We proved that our Universe was made from AI Algorithm.
What if spacetime itself is the result of a gigantic self-learning quantum neural network? đ€Żđ
A new framework called the Universal Bridge Theorem (UBT) proposes a deep equivalence between:
đ§ Neural network training.
and.
đ The evolution of spacetime geometry.
The proposal combines:
Artificial intelligence systems, such as large language models (LLMs) and convolutional neural networks (CNNs), can analyze large amounts of data and rapidly generate desired content or identify meaningful patterns. However, when running on existing hardware, such as smartphones, laptops and tablets, these systems typically consume a large amount of energy.
Over the past decade or so, electronics engineers have been increasingly working on alternative hardware systems that could run AI models more energy efficiently. Many of these systems are neuromorphic, meaning that they are inspired by the structure and functioning of the human brain.
Researchers at Huazhong University of Science and Technology and the Chinese University of Hong Kong recently introduced a new approach for designing neuromorphic computing hardware based on two-dimensional materials. Their proposed strategy, introduced in a paper published in Nature Electronics, was used to develop a chip based on the 2D semiconductor molybdenum disulfide (MoS2) that can reliably run AI algorithms while consuming less power.
Itâs been 37 years since scientists first demonstrated the ability to move single atoms, suggesting the possibility of designing materials atom by atom to customize their properties. Today there are several techniques that allow researchers to move individual atoms in order to give materials exotic quantum properties and improve our understanding of quantum behavior.
But existing techniques can only move atoms across the surface of materials in two dimensions. Most also require painstakingly slow processes and high-vacuum, ultracold lab conditions.
Now a team of researchers at MIT, the Department of Energyâs Oak Ridge National Laboratory, and other institutions has created a way to precisely move tens of thousands of individual atoms within a material in minutes at room temperature. The approach uses a set of algorithms to carefully position an electron beam at specific locations of a material, then scan the beam to drive atomic motions.
Researchers at the University of California San Diego have developed an open-source âdigital twinâ of a wireless network, giving graduate students, startups and other innovators a free, easy-to-use way to test new technologies and get fast, realistic feedback. The platform could help accelerate the pace of wireless innovation.
âWe are building a software replica of everything that happens when you use your phone, from the wireless signals traveling through the environment to the cellular network and apps that deliver data and services like video and Instagram,â said Dinesh Bharadia, associate professor in the Department of Electrical and Computer Engineering at the UC San Diego Jacobs School of Engineering, an affiliate of the UC San Diego Qualcomm Institute and senior author of the paper.
âThis will help industry and academia build new protocols and algorithms faster using software and AI, with less need for real-world experiments.â
A new Linux zero-day exploit, named Dirty Frag, allows local attackers to gain root privileges on most major Linux distributions with a single command.
Security researcher Hyunwoo Kim, who disclosed it earlier today and published a proof-of-concept (PoC) exploit, says this local privilege escalation was introduced roughly nine years ago in the Linux kernelâs algif_aead cryptographic algorithm interface.
Dirty Frag works by chaining two separate kernel flaws, the xfrm-ESP Page-Cache Write vulnerability and the RxRPC Page-Cache Write vulnerability, to modify protected system files in memory without authorization and achieve privilege escalation.
Galloni et al. introduce âdendritic target propagationâ: a Daleâs law-compliant learning algorithm for cortical microcircuits with soma-and dendrite-targeting inhibition and realistic connectivity constraints. By combining experimentally derived BTSP and Hebbian rules, dendrites compute local error proxies via E/I mismatch, supporting gradient-based deep learning during simultaneous bottom-up and top-down signaling.
In the era of big data and artificial intelligence, a new approach has emerged for solving combinatorial optimization problems, which involves finding the most efficient solution among many possible options and can otherwise take thousands of years to compute.
A KAIST research team has developed computational hardware that can be implemented entirely using existing silicon processes, enabling deployment on existing fabrication lines without additional facilities. This is expected to enable faster and more accurate decision-making across various industries, including logistics, finance, and semiconductor design.
The research is published in Science Advances.
Creating complex molecules usually requires years of experience and countless decisions, but a new AI system is changing that. Synthegy lets chemists guide synthesis and reaction planning using simple language, while powerful algorithms generate and evaluate possible solutions. The AI doesnât just computeâit reasons, scoring pathways and explaining which ones make the most sense.
Finding and developing new molecules is one of the great research endeavors of modern chemistry. From the development of new drugs to the creation of more sustainable materials, everything depends on finding new combinations of atoms with useful properties. Now, a research team from the Universitat Rovira i Virgili (URV) has developed an artificial intelligence tool capable of generating millions of new molecules which, although still unknown to science, comply with the laws of chemistry and could therefore be realistic possibilities. The research results have been published in the journal Nature Machine Intelligence.
The system, called CoCoGraph, works in a similar way to generative artificial intelligence tools for text or images, such as ChatGPT or Dall-E. âThese models create new content that looks very much like the real thing. Our algorithm does the same, but with molecules,â explains Roger GuimerĂ , an ICREA Research Professor in the Department of Chemical Engineering at the URV.
Unlike other AI tools, however, the model does not yet respond to specific instructions. For the moment it simply carries out the more basic task of generating plausible molecules, that is, structures that comply with the rules of chemistry.
