AI Model Simulates 500 Million Years of Evolution to create new Proteins! ESM3 is a LLM for Biology.

1. ESM3 simulates 500 million years of evolution to create new proteins.

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ESM3 leverages AI to simulate extensive evolutionary processes, enabling the creation of novel proteins with unprecedented control and efficiency.

• AI models like ESM3 can generate new proteins through simulated evolution equivalent to 500 million years of natural evolution.
• This approach offers a revolutionary method to design and engineer proteins with specific functions and properties.
• ESM3's ability to simulate evolution at scale provides scientists with a powerful tool for protein design and discovery.

2. Proteins are programmable entities with diverse functions.

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Proteins serve as programmable building blocks of life, exhibiting a wide range of functions crucial for various biological processes.

• Proteins play essential roles in biological systems, from powering motion to information processing.
• Ribosomes fabricate proteins based on RNA codes, highlighting the programmable nature of proteins.
• Understanding protein sequences, structures, and functions is key to unlocking their potential for diverse applications.

3. AI models like ESM3 revolutionize protein design through multimodal prompting.

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ESM3's ability to generate proteins in all three modalities - sequence, structure, and function - offers unprecedented control and innovation in protein design.

• ESM3's multimodal prompting allows for the creation of proteins with specific structures, functions, and sequences.
• This approach enables scientists to design proteins tailored for various applications with enhanced precision.
• The model's capacity to combine sequence, structure, and function provides a powerful tool for protein engineering.

4. AI models like ESM3 enable rapid and accurate protein design.

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ESM3's ability to achieve atomic-level accuracy in protein structure generation showcases the model's advanced capabilities in rapid and precise protein design.

• ESM3's capacity to generate proteins with high accuracy at the atomic level demonstrates its potential for challenging protein design tasks.
• The model's rapid generation of diverse protein candidates highlights its efficiency and effectiveness in protein engineering.
• ESM3's feedback mechanism enhances the quality of its protein generations, contributing to improved design outcomes.

5. OpenAI's commitment to open science accelerates progress in protein design.

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OpenAI's decision to release ESM3's code and model weights fosters collaboration, accelerates research, and maximizes positive impact in the scientific community.

• Sharing research and code promotes understanding, reduces risks, and enhances innovation in protein design.
• Open sourcing ESM3 encourages researchers to explore and build upon the model's capabilities for diverse applications.
• The open model approach aligns with OpenAI's vision of advancing science through collaborative efforts.

6. AI-driven protein design offers innovative solutions for various industries.

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ESM3's capabilities in protein design present groundbreaking opportunities for industries like biotechnology, healthcare, and environmental sustainability.

• The application of AI models like ESM3 in protein design can revolutionize drug development, environmental remediation, and biotechnological advancements.
• ESM3's potential to create custom proteins tailored for specific functions opens doors for novel solutions in diverse sectors.
• Industries can leverage AI-driven protein design to address complex challenges and drive innovation in various fields.

7. New AI chip claims to be fastest ever, surpassing Nvidia's GPUs.

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A company introduces a new AI chip that can run a 70 billion parameter model at over 500,000 tokens per second, outperforming Nvidia GPUs significantly.

• The new chip is specialized for running inference on Transformer models like GPTs.
• It is more efficient and cost-effective compared to Nvidia's latest Blackwell chip.
• This chip is a potential competitor for Nvidia in the AI hardware market.

8. Specialized AI chips offer superior performance for specific tasks.

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ASICs are designed for running inference on Transformer models like GPTs, providing optimized performance for popular AI applications.

• ASICs are not versatile like Nvidia GPUs but excel in specific AI model tasks.
• Popular models like GPTs, Gemini, and Sora benefit from Transformer architecture.
• These chips are faster and more cost-effective than general-purpose GPUs for AI tasks.