Lifeboat Foundation

Apple’s own System on Chips (SoC) designs used in iPhones, iPads and now Macs (with ‘Apple Silicon’ branding) are a key source of competitive advantage for the Cupertino giant. The Arm instruction set compatible, but Apple designed, processors used in these SoCs, consistently outperform competitors’ designs.

Apple is the modern exemplar of the maxim from Herman Hauser, founder of Acorn, Apple’s partner in the original Arm joint venture, that ‘there will be two types of computer company in the future, those with silicon design capability and those that are dead ’.

But Apple’s first attempt to design its own processor came over twenty years before the appearance of the first iPhone. We’ve seen in the RISC Wars Part 1: the Cambrian Explosion how, as the 1980s progressed, almost every semiconductor manufacturer and computer maker felt the need to have their own processor design. Apple was no exception.

Researchers have developed a way to address many quantum dots with only a few control lines using a chessboard-like method. This enabled the operation of the largest gate-defined quantum dot system ever. Their result is an important step in the development of scalable quantum systems for practical quantum technology.

Quantum dots can be used to hold qubits, the foundational building blocks of a quantum computer. Currently, each qubit requires its own addressing line and dedicated control electronics. This is highly impractical and in stark contrast with today’s computer technology, where billions of transistors are operated with only a few thousand lines.

Can we learn robot manipulation for everyday tasks, only by watching videos of humans doing arbitrary tasks in different unstructured settings? Unlike widely adopted strategies of learning task-specific behaviors or direct imitation of a human video, we develop a a framework for extracting agent-agnostic action representations from human videos, and then map it to the agent’s embodiment during deployment. Our framework is based on predicting plausible human hand trajectories given an initial image of a scene. After training this prediction model on a diverse set of human videos from the internet, we deploy the trained model zero-shot for physical robot manipulation tasks, after appropriate transformations to the robot’s embodiment. This simple strategy lets us solve coarse manipulation tasks like opening and closing drawers, pushing, and tool use, without access to any in-domain robot manipulation trajectories. Our real-world deployment results establish a strong baseline for action prediction information that can be acquired from diverse arbitrary videos of human activities, and be useful for zero-shot robotic manipulation in unseen scenes.

With careful development and thoughtful approaches, Kazi believes the changes AI will bring to healthcare will be some of the most exciting technological advances of our lifetime.

Learn more about building a modern data foundation to deliver the next generation of personalized healthcare with AI, ML, and generative AI.

Applying deep learning to large-scale genomic data of species or populations is providing new opportunities to understand the evolutionary forces that drive genetic diversity. This Review introduces common deep learning architectures and provides comprehensive guidelines to implement deep learning models for population genetic inference. The authors also discuss current opportunities and challenges for deep learning in population genetics.

A new nanoscience study led by a researcher at the Department of Energy’s Oak Ridge National Laboratory takes a big-picture look at how scientists study materials at the smallest scales.

The paper, published in Science Advances, reviews leading work in subsurface nanometrology, the science of internal measurement at the nanoscale level, and suggests quantum sensing could become the foundation for the field’s next era of discoveries. Potential applications could range from mapping intracellular structures for targeted drug delivery to characterizing quantum materials and nanostructures for the advancement of quantum computing.

“Our goal was to define the state of the art and to consider what’s been done and where we need to go,” said Ali Passian, an ORNL senior research scientist and senior author of the study.

A family of large-genome bacteriophages assembles a protective protein shell around its replicating DNA called the ‘phage nucleus’. Here, Enustun et al. use proteomics to identify a set of proteins associated with the phage nucleus that aid macromolecule transport through the nuclear shell.

Einstein’s theory of relativity say black holes are ‘bald’, but a new tweak to his research may give the mysterious objects their long-sought ‘hair.’

A study recently published in the journal Nanophotonics reveals that by rapidly modulating the refractive index – which is the ratio of the speed of electromagnetic radiation in a medium compared to its speed in a vacuum – it’s possible to produce photonic time crystals (PTCs) in the near-visible part of the spectrum.

The study’s authors suggest that the ability to sustain PTCs in the optical domain could have profound implications for the science of light, enabling truly disruptive applications in the future.

PTCs, materials in which the refractive index rises and falls rapidly in time, are the temporal equivalent of photonic crystals in which the refractive index oscillates periodically in space causing, for example, the iridescence of precious minerals and insect wings.