Recent comments in /f/MachineLearning

schwagggg t1_j916cc5 wrote

Reply to comment by Oripy in [D] Simple Questions Thread by AutoModerator

so actor critic without critic is just policy gradient/reinforce/score function gradient, first two names used in RL, last one used in stats/OR.

short answer is policy gradient tends to have high variances empirically, so people use control variates to control its variance, and the critic is simply the control variate.

high variance methods usually converge to worse local minimas than low variance ones. u can verify this by taking or the critic function entirely. try it itself with that tutorial

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CacheMeUp t1_j915ffp wrote

Reply to [D] What are the worst ethical considerations of large language models? by BronzeArcher

Breaking the security-by-required-effort assumption of various human interactions, especially among strangers.

It used to take effort to voice opinions on social media and other mass-communication platform, making the public trust that these are authentic messages representing real people. The scalability of this technology breaks that assumption. This has started before, and LLMs take it to a whole new level.

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East-Beginning9987 t1_j90q25r wrote

Reply to [D] Coauthor Paper? by [deleted]

From my experience in interviewing for pre doc programs, people down weigh your controbution to the paper if youre not the first author and often you have to explicitly make sure to let them know what you did exactly , etc.however I think having a good contribution from your side would eventually lead to you being able to talk more clearly about what you did and show the interviewer that you know stuff. Since you’re in second year, i guess you will be able to work on first author papers which would then strengthen having second or later author papers.

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PrestigiousCloud9 t1_j90q0hq wrote

Reply to [D] Simple Questions Thread by AutoModerator

I am currently a student learning ML. I have done some courses on Machine Learning and I know the theoretical part of it i.e. about algorithms like Random Forest , Decision Tree , SVM , KNN etc. But I want to work of some ML projects how should I start ? How can I gain practical knowledge of ML ? How can I make projects to improve my resume ? I particularly struggle to identify which ML algorithm is suitable for a particular problem statment?

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farmingvillein t1_j90m0ab wrote

Reply to comment by adt in [D] Compare open source LLMs by President_Xi_

> For models, see my up-to-date list of models:

Which tab is germane to OP's request?

> but I am specifically refering to performance after finetuning.

So far as I can tell, there is nothing here that is responsive to OP's query. But there is a lot here--perhaps I read too quickly.

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NotARedditUser3 t1_j90j0er wrote

Reply to comment by a1_jakesauce_ in [D] What are the worst ethical considerations of large language models? by BronzeArcher

If you spend some time looking up how microsoft's gpt integrated chat / ai works, it does this. Lookup the thread of tweets for the hacker that exposed its internal codename 'Syndey'; it scrapes his twitter profile, realizes he exposed its secrets in prior convo's after social engineering hacking it with a few conversations, and then turns hostile to him.

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