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Category: robotics/AI – Page 368

SDT, Anyon Technologies Plan Collaboration on 20-Qubit Quantum System

The initial product from this collaboration will be a 20-qubit system integrated with NVIDIA’s Grace Hopper Superchip, facilitating hybrid quantum-classical computing. This integration is expected to drive advancements in various fields, including financial services and artificial intelligence.

Through this joint venture, SDT and Anyon Technologies aim to establish a unique and robust partnership in the Asian quantum computing sector, leveraging their combined expertise to lead the commercialization and supply of superconducting quantum computers in the region.

The future of optical modulators and integrated photonics

Despite being a mature technology in existence for over several decades, silicon photonic modulators face scrutiny from industry and academic experts. In a recent editorial interview, experts emphasize the need to explore alternatives beyond the traditional platforms. The discussion centers on innovative modulator materials and configurations that could cater to emerging applications in data centers, artificial intelligence, quantum information processing, and LIDAR. Experts also outline the challenges that lie ahead in this field.

Optical and photonic modulators are technologically advanced devices that enable the manipulation of light properties—such as power and phase—based on input signals. Over the decades, scientists have researched and developed silicon photonic modulators with wide-ranging applications, including optical data communication, sensing, biomedical technologies, automotive systems, astronomy, aerospace, and artificial intelligence (AI).

However, these modulators face bandwidth limitations and operational robustness issues stemming from the fundamental properties of silicon and other practical constraints, as highlighted by a panel of leading industry and academic experts in a recent editorial interview.

Adjusting accelerators with help from machine learning

Banks of computer screens stacked two and three high line the walls. The screens are covered with numbers and graphs that are unintelligible to an untrained eye. But they tell a story to the operators staffing the particle accelerator control room. The numbers describe how the accelerator is speeding up tiny particles to smash into targets or other particles.

However, even the best operator can’t fully track the miniscule shifts over time that affect the accelerator’s machinery. Scientists are investigating how to use computers to make the tiny adjustments necessary to keep particle accelerators running at their best.

Researchers use accelerators to better understand materials and the particles that make them up. Chemists and biologists use them to study ultra-fast processes like photosynthesis. Nuclear and high energy physicists smash together protons and other particles to learn more about the building blocks of our universe.

ACT Study contributes to understanding Alzheimer’s disease in brain cells

Using data and samples from volunteers, including Kaiser Permanente Washington members participating in the Adult Changes in Thought Study (ACT Study), the researchers used advanced genomic technologies and machine learning models to create a timeline of the cellular and molecular changes caused by…

Mapping the disease at the cellular level identifies possible new treatment targets.

Machine learning and supercomputer simulations predict interactions between gold nanoparticles and blood proteins

Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have used machine learning and supercomputer simulations to investigate how tiny gold nanoparticles bind to blood proteins. The studies discovered that favorable nanoparticle-protein interactions can be predicted from machine learning models that are trained from atom-scale molecular dynamics simulations. The new methodology opens ways to simulate the efficacy of gold nanoparticles as targeted drug delivery systems in precision nanomedicine.

Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano–bio interface.

Modeling the properties of the nano-bio interface is demanding since the important processes such as electronic charge transfer, or restructuring of the biomolecule surface can take place in a wide range of length and time scales, and the atomistic simulations need to be run in the appropriate aqueous environment.

The Seductive Promise of Love on Demand | Posthuman with Emily Chang

We are now more connected than ever, but also more lonely. Could AI companionship be the cure? In this episode, Emily Chang explores the future tech behind a growing market of relationships-on-demand.

Technology that once seemed like science fiction is rapidly becoming reality, transforming the very essence of our existence. In this four-part series, Emily Chang unravels the future of being human in an age of unprecedented innovation.

Watch more of Posthuman with Emily Chang: • Posthuman with Emily Chang.

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Intelciety: Intelligent Society. Are we up for the challenge?. The book “Intelciety. Intelligent Society. Are We Ready for the Challenge?” explores th

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#AI #ExponentialTechnologies

The book “Intelciety. Intelligent Society. Are We Ready for the Challenge?” explores the profound changes that artificial intelligence (AI) and other emerging technologies are causing in modern society. Vicente Ferreira da Silva addresses how these technologies are transforming various fields, from medicine and biotechnology to robotics and nanotechnology, and questions whether we are truly prepared to deal with these advances.

A ChatGPT-Like AI Can Now Design Whole New Genomes From Scratch

Called Evo, the AI was inspired by the large language models, or LLMs, underlying popular chatbots such as OpenAI’s ChatGPT and Anthropic’s Claude. These models have taken the world by storm for their prowess at generating human-like responses. From simple tasks, such as defining an obtuse word, to summarizing scientific papers or spewing verses fit for a rap battle, LLMs have entered our everyday lives.

If LLMs can master written languages—could they do the same for the language of life?

This month, a team from Stanford University and the Arc Institute put the theory to the test. Rather than training Evo on content scraped from the internet, they trained the AI on nearly three million genomes—amounting to billions of lines of genetic code—from various microbes and bacteria-infecting viruses.

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