Multi Agent & Multi Modal AI does Physics (MIT)

New Multi Agent, Multi Modal AI research by MIT: AtomAgents. Exploring Physics Through a Network of AI Agents, new AI research for material science by MIT.
Let's Do Physics w/ Network of AI Agents, to discover new materials, done by Massachusetts Institute of Technology (MIT).
AtomAgents, a generative AI platform aimed at revolutionizing the process of alloy design and analysis. This platform utilizes a multi-agent system where each AI agent specializes in specific tasks such as knowledge retrieval, multimodal data integration, and physics-based simulations. AtomAgents leverages large language models and domain-specific AI to enhance predictive accuracy and reduce the need for human intervention, aiming to accelerate the materials design process significantly.
In its operational framework, AtomAgents employs a collaborative approach among multiple AI agents to handle diverse tasks from data processing and simulation execution to result analysis. This setup allows the system to utilize advanced machine learning models, including deep surrogate models that link material properties with structural and chemical features. The system demonstrates its utility by autonomously designing metallic alloys with superior properties compared to traditional materials. By doing so, AtomAgents showcases potential applications across various sectors, including biomedical materials engineering, renewable energy, and environmental sustainability, highlighting its ability to drive innovation in materials science.
Key contributions of AtomAgents are the integration of AI with detailed physics-based modeling and the capability to manage and analyze multimodal data. These advancements facilitate a deeper understanding of material properties and behaviors, enhancing the efficiency of design processes. By integrating AI-driven innovations with traditional scientific approaches, AtomAgents not only promises to accelerate the discovery and development of new materials but also aims to reduce the environmental impacts associated with material production.
All rights w/ authors:
AtomAgents: Alloy design and discovery through
physics-aware multi-modal multi-agent artificial intelligence
arxiv.org/pdf/...
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[@danberm1755]

I bet that there's a way to boost these significantly. Keep in mind that GPT 4 can actually pass standardized tests quite well.

[@andydataguy]

Found it 💜 this paper is underrated AF

[@jackflash6377]

Well done and informative. Thank you.

[@LaboriousCretin]

Good video. Thank you for sharing.

[@wwkk4964]

14% or 1 in 7. Maybe it can work on tasks that would be done on a sunday by humans?

[@densonsmith2]

Since we see that using slightly worse LLM or VLM makes the overall system much worse it seems likely if the LLM or VLM is slightly better the success rate of the overall system might improve by a lot?

[@xhridhar]

We will have to wait a few months or more I guess for a model that will return 95% or more success rate . Only at that rate of success , it can be in a production environment for complex tasks

[@TechnoMageCreator]

Not really, I feel using chatGPT 4 Turbo instead of Claude 3.5 and other AI in parallel and combine and reiterate answers that would have brought a much greater results

[@joehopfield]

Physics informed neural networks + management informed neural networks. Love it.
But no match for the destructive power of middle management and executive suite neural networks . 😢