Lifeboat Foundation

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Accessibility empowers innovation for everyone.

Continue reading “#BuildFor2030: Empowering an inclusive and accessible world” »

In a groundbreaking announcement, physicists from University College London (UCL) have presented a radical theory that unifies the realms of gravity and quantum mechanics while preserving the classical concept of spacetime, as outlined by Einstein.

This innovative approach, detailed in two simultaneously published papers, challenges over a century of scientific consensus and proposes a revolutionary perspective on the fundamental nature of our universe.

Modern physics rests on two contradictory pillars: quantum theory, which rules the microscopic world, and Einstein’s theory of general relativity, explaining gravity through spacetime curvature. These theories, despite their individual successes, have remained irreconcilable, creating a significant rift in our understanding of the universe.

A breakthrough in nanofluidics is set to revolutionize our grasp of molecular dynamics at minuscule scales. Collaborative efforts from scientists at EPFL and the University of Manchester have uncovered a previously hidden world by using the newly found fluorescent properties of a graphene-like 2D material, boron nitride. This innovative approach enables scientists to track individual molecules within nanofluidic structures, illuminating their behavior in ways never before possible. The study’s findings were recently published in the journal Nature Materials.

Nanofluidics, the study of fluids confined within ultra-small spaces, offers insights into the behavior of liquids on a nanometer scale. However, exploring the movement of individual molecules in such confined environments has been challenging due to the limitations of conventional microscopy techniques. This obstacle prevented real-time sensing and imaging, leaving significant gaps in our knowledge of molecular properties in confinement.

In the age of rapid technological advancements, a new player has emerged on the scene, promising to revolutionize the way we transmit data wirelessly. Li-Fi, or Light Fidelity, is a cutting-edge technology that employs visible light to transmit information, offering an innovative alternative to traditional radio frequency-based wireless communication systems.

The IEEE published the 802.11bb standard for light-based networking in July 2023, an extension of the Wi-Fi specification enabling wireless networking using visible and infrared light rather than the radio spectrum. The standard outlines adjustments to the physical and medium access control layers, allowing wireless networking through light source modulation imperceptible to the human eye.

The Li-Fi specification mandates bidirectional transmission within the 800nm to 1,000nm electromagnetic spectrum range, ensuring a minimum throughput of 10 Mb/s and a maximum of 9.6 Gb/s at the MAC data service access point. By comparison, Wi-Fi operates within wavelengths of 120mm (2.4 GHz) and 60mm (5 GHz), with speeds that vary across versions, including Wi-Fi 6, which reaches up to 9.6 Gb/s, akin to Li-Fi capabilities.

UK MoD enhances combat training with ChatGPT in SimStriker robots, enabling dynamic soldier-target conversations for realistic scenarios.

SimStriker: A breakthrough in close combat training

SimStriker, developed by 4GD in 2020, has already been used at the SmartFacility in Colchester, UK, where it currently serves the British Army’s 16th Air Assault Brigade. The facility logged over 1,200 hours of training in 2022, engaging various users, including the Ministry of Defence police and civilian participants.

Old rocket parts, broken satellites, lost screws and other space debris have been floating near Earth’s orbit for more than 50 years. Innovative solutions are needed to remedy this dangerous situation.

Life Science Animation presents a groundbreaking 2D video animation that delves into the world of peptide-based cancer immunotherapies. In collaboration with Micropep, we explore Enterome’s OncoMimics platform, a novel approach to activating pre-existing memory T cell immunity for a more robust anti-tumor response.

Current immunotherapies often fall short as they only stimulate naïve T cell responses, leading to limited efficacy. Enterome’s innovative solution utilizes bacterial peptides, known as OncoMimics, that mimic tumor antigens. These OncoMimics are derived from common microbiome bacteria and are naturally tolerated by the human body.

Continue reading “Revolutionizing Cancer Treatment with Enterome’s OncoMimics: A 2D Animation” »

Washington is laying down rules for the use of seemingly mundane AI that could, in fact, be incredibly dangerous.

Between fancy global summits, OpenAI’s boardroom drama and rumoured technical breakthroughs, the world has recently been paying close attention to the frontiers of AI research.

StreamingLLM is an innovative framework that allows large language models to handle text of infinite length without the need for finetuning. This technique preserves attention sinks to maintain a near-normal attention score distribution. When the sequence of the conversation with the LLM surpasses the model’s context length, retains the KV cache for the attention sink tokens—four initial tokens are sufficient—and discards subsequent tokens to make room for the sliding window tokens. This approach enables the model to extend its context and stabilize its performance without having to recompute the entire KV values.

“The introduction of four initial tokens, as attention sinks, suffices to restore the LLM’s performance,” the researchers write. “In contrast, adding just one or two doesn’t achieve full recovery. We believe this pattern emerges because these models didn’t include a consistent starting token across all input samples during pre-training.”

Under the framework, the KV cache comprises the attention sinks and the rolling KV cache that retains the most recent tokens vital for language modeling. The researchers emphasize the versatility of, stating, design is versatile and can be seamlessly incorporated into any autoregressive language model that employs relative positional encoding.”