**OFF TOPIC** but perhaps of interest to some on this list. I apologize in
advance to those who may be offended.
The byline:
"ChatGPT's odds of getting code questions correct are worse than a coin flip
But its suggestions are so annoyingly plausible"
*
Thanks.
https://www.wsj.com/articles/with-ai-hackers-can-simply-talk-computers-into-misbehaving-ad488686?mod=hp_lead_pos10
Ever heard of AI prompt injection?
*Stephen Dawson, DSL*
/Executive Strategy Consultant/
Business & Technology
+1 (865) 804-3454
http://www.shdawson.com
On 8/13/23 13:49
While working on 'random walk' applications, I got interested in
optimizing noisy objective functions. As an (artificial) example, the
following is the Rosenbrock function, where Gaussian noise of standard
deviation `sd = 0.01` is added to the function value.
fn <- function(x)
(1
This is a huge topic.
Differential evolution (DEoptim package) would be one good starting
point; there is a simulated annealing method built into optim() (method
= "SANN") but it usually requires significant tuning.
Also genetic algorithms.
You could look at the NLopt list of algorit
It does often behave better if you say to it "that doesn't seem to be
working" and perhaps some error message
It is afterall a language tool. Its function is to provide text that seems
real.
If you ask it a science question and ask it to provide references in
Vancouver format, it can format the r
Thanks, Ben.
For certain reasons, I would *not* like to apply global optimization solvers,
e.g., for reasons of higher dimensions and longer running times.
I was hoping for suggestions from the "Stochastic Programming" side.
And please, never suggest `optim` with method "SANN".
See the Optimizati
Hi Bert,
The article notes that chatGPT often gets the concept wrong, rather
than the facts. I think this can be traced to the one who poses the
question. I have often encountered requests for help that did not ask
for what was really wanted. I was recently asked if I could
graphically concatenate
More to provide another perspective, I'll give the citation of some work
with Harry Joe and myself from over 2 decades ago.
@Article{,
author = {Joe, Harry and Nash, John C.},
title = {Numerical optimization and surface estimation with imprecise
function evaluations},
journal = {Statist
On Sun, 13 Aug 2023, Hans W writes:
> While working on 'random walk' applications, I got interested in
> optimizing noisy objective functions. As an (artificial) example, the
> following is the Rosenbrock function, where Gaussian noise of standard
> deviation `sd = 0.01` is added to the function v
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