Research

Research Vision

At Tidal, our research is driven by a singular vision: to create steerable and trustworthy collective systems where humans and AI collaborate seamlessly, enhancing decision-making and fostering innovation. Our research efforts are tightly integrated with our product development, each informing and advancing the other.

We're not just building a product; we're pushing the boundaries of what's possible in collective intelligence and AI alignment.

Core Research Areas

1. Collective AI Systems

Our research in collective AI systems forms the backbone of Tidal's Foundational Model scaffolding and agent management system. We're exploring how multiple AI agents can work together effectively, mimicking and enhancing human collective intelligence.

Key research questions include:

• How can we optimize information flow between multiple AI agents?
• What mechanisms enable effective task distribution and coordination in multi-agent systems?
• How do we balance specialization and generalization in a collective AI system?

This research directly feeds into the development of Tidal's core infrastructure, enabling users to interact with multiple AI agents simultaneously and efficiently.

2. Human-AI Collaboration

As we develop Tidal's interface and interaction models, our research in human-AI collaboration is crucial. We're investigating how to create synergistic relationships between human intelligence and AI capabilities.

Our focus areas include:

• Designing intuitive interfaces for complex multi-agent interactions
• Developing models for effective task handoff between humans and AI agents
• Investigating trust-building mechanisms in human-AI collaborative systems

These insights are vital for ensuring that Tidal not only manages AI agents but also facilitates meaningful collaboration between humans and AI.

3. Multi-Agent Learning Algorithms

Our research into multi-agent learning algorithms is key to realizing Tidal's vision of a self-improving AI ecosystem. We're drawing inspiration from cultural evolution and social learning theories to develop novel approaches to multi-agent learning.

Current research directions include:

• Developing algorithms for knowledge sharing and collective learning among AI agents
• Investigating the emergence of 'cultural artifacts' in AI systems
• Creating adaptive learning mechanisms that balance individual and collective knowledge

This research underpins Tidal's ability to create a system of Foundational Models that self-learn over time, improving inter-Foundational Model coordination and overall system performance.

4. Geometric Deep Learning

Our work in geometric deep learning is crucial for developing Tidal's principle graph and improving the interpretability of our AI systems. We're exploring how to represent complex relational data and decision-making processes in ways that are both computationally efficient and humanly interpretable.

Key areas of investigation include:

• Developing graph-based representations of principles and decision-making processes
• Creating algorithms for reasoning over principle graphs
• Investigating methods for translating between natural language and geometric representations

This research is essential for creating the interpretable systems at the heart of Tidal, making AI decision-making processes more transparent and understandable.

5. AI Alignment

Our research in AI alignment is fundamental to Tidal's ultimate goal of creating trustworthy and beneficial AI systems. We're investigating how to ensure that AI systems behave in ways that are consistent with human values and intentions.

Our research in this area includes:

• Developing formal models of human values and preferences
• Creating mechanisms for value learning in multi-agent systems
• Investigating robustness and corrigibility in AI systems

This research is crucial for realizing Tidal's vision of an alignment system where transparency and cooperation are optimized, ensuring that our AI systems remain beneficial as they grow more powerful.

Theory of Change

Our research is guided by a clear theory of change: by advancing our understanding and capabilities in these key areas, we can create AI systems that are not just powerful, but also aligned, interpretable, and collaborative. We believe that true progress in AI requires not just technological advancement, but also a deep consideration of how these technologies integrate with and benefit human society.

Through our research:

1. We aim to demonstrate that collective AI systems can outperform individual AI agents, much as human collective intelligence often surpasses individual human intelligence.
2. We seek to prove that transparency and interpretability are not just ethical imperatives, but also key to building more capable and trustworthy AI systems.
3. We strive to show that alignment and capability are not trade-offs, but mutually reinforcing goals in AI development.

By pursuing these research directions and embodying our findings in the Tidal product, we're working towards a future where AI systems are not black boxes or potential threats, but transparent, cooperative partners in tackling complex challenges and advancing human knowledge and capabilities.