Modeling Ecosystems - Revision history

Tom: /* Scaling */

2025-02-28T22:23:10Z

‎Scaling

Tom: /* All Models are Wrong */

2024-10-26T19:18:49Z

‎All Models are Wrong

Tom: /* Scaling */

2024-10-26T19:15:30Z

‎Scaling

Tom: /* All Models are Wrong */

2024-10-26T19:15:13Z

‎All Models are Wrong

Tom: /* All Models are Wrong */

2024-10-26T19:12:33Z

‎All Models are Wrong

Tom: /* Scaling */

2024-10-26T19:11:42Z

‎Scaling

Tom: /* All Models are Wrong */

2024-10-26T19:02:54Z

‎All Models are Wrong

Tom: /* All Models are Wrong */

2023-08-03T22:17:26Z

‎All Models are Wrong

Tom: /* All Models are Wrong */

2023-08-03T21:52:15Z

‎All Models are Wrong

Tom: /* All Models are Wrong */

2023-04-02T18:52:53Z

‎All Models are Wrong

@@ Line 14: / Line 14: @@
 ===Scaling===
 Some abstract models, like fractals, work well at all scales. But when we are looking for verisimilitude in the models of individuals, like leaves, or hair, they become unimaginable computationally expensive. Tim Sweeney of Epic Games put it this way:<ref>Anna Wiener, ''Get Real''  The New Yorker p40ff (2924-04-22) </ref><blockquote>When you're standing on a mountaintop, looking out into the distance you're seeing the effect of trillions of leaves of trees. In the aggregate they don't behave as ordinary solid objects. At a certain distance, trees become sort of transparent. When you look at the real world and see all the areas where computer graphics are falling short of the real world, you tend to realize we have a lot of work yet do do."</blockquote>
-See the wiki page on [[Phase Transitions]] for how aggregates can model, for example, the flight of starlings.
+See the wiki page on [[Phase Transitions]] for how aggregates can model [[Reality]], for example, the flight of starlings.
 ==References==

@@ Line 13: / Line 13: @@
 Artificial Intelligence (AI) has been an experimental science only since 1960 and so has not produced models of intelligence that are particularly close to the real thing. Brian Christian described "the Alignment Problem"<ref>Brian Christian ''the Alignment Problem'' W. W. Norton (2020-10-06) ISBN 978-0-393-86833-3</ref> as the difficulty that AI has with aligning the results of providing an AI with the results obtained. It is worse than you might imagine. When [[Machine Learning]] is introduced into society before it has been fully vetted, we must expect that the results will not align with human values. By 2016 the US already had an AI deployed for predicting the future of criminals what was blatantly racist<ref> Julia Angwin +3, '' Machine Bias'' ProPublica (2016-05-23) https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing</ref> But since the AI was trained on real-word data, it was just doing the same thing any any human would do given the same inputs since society as a whole was also racist. The point here is that both the racist profiler and the physics standard model are doing the same thing. it just that one system is more regular than the other.
 ===Scaling===
-Some abstract models, like fractals, work well at all scales. But when we are looking for verisimilitude the models of individuals, like leaves, or hair, becomes unimaginable computationally expensive. Tim Sweeney of Epic Games put it this way:<ref>Anna Wiener, ''Get Real''  The New Yorker p40ff (2924-04-22) </ref><blockquote>When you're standing on a mountaintop, looking out into the distance you're seeing the effect of trillions of leaves of trees. In the aggregate they don't behave as ordinary solid objects. At a certain distance, trees become sort of transparent. When you look at the real world and see all the areas where computer graphics are falling short of the real world, you tend to realize we have a lot of work yet do do."</blockquote>
+Some abstract models, like fractals, work well at all scales. But when we are looking for verisimilitude in the models of individuals, like leaves, or hair, they become unimaginable computationally expensive. Tim Sweeney of Epic Games put it this way:<ref>Anna Wiener, ''Get Real''  The New Yorker p40ff (2924-04-22) </ref><blockquote>When you're standing on a mountaintop, looking out into the distance you're seeing the effect of trillions of leaves of trees. In the aggregate they don't behave as ordinary solid objects. At a certain distance, trees become sort of transparent. When you look at the real world and see all the areas where computer graphics are falling short of the real world, you tend to realize we have a lot of work yet do do."</blockquote>
 See the wiki page on [[Phase Transitions]] for how aggregates can model, for example, the flight of starlings.

@@ Line 14: / Line 14: @@
 ===Scaling===
 Some abstract models, like fractals, work well at all scales. But when we are looking for verisimilitude the models of individuals, like leaves, or hair, becomes unimaginable computationally expensive. Tim Sweeney of Epic Games put it this way:<ref>Anna Wiener, ''Get Real''  The New Yorker p40ff (2924-04-22) </ref><blockquote>When you're standing on a mountaintop, looking out into the distance you're seeing the effect of trillions of leaves of trees. In the aggregate they don't behave as ordinary solid objects. At a certain distance, trees become sort of transparent. When you look at the real world and see all the areas where computer graphics are falling short of the real world, you tend to realize we have a lot of work yet do do."</blockquote>
-See the wiki page on [[Phase Transitions]] for how aggregates can be model, for example, the flight of starlings.
+See the wiki page on [[Phase Transitions]] for how aggregates can model, for example, the flight of starlings.
 ==References==

@@ Line 14: / Line 14: @@
 ===Scaling===
 Some abstract models, like fractals, work well at all scales. But when we are looking for verisimilitude the models of individuals, like leaves, or hair, becomes unimaginable computationally expensive. Tim Sweeney of Epic Games put it this way:<ref>Anna Wiener, ''Get Real''  The New Yorker p40ff (2924-04-22) </ref><blockquote>When you're standing on a mountaintop, looking out into the distance you're seeing the effect of trillions of leaves of trees. In the aggregate they don't behave as ordinary solid objects. At a certain distance, trees become sort of transparent. When you look at the real world and see all the areas where computer graphics are falling short of the real world, you tend to realize we have a lot of work yet do do."</blockquote>
 ==References==

@@ Line 9: / Line 9: @@
 ==All Models are Wrong==
-The professor of statistics George Box noted<ref>George E. P. Box +2, ''Statistics for Experimenters'' Wiley (1978)  ISBN: ‎ 978-0471093152 </ref> that "All models are wrong; but some models are useful." In physics there as been sufficient modeling since Newton to narrow the discrepancy of reality and the model to a very small percentage. But that does not imply that they are correct, just useful. Box also noted:<blockquote>Since all models are wrong the scientist cannot obtain a "correct" one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so over-elaboration and over-parameterization is often the mark of mediocrity.</blockquote>
+The professor of statistics George Box noted<ref>George E. P. Box +2, ''Statistics for Experimenters'' Wiley (1978)  ISBN: ‎ 9780471093152 </ref> that "All models are wrong; but some models are useful." In physics there as been sufficient modeling since Newton to narrow the discrepancy of reality and the model to a very small percentage. But that does not imply that they are correct, just useful. Box also noted:<blockquote>Since all models are wrong the scientist cannot obtain a "correct" one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so over-elaboration and over-parameterization is often the mark of mediocrity.</blockquote>
 Artificial Intelligence (AI) has been an experimental science only since 1960 and so has not produced models of intelligence that are particularly close to the real thing. Brian Christian described "the Alignment Problem"<ref>Brian Christian ''the Alignment Problem'' W. W. Norton (2020-10-06) ISBN 978-0-393-86833-3</ref> as the difficulty that AI has with aligning the results of providing an AI with the results obtained. It is worse than you might imagine. When [[Machine Learning]] is introduced into society before it has been fully vetted, we must expect that the results will not align with human values. By 2016 the US already had an AI deployed for predicting the future of criminals what was blatantly racist<ref> Julia Angwin +3, '' Machine Bias'' ProPublica (2016-05-23) https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing</ref> But since the AI was trained on real-word data, it was just doing the same thing any any human would do given the same inputs since society as a whole was also racist. The point here is that both the racist profiler and the physics standard model are doing the same thing. it just that one system is more regular than the other.

← Older revision		Revision as of 19:02, 26 October 2024
Line 12:		Line 12:

	Artificial Intelligence (AI) has been an experimental science only since 1960 and so has not produced models of intelligence that are particularly close to the real thing. Brian Christian described "the Alignment Problem"<ref>Brian Christian ''the Alignment Problem'' W. W. Norton (2020-10-06) ISBN 978-0-393-86833-3</ref> as the difficulty that AI has with aligning the results of providing an AI with the results obtained. It is worse than you might imagine. When [[Machine Learning]] is introduced into society before it has been fully vetted, we must expect that the results will not align with human values. By 2016 the US already had an AI deployed for predicting the future of criminals what was blatantly racist<ref> Julia Angwin +3, '' Machine Bias'' ProPublica (2016-05-23) https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing</ref> But since the AI was trained on real-word data, it was just doing the same thing any any human would do given the same inputs since society as a whole was also racist. The point here is that both the racist profiler and the physics standard model are doing the same thing. it just that one system is more regular than the other.		Artificial Intelligence (AI) has been an experimental science only since 1960 and so has not produced models of intelligence that are particularly close to the real thing. Brian Christian described "the Alignment Problem"<ref>Brian Christian ''the Alignment Problem'' W. W. Norton (2020-10-06) ISBN 978-0-393-86833-3</ref> as the difficulty that AI has with aligning the results of providing an AI with the results obtained. It is worse than you might imagine. When [[Machine Learning]] is introduced into society before it has been fully vetted, we must expect that the results will not align with human values. By 2016 the US already had an AI deployed for predicting the future of criminals what was blatantly racist<ref> Julia Angwin +3, '' Machine Bias'' ProPublica (2016-05-23) https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing</ref> But since the AI was trained on real-word data, it was just doing the same thing any any human would do given the same inputs since society as a whole was also racist. The point here is that both the racist profiler and the physics standard model are doing the same thing. it just that one system is more regular than the other.
		+	===Scaling===
		+	Some abstract models, like fractals, work well at all scales. But when we are looking for verisimilitude the models of individuals, like leaves, or hair, becomes unimaginable

	==References==		==References==