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You explain this like humans cant make decisions for themselves. all doom scenar…
ytc_UgzKsSVy3…
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EU wants EVs, ESG, AI and the list goes on, all of this before 2030. Where are t…
ytc_UgxslAfeP…
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I agree with this totally. I recently began using AI to help me write code. It w…
rdc_n00db2h
G
This is great coversation. Direct, Easy to understand for anyone and potential i…
ytc_UgxtlGXf-…
G
Thank G-d someone sees what I was trying to say!
Even as it gets better, it st…
rdc_oh4gez6
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Solution for motorcycle..... drive faster than traffic. Can't hit what is going …
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30:10
"AI will cure cancer."
(the reality) health insurance uses AI and it jus…
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This question needs to be answered a part of this debate. Thought experiment: a…
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Comment
Typically, gamma is viewed as part of the problem, not of the algorithm. A reinforcement learning algorithm tries for each state to optimise the cumulative discounted reward:
r1 + gamma*r2 + gamma^2*r3 + gamma^3*r4 ...
where rn is the reward received at time step n from the current state. So, for one choice of gamma the algorithm may optimise one thing, and for another choice it will optimise something else.
However, when you have defined a certain high-level goal, there still often remains a modelling choice, as many different gamma's might satisfy the requirements of the goal.
In general, most algorithms learn faster when they don't have to look too far into the future. So, it sometimes helps the performance to set gamma relatively low. A general rule of thumb might be: determine the lowest gamma min_gamma that still satisfies your high-level goal, and then set the gamma to gamma = (min_gamma + 1)/2.
Hope that solves your query.
youtube
AI Governance
2020-10-20T11:4…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | none |
| Reasoning | unclear |
| Policy | none |
| Emotion | indifference |
| Coded at | 2026-04-27T06:26:44.938723 |
Raw LLM Response
[
{"id":"ytr_Ugy6Z95H4v2LC9IASRV4AaABAg.9EYLk423JyI9Ep75vu312f","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgyJMpeIHjcqnIf6Y8h4AaABAg.9E8GhxSDC279F1boaf47QE","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgwPbs2bnV77PjCfhVd4AaABAg.9CTXRS-k8hy9CoQzbBKFcV","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgxG7kzon6DLRVupZgp4AaABAg.9BZRM0p718N9C5HvQ4GT3d","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgzHllJRZoEZOWi2lpF4AaABAg.9Ai2NdWCd4w9C5HgCvJBZE","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgzgODDBLXrwJ2fZ0hx4AaABAg.99-PLRlb7X59C5IFdJOXTm","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_Ugx6Kj_6y2_xnNW2dK94AaABAg.97TxpHm0YzE97hYkPCE3mm","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgxEmLvHkBgAbOV_D7l4AaABAg.90THB0z1tbM92U4VgjA9ds","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytr_UgxnIjAVfQcdYHp72694AaABAg.9-nQirXxply93-nOhZOEa2","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgzymuNIIrFmE0ki5D54AaABAg.8zbUzxAfKXO8zwbXpqvP3W","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"}
]