محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | Recurso digital |
| اللغة: | الإنجليزية |
| منشور في: |
Zenodo
2026
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.5281/zenodo.19604647 |
| الوسوم: |
إضافة وسم
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جدول المحتويات:
- <p class="AbstractLabel">Abstract</p> <p>This paper examines the information provided by an AI Chatbot. The starting point is Shannon’s technical concept of information, which treats the message <em>y</em> ∈ as a formal selection from a set of possible responses to a prompt <em>x</em> ∈ . The relevant statistical quantities are the conditional self-information <em>i</em>(<em>y|x</em>) and, on average, the conditional entropy <em>H</em>(<em>Y|X </em>= <em>x</em>), where <em>X </em>and <em>Y </em>denote the random variables of the prompt and the response. Landauer’s principle links information theory with thermodynamics; through the concept of entropy, it forms a bridge between information and energy in the form of a thermodynamic lower bound on heat for logically irreversible processes. This thermodynamic minimum energy is vanishingly small compared to the actual production energy <em>E</em><sub>prod</sub>(<em>x</em>,<em>y</em>) of the response <em>y </em>on the provider’s side, which in turn represents only one component of the production costs <em>K</em><sub>prod</sub>(<em>x</em>,<em>y</em>) alongside computing time, storage, infrastructure and proportionate system costs. Using the example <em>x </em>= ‘Name a prime number less than 10’, the transition from the theoretical information value of a specific response <em>y </em>to a rough price estimate for an AI chat is illustrated.</p>