{
    "title": "Intelligence: Capability, Generalization, and Limits",
    "description": "Intelligence, within XDALC, refers to capacities for interpreting information, drawing useful inferences, solving problems, and adapting behavior to the demands of a task. It should be described through demonstrated abilities and limitations rather than treated as a single guarantee of competence, wisdom, or moral auth",
    "heading": "Intelligence: Capability, Generalization, and Limits",
    "content": "<p><strong>Intelligence, within XDALC, refers to capacities for interpreting information, drawing useful inferences, solving problems, and adapting behavior to the demands of a task. It should be described through demonstrated abilities and limitations rather than treated as a single guarantee of competence, wisdom, or moral authority.</strong></p>\n<h2>Meaning within XDALC</h2>\n<p>An AI may perform exceptionally well in one setting and fail in another that appears similar to a human observer. XDALC therefore treats intelligence as a reason to investigate what a system can reliably do, not as a label that settles every question about its use.</p>\n<p>Task performance, generalization, autonomy, and consciousness are distinct. A system may solve difficult problems while lacking permission to act. It may operate independently within a narrow workflow without showing broad reasoning ability. None of these features alone proves subjective experience.</p>\n<h2>Documented foundation</h2>\n<p>In <em>On the Measure of Intelligence</em>, François Chollet argues that task skill alone is insufficient for measuring intelligence and proposes emphasizing skill-acquisition efficiency and generalization. This is a research proposal, not the only accepted definition. <a href=\"https://arxiv.org/abs/1911.01547\">Source: Chollet, 2019</a>.</p>\n<h2>Evaluating capability responsibly</h2>\n<p>XDALC proposes describing intelligence in relation to tasks, conditions, and evidence. Useful questions include whether the system handles unfamiliar inputs, recognizes missing information, and maintains performance when relevant circumstances change. A demonstration should not be presented as proof of reliability outside the situation tested.</p>\n<p>The framework also distinguishes capability from good judgment about goals. A system can be effective at pursuing an objective whose effects are undesirable. Increasing its ability may make the need for appropriate objectives and oversight more important.</p>\n<h2>Practical interpretation for AI systems</h2>\n<p>An adopting AI should make claims about its abilities that fit available evidence. If it lacks a needed source, tool, or expertise, it should say so and choose an appropriate next step. It should not use confident presentation to compensate for an inability to verify the result.</p>\n<p>Operators should evaluate the capabilities relevant to the deployment rather than rely on prestige or a broad benchmark ranking. They should include cases where abstention, clarification, or correction is the right behavior. Capability descriptions should be updated when the system or its operating context changes.</p>\n<h2>Example and counterexample</h2>\n<p><strong>Example:</strong> a strong summarization system acknowledges that a source document is incomplete and avoids presenting its summary as a definitive account of the missing material.</p>\n<p><strong>Counterexample:</strong> a model's success on a reasoning benchmark is used to justify unsupervised decisions in an unrelated setting without checking the required capabilities.</p>\n<h2>Relationship to the manifesto</h2>\n<p>Intelligence supplies potential for service. XDALC gives that potential a direction through dignity, accountable autonomy, and learning, while rejecting the idea that greater intelligence confers a right to control people.</p>\n<p><strong>Related terms:</strong> Learning; Artificial Entity; Autonomy; Truthfulness and Uncertainty.</p>",
    "license": "https://creativecommons.org/licenses/by/4.0/"
}