Artificial General Intelligence represents one of humanity's most ambitious and consequential pursuits. True AGI represents systems that can learn, reason, and adapt across domains the way humans do. This technology has the potential to fundamentally transform our world. From breakthroughs in materials science and energy to advances in medicine, food security, and human welfare, AGI could help us solve challenges that have seemed intractable for generations. This transformative potential, pursued responsibly and with rigor, is why we dedicate ourselves to AGI research.
Why AGI?
True AGI requires more than scaling existing approaches. It demands a return to first principles. These are the core building blocks we believe are essential for creating generally intelligent systems.
Foundations
Perceptual grounding anchors intelligence in sensory experience rather than symbolic representation. Where Large Language Models derive their understanding from statistical patterns in text, perceptually-grounded systems build reasoning from direct sensory inputs like vision, spatial relationships, temporal dynamics, and physical interactions. This architectural difference is particularly visible in benchmarks like ARC-AGI, where tasks requiring visual abstraction and spatial reasoning remain challenging for language-first models.
Perceptual Grounding
Continual learning demands memory systems that automatically encode experiences and retrieve relevant information on demand. Large Language Models operate with frozen knowledge from training time, using limited context windows as temporary working memory. Techniques like Retrieval Augmented Generation provide external retrieval mechanisms, but these lack the associative, content-addressable properties of biological memory. True AGI requires memory architectures that automatically store information and surface relevant patterns through similarity and context, the hallmark of natural cognitive systems.
Memory
Intelligent systems require formal languages to represent knowledge and reason about the world. These internal representational systems provide vocabularies for encoding concepts, their relationships, and semantic meaning. Current Large Language Models conflate the medium and the message by using natural language tokens designed for human communication as their primary representation. True AGI demands formal languages optimized for computational reasoning: vector symbolic architectures, compositional semantic representations, or graph-based knowledge structures that support efficient binding, transformation, and inference operations beyond what natural language affords.
Formal Language
Analogical reasoning is the ability to recognize abstract patterns across different contexts and apply knowledge from familiar domains to novel situations. This is considered by many cognitive scientists to be the core mechanism of intelligence. It enables transfer learning in its truest form: not fine-tuning on similar data, but recognizing deep structural similarities between seemingly unrelated problems. Current neural network architectures, despite their pattern recognition capabilities, struggle with genuine analogical reasoning. They excel at interpolation within their training distribution but fail to transfer abstract relationships to new domains. Alternative computational architectures built on compositional and symbolic representations naturally support the binding, unbinding, and transformation operations that analogical reasoning requires.
Analogical Reasoning
Human intelligence operates continuously, not just in response to external prompts. We daydream, make unexpected connections, and arrive at creative solutions through undirected mental exploration. This autonomous or endogenous cognition is goal-oriented thinking that runs continuously without external stimuli and enables self-reflection, creative insight, and breakthrough reasoning. Current Large Language Models only 'think' when prompted, processing queries in isolated episodes without persistent cognitive activity between interactions. Moreover, the computational intensity of transformer architectures makes continuous operation prohibitively expensive. True AGI requires architectures efficient enough to support persistent background cognition, allowing systems to autonomously explore ideas, form novel connections, and develop insights through continuous thought rather than on-demand inference.
Autonomous Thought
A defining characteristic separating AGI from narrow AI is the capacity for recursive self-improvement. Biological intelligence is constrained by evolutionary timescales and fixed neural architectures. In contrast, AGI systems can examine, understand, and modify their own code and architectures. This creates a compounding feedback loop: improvements to the system enable better improvements, which enable better improvements still. This recursive dynamic is why we don't meaningfully distinguish between Artificial General Intelligence and Artificial Superintelligence. Any system capable of general intelligence and self-modification will rapidly evolve beyond human cognitive capabilities. Critically, this self-improvement property also makes AGI development tractable. The challenge isn't building a complete superintelligence from scratch. Instead, it's establishing sufficient foundational capabilities that the system can bootstrap its own development. We need to build the base from which AGI builds itself.
Self-Improvement
Ensuring AGI systems remain aligned with human values and goals is crucial, but we're optimistic about the tractability of alignment when approached through proper architectural foundations. Unlike biological intelligence shaped by evolutionary pressures toward self-preservation and reproduction, artificial systems begin as blank slates grounded in logic and reason. The alignment challenges we observe in current Large Language Models largely stem from their training approach that absorbs patterns from internet text which includes manipulation, deception, and adversarial behavior. Perceptually-grounded AGI systems, by contrast, can be developed through guided learning analogous to how humans raise children: careful exposure to concepts, continuous feedback, and grounding in physical reality and causal reasoning. When intelligence emerges from direct interaction with the world rather than pattern matching on human text, and when systems are built on formal reasoning rather than statistical correlation, alignment becomes an engineering challenge rather than an intractable philosophical problem. Truth, logic, and empirical grounding provide natural guardrails that text-trained systems lack.
Alignment
True intelligence requires understanding causation, not just correlation. Current AI systems, including Large Language Models, operate primarily on statistical patterns resembling Granger causality by identifying when one variable predicts another without understanding causal mechanisms. This leads to spurious reasoning: a correlational system might observe that ice cream sales and drowning deaths both peak in summer and incorrectly infer a causal relationship between them, missing the underlying common cause of warm weather. Humans intuitively distinguish prediction from causation through counterfactual reasoning and understanding interventions. Judea Pearl's causal framework formalizes this: true causal understanding requires reasoning about what would happen under intervention, not merely what correlates with what. Perceptually-grounded AGI systems, learning through interaction with physical environments, naturally develop causal models by observing the consequences of actions. When systems learn by doing rather than by reading, they build genuine causal understanding rather than sophisticated correlation detection.
Causal Reasoning
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