Lifeboat Foundation

Why it matters: While AI algorithms are seemingly everywhere, processing on the most popular platforms require powerful server GPUs to provide customers with their generative services. Arm is introducing a new dedicated chip design, set to provide AI acceleration even in the most affordable IoT devices starting next year.

The Arm Cortex-M52 is the smallest and most cost-efficient processor designed for AI acceleration applications, according to the company. This latest design from the UK-based fabless firm promises to deliver “enhanced” AI capabilities to Internet of Things (IoT) devices, as Arm states, without the need for a separate computing unit.

Paul Williamson, Arm’s SVP and general manager for the company’s IoT business, emphasized the need to bring machine learning optimized processing to “even the smallest and lowest-power” endpoint devices to fully realize the potential of AI in IoT. Despite AI’s ubiquity, Williamson noted, harnessing the “intelligence” from the vast amounts of data flowing through digital devices requires IoT appliances that are smarter and more capable.

An algorithm that can analyse hundreds of millions of genetic sequences has identified DNA-cutting genes and enzymes that are extremely rare in nature.

Light confined to an accelerating optical cavity could display a photonic counterpart of the electronic quantum Hall effect.

Place a conductor in a magnetic field and the electrical current driven by an applied voltage will not flow in a straight line but in a direction perpendicular to the electric field—a behavior known as the Hall effect [1]. Reduce the temperature to the point where the electrons manifest quantum-mechanical behavior, and the plot thickens. The conductivity (defined as the ratio between the sideways current and the voltage) exhibits discrete jumps as the magnetic field is varied—the quantum Hall effect [2]. Since electrons at low temperature and photons obey a similar wave equation [3], should we also expect a quantum Hall effect for light? This question has been bubbling under the surface for the past decade, leading to the observation of some aspects of an optical quantum Hall effect [4, 5]. But the analogy between photons and electrons remains incomplete.

An open-source advanced supercomputer algorithm predicts the patterning and dynamics of living materials, allowing for the exploration of their behaviors across space and time.

Biological materials consist of individual components, including tiny motors that transform fuel into motion. This process creates patterns of movement, leading the material to shape itself through coherent flows driven by constant energy consumption. These perpetually driven materials are called “active matter.”

The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand the shape, flows, and form of living materials. The active matter theory consists of many challenging mathematical equations.

Nov 22 (Reuters) — Ahead of OpenAI CEO Sam Altman’s four days in exile, several staff researchers sent the board of directors a letter warning of a powerful artificial intelligence discovery that they said could threaten humanity, two people familiar with the matter told Reuters.

The previously unreported letter and AI algorithm was a key development ahead of the board’s ouster of Altman, the poster child of generative AI, the two sources said. Before his triumphant return late Tuesday, more than 700 employees had threatened to quit and join backer Microsoft (MSFT.O) in solidarity with their fired leader.

The sources cited the letter as one factor among a longer list of grievances by the board that led to Altman’s firing. Reuters was unable to review a copy of the letter. The researchers who wrote the letter did not immediately respond to requests for comment.

AI has mastered some of the most complex games known to man, but models are generally tailored to solve specific kinds of challenges. A new DeepMind algorithm that can tackle a much wider variety of games could be a step towards more general AI, its creators say.

Using games as a benchmark for AI has a long pedigree. When IBM’s Deep Blue algorithm beat chess world champion Garry Kasparov in 1997, it was hailed as a milestone for the field. Similarly, when DeepMind’s AlphaGo defeated one of the world’s top Go players, Lee Sedol, in 2016, it led to a flurry of excitement about AI’s potential.

DeepMind built on this success with AlphaZero, a model that mastered a wide variety of games, including chess and shogi. But as impressive as this was, AlphaZero only worked with perfect information games where every detail of the game, other than the opponent’s intentions, is visible to both players. This includes games like Go and chess where both players can always see all the pieces on the board.

Biological materials are made of individual components, including tiny motors that convert fuel into motion. This creates patterns of movement, and the material shapes itself with coherent flows by constant consumption of energy. Such continuously driven materials are called active matter.

The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand the shape, flow, and form of living materials. The active matter theory consists of many challenging mathematical equations.

Scientists from the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) in Dresden, the Center for Systems Biology Dresden (CSBD), and the TU Dresden have now developed an algorithm, implemented in an open-source supercomputer code, that can for the first time solve the equations of active matter theory in realistic scenarios.

Quantum advantage is the milestone the field of quantum computing is fervently working toward, where a quantum computer can solve problems that are beyond the reach of the most powerful non-quantum, or classical, computers.

Quantum refers to the scale of atoms and molecules where the laws of physics as we experience them break down and a different, counterintuitive set of laws apply. Quantum computers take advantage of these strange behaviors to solve problems.

Continue reading “A scientist explains an approaching milestone marking the arrival of quantum computers” »

Researchers confront a formidable challenge within the expansive domain of materials science—efficiently distilling essential insights from densely packed scientific texts. This intricate dance involves navigating complex content and generating coherent question-answer pairs that encapsulate the core of the material. The complexity lies in the substantial task of extracting pivotal information from the dense fabric of scientific texts, requiring researchers to craft meaningful question-answer pairs that capture the essence of the material.

Current methodologies within this domain often lean on general-purpose language models for information extraction. However, these approaches need help with text refinement and the accurate incorporation of equations. In response, a team of MIT researchers introduced MechGPT, a novel model grounded in a pretrained language model. This innovative approach employs a two-step process, utilizing a general-purpose language model to formulate insightful question-answer pairs. Beyond mere extraction, MechGPT enhances the clarity of key facts.

The journey of MechGPT commences with a meticulous training process implemented in PyTorch within the Hugging Face ecosystem. Based on the Llama 2 transformer architecture, the model flaunts 40 transformer layers and leverages rotary positional embedding to facilitate extended context lengths. Employing a paged 32-bit AdamW optimizer, the training process attains a commendable loss of approximately 0.05. The researchers introduce Low-Rank Adaptation (LoRA) during fine-tuning to augment the model’s capabilities. This involves integrating additional trainable layers while freezing the original pretrained model, preventing the model from erasing its initial knowledge base. The result is heightened memory efficiency and accelerated training throughput.