When I was a student I thought professors are people who know lots of stuff. Then they went and made me a professor. After getting over my terror of not knowing stuff, I realized I had it all wrong. Here are a bunch of things that are far more important than how much you know.
- Knowing what you know and what you don’t know.
- Being good at teaching what you know.
- Being comfortable with saying you don’t know.
- Admitting when you realize you got something wrong.
- Effectively communicating uncertainty when necessary.
- Spotting BS.
- Recognizing others with expertise.
- Recognizing that there are different domains of expertise.
- Recognizing that there are different kinds of expertise including lived experience.
- Drawing from others’ expertise without deferring to authority.
- Accepting the unknowable (this one's my favorite). There’s so much we simply can’t know in our present state of understanding. But we seem to be wired to seek explanations, so pseudo-experts take advantage of us. Let’s fight this bias because it’s at the root of many problems.
I wrote this thread with academia in mind but it's really gratifying that it seems to be resonating with people in other fields as well.

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More from @random_walker

21 Oct
Many face recognition datasets have been taken down due to ethical concerns. In ongoing research, we found that this doesn't achieve much. For example, the DukeMTMC dataset of videos was used in 135 papers published *after* it was taken down in June 2019. freedom-to-tinker.com/2020/10/21/fac…
A major challenge comes from derived datasets. In particular, the DukeMTMC-ReID dataset is a popular dataset used for person re-identification and continues to be free for anyone to download. 116 of 135 papers that use DukeMTMC after its takedown actually use a derived dataset.
This is a widespread problem. MS-Celeb was removed due to criticism but lives on through MS1M-IBUG, MS1M-ArcFace, MS1M-RetinaFace… all still public. The original dataset is also available via Academic Torrents. One popular dataset, LFW, has spawned at least 14 derivatives.
Read 6 tweets
5 Oct
At Princeton CITP, we were concerned by media reports that political candidates use psychological tricks in their emails to get supporters to donate. So we collected 250,000 emails from 3,000 senders from the 2020 U.S. election cycle. Here’s what we found. electionemails2020.org
Let me back up: this is a study by @aruneshmathur, Angelina Wang, @c_schwemmer, Maia Hamin, @b_m_stewart, and me. We started last year by buying a list of all candidates running for federal and state elections in the U.S. We also acquired lists of PACs and other orgs.
Next, the key bit for data collection: we created a bot that was able to find these candidates’ websites through search engines, look for email sign up forms, fill them in, and collect the emails in a giant inbox. We verified manually that each step works pretty accurately.
Read 11 tweets
17 Sep
Expertise is important for scholars, but after 5-10 years the benefits of continuing to deepen your expertise are tiny compared to broadening it.

Universities are perfectly set up to prevent breadth of expertise by hiring people for life and putting them into siloed departments.
How have I not heard this before? It's a few weeks too late to use it on the students on PhD orientation day!
The intellectual superiority of depth over breadth is a pervasive fiction in academia that sustains the culture of fetishizing specialization. I tried to fight this culture early in my career, but realized it was like punching a bag of sand.
Read 5 tweets
26 Aug
An amazing benefit of my privilege is being able to say "I didn't understand that. Could you explain it again?" as many times as necessary without having to worry that people will think I'm stupid.
If you didn't understand something I said, please ask me as many times as necessary. In fact, I'm delighted when this happens. As a professor, knowing when something I explained didn't make sense is extremely valuable feedback that helps me do better.
I'm a tenured computer science professor who looks like what many people expect a tenured computer science professor to look like. The follow up I get after someone asks "So what do you do?" is nearly always "Oh, you must be really smart."
Read 4 tweets
30 Jul
By the same token, it should be a sobering moment for computer science academia. With few exceptions, work that tries to bring accountability to big tech companies is relegated to the fringes of our discipline. CS these days cozies up to power far more than speaking truth to it.
There's a lot of concern today about industry funding of specific researchers. That's important, but a 100x deeper problem is that the tech industry warps CS academia's concept of what is even considered a legitimate research topic. This influence is both pervasive and invisible.
Most of the industry influence happens without any money changing hands. Academia's dependence on industry data is one way. Another is that most grad students go on to industry jobs and naturally prefer to work on topics that increase their employability.
Read 6 tweets
27 Jul
Academia forces you to pay a "cleverness tax" if you want to succeed—it's a tax on your time that goes towards constantly convincing others that your work is clever enough for publication, getting a PhD, tenure, and promotion. It's one of the things that pushes people out.
Reviewer 3: I see you’ve solved global hunger, but it was always obvious that you could do that by working really hard, so we haven’t learned anything from your paper. Perhaps you could try solving global hunger using only purple foods? That would be novel.
The cleverness tax is higher for scholars whose work doesn’t fit their discipline’s stereotyped notions of what clever work is supposed to look like. You’re often forced to pick between having a real impact on the world and just staying in the game.
Read 4 tweets

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