes them stronger and more likely to be resilient against certain environmental threats, like heat stress or ocean acidification.
An interesting paper where Schuette et al. develop a generative diffusion-based AI model for predicting the 3D structure of chromatin. Their model takes chromatin accessibility sequence data as input and outputs a statistical distribution of predicted 3D chromatin structures. Remarkably, their model generalizes across cell types, making it broadly useful! #computationalbiology #ai #generativeai
Computational approaches for predicting chromatin conformations de novo using only sequencing data remain scarce. Compared to existing polymer simulation–based prediction approaches, ChromoGen maintains unique advantages. The generative nature of ChromoGen enables efficient production of statistically independent samples, thus avoiding the inefficient navigation of state space that polymer simulations require to produce a diverse set of conformations. Moreover, ChromoGen’s transformer-based front end provides additional advantages, extracting features from sequencing data and placing the information in low-dimensional embeddings that the diffusion model handles efficiently. This powerful design markedly reduces the computational cost of each diffusion step, providing a practical means to achieve cell type–specific de novo predictions with the full benefit of DNA sequence and chromatin accessibility data. In contrast, incorporating DNA sequence information into polymer models has long been a challenging task that is often indirectly addressed by incorporating various histone marks.
In its current form, ChromoGen can be immediately applied to any cell type with available DNAse-seq data, enabling a vast number of studies into the heterogeneity of genome organization both within and between cell types to proceed. However, several improvements could enhance its utility. Notably, the current model exclusively predicts chromatin conformations in 1.28-Mb regions at 20-kb resolution, the latter restriction primarily stemming from our decision to maximize resolution within the constraints imposed by the available Dip-C data. However, higher-resolution single-cell datasets are becoming available, such as those at 5-kb resolution (50), and we anticipate that ChromoGen will require no modifications to perform well after training on these improved datasets. Similarly, we anticipate that ChromoGen can be directly applied to longer genomic regions if using a lower resolution, e.g.
For decades, end users and systems designers have valued radar technology for its reliability. Especially in adverse weather conditions in which sensors based on other modalities are apt to fail, radar is a dependable technique offering broad application potential.
As a result of this robustness and widespread applicability, radar today is established as a standard sensing system in several high-growth technology sectors. The automotive industry, for example, has been a key driver of radar sensor miniaturization and overall performance improvements. The commercialization of radar for passenger vehicles predates the turn of the century, and radar sensors are also now commonly deployed in advanced driver-assistance systems, including for adaptive cruise control, autonomous emergency braking, and blind-spot assist.
AI transformational impact is well under way. But as AI technologies develop, so too does their power consumption. Further advancements will require AI chips that can process AI calculations with high energy efficiency. This is where spintronic devices enter the equation. Their integrated memory and computing capabilities mimic the human brain, and they can serve as a building block for lower-power AI chips.
Now, researchers at Tohoku University, National Institute for Materials Science, and Japan Atomic Energy Agency have developed a new spintronic device that allows for the electrical mutual control of non-collinear antiferromagnets and ferromagnets. This means the device can switch magnetic states efficiently, storing and processing information with less energy—just like a brain-like AI chip.
The breakthrough can potentially revolutionize AI hardware via high efficiency and low energy costs. The team published their results in Nature Communications on February 5, 2025.
The development of artificial intelligence has entered a pivotal phase. With groundbreaking advancements in large models such as ChatGPT and Sora, AI is approaching what has been termed as “technological singularity”.The allure of AI’s potential is undeniable, but its immense potential is accompanied by significant risks including deepfakes, frauds and autonomous weapons systems.
The complexities and interconnectedness of AI pose a new global challenge. Hence, building a coordinated global governance framework for AI is no longer optional; it is an urgent necessity.
AI transcends national boundaries, creating both global opportunities and risks that no country alone can manage. Hence, countries across the world need to work together to eliminate the risks.
Basically chat gpt can allow people that need to do more jobs can actually do several if not all jobs needed. Also essentially increase ones mental capacity and mental health due to that chat gpt can be almost like an external brain interface that can do nearly any job so that people can make even more money. Also people think this would replace people I believe it augments people like Ironman from marvel comics allowing to do tasks in seconds.#Ironman
A new breed of overemployed workers has emerged, turning to artificial intelligence (AI)-powered language models like ChatGPT to handle a significant portion of their job responsibilities.
“ChatGPT does like 80% of my job,” stated one worker, while another, currently holding down four robot-performed jobs, says, “Five would be overkill.”
As the popularity of AI-powered tools like ChatGPT continues to soar, concerns are growing about the impact on the global job market. With the potential for jobs to be automated and replaced by chatbots, experts are warning of a possible future where human workers become obsolete.
Aurora, the exascale supercomputer at Argonne National Laboratory, is now available to researchers worldwide, as announced by the system’s operators from the U.S. Department of Energy on January 28, 2025. One of the goals for Aurora is to train large language models for science.
According to official reports, among the world’s fastest supercomputers, there are currently only three systems that reach at least one exaflop. An exaflop is a quintillion (10¹⁸) calculations per second—that’s like a regular calculator computing continuously for 31 billion years, but completing everything in just a single second. Or, to put it briefly: exaflop supercomputers are incredibly fast.
The fastest among the swift three is El Capitan at the Lawrence Livermore National Laboratory with 1.742 exaflops per second under the HPL benchmark (High-Performance Linpack, a standardized test for measuring the computing power of supercomputers). It is followed by Frontier with 1.353 exaflops/s at the Oak Ridge National Laboratory. The trio is completed by Aurora with 1.012 exaflops/s. Incidentally, all three laboratories belong to the U.S. Department of Energy (DOE).
Scientists have developed a new type of compact camera engineered for computer vision. Developed by scientists from the University of Washington and Princeton University, the prototype uses optics for computing and reduces power consumption. It also enables the camera to identify objects at the speed of light.
Their device also represents a new approach to the field of computer vision, a type of artificial intelligence that allows computers to recognize objects in images and video.
In this video, Dr. Ben Goertzel, CEO of SingularityNET, TrueAGI and the Artificial Superintelligence Alliance (ASI Alliance), analyzes DeepSeek LLM as an efficiency advancement rather than an AGI breakthrough. The model’s open-source implementation and technical architecture (mixture of experts and multi-token training) improve accessibility while maintaining performance. This development demonstrates the continued democratization of AI capabilities and may redirect industry focus toward alternative computing architectures and decentralized systems.
0:00 Intro. 00:33 Initial Thoughts on DeepSeek. 01:25 Efficiency Gains and Their Implications. 02:58 Technological Singularity and Rapid Advances. 04:07 DeepSeek’s Underlying Technology. 07:27 Open Source Approach and Its Benefits. 09:58 China’s Role in AI and Open Source. 12:20 Broader Implications for AI and AGI 15:42 Conclusion: The Path to Technological Singularity.
#AGI #Deepseek #AI –
SingularityNET was founded by Dr. Ben Goertzel with the mission of creating a decentralized, democratic, inclusive, and beneficial Artificial General Intelligence (AGI). An AGI is not dependent on any central entity, is open to anyone, and is not restricted to the narrow goals of a single corporation or even a single country.
The SingularityNET team includes seasoned engineers, scientists, researchers, entrepreneurs, and marketers. Our core platform and AI teams are further complemented by specialized teams devoted to application areas such as finance, robotics, biomedical AI, media, arts, and entertainment.
“The Consciousness Explosion: A Mindful Human’s Guide to the Coming Technological and Experiential Singularity” by Ben Goerztel with Gabriel Axel Montes0:00…
