Understanding synapse loss in Alzheimer’s disease has been hampered by a lack of human model systems. Here, the authors show that manipulation of physiological or pathological Aβ has differing effects on synapses in live human brain slice cultures.
Long before human minds contemplated their own existence, information was already flowing. Not as bits in silicon, but as a fundamental flux of differentials in the fabric of reality itself. The universe, at its most elemental level, may be understood not merely as matter and energy, but as a vast information-processing system — a perspective that opens new avenues for understanding the enigma of consciousness. The question that has bedeviled philosophers and scientists alike is not simply what consciousness is, but how it emerges, and whether it represents something unique in the cosmic landscape or is merely a sophisticated expression of processes inherent to reality itself.
Panpsychism — the view that consciousness is fundamental and ubiquitous throughout the universe — has experienced a renaissance in recent philosophical discourse. Yet despite its elegant simplicity, it leaves crucial questions unanswered, particularly regarding the mechanism by which consciousness manifests in systems of varying complexity. This essay proposes that consciousness can be more productively understood as an autonomous region of information processing within a general field of information, a perspective that synthesizes insights from systems theory, information dynamics, the science of living systems, and recent research on microtubular functions to transcend traditional panpsychist frameworks.
To appreciate consciousness as an emergent property of information processing, we must first recognize information’s fundamental role in the universe. Wheeler’s famous dictum “it from bit” suggests that physical reality emerges from information (Wheeler, 1990). This perspective has been substantiated by advances in quantum information theory, which demonstrates that information is not merely about reality but constitutive of it. As Vedral (2010, p. 3) argues, “Quantum physics requires us to abandon the distinction between information and reality.” The quantum world reveals itself not as a collection of things but as potentialities and relationships — informational patterns that coalesce into what we perceive as physical reality.
New research is bringing us closer to answering one of the greatest questions in biology.
Scientists and engineers from the UK Atomic Energy Authority (UKAEA) and the University of Bristol have successfully created the world’s first carbon-14 diamond battery.
This new type of battery has the potential to power devices for thousands of years, making it an incredibly long-lasting energy source.
The battery leverages the radioactive isotope, carbon-14, known for its use in radiocarbon dating, to produce a diamond battery.
A new study involving over 700 older adults suggests that taking one gram of omega-3 daily may help slow biological aging, with effects visible in molecular markers known as epigenetic clocks.
When combined with vitamin D and regular exercise, the anti-aging benefits became even more pronounced, lowering the risks of frailty and cancer as well.
Omega-3 linked to slower aging in humans.
Unmanned aerial vehicles (UAVs), commonly known as drones, have already proved to be valuable tools for a wide range of applications, ranging from film and entertainment production to defense and security, agriculture, logistics, construction and environmental monitoring. While these technologies are already widely used in many countries worldwide, engineers have been trying to enhance their capabilities further so that they can be used to tackle even more complex problems.
Researchers at Pohang University of Science and Technology and the Agency for Defense Development (ADD)’s AI Autonomy Technology Center in South Korea recently developed a drone with foldable wings that could be more maneuverable than conventional drones. Their drone draws inspiration from the winged flying squirrel, a type of squirrel that uses loose flaps of skin attached from their wrists to their ankles to glide from tree to tree.
“The flying squirrel drone is inspired by the movements of flying squirrels, particularly their ability to rapidly decelerate by spreading their wings just before landing on trees,” Dohyeon Lee, Jun-Gill Kang and Soohee Han, co-authors of the paper, told Tech Xplore. “We initiated this research with the belief that, like flying squirrels, drones could expand their dynamic capabilities by utilizing aerodynamic drag.”
In recent years, computer scientists have created various highly performing machine learning tools to generate texts, images, videos, songs and other content. Most of these computational models are designed to create content based on text-based instructions provided by users.
Researchers at the Hong Kong University of Science and Technology recently introduced AudioX, a model that can generate high quality audio and music tracks using texts, video footage, images, music and audio recordings as inputs. Their model, introduced in a paper published on the arXiv preprint server, relies on a diffusion transformer, an advanced machine learning algorithm that leverages the so-called transformer architecture to generate content by progressively de-noising the input data it receives.
“Our research stems from a fundamental question in artificial intelligence: how can intelligent systems achieve unified cross-modal understanding and generation?” Wei Xue, the corresponding author of the paper, told Tech Xplore. “Human creation is a seamlessly integrated process, where information from different sensory channels is naturally fused by the brain. Traditional systems have often relied on specialized models, failing to capture and fuse these intrinsic connections between modalities.”
In this third episode of the AI Bros podcast Bruce and John talk about everything from how AI is being utilized in existing platforms, to Character AI’s sync…
A team of AI researchers at the University of California, Los Angeles, working with a colleague from Meta AI, has introduced d1, a diffusion-large-language-model-based framework that has been improved through the use of reinforcement learning. The group posted a paper describing their work and features of the new framework on the arXiv preprint server.
Over the past couple of years, the use of LLMs has skyrocketed, with millions of people the world over using AI apps for a wide variety of applications. This has led to an associated need for large amounts of electricity to power data centers running the computer-intensive applications. Researchers have been looking for other ways to provide AI services to the user community. One such approach involves the use of dLLMs as either a replacement or complementary approach.
Diffusion-based LLMs (dLLMs) are AI models that arrive at answers differently than LLMs. Instead of taking the autoregressive approach, they use diffusion to find answers. Such models were originally used to generate images—they were taught how to do so by adding overwhelming noise to an image and then training the model to reverse the process until nothing was left but the original image.