> Why does anyone need to know what order you’re going to present things in.
I agree with the sentiment, and many talks do this really badly ("Here is our outline, we start with an introduction, and end with a summary"), but it is worth mentioning that the alternative isn't no structure at all, but trying to convey a bigger picture to your audience for them to anchor each section in once you actually start your talk. This could be done like the OP suggests ("Just tell people the key idea upfront"), but there are other ways: instead of telling people the end result, tell them the question you set out to answer, and present your talk as this journey. look at the same thing/topic through different lenses/perspectives. Present a rough outline of a proof you are going to go through, or a case study you are about to present before going through the details sequentially.
> How does one become a good collaborator? The golden rule: Do not block.
Not only is this great advice for effective collaboration, it is also a very nice habit to have in any place where people's impression of your ability determines your future (career) trajectory
* His points about collaboration are excellent. So many research students think that their brains are their best asset. There are many smart people. There are much fewer smart people who can communicate and collaborate well. Be one of those people.
* His points about papers are completely on spot. There are simply too many papers, and many actually aren't that good. The light bulb moment for me was realizing that this author didn't write this paper to help me solve my problem, they wrote it to describe how they solved their problem. Finding the right way to measure whether "their problem ≅ your problem" is key.
* Do a lot of learning on your own. If you don't you're only hurting yourself.
I haven't seen the associated talk, but (a) I would imagine the author chuckled while reading this, because it's sort of a joke among scholars, and (b) the point is likely focused much more on the context of presenting research (e.g., at conferences) rather than a blanket ironclad rule for all presentations you ever make ever.
While I think there's some validity to your point that the author's presentation suffers excess verbosity, I'm not too worried about it because the linked slides seem more meant to act as a reference document than an example of a good presentation, and the level of text is just fine for that purpose.
Yeah I’m the author, this was a joke. I also wanted to convey to the students in the room that this was not a high quality presentation, more so text just converted into presentation form.
There are presentations that you actually present to an audience, to which this point is valid.
But lots of presentations, including this one I think, are merely used as a means of conveying information (yeah, not my favorite way of doing so, but being a contrarian doesn't do anybody's career any good), and those are indeed intended to be read and need to have explicitly all the information that you otherwise would be speaking and addressing.
I always thought this was funny. We were taught this in grad school, but hardly anybody followed this guideline. "If there's too much text on the slide, the audience will be busy reading the slide, and not paying attention to you". I try to have just the main point on the slide, then I can talk around it. The details should be in the slide notes, if you need reminders.
As for the notebook part, it's fascinating because it genuinely is kind of like software design if you try and impose more structure onto it - how is it reusable for the future? Can you genuinely predict the space of possible writings that you want, or does it, in the authors words, homogenize?
I've taken an approach where I treat the act of deep writing (or shallow writing, or any writing) as means in it of themselves. Not sure how absolutely effective this is but I can definitely say that my thinking changed.
#1 step to writing is to stop giving a f and I didn't not learn that until I was 21. Too easy to get caught up in schoolwork and the "proper" way to do things I feel.
This seems like a pile of generally good, and some non-obvious advice, that's also useful outside of the boundaries of ML (it would also apply to a PhD in Neuroscience for example).
Indeed. It seems, at least in America (I’m less familiar with the situation abroad) that computer science researchers who want to do longer-term work are getting squeezed. Less funding means fewer research positions in academia. Industry has many opportunities, especially in AI, but industry tends to favor shorter-term, product-focused research as opposed to longer-term work with fewer immediate prospects for productization. This is a great environment for many researchers, but researchers who want to work on longer-term, “blue-skies” projects might not find a suitable position in industry these days.
There are still opportunities, but they aren’t paid nearly as well as less researchy positions in industry. US post-doc salaries at state universities aren’t that high.
I want to watch the presentation, is there a recording, does anyone know? When the Overview slide starts with "Don’t do overview slides, it’s bad practice", I feel like I'm missing quite some context here.
> Why does anyone need to know what order you’re going to present things in.
I agree with the sentiment, and many talks do this really badly ("Here is our outline, we start with an introduction, and end with a summary"), but it is worth mentioning that the alternative isn't no structure at all, but trying to convey a bigger picture to your audience for them to anchor each section in once you actually start your talk. This could be done like the OP suggests ("Just tell people the key idea upfront"), but there are other ways: instead of telling people the end result, tell them the question you set out to answer, and present your talk as this journey. look at the same thing/topic through different lenses/perspectives. Present a rough outline of a proof you are going to go through, or a case study you are about to present before going through the details sequentially.
> How does one become a good collaborator? The golden rule: Do not block.
Not only is this great advice for effective collaboration, it is also a very nice habit to have in any place where people's impression of your ability determines your future (career) trajectory
This is an excellent overview.
* His points about collaboration are excellent. So many research students think that their brains are their best asset. There are many smart people. There are much fewer smart people who can communicate and collaborate well. Be one of those people.
* His points about papers are completely on spot. There are simply too many papers, and many actually aren't that good. The light bulb moment for me was realizing that this author didn't write this paper to help me solve my problem, they wrote it to describe how they solved their problem. Finding the right way to measure whether "their problem ≅ your problem" is key.
* Do a lot of learning on your own. If you don't you're only hurting yourself.
Some excellent advice there. I can also confirm them from hardware engineering perspective.
> Also a lot of bullet points are bad
> Slides should have maybe a sentence of text at most
Proceeds to have slides with many bullet points and more than several sentences of text per.
I don't find issue with the slides as they are but if you're going to make arbitrary rules why not follow them yourself?
I haven't seen the associated talk, but (a) I would imagine the author chuckled while reading this, because it's sort of a joke among scholars, and (b) the point is likely focused much more on the context of presenting research (e.g., at conferences) rather than a blanket ironclad rule for all presentations you ever make ever.
While I think there's some validity to your point that the author's presentation suffers excess verbosity, I'm not too worried about it because the linked slides seem more meant to act as a reference document than an example of a good presentation, and the level of text is just fine for that purpose.
Yeah I’m the author, this was a joke. I also wanted to convey to the students in the room that this was not a high quality presentation, more so text just converted into presentation form.
There are presentations that you actually present to an audience, to which this point is valid.
But lots of presentations, including this one I think, are merely used as a means of conveying information (yeah, not my favorite way of doing so, but being a contrarian doesn't do anybody's career any good), and those are indeed intended to be read and need to have explicitly all the information that you otherwise would be speaking and addressing.
I always thought this was funny. We were taught this in grad school, but hardly anybody followed this guideline. "If there's too much text on the slide, the audience will be busy reading the slide, and not paying attention to you". I try to have just the main point on the slide, then I can talk around it. The details should be in the slide notes, if you need reminders.
My guess is probably there is a difference between slides used for presentation and slides used for reading myself alone
As for the notebook part, it's fascinating because it genuinely is kind of like software design if you try and impose more structure onto it - how is it reusable for the future? Can you genuinely predict the space of possible writings that you want, or does it, in the authors words, homogenize?
I've taken an approach where I treat the act of deep writing (or shallow writing, or any writing) as means in it of themselves. Not sure how absolutely effective this is but I can definitely say that my thinking changed.
#1 step to writing is to stop giving a f and I didn't not learn that until I was 21. Too easy to get caught up in schoolwork and the "proper" way to do things I feel.
“Your objective is to find the weirdest niche possible that still has the potential to change everything.”
“Virality based social media is inherently homogenizing.”
Some nice nuggets in here!
This seems like a pile of generally good, and some non-obvious advice, that's also useful outside of the boundaries of ML (it would also apply to a PhD in Neuroscience for example).
And, even if you're in the "real world", anyone who wants to do fun hobby projects with community on the side :)
Except maybe the "aim for 2 papers a year"
I wish !
Biggest drawback these days is funding (Govt. is not liking research with all fund cuts) and long term opportunities for folks doing research.
Indeed. It seems, at least in America (I’m less familiar with the situation abroad) that computer science researchers who want to do longer-term work are getting squeezed. Less funding means fewer research positions in academia. Industry has many opportunities, especially in AI, but industry tends to favor shorter-term, product-focused research as opposed to longer-term work with fewer immediate prospects for productization. This is a great environment for many researchers, but researchers who want to work on longer-term, “blue-skies” projects might not find a suitable position in industry these days.
There are still opportunities, but they aren’t paid nearly as well as less researchy positions in industry. US post-doc salaries at state universities aren’t that high.
The best research advice is always unsolicited. The official version tells you how it is supposed to work, not how it actually does.
I want to watch the presentation, is there a recording, does anyone know? When the Overview slide starts with "Don’t do overview slides, it’s bad practice", I feel like I'm missing quite some context here.
> • Slides should have maybe a sentence of text at most
proceeds to present slides which are walls of text lolol