Advances in technology raise questions about the need to send people to space — and the risks and cost.
Category: robotics/AI – Page 98
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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.
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.
At CES 2025, Nvidia CEO Jensen Huang kicks off CES, the world’s largest consumer electronics show, with a new RTX gaming chip, updates on its AI chip Grace Blackwell and its future plans to dig deeper into robotics and autonomous cars.
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Nvidia’s new chips target the gaming, robotics, and autonomous vehicles industries, partnerships with Toyota and others highlight Nvidia’s AI expansion in self-driving technology.
A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost
Posted in biotech/medical, information science, robotics/AI | Leave a Comment on A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost
Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.
Join NVIDIA CEO Jensen Huang at CES on January 6 at 6:30 p.m. PT for an exclusive look at groundbreaking innovations in AI, gaming, robotics, and beyond.
Google is establishing a team to build AI that can simulate the physical world, led by one of the former co-leads on OpenAI’s Sora model.