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AI Assistants Boost Beginners More Than Experts, Study Shows Correlation

There once was an AI named Chat who was really good at repeating back information it already knew. One day, Chat was given to some office workers [1] to help them with their jobs. Some of the workers were experts at their jobs, while others were still learning.

At first, Chat helped all the workers get more work done faster - even the experts! But soon, the experts noticed something funny. The workers who were still learning got way MORE help from Chat. The new workers improved a lot using Chat, doing their work faster and better than ever before!

The experts wondered why Chat didn't help them as much. That's when they realized - that Chat is an expert at repeating back facts but can't come up with brand new ideas. So, for workers who already knew those facts, Chat didn't offer them that much new help. But for newer workers still learning those basics, Chat was able to teach them so much more!

This shows a correlation - as in, two things that relate to each other and change together. The more expert a worker already was, the less helpful Chat was for them. But for newer workers, Chat could help them almost as much as the experts! It's because of their different starting points. Chat has a limit to how expert it can be. So, the closer a worker already was to Chat's expertise, the less new stuff Chat offered them.

The experts and newbies improved at different rates thanks to Chat. Their own expertise compared to Chat's matters for how much more they can learn. That connection in how much they improve is the correlation!

The SDTEST^® gives clues to someone's motivational values. However, additional polls can provide more pieces of the puzzle.

Imagine also giving an "A.I. and the end of civilization" poll. It asks people to rate at the agree or disagree level.

Now imagine 100 people who took both tests. You could match up each person's SDTEST^® colors with their rated answers about the danger of AI.

Comparing tests gives an expanded picture of values in action. More puzzle pieces make the whole image more apparent!

Multiple tests can work together, like colors blending on a palette. Other polls reveal what engages your values, like what is the perception of the danger of AI. Combined, they paint a richer picture of what motivates our thoughts and deeds.

Below you can read an abridged version of the results of our VUCA poll “A.I. and the end of civilization“. The full results of the poll are available for free in the FAQ section after login or registration.

Deallusrwydd artiffisial a diwedd gwareiddiad

siartiau Cydberthyniad

VUCA

Gwerth feirniadol o'r cyfernod cydberthyniad

Dosbarthiad Arferol, gan William Sealy Gosset (Myfyriwr) r = 0.084

Dosbarthiad nad yw'n arferol, gan Spearman r = 0.0036

Dangos canlyniadau yn unig > r = 0.084

Nosbarthiadau	Nad yw'n normal	Normal	Normal	Normal	Normal	Normal	Normal	Normal
Pob cwestiwn Pob cwestiwn 1) Diogelwch (faint ydych chi'n cytuno neu'n anghytuno?) 2) Rheoli (faint ydych chi'n cytuno neu'n anghytuno?)
1) Diogelwch (faint ydych chi'n cytuno neu'n anghytuno?)
Answer 1	-	Gadarnhaol gwan 0.0782	Negyddol gwan -0.0194	Gadarnhaol gwan 0.1068	Negyddol gwan -0.1237	Gadarnhaol gwan 0.0211	Negyddol gwan -0.0558	Gadarnhaol gwan 0.0212
Answer 2	-	Gadarnhaol gwan 0.0296	Gadarnhaol gwan 0.0231	Gadarnhaol gwan 0.0400	Negyddol gwan -0.0012	Gadarnhaol gwan 0.0132	Negyddol gwan -0.0053	Negyddol gwan -0.0711
Answer 3	-	Negyddol gwan -0.0075	Negyddol gwan -0.0330	Negyddol gwan -0.0036	Gadarnhaol gwan 0.0550	Negyddol gwan -0.0054	Negyddol gwan -0.0144	Negyddol gwan -0.0108
Answer 4	-	Gadarnhaol gwan 0.0045	Gadarnhaol gwan 0.0259	Gadarnhaol gwan 0.0324	Negyddol gwan -0.0203	Negyddol gwan -0.0601	Negyddol gwan -0.0206	Gadarnhaol gwan 0.0472
Answer 5	-	Gadarnhaol gwan 0.0071	Negyddol gwan -0.0184	Negyddol gwan -0.0280	Gadarnhaol gwan 0.0166	Gadarnhaol gwan 0.0164	Gadarnhaol gwan 0.0252	Negyddol gwan -0.0339
Answer 6	-	Negyddol gwan -0.0457	Negyddol gwan -0.0552	Negyddol gwan -0.1004	Gadarnhaol gwan 0.0896	Gadarnhaol gwan 0.0092	Gadarnhaol gwan 0.0466	Gadarnhaol gwan 0.0190
Answer 7	-	Negyddol gwan -0.0566	Gadarnhaol gwan 0.0719	Negyddol gwan -0.0421	Negyddol gwan -0.0405	Gadarnhaol gwan 0.0116	Gadarnhaol gwan 0.0233	Gadarnhaol gwan 0.0372
2) Rheoli (faint ydych chi'n cytuno neu'n anghytuno?)
Answer 8	-	Gadarnhaol gwan 0.0261	Gadarnhaol gwan 0.0447	Gadarnhaol gwan 0.0766	Gadarnhaol gwan 0.0706	Negyddol gwan -0.0209	Negyddol gwan -0.1053	Negyddol gwan -0.0740
Answer 9	-	Gadarnhaol gwan 0.0256	Negyddol gwan -0.0460	Negyddol gwan -0.0393	Negyddol gwan -0.0056	Gadarnhaol gwan 0.0960	Negyddol gwan -0.0292	Negyddol gwan -0.0080
Answer 10	-	Gadarnhaol gwan 0.0471	Negyddol gwan -0.0248	Negyddol gwan -0.0245	Negyddol gwan -0.0183	Negyddol gwan -0.0291	Gadarnhaol gwan 0.0256	Gadarnhaol gwan 0.0316
Answer 11	-	Gadarnhaol gwan 0.0191	Gadarnhaol gwan 0.0042	Gadarnhaol gwan 0.0053	Negyddol gwan -0.0398	Gadarnhaol gwan 0.0012	Negyddol gwan -0.0115	Gadarnhaol gwan 0.0295
Answer 12	-	Negyddol gwan -0.0507	Gadarnhaol gwan 0.0353	Gadarnhaol gwan 0.0299	Gadarnhaol gwan 0.0534	Negyddol gwan -0.0671	Gadarnhaol gwan 0.0424	Negyddol gwan -0.0536
Answer 13	-	Negyddol gwan -0.1113	Negyddol gwan -0.0431	Gadarnhaol gwan 0.0030	Gadarnhaol gwan 0.0204	Negyddol gwan -0.0120	Gadarnhaol gwan 0.0925	Gadarnhaol gwan 0.0106
Answer 14	-	Negyddol gwan -0.0076	Gadarnhaol gwan 0.0455	Negyddol gwan -0.0575	Negyddol gwan -0.0913	Negyddol gwan -0.0036	Gadarnhaol gwan 0.0486	Gadarnhaol gwan 0.0789

Allforio i MS Excel

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Iawn

[1] https://www.ft.com/content/b2928076-5c52-43e9-8872-08fda2aa2fcf

2023.11.27

Valerii Kosenko

Perchennog y Cynnyrch Saas Pet Project Sdtest®

Roedd Valerii yn gymwys fel addysgegydd cymdeithasol-seicolegydd ym 1993 ac ers hynny mae wedi cymhwyso ei wybodaeth mewn rheoli prosiect.
Cafodd Valerii radd meistr a chymhwyster y prosiect a rheolwr rhaglen yn 2013. Yn ystod rhaglen ei feistr, daeth yn gyfarwydd â Map Ffordd Project (GPM Deutsche Gesellschaft für Projektmanagement e. V.) a Spiral Dynamics.
Cipiodd Valerii amryw o brofion dynameg troellog a defnyddio ei wybodaeth a'i brofiad i addasu'r fersiwn gyfredol o SDTest.
Valerii yw awdur archwilio ansicrwydd y V.U.C.A. Cysyniad gan ddefnyddio dynameg troellog ac ystadegau mathemategol mewn seicoleg, mwy nag 20 o bolau rhyngwladol.

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