CT 00 - Laptörténet

Jkv1: /* Title */

2026-01-09T05:13:00Z

‎Title

Jkv1: /* Chapter#.8.4. Conversations with LLMs */

2026-01-09T05:08:13Z

‎Chapter#.8.4. Conversations with LLMs

Jkv1, 2025. október 28., 03:18-n

2025-10-28T03:18:32Z

Jkv1, 2025. szeptember 30., 00:52-n

2025-09-30T00:52:12Z

Jkv1: /* Chapter#.8.3. References */

2025-09-30T00:29:22Z

‎Chapter#.8.3. References

Jkv1: /* Chapter#1.4. Utilities (estimation of informational added-values) */

2025-04-07T11:50:44Z

‎Chapter#1.4. Utilities (estimation of informational added-values)

Jkv1: /* Chapter#1.4. Utilities (estimation of informational added-values) */

2025-04-07T11:42:12Z

‎Chapter#1.4. Utilities (estimation of informational added-values)

Jkv1: /* Chapter#2.3. KPIs */

2025-04-07T10:22:15Z

‎Chapter#2.3. KPIs

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

2025-04-07T10:18:33Z

‎Chapter#2.2. Proving, goodness, objectivity

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

2025-04-07T10:07:50Z

‎Chapter#2.2. Proving, goodness, objectivity

@@ 8. sor: / 8. sor: @@
   + https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx)
   + https://miau.my-x.hu/temp/2025tavasz/rules/ (steps toward robot-lector:-)
 =Title=
 Which concepts can be verified based on partial data about log-information in an e-car?
 =Subtitle=
 (or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)

← Régebbi változat		A lap 2026. január 9., 05:08-kori változata
186. sor:		186. sor:

	==Chapter#.8.4. Conversations with LLMs==		==Chapter#.8.4. Conversations with LLMs==
		+	Each final thesis have to present an annex with the appropriate LLM-documentation: exact prompts + exact LLM-outputs for relevant parts of the development

@@ 7. sor: / 7. sor: @@
   especially: https://miau.my-x.hu/bprof/kje_bprof_szakdolgozat_specialitasok.docx
   + https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx)
 =Title=

@@ 4. sor: / 4. sor: @@
   History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
   History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history
 =Title=

@@ 174. sor: / 174. sor: @@
 ==Chapter#.8.2. Figures==
 ==Chapter#.8.3. References==
 ==Chapter#.8.4. Conversations with LLMs==

@@ 77. sor: / 77. sor: @@
 **Costs: basically wages (where employees/experts are writting messages for the social media)
 **Impacts: in ideal case, the share of the particular university is not decreasing compared to the competitive institutions
-**Expectation: the
+**Expectation: the income through the human activities must be higher than the costs of the human activities, atl least zero (0 EUR)
 *AI-driven support:
-**Costs: redurced wages
+**Costs: redurced wages, but licence fees for AI (concept testing) - human experts produce concepts based on the particular data, robots are verifying concepts
-**Impacts: in ideal case, the share of the particular university is massive increasing compared to the competitive institutions
+**Costs of the AI-oriented development (10.000 EUR/licence)
 ==Chapter#1.5. Motivation==

@@ 72. sor: / 72. sor: @@
 There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
 Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)
 ==Chapter#1.5. Motivation==

@@ 132. sor: / 132. sor: @@
 *...
 All matching-oriented KPIs are relevant!
 Numeric KPIs:
 *sum of absulote difference between facts and estimations:
 *sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
 *correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
 *...
 All numeric KPIs are relevant!

← Régebbi változat		A lap 2025. április 7., 10:18-kori változata
118. sor:		118. sor:

	Source#7:		Source#7:
		+	*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
		+	*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
		+	''"Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
		+	''" Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
		+	*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
		+	*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
		+	The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

	==Chapter#2.3. KPIs==		==Chapter#2.3. KPIs==

@@ 111. sor: / 111. sor: @@
 Source#6:
 Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
 ''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.