Separation Before Depth
An exploration of why continuous learning requires architectural separation of new and old knowledge, rather than just temporal depth.
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Progress in the Open
Understanding new forms of intelligence requires openness and dialogue.
Our resources bring together research signals, applied insights, and industry perspectives
as we continue developing continuous learning systems in real environments.
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Intelligence as a Temporal Process
Intelligence is not a static property that a system either has or lacks. It is a process with a temporal structure. We explore how systems relate to their own past, present, and future.
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Learning From the World Versus Learning About the World
Three approaches to building more general artificial intelligence are now visible, but they diverge on whether learning should flow from building a rich pretrained world model or from interactive consequence.
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Why the Search for More General Intelligence Likely Requires an Idea Beyond Deep Learning
Intelligence is a process embedded in time: prediction under consequence. We explore why more general forms of artificial intelligence will likely require architectural properties that couple learning directly to ongoing interaction with changing environments.
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When Change Happens: Understanding Drift in Learning Systems
Learning systems face challenges when the statistical patterns of the world evolve faster than the model can adapt. We explore data, label, and concept drift.
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Stateful vs. Stateless Systems - Why the Distinction Matters for Artificial Intelligence
Understanding the distinction between stateful and stateless systems helps clarify an important architectural question in artificial intelligence.
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Aleatoric and Epistemic Uncertainty in Continual Learning Systems
Treating uncertainty as a design constraint is essential for continually learning systems. A system that updates its internal state over time cannot postpone uncertainty until after the fact.
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Why Intelligence Cannot Rely Only on What It Learned Before
An intelligent system shouldn't necessarily require static, pre-programmed knowledge to solve novel future problems.
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What “Safe” Means for a System That Never Stops Updating
In systems that learn continuously, safety must be evaluated differently. It must apply to the mechanism of change itself, rather than just a favorable snapshot of present behavior.
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The Analog Nature of Intelligence
Biological intelligence emerged inside continuous signals and causal time. We explore what the digital, discrete abstractions of modern machine learning assume away.
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When the Rules Move
Prediction becomes exponentially harder when the underlying rules shift frequently. Continuous coupling to consequence is an architectural necessity, not just a feature.
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Why Intelligence Evolved as a Prediction Mechanism
Intelligence is best understood as action-conditioned prediction under consequence. Discover what this framing implies for the future of AI architecture.
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What Continual Learning Actually Means - and What It Doesn’t
A first-principles clarification of continual learning as a runtime property: selective adaptation under consequence, distinct from retraining cycles, scale, or constant parameter drift.
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Why Generalization Is Not the Same as Intelligence
Strong generalization can still fall short of true intelligence. We examine the critical difference between representational breadth and adaptive coupling to consequence over time.
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Why Intelligence Exists - and What That Means for AI
A first-principles exploration of why intelligence emerged, how prediction and resource allocation shape adaptive behavior, and what this implies for the future of artificial intelligence.
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