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Multimodal retrieval-augmented generation (RAG) is transforming how AI applications handle complex information by merging retrieval and generation capabilities across diverse data types, such as text, images, and video.

Unlike traditional RAG, which typically focuses on text-based retrieval and generation, multimodal RAG systems can pull in relevant content from both text and visual sources to generate more contextually rich, comprehensive responses.

This article outlines examples of where AI has been utilized to predict disease outbreaks and how AI models could help inform future strategies for controlling the spread of infectious diseases to prevent possible pandemics.

AI’s contribution to pandemic preparedness

In August 2024, the World Health Organization (WHO) updated its list of pathogens that could spark the next pandemic, which grew to include more than 30 pathogens. The microorganisms were selected based on available evidence showing them to be highly transmissible and virulent, with limited access to vaccines and treatments. While some pathogens on the list may never cause an epidemic, the growing number of pathogens of concern highlights the need for new tools to help predict and control the spread of infectious diseases.

But there’s a quantum catch.

John Larison’s new novel The Ancients suggests some societies are built for cataclysm.

Nvidia CEO Jensen Huang also announced new AI tools for creating autonomous agents during a keynote address at CES.

“The Chinese wisely recognize that if a country standardizes on China’s AI platform, it likely will continue to rely on that platform in the future,” Smith said.

The US should move quickly to promote its AI technology as superior and more trustworthy, enlisting allies in the effort, he recommended.

For its part, Microsoft is on pace to invest about $80 billion this year to build out AI datacenters, train AI models and deploy cloud-based applications around the world, according to Smith.

In “time expansion experiences,” time typically appears to expand by many orders of magnitude.

United Airlines is speeding up its timeline for connecting its fleet to Starlink, Elon Musk’s satellite network that provides high-speed internet around the globe.

The airline expects to begin testing Starlink in February with the first commercial flight anticipated in the spring on a United Embraer E-175 aircraft, used for regional flights. United plans to outfit its entire two-cabin regional fleet, about 200 planes, with the service by the end of 2025.

And United anticipates seeing its first Starlink-connected mainline aircraft take off before the end of the year.

Dipole toroidal modes are a unique set of excitations that are predicted to occur in various physical systems, ranging from atomic nuclei to metamaterials. What characterizes these excitations, or modes, is a toroidal distribution of currents, which results in the formation of vortex-like structures.

A classic example is smoke rings, the characteristic “rings” of smoke produced when puffs of smoke are released into the air through a narrow opening. Physics theories have also predicted the existence of toroidal dipole excitations in atomic nuclei, yet observing these modes has so far proved challenging.

Researchers at Technische Universitat Darmstadt, the Joint Institute for Nuclear Research, and other institutes recently identified candidates for toroidal dipole excitations in the nucleus ⁵⁸ Ni for the very first time. Their paper, published in Physical Review Letters, opens new possibilities for the experimental observations of these elusive modes in heavy nuclei.