The GAIA Vision

  • Perceived threat vs. real threat

    While the risk of adversarial superintelligent AI cannot be discarded, we focus on the immediate danger posed by billions of increasingly autonomous (yet unaccountable and misspecified) AI agents creating the conditions for systemic failure: unreliable AVs, drones and other robotics; crash-prone markets; more brittle supply chains; etc.

  • The danger of misspecification

    With ever more agentic AIs in place the main question will be: How do we specify goals and targets for these tools so that they are aligned with our, humanity’s, objectives? As most of these systems are functioning as blackboxes, the risk for cascading catastrophic effects is exploding with an increasing degree of autonomy and interconnectedness.

  • The solution

    To address these issues, the GAIA vision proposes a system of coordination and governance for autonomous, globally connected AIs by creating a shared, decentralized WWW of world models. By bootstrapping the GAIA network from safe neurosymbolic AI approaches we will eventually arrive in a complex web of models checking on each other.

How GAIA works

  1. The Repository: A Github for world models bootstrapped then growing over time

  2. Protocol: Defining the rules for communication, updating and behaviour of world models

  3. Decision Support: Extract information from the GAIA world models for important decisions in your organization

  4. Convergence: Akin to Darwinian processes models compete between each other for top score wrt. quality, accuracy and transparency

  5. Knowledge economy: The knowledge explosion ensues driven by increasing capabilities within a safe space bounded by GAIA

Why GAIA?

We are currently witnessing an unordered and unstructured system of AI models integrated into the fabric of society.
A system of models defined by opacity, volatility and bias:

  • Manual data curation and model updates

  • Slow processes and verification

  • Biased decisions and misalignment

The future is GAIA-esque:

  • Automated models updates and with self- and crosschecks

  • Scalable in any dimension: Data, model size, connectedness

  • Trustworthy and reliable world models

GAIA-enabled systemically safe ai

Model-based, real-time, explainable risk analysis based on Active Inference

  • First-principles metric "fully loads" uncertainty and risk

  • Stakeholders can see likely system trajectories, given current knowledge

  • Modelers can target effort towards most impactful model improvements

Cybernetic AI gatekeeper

  • Algorithmically "takes over" agent behavior if risk exceeds a threshold

  • Basis for transparent, trustworthy AI governance by the collective

The road ahead