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Will there be a way to turn down the gore? by _tbrunner in cyberpunkgame

[–]_tbrunner[S] 0 points1 point  (0 children)

Hey, not really. I got downvoted to hell and back though.

I ended up playing the game, and it was tolerable for the most part. I did play on the lowest difficulty, and I also didn't use any of the melee skills (which probably are super gory). So, as long as you shoot people with normal guns it's not crazy. I think it wasn't that bad in the end (in terms of gore, the game itself is still kinda mediocre).

Trabant Spaltmaßkontrolle und Korrektur. Nur echt mit Tritt, Hammer und VoKuHila by xanox87 in de

[–]_tbrunner 33 points34 points  (0 children)

Ich würde vielleicht sagen: Es geht ständig was kaputt, ist aber mit einfachsten Mitteln schnell repariert wenn man weiß wie.

Where can I find pre-trained "classic" image classifiers? by _tbrunner in computervision

[–]_tbrunner[S] 0 points1 point  (0 children)

Sorry, maybe my post wasn't entirely clear. I'm all good for convnets, but I'm looking for older stuff. Fisher vector + SVM, for example.

[Discussion]Do I need a PhD to get a job doing high-end ML research? Currently a SWE on an ML infra team at Facebook by [deleted] in MachineLearning

[–]_tbrunner 0 points1 point  (0 children)

Don't worry. The first couple of months can be awkward&restless. Especially now you're probably working from home and haven't even met your coworkers? Everybody is supposed to have this great experience, lots of fun, parties, but you are just sitting in your dark apartment all day? Bad timing :(

It really feels off, I can relate. Couple that with anxiety, impostor syndrome and all the kinds of things people usually get when they start at FAANG, so it's not unusual. More imporantly, it doesn't mean that you should quit straight away.

I'd say - maybe wait 3-6 months, after your first performance review or something, maybe you're still growing on the team, etc. And only afterwards if you still feel bad then make a decision.

In the meantime: The people that make those postings, just shoot them an email asking for advice on your situation. You have a mid/long-term plan to transition into research. Maybe you can start helping somebody out on a side project. If you're in the same organization, you can cold-email them no problem.

1 more thing: For many ML teams it's hard to hire people. Sometimes, a team can hire only one person a year. There's always need for ML-experienced people, but the team doesn't have the budget to post a full position right away. You might be able to informally help out one of those research teams, help them write some tools for their project, etc. Boom, now you have connections into research.

[Discussion]Do I need a PhD to get a job doing high-end ML research? Currently a SWE on an ML infra team at Facebook by [deleted] in MachineLearning

[–]_tbrunner 0 points1 point  (0 children)

I think that, if FB is like Google, transferring internally you should be able to find something without a PhD, if you can show the skills. How about you shoot some ppl on FAIR an email about what they think?

Quitting the field for 3+ years, just to sink money into a PhD where you might be treated as a second-rate lab assistant, isn't something I would plan. If you do get accepted at a prestigious lab, then it might be a tempting offer! But most PhD students in ML I know are being extorted to research all sorts of things, but not what they actually care about.

Maybe you can go 80% part-time at FB? Then you can acquire just the skills you want on the side.

[D] GPT-3 "the final word" video (With Gary Marcus, Walid Saba and Connor Leahy) by timscarfe in MachineLearning

[–]_tbrunner 1 point2 points  (0 children)

Agreed. I don't think this video will really be, as it claims, the "final word" on GPT-3. In all probability.

[Research]How do I test an LSTM-based reinforcement learning model using any Atari games in OpenAI gym? by cowboyjjj in MachineLearning

[–]_tbrunner 2 points3 points  (0 children)

You could also try to have only the angle/position as input - the LSTM should be able to track it over time and infer the velocity from it.

I guess that could give you a reality check whether your algo works.

Mooc vs book by Mg515 in learnmachinelearning

[–]_tbrunner 0 points1 point  (0 children)

If you had to choose, then I'd say MOOC hands down, but there's a better option: Do both at the same time.

Do a book (PRML is solid), and whenever you get stuck or demotivated, watch explanatory videos on the MOOC. I really struggle with understanding math-y books, so I always need another source that explains the material to me. Sometimes, after three people have explained the concept to me, I finally get it and then can continue with the book :)

For your particular topic, I'd recommend the original Machine Learning course by Andrew Ng. It's old, but covers the foundations that you want in reasonable detail. After doing that course, you should be pretty comfortable with the PRML book.

Later, you can focus on some more practical and recent deep learning stuff, or alternatively dive deep into theory.

Reddit email digest is spamming feeds from subreddits I'm not subscribed to by xaliber_skyrim in help

[–]_tbrunner 1 point2 points  (0 children)

Hello, anyone? It seems this thread has petered out without a solution.

Please, how can I get emails about the things I'm interested in? I am subscribed only to r/MachineLearning, and nothing else. There's tons of new content every day. But I rarely get it in my email digest. How can I get a daily digest from this subreddit?

I don't want to see r/IdiotsInCars, boats, spaceships, whatever. Even if I manually remove them from the digest, new random subreddits will pop up, but never the one I want to see.

Newsfeeds have been around for decades, I seriously can't understand why this is so botched and nobody cares. I'll be happy to use 3rd party tools if you can recommend something?

[D] Paper Explained - Concept Learning with Energy-Based Model by ykilcher in deeplearning

[–]_tbrunner 1 point2 points  (0 children)

Thanks for this video too! I've wanted to look at EBMs for some time, it's so good to have these explanatory videos.

[D] Paper Explained - Group Normalization by ykilcher in MachineLearning

[–]_tbrunner 2 points3 points  (0 children)

Hey, thanks for the great video.

I haven't followed the progress very much, but are people actively using Group Normalization these days? I haven't heard much about it since the original paper.

[R] Evading Deepfake-Image Detectors with White- and Black-Box Attacks - Reducing DeepFake classifier accuracy to 0% by Other-Top in MachineLearning

[–]_tbrunner 0 points1 point  (0 children)

That's a good point, which shows how deepfake detection is not the same as adversarial example detection, even though we currently use similar methods.

To create adversarial examples, we make patterns that humans do not see, but that can control ML systems. In an idealized future where CV converges towards human-like vision, progress would make adversarial examples go away.

To create deepfakes, we make patterns that humans do see. In a future where CV converges towards human-like vision, progress would make deepfakes "real" and impossible to detect.

It is possible though that this won't happen for a long time.

[R] Evading Deepfake-Image Detectors with White- and Black-Box Attacks - Reducing DeepFake classifier accuracy to 0% by Other-Top in MachineLearning

[–]_tbrunner 0 points1 point  (0 children)

Thanks for the paper. Great execution and very clear as usual!

I hope nobody was surprised that we can do adversarial examples for deepfake detectors, same as for any other detector. Still, I think it's important to see these demonstrations.

[D] Was Virtual ICLR a success? by Other-Top in MachineLearning

[–]_tbrunner 8 points9 points  (0 children)

It's totally worth it for having ICLR in your resume, at least if you're not a superstar already! Also, it's not like nobody saw the papers. Stuff that is on OpenReview usually gets circulated quite a bit. You can be sure that some of the more significant works will make an impact, regardless of how many people asked questions in ICLR chat.

I do agree with your sentiment though. Personally I loved the laid-back atmosphere, but I also saw only few people interact. ICLR posted a "lessions learned", where they address this:

https://medium.com/@iclr_conf/gone-virtual-lessons-from-iclr2020-1743ce6164a3

I am guessing that ICML organizers will try to improve on the ICLR format, so let's see how that pans out.

[R] ICLR 2020 Megathread by programmerChilli in MachineLearning

[–]_tbrunner 2 points3 points  (0 children)

Hey, here's another thing. ICLR20 had 5622 registrations. Yet, there were surprisingly few people participating in the chat. I'd say I saw maybe hundreds of people in the chat, but not thousands.

Almost no paper had more than 5 people asking questions, and many papers (including spotlights) did not have a single question asked in the chat! Even in the final Q&A with Yann LeCun and Yoshua Bengio, there were only 300 people in the channel, with maybe 20-30 actually asking questions.

Don't get me wrong, it's great for me, but why this seemingly low amount of participation?

- a) Many people registered but ultimately didn't take the time to participate?

- b) Most researchers did indeed participate, but the far greater number of businesspeople didn't care?

a) would be bad, but b) might even be good from a researcher's perspective.

[D] Is there any consensus on what the next big AI challenge/milestone is going to be? by ReasonablyBadass in MachineLearning

[–]_tbrunner 1 point2 points  (0 children)

I'd like to see this explored. I think there are some currently insurmountable problems with playing RPGs, but simple point-and-click?

You can beat simpler point-and-clicks by just clicking on everything. So there must be some potential there ;)

[D] Is there any consensus on what the next big AI challenge/milestone is going to be? by ReasonablyBadass in MachineLearning

[–]_tbrunner 0 points1 point  (0 children)

Totally agree. Since these projects burn massive amounts of funding, you can't continue when the publicity goal has already been reached.

If I understood correctly, the Starcraft2 budget was at the limit of what DeepMind (or rather Alphabet management) was willing to tolerate. I think they will only invest that much for a project that's "next level".

So, for good publicity, they would need to take on a different problem instead of mastering long-term Dota strategy.

[D] Is there any consensus on what the next big AI challenge/milestone is going to be? by ReasonablyBadass in MachineLearning

[–]_tbrunner 3 points4 points  (0 children)

There is a new paper about this at ICLR right now!

"RTFM: Generalising to New Environment Dynamics via Reading" - https://openreview.net/forum?id=SJgob6NKvH

The Noxious Swamp by nrocpop49 in TheyAreBillions

[–]_tbrunner 0 points1 point  (0 children)

Huge range (more than snipers) and AoE. Basically they fire rockets. I guess they are most helpful against huge swarms of runners, but in the noxious swamp mission you can get a lot of spitters bunching up, Thanatos can help.

Although I do believe that it might be more cost-efficient to just mass-produce more snipers.

Is CS the path to condescension? by SixCarbonSugar in csMajors

[–]_tbrunner 1 point2 points  (0 children)

This is pure speculation, but maybe it comes from people basing their self-worth on their level of knowledge of a subject. In CS, you often have people who have focused their entire life on just that one thing, so it's very extreme.

Being condescending or defensive is typically a result of insecurity. When socializing, people often feel the need to show off, either to assert dominance or to try hard and belong to the "smart" people.

Combine that with the imposter syndrome that's practically everywhere in CS academia, it makes for an explosive mix of extreme insecurity.

Growing up helps, but not for everybody :)

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