Lifeboat Foundation: Safeguarding Humanity
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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 . 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 .”

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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.”

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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.

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The ability to quickly recognize sounds, particularly the vocalizations made by other animals, is known to contribute to the survival of a wide range of species. This ability is supported by a process known as categorical perception, which entails the transformation of continuous auditory input (e.g., gradual changes in pitch or tone) into distinct categories (i.e., vocalizations that mean something specific).

Various past studies have tried to shed light on the neural underpinnings of categorical perception and the categorization of vocalizations. While they broadly identified some that could play a part in these abilities, the precise processes through which animals categorize their peer’s categorizations have not yet been fully elucidated.

Researchers at Johns Hopkins University recently carried out a study investigating how vocalizations are represented in the brain of big brown bats, which are scientifically known as Eptesicus fuscus. Their findings, published in Nature Neuroscience, suggest that the categories of vocalizations are encoded in the bat midbrain.

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