Emily M. Bender Profile picture
Sep 19 25 tweets 12 min read
This article in the Atlantic by Stephen Marche is so full of #AIhype it almost reads like a self-parody. So, for your entertainment/education in spotting #AIhype, I present a brief annotated reading:


Straight out of the gate, he's not just comparing "AI" to "miracles" but flat out calling it one and quoting Google & Tesla (ex-)execs making comparisons to "God" and "demons".

/2 Screencap from linked article: "Miracles can be perplex
This is not the writing of someone who actually knows what #NLProc is. If you use grammar checkers, autocorrect, online translation services, web search, autocaptions, a voice assistant, etc you use NLP technology in everyday life. But guess what? NLP isn't a subfield of "AI".
/3 Screencap, same article: "Early artificial intelligence
Here's the author is claiming to have inside knowledge of some "esoteric" technology development that, unbeknownst to the average human, is going to be very disruptive. But note the utter lack of citations or other grounding for this claim.

/4 Screencap, same article: "Or rather: If you are using N
Okay, agreed on fake-it-til-you-make-it, but "direct thrust at the unfathomable" and "not even the engineers understand" are just unadulterated hype. If they don't understand how it works, how are they even measuring that it works?

/5 Screencap, same article: "Science fiction, and our own
Protip: They aren't really. The capabilities that the AI boosters claim to have built are ones that we don't have effective benchmarks for, & actually can't, in principle. See: AI and the Everything in the Whole Wide World Benchmark by @rajiinio et al /6

et al = @cephaloponderer @alexhanna @amandalynneP and me.

For a quick overview, see this article by @bendee983


@cephaloponderer @alexhanna @amandalynneP @bendee983 Okay, back to the hype. This is weirdly ominous and again provides no supporting evidence. You can't see it, but that doesn't mean it isn't there ... is not an argument that it is!

/8 Screencap, same article: "But the confusion surrounding
This is kinda fun, because I was musing a few weeks ago about how we don't usually go to "superhuman" for other tools. And it does sound ridiculous, doesn't it?

/9 Screencap, same article: "All technology is, in a sense
If you don't know how something works, but can test that it works (w/certain degree of reliability), then it is usable. It's true that deep learning is opaque on the how. But we can't let any engineers off the hook in terms of testing the functionality of their systems.

/10 Screencap, same article: "But the sorcery of artificial
"What technologists call 'parameters'" makes this sound so ominous and mysterious. Our "little animal brains" have ~86 billion neurons (source: brainfacts.org/in-the-lab/mee…). So not a different scale (and with much more complexity).

/11 Screencap, same article: "The details of how this could
More to the point: None of this is inevitable. DL systems aren't naturally occurring phenomena that we can try to understand or just stand in awe of. They are things we are building & choosing to use. We can choose not to, at least w/p sufficient testing for each use case.

Also, because it feels gross to compare language model parameters to human neurons, I want to plug again this great article by @AlexBaria and @doctabarz on the computational metaphor.


Back to Marche: I don't think we should necessarily believe the people who got super rich off of surveillance capitalism when they say "oh noes, can't regulate, it would stop the development of the technology".

/14 Screencap, same article: "This unfathomability poses a
Again, whether or not we try to build this (and with what regulatory guardrails) is a CHOICE. But also: it would be pretty easy with today's stochastic parrots to sometimes at least get an answer like that. (While other times getting hate speech...)

/15 Screencap, same article: "Others have headed into deepe
Uh, just because you put these things in a list does not make them all the same kind of thing ("language game").

/16 Screencap, same article: "What we are doing is teaching
Yeah, just because the people who built the thing say it does something "in a ways that's not dissimilar from the way you and I do" doesn't make it true. Do they have the expertise to evaluate that? How did they evaluate that?

/17 Screencap: "PaLM, Google’s latest foray into NLP, has
Oh, and again, while "contemporary NLP" does use neural LMs for a lot of things, I wouldn't say it "derives" from them. There is more to the field than just throwing neural nets are poorly conceived tasks.

What comes next is some GPT-3 authored additional hype, stating with the prompt "And if AI harnesses the power promised by quantum computing," Marche does acknowledge it (in the following paragraph). He is also responsible for deciding to include it.

/19 Screencap (GPT-3 authored text): "And if AI harnesses tScreencap (Marche): "Our AI future will be weird and su
(Note that Marche also doesn't tell us how many tries he took to get the one he chose to include.)

It's not doing any of these things, actually. Having synthetic text in the style of someone who has died is not bringing them back from the dead. I'm not sure what an "imitation" of consciousness is, nor how it would benefit us.

/21 Screencap: "Technology is moving into realms that were
And it is certainly not "piercing the heart of how language works between people".

On how LM-geneated text is nothing like human linguistic behavior, see Bender & Koller 2020 and also this episode of Factually!


And one last screencap before I end. Where is the evidence for any of these claims? None is provided.

/23 Screencap: "A fragment of humanity is about to leap for
So, I hope that was enjoyable and/or informative. I give this one #threemarvins. Could 2022 be the year of peak #AIhype? That sure would be nice. 24/24
Postscript 1: Important additional info on the (sigh) comparison of 100B parameter networks to human brains

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More from @emilymbender

Aug 25
This piece is stunning: stunningly beautifully written, stunningly painful, and stunningly damning of family policing, of the lack of protections against data collection in our country, & of the mindset of tech solutionism that attempts to remove "failable" humans decision makers
.@UpFromTheCracks 's essay is both a powerful call for the immediate end of family policing and an extremely pointed case study in so many aspects of what gets called #AIethics:

1. What are the potentials for harm from algorithmic decision making?

2. The absolutely essential effects of lived experience and positionality to understanding those harms.
3. The ways in which data collection sets up future harms.

Read 5 tweets
Aug 24
Just to recap the morning so far (admittedly, some of these news stories are from a couple of days ago):
Read 5 tweets
Aug 11
Read the recent Vox article about effective altruism ("EA") and longtermism and I'm once again struck by how *obvious* it is that these folks are utterly failing at ceding any power & how completely mismatched "optimization" is from the goals of doing actual good in the world.
Just a few random excerpts, because it was so painful to read...

"Oh noes! We have too much money, and not enough actual need in today's world."

First: This is such an obvious way in which insisting on only funding the MOST effective things is going to fail. (Assuming that is even knowable.)

>> Screencap reading: "EA...
Read 19 tweets
Jul 26
In Stochastic Parrots, we referred to attempts to mimic human behavior as a bright line in ethical AI development" (I'm pretty sure that pt was due to @mmitchell_ai but we all gladly signed off!) This particular instance was done carefully, however >>

@mmitchell_ai Given the pretraining+fine-tuning paradigm, I'm afraid we're going to see more and more of these, mostly not done with nearly the degree of care. See, for example, this terrible idea from AI21 labs:


@mmitchell_ai As Dennett says in the VICE article, regulation is needed---I'd add: regulation informed by an understanding of both how the systems work and how people react to them.


>> Screenshot from linked article, reading: "“Mainly it
Read 6 tweets
Jul 25
Thinking back to Batya Friedman (of UW's @TechPolicyLab and Value Sensitive Design Lab)'s great keynote at #NAACL2022. She ended with some really valuable ideas for going forward, in these slides:

Here, I really appreciated 3 "Think outside the AI/ML box".

>> Screenshot of slide, with t...
As societies and as scientific communities, we are surely better served by exploring multiple paths rather than piling all resources (funding, researcher time & ingenuity) on MOAR DATA, MOAR COMPUTE! Friedman points out that this is *environmentally* urgent as well.

Where above she draws on the lessons of nuclear power (what other robust sources of non-fossil energy would we have now, if we'd spread our search more broadly back then?) here she draws on the lessons of plastics: they are key for some use case (esp medical). >> Screenshot of slide, with t...
Read 7 tweets
Jul 3
Not it effing can't. This headline is breathtakingly irresponsible.

h/t @hypervisible

Some interesting 🙃 details from the underlying Nature article:

1. Data was logs maintained by the cities in question (so data "collected" via reports to police/policing activity).
2. The only info for each incident they're using is location, time & type of crime.

3. A prediction was counted as "correct" if a crime (by their def) occurred in the (small) area on the day of prediction or one day before or after.

Read 13 tweets

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