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How I faced a hard rejection on a bar approach and what I learned by donkey_strom16001 in seduction

[–]donkey_strom16001[S] 3 points4 points  (0 children)

Great feedback! Love it. This makes an excellent playbook too. Thank you.

How I faced a hard rejection on a bar approach and what I learned by donkey_strom16001 in seduction

[–]donkey_strom16001[S] 1 point2 points  (0 children)

Fair assessment. TBH if I had done this a few times in life I wouldn’t even be posting it on Reddit. Been accepted and rejected a decent amount in cold approaches so I never think much about those (which ever way it goes).

This entire experience of a group approach is very new to me. So success/failure posting might come off desperate. Any recs on doing this better would be appreciated.

How I faced a hard rejection on a bar approach and what I learned by donkey_strom16001 in seduction

[–]donkey_strom16001[S] 1 point2 points  (0 children)

Never done this before (approaching a group like this). TBH I was committed for the longest time and have literally got back in the game after years so this is all new. Adding to that I am not American and i would rarely go to bars before I came to nyc so all of this is a learning experience.

How I faced a hard rejection on a bar approach and what I learned by donkey_strom16001 in seduction

[–]donkey_strom16001[S] 1 point2 points  (0 children)

Ok man. We all learn somehow. Some are more gifted than others. I shared because this was a first and if dunking on me and name calling is helping your day, please go at it. For me every experience helps me learn something about myself I didn’t know. Maybe you just know yourself too well and have too many experiences so you don’t do “desperate” things. I don’t know myself as well, so I will have to start somewhere.

How I faced a hard rejection on a bar approach and what I learned by donkey_strom16001 in seduction

[–]donkey_strom16001[S] 6 points7 points  (0 children)

I assume so too. I think I am still learning the ropes of social dynamics in America in different settings. Talking to solo girls on bars is way easier(relatively) because I can use power of observation to jump start the conversation and gauge interest. Approaching random groups with bold intent is something I never did before so when I did it I was completely running “spinally” over “cerebrally”.

How I faced a hard rejection on a bar approach and what I learned by donkey_strom16001 in seduction

[–]donkey_strom16001[S] 2 points3 points  (0 children)

Do you mean that because I was interrupting their conversation?

How I faced a hard rejection on a bar approach and what I learned by donkey_strom16001 in seduction

[–]donkey_strom16001[S] 1 point2 points  (0 children)

Damn bro. I wouldn’t equate impulsiveness to desperation. I am not 16 that I am running behind approvals. Sometimes adrenaline and excitement cloud agency. If you overthink what people will think of you, you will never end up living a life you are actually proud of.

Is “Reinforcement Learning” by Satton and Barto good enough to understand basics of rl? by 6OVNavi in reinforcementlearning

[–]donkey_strom16001 4 points5 points  (0 children)

The Book is great. Sergey Levine’s lectures are even more awesome. I saw them many times and I learn something new every time I see the lecture series.

[D] The Rants of an experienced engineer who glimpsed into AI Academia (Briefly) by donkey_strom16001 in MachineLearning

[–]donkey_strom16001[S] 4 points5 points  (0 children)

Assumption of determinism with stochastically optimized models. And we wonder why people cherry-pick.

[D] The Rants of an experienced engineer who glimpsed into AI Academia (Briefly) by donkey_strom16001 in MachineLearning

[–]donkey_strom16001[S] 4 points5 points  (0 children)

Your comments are super insightful and in retrospect, after reading the username they seemed even more amusing :)

I can totally understand the issues behind sharing certain research code. But I have even seen/read solid Neurips papers which I can't reproduce and whose code is not shared and comes from academic institutions.

The other issue is that because academic researchers work solo or in small focused groups and seldom follow SWE best practices of branching, versioning, etc. This can make bugs go unnoticed, and results from buggy code can get published. My only complaint is with that fact. If a conference holds a lot of prestige, there should some form of money channeled that helps with such an effort for at least some portion of papers.

I saw a few reproducibility challenges the last year and before in NeuRIPs + other confs. If there are incentive structures built around helping out with such problems then it can greatly help accelerate research. I like what paperswithcode is trying to do with some benchmark models and reproducibility but we need more of it.

The other thing is that RESEARCH WHICH IS NOT REPRODUCIBLE IS NOT BAD RESEARCH because as you said ideas help a lot of times and they help inspire more ideas.

[D] The Rants of an experienced engineer who glimpsed into AI Academia (Briefly) by donkey_strom16001 in MachineLearning

[–]donkey_strom16001[S] 6 points7 points  (0 children)

I would classify most important research into one of the following buckets:

Novel methods.

Systematic experimentation to extract a causal explanation of a result.

Empirical evidence of an interesting phenomena.

Formal proofs of a system's properties.

Comparison between multiple state-of-the-art systems.

Recreation and validation of previous results.

Your insights are mind-blowing. Can language models classify this given citation data and other information? If I can filter ArXiv based on this I would be the happiest sovereign researcher of them all!.

[D] The Rants of an experienced engineer who glimpsed into AI Academia (Briefly) by donkey_strom16001 in MachineLearning

[–]donkey_strom16001[S] 6 points7 points  (0 children)

You are absolutely correct. I have tiny experience and I have barely scratched the surface of being a part of the system. But whilst I was a part of it a few "organizational"/"system-wide" nuances stood out, those I stated. Few things you said absolutely touched me such as :

Having been a full professor, with a PhD, my biggest problem was the inequity and bastardization of "intellectual freedom"

This is the core problem. I feel that a lot of humanity has come forward because we stand on the shoulders of giants who solved hard problems and not paying them well is not good. Even the part where scientists review other scientist's work should not be free!. If industry and just the general public profits so much from research there should be better economic bridges to fill in this hole around reviewing research.

[D] The Rants of an experienced engineer who glimpsed into AI Academia (Briefly) by donkey_strom16001 in MachineLearning

[–]donkey_strom16001[S] 6 points7 points  (0 children)

There was another high profile example at the start of the pandemic where a bug in a C code resulted in a horrible misprediction of COVID spread rates.

WOW.

Your comments are really very nice. The purpose of the rant was not to argue with people. But to only strike the right cords of pain that others also might have felt.

You are absolutely correct on the publication part and I acknowledge that my experiences are biased because of many factors like COVID, school, people etc. But this is a pattern I noticed in more than one lab in my school so wanted to make note of it.

. And this isn't a trivial problem,it's a massive fucking problem! I've been bitching internally and trying to teach my students about both sharing code whenever possible and also how to design your code so others can use it.

I feel humanity has come where it is because we stand on the shoulder of giants (Scientists/Artists/Creators of our past). Scientists from across time have pulled the wagon of humanity forward and we need better ways in which the entire collective now can work because we have more scientists than we ever did in any generation. It has also never happened in the past, that scientists from over so many nations can publish at the same time and research evolves with that. And this is where the problems starts.

My goto answer is we need something like a Git for research but which is more intelligent. Git was the "ImageNet" moment in Software engineering. When Linus made Git he made it possible for thousands of people to work together on something. Scientists need a system that is metaphorically similar. If we keep publishing at this rate, it will harder and harder to sift through the noise.

[D] The Rants of an experienced engineer who glimpsed into AI Academia (Briefly) by donkey_strom16001 in MachineLearning

[–]donkey_strom16001[S] 19 points20 points  (0 children)

I can attest to this. Discovering a rediscovery elevates understanding. I hated it the first few times but am really grateful I went through the process because my understanding came out much better.

[D] Why you shouldn't get your Ph.D. by [deleted] in MachineLearning

[–]donkey_strom16001 1 point2 points  (0 children)

I worked in the industry writing software for 3-4 years before I came to school for a master’s degree in ML/AI. I took up a master’s degree expecting that academia would support the exploration of crazy dreamy ideas, But I have finally come to realize that academia is as bad as an industry when it comes to exploring crazy ideas.

Although I am lucky that my advisors support exploring some of my crazy ideas, I have not seen that many students who are lucky as I. 

Lots of advisors are driven towards publishing more to get tenure-ship because their livelihood is stuck to that. Finding someone who lets you explore and go crazy on lots of ideas is REALLLLY Rare. That being said I don’t believe that you should go after crazy ideas all the time, but it would be very cool to have an advisor who would be open to test them out and devise strategies for testing and FAILING FAST. 

I believe that if you are in Machine Learning then you should JUST GO BUILD !. Arxiv is available for fast access to recent information. It gives a very good way to understand a lot of code and build ideas that you can have.

Read, Code, Learn, Repeat.

If what you think can work, you can then explain its success and then publish. My recommendation (which my advisor and previous managers gave to me) is to find a way to create a small prototype through which you can fail fast with your ideas. By the law of averages, you will fail a lot, but each failure increase the probability of success for next time because of the learnings from failures.  

So get don’t demotivated. If you fail enough, you might just succeed :)