inference training of LLM models is an active area of ai research. Is this training the key technology for self-teleoligical ai systems?
Inference-time training (or inference-time optimization) is indeed a critical frontier, but whether it is the "key" to self-teleological AI depends on how strictly we apply Aristotelian definitions.
While traditional inference is just "running" a fixed model, new techniques allow the AI to "think" and self-correct during the output process. This creates a synthetic teleology where the system appears to pursue a goal through its own internal adjustments.
- Why Inference-Time Training Mimics "Self-Teleology"
In an Aristotelian sense, a self-teleological being (like a plant or animal) has an internal principle of change. Inference-time training brings AI closer to this by moving from "static execution" to iterative self-refinement:
Internal Feedback Loops: Systems like OpenAI’s o1 use Chain-of-Thought (CoT) and reinforcement learning to self-correct and verify their own reasoning steps before finalizing an answer.
Plasticity During Use: Instead of only learning during a massive, external training phase, the model uses "compute-at-inference" to optimize its path toward a solution in real-time. Emergent Intentionality: By evaluating its own intermediate outputs against a reward signal, the AI exhibits goal-directed behavior that isn't hard-coded as a specific instruction, but emerges as a strategy to reach the "best" outcome.
- The Aristotelian Counter-Argument: Still an Artifact?
Despite these advances, most philosophers argue this is still extrinsic teleology (purpose imposed from without) for two reasons:
- The Reward Function is External: The "goal" the AI pursues during inference is still defined by a human-designed reward function. The AI does not choose to want to be accurate; it is mechanically optimized to satisfy an external metric. Instrumental vs. Natural: An Aristotelian substance has a telos that serves its own flourishing (e.g., a cat hunting to stay alive).
- An AI's "thinking" during inference serves a human-directed task, making its purpose instrumental rather than natural.
- The "Key" Technology?
Inference-time training is the key to Autonomy, but perhaps not yet to Substance. It allows the artifact to operate without a "babysitter," but the "internal principle of change" is still a simulated one, triggered by external prompts rather than an internal drive for existence.
Do you think a system that can re-program its own reward function would finally cross the line from "complex artifact" to "Aristotelian substance"?