💯 this! Overfunding is bad for the overfunded fields, bad for researchers in the overfunded fields, and bad for fields left to starve, and bad for society as a result of both of those.

Re bad for the field, see @histoftech 's tweet and the tweet by @ChristophMolnar they are QT-ing.

Re bad for researchers in the overfunded fields, see all the discourse around how do we keep up with arXiv??

Re bad for researchers: It puts on a SOTA treadmill, with incentives to publish papers which have expiration dates:

Re bad for researchers + bad for the field: SOTA treadmill etc are bad for diversity, too.

Re bad for the field: to the extent that all of this thrashing about is generating new knowledge, there is also a lot of duplicated effort (and minimal careful reproduction work):

Re bad for the field + bad for adjacent fields, the concentration of funding in CS warps framing of interdisciplinary problems (= all the potential real world applications of CS) to a primarily CS lens:

To be clear: It is not that the world has too many researchers. It's that the funding incentives overcentrate us in one area. Imagine what we could do if those resources (from all sources, industry, government, philanthropy) were more evenly distributed!

Very much the opposite, in fact, we need more researchers, esp people w/perspectives that are absolutely key to understanding things like the power structures through which tech reproduces harm, but whose knowledge is devalued in the current paradigm.

Coda 1: I always cringe when people cite risks of AI winter among (and often even firsts!) among the problems with #AIhype

The world + ML-adjacent research fields, would likely be better off with a cool-down.

Coda 2: Just going to end with this, because it was one of my favorite tweets and I think it fits here:

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More from @emilymbender

Jan 11
Quick! Think of an example of word sense ambiguity in English other than bank/river bank/financial institution!
(Mostly this is just commentary on how over-used that one example is, but I'm also kind of curious what people come up with.)
And if you want to read more about the ways in which words can have multiple senses, check out Ch 4 of Bender & Lascarides 2019:

Read 4 tweets
Dec 2, 2021
“I’ve been frustrated for a long time about the incentive structures that we have in place and how none of them seem to be appropriate for the kind of work I want to do,” -- @timnitGebru on the founding of @DAIRInstitute

@timnitGebru @DAIRInstitute “how to make a large corporation the most amount of money possible and how do we kill more people more efficiently,” Gebru said. “Those are […] goals under which we’ve organized all of the funding for AI research. So can we actually have an alternative?”
“AI needs to be brought back down to earth,” said Gebru, founder of DAIR. “It has been elevated to a superhuman level that leads us to believe it is both inevitable and beyond our control. >>
Read 5 tweets
Dec 2, 2021
A few thoughts on citational practice and scams in the #ethicalAI space, inspired by something we discovered during my #ethNLP class today:

Today's topic was "language variation and emergent bias", i.e. what happens when the training data isn't representative of the language varieties the system will be used with.

The course syllabus is here, for those following along:

Week by week, we've been setting our reading questions/discussion points for the following week as we go, so that's where the questions listed for this week come from.

Read 14 tweets
Nov 4, 2021
"Bender notes that Microsoft’s introduction of GPT-3 fails to meet the company’s own AI ethics guidelines, which include a principle of transparency" from @jjvincent on the @verge:

@jjvincent @verge In a bit more detail, here's what Microsoft says in their blog:


>> Screen cap of Microsoft blog reading: "That’s why Mic
@jjvincent @verge The principles are well researched and sensible, and working with their customers to ensure compliance is a laudable goal. However, it is not clear to me how GPT-3 can be used in accordance with them.

Read 9 tweets
Nov 3, 2021
About once a week, I get email from someone who'd like me to take the time to personally, individually, argue with them about the contents of Bender & Koller 2020. (Or, I gather, just agree that they are right and we were wrong.)

I don't answer these emails. To do so would be a disservice to, at the very least, the students to whom I do owe my time, as well as my own research and my various other professional commitments.

It's not that I object to people disagreeing with me! While I am committed to my own ideas, I don't have the hubris to believe I can't be wrong.

Read 10 tweets
Sep 6, 2021
Thanks for the ping, @michaelbrundage

I don't think there's anything specific to LLMs here. Rather, this is endemic to the way ML is applied these days:
1. Someone creates a dataset & describes it as a benchmark for some skill. Sometimes, the skill is well-scoped & the benchmark represents a portion of it reasonably (eg ASR for news in X language). Others, the skill is bogus (all the physiognomy tasks; IQ from text samples; &c).
2. ML researchers use the benchmarks to test different approaches to learning. This can be done well (for well-scoped tasks): which algorithms are suited to which tasks and why? (Requires error analysis, and understanding the task as well as the algorithm.)
Read 21 tweets

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