Bio x AI Hackathon

Our Vision, Mission and Desired Outcomes

The Bio x AI Hackathon represents a catalyst for the open-source revolution at the convergence of biotechnology and artificial intelligence. We've created this platform to foster a vibrant community where developers, researchers, and innovators can collaborate to build powerful tools and solutions that accelerate scientific discovery.

Through this hackathon, we aim to:

1. Accelerate Scientific Innovation: Identify and reward projects that have genuine potential to advance scientific research through AI-powered tools

2. Bridge Communities: Connect AI developers with the scientific research community to solve real-world problems facing researchers today

3. Democratize Access: Promote solutions that make scientific tools and data more accessible to researchers worldwide

4. Foster Open Collaboration: Establish sustainable open-source projects that will continue to evolve and serve the scientific community beyond this hackathon

5. Educate and Inspire: Raise awareness about the unique challenges and opportunities at the intersection of AI and academic research

Your role a hacker is critical to building projects that align with these goals and have the potential for lasting impact in the scientific community.

Hackathon Scope: Advancing Scientific Discovery through Knowledge Graphs

The Bio x AI Hackathon focuses specifically on scientific outcomes derived from knowledge graph creation and utilization. We aim to build tools that organize scientific information, generate meaningful connections between disparate research, and produce novel hypotheses that can advance scientific discovery. Projects should leverage the power of AI to extract, structure, analyze, and derive insights from scientific data, with a particular emphasis on basic and translational research.

Beyond "Fixing Science": Embracing Digitally Native Scientific Systems

We ask all participants to explicitly refrain from building solutions that merely "fix the current scientific system." While identifying flaws in existing scientific processes is valuable, the world is quickly moving past the capabilities of the traditional publishing systems. This hackathon aims to catalyze more fundamental innovation.

Instead, please envision and build truly digitally native systems for science—approaches that reimagine how scientific knowledge could be created, shared, verified, and built upon if designed from first principles for the digital age. These new paradigms should:

• Transcend legacy constraints rather than patching existing workflows

• Leverage the unique properties of knowledge graphs, blockchain, and AI that weren't available when current scientific systems were designed

• Prioritize interoperability and machine-actionability of scientific knowledge from the ground up

• Design for collaboration across disciplines, organizations, and computational systems

This orientation toward digitally native solutions will help ensure the hackathon generates truly transformative approaches rather than incremental improvements to fundamentally limited systems.

Expected Outputs: Eliza Plugins as Open Science Tools

Understanding the Eliza Framework (For Non-Technical Judges)

Eliza is an AI framework that allows researchers to interact with information and tools through natural language. Think of Eliza as an intelligent scientific assistant that can understand researchers' questions, access relevant data, run appropriate analyses, and present results in an understandable way.

The power of Eliza comes from its extensibility through "plugins" - specialized modules that add specific capabilities to the base system. This is similar to how you might download apps to your smartphone to give it new functionalities, or how a construction worker might have specific tools in their tool belt.

What Are Plugins and Why They Matter

Plugins are self-contained pieces of software that:

• Solve a specific scientific problem or provide a particular capability

• Connect to Eliza through standardized interfaces

• Can be developed independently but work together in the larger ecosystem

• Allow researchers to access specialized tools without technical expertise

For example, a plugin might enable Eliza to:

• Extract structured data from scientific papers

• Generate visualizations of complex biological pathways

• Connect to specific scientific databases or instruments

• Apply specialized analytical techniques to research questions

Hackathon Outputs as Plugins

We've specifically designed this hackathon to produce plugins because they are:

1. Appropriately Scoped: Typical plugins can be developed in a weekend given the right guidance and access to information, unlike full-scale platforms which would require much longer development cycles.

2. Standardized: By following consistent design patterns and interfaces, plugins can be readily integrated into the existing Eliza ecosystem. A large community of developers and support resources form a foundation for developers

3. Maintainable: Their focused functionality and modular nature make plugins easier to maintain, update, and improve over time.

4. Composable: Different plugins can be combined to create powerful workflows that are greater than the sum of their parts. Within the two-month timeframe of the hackathon, we expect hackers to not only build plugins, but experiment with chaining them together

Open Source Requirements

All hackathon submissions must adhere to open source principles:

• MIT License Requirement: All code must be released under the MIT License, which allows for maximum reuse, modification, and sharing while still providing attribution to the original creators.

• Public Repositories: Code must be hosted in public repositories (e.g., GitHub) with clear documentation.

• Transparency: Development processes, dependencies, and limitations should be clearly documented.

• Community Contribution: Projects should be structured to enable ongoing community contributions after the hackathon.

This open approach ensures that the tools developed during the hackathon can continue to evolve, be adopted by the broader scientific community, and make lasting impacts on how research is conducted.