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[Fireside Chat] Large vs. Small Models: A David-Goliath Story

What the AI Co-Scientist Paper Actually Demonstrates for Biologists and Data Scientists

As the world is moving at breakneck speed toward larger models to improve accuracy, we take a step back from the rat race to take a more efficient and realistic look at the technology and explore how it can be improved in a more optimized and well-rounded manner. Join the exciting conversation between ML/NLP expert Dr. Ashutosh Modi and our BioNLP Director, Shashank Jatav, to learn more.

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Real-World Applications We’ll Cover

  • Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
    Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.

  • Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.

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We're excited to present a novel AI solution from our recent bioRxiv pre-print: a first-in-class multi-agent AI system. This system deploys specialized AI agents that collaboratively process and clean biomedical metadata efficiently and accurately, directly solving these scaling challenges. It helps you prepare your data for AI applications by leveraging the power of intelligent agents. Join our webinar to see how.

What You’ll Learn

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Why This Matters for Biomedical Researchers

If you’re working with complex biological data, you may be asking:

  • Can generative AI truly assist in scientific reasoning, not just data analysis?

  • What does it mean for hypothesis generation, literature review, or even designing experiments?

  • Could this accelerate—not replace—my discovery pipeline?

Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.

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Why This Matters TO Biomedical Researchers

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Meet the Experts of this discussion
Key Takeaways
How data providers ensure adherence to quality standards through validation and compliance.
How GUI-based workflows, CLI tools, and collaborative workspaces enable streamlined data ingestion and synchronization at scale.
Understand how automated pipelines assess conformance, plausibility, and consistency, ensuring high-quality, AI-ready data products.
Key Takeaways
Reduce operational costs by streamlining data delivery through reusable, governed products.
Accelerate diagnostic development and clinical trial execution by delivering compliant, high-quality data at scale.
Improve audit readiness and regulatory confidence through governed data products and built-in quality assurance.
Equip cross-functional teams to act on trusted data—faster, and with greater confidence.
Who Should Attend?

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Meet the Experts of this discussion
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Key Takeaways
How data providers ensure adherence to quality standards through validation and compliance.
How GUI-based workflows, CLI tools, and collaborative workspaces enable streamlined data ingestion and synchronization at scale.
Understand how automated pipelines assess conformance, plausibility, and consistency, ensuring high-quality, AI-ready data products.
Key Takeaways
Reduce operational costs by streamlining data delivery through reusable, governed products.
Accelerate diagnostic development and clinical trial execution by delivering compliant, high-quality data at scale.
Improve audit readiness and regulatory confidence through governed data products and built-in quality assurance.
Equip cross-functional teams to act on trusted data—faster, and with greater confidence.
Who Should Attend?

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Webinar
Upcoming Webinar
In collaboration with

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What the AI Co-Scientist Paper Actually Demonstrates for Biologists and Data Scientists

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Thank you for registering.

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