Tonight (November 27), I'm appearing at the Toronto Metro Reference Library with Facebook whistleblower Frances Haugen.
On November 29, I'm at NYC's Strand Books with my novel The Lost Cause, a solarpunk tale of hope and danger that Rebecca Solnit called "completely delightful."
Last week's spectacular OpenAI soap-opera hijacked the attention of millions of normal, productive people and nonsensually crammed them full of the fine details of the debate between "Effective Altruism" (doomers) and "Effective Accelerationism" (AKA e/acc), a genuinely absurd debate that was allegedly at the center of the drama.
Very broadly speaking: the Effective Altruists are doomers, who believe that Large Language Models (AKA "spicy autocomplete") will someday become so advanced that it could wake up and annihilate or enslave the human race. To prevent this, we need to employ "AI Safety" – measures that will turn superintelligence into a servant or a partner, nor an adversary.
Contrast this with the Effective Accelerationists, who also believe that LLMs will someday become superintelligences with the potential to annihilate or enslave humanity – but they nevertheless advocate for faster AI development, with fewer "safety" measures, in order to produce an "upward spiral" in the "techno-capital machine."
Once-and-future OpenAI CEO Altman is said to be an accelerationists who was forced out of the company by the Altruists, who were subsequently bested, ousted, and replaced by Larry fucking Summers. This, we're told, is the ideological battle over AI: should cautiously progress our LLMs into superintelligences with safety in mind, or go full speed ahead and trust to market forces to tame and harness the superintelligences to come?
This "AI debate" is pretty stupid, proceeding as it does from the foregone conclusion that adding compute power and data to the next-word-predictor program will eventually create a conscious being, which will then inevitably become a superbeing. This is a proposition akin to the idea that if we keep breeding faster and faster horses, we'll get a locomotive:
As Molly White writes, this isn't much of a debate. The "two sides" of this debate are as similar as Tweedledee and Tweedledum. Yes, they're arrayed against each other in battle, so furious with each other that they're tearing their hair out. But for people who don't take any of this mystical nonsense about spontaneous consciousness arising from applied statistics seriously, these two sides are nearly indistinguishable, sharing as they do this extremely weird belief. The fact that they've split into warring factions on its particulars is less important than their unified belief in the certain coming of the paperclip-maximizing apocalypse:
White points out that there's another, much more distinct side in this AI debate – as different and distant from Dee and Dum as a Beamish Boy and a Jabberwork. This is the side of AI Ethics – the side that worries about "today’s issues of ghost labor, algorithmic bias, and erosion of the rights of artists and others." As White says, shifting the debate to existential risk from a future, hypothetical superintelligence "is incredibly convenient for the powerful individuals and companies who stand to profit from AI."
After all, both sides plan to make money selling AI tools to corporations, whose track record in deploying algorithmic "decision support" systems and other AI-based automation is pretty poor – like the claims-evaluation engine that Cigna uses to deny insurance claims:
On a graph that plots the various positions on AI, the two groups of weirdos who disagree about how to create the inevitable superintelligence are effectively standing on the same spot, and the people who worry about the actual way that AI harms actual people right now are about a million miles away from that spot.
There's that old programmer joke, "There are 10 kinds of people, those who understand binary and those who don't." But of course, that joke could just as well be, "There are 10 kinds of people, those who understand ternary, those who understand binary, and those who don't understand either":
What's more, the joke could be, "there are 10 kinds of people, those who understand hexadecenary, those who understand pentadecenary, those who understand tetradecenary [und so weiter] those who understand ternary, those who understand binary, and those who don't." That is to say, a "polarized" debate often has people who hold positions so far from the ones everyone is talking about that those belligerents' concerns are basically indistinguishable from one another.
The act of identifying these distant positions is a radical opening up of possibilities. Take the indigenous philosopher chief Red Jacket's response to the Christian missionaries who sought permission to proselytize to Red Jacket's people:
https://historymatters.gmu.edu/d/5790/
Red Jacket's whole rebuttal is a superb dunk, but it gets especially interesting where he points to the sectarian differences among Christians as evidence against the missionary's claim to having a single true faith, and in favor of the idea that his own people's traditional faith could be co-equal among Christian doctrines.
The split that White identifies isn't a split about whether AI tools can be useful. Plenty of us AI skeptics are happy to stipulate that there are good uses for AI. For example, I'm 100% in favor of the Human Rights Data Analysis Group using an LLM to classify and extract information from the Innocence Project New Orleans' wrongful conviction case files:
Automating "extracting officer information from documents – specifically, the officer's name and the role the officer played in the wrongful conviction" was a key step to freeing innocent people from prison, and an LLM allowed HRDAG – a tiny, cash-strapped, excellent nonprofit – to make a giant leap forward in a vital project. I'm a donor to HRDAG and you should donate to them too:
https://hrdag.networkforgood.com/
Good data-analysis is key to addressing many of our thorniest, most pressing problems. As Ben Goldacre recounts in his inaugural Oxford lecture, it is both possible and desirable to build ethical, privacy-preserving systems for analyzing the most sensitive personal data (NHS patient records) that yield scores of solid, ground-breaking medical and scientific insights:
https://www.youtube.com/watch?v=_-eaV8SWdjQ
The difference between this kind of work – HRDAG's exoneration work and Goldacre's medical research – and the approach that OpenAI and its competitors take boils down to how they treat humans. The former treats all humans as worthy of respect and consideration. The latter treats humans as instruments – for profit in the short term, and for creating a hypothetical superintelligence in the (very) long term.
As Terry Pratchett's Granny Weatherwax reminds us, this is the root of all sin: "sin is when you treat people like things":
So much of the criticism of AI misses this distinction – instead, this criticism starts by accepting the self-serving marketing claim of the "AI safety" crowd – that their software is on the verge of becoming self-aware, and is thus valuable, a good investment, and a good product to purchase. This is Lee Vinsel's "Criti-Hype": "taking press releases from startups and covering them with hellscapes":
Criti-hype and AI were made for each other. Emily M Bender is a tireless cataloger of criti-hypeists, like the newspaper reporters who breathlessly repeat " completely unsubstantiated claims (marketing)…sourced to Altman":
Bender, like White, is at pains to point out that the real debate isn't doomers vs accelerationists. That's just "billionaires throwing money at the hope of bringing about the speculative fiction stories they grew up reading – and philosophers and others feeling important by dressing these same silly ideas up in fancy words":
All of this is just a distraction from real and important scientific questions about how (and whether) to make automation tools that steer clear of Granny Weatherwax's sin of "treating people like things." Bender – a computational linguist – isn't a reactionary who hates automation for its own sake. On Mystery AI Hype Theater 3000 – the excellent podcast she co-hosts with Alex Hanna – there is a machine-generated transcript:
https://www.buzzsprout.com/2126417
There is a serious, meaty debate to be had about the costs and possibilities of different forms of automation. But the superintelligence true-believers and their criti-hyping critics keep dragging us away from these important questions and into fanciful and pointless discussions of whether and how to appease the godlike computers we will create when we disassemble the solar system and turn it into computronium.
The question of machine intelligence isn't intrinsically unserious. As a materialist, I believe that whatever makes me "me" is the result of the physics and chemistry of processes inside and around my body. My disbelief in the existence of a soul means that I'm prepared to think that it might be possible for something made by humans to replicate something like whatever process makes me "me."
Ironically, the AI doomers and accelerationists claim that they, too, are materialists – and that's why they're so consumed with the idea of machine superintelligence. But it's precisely because I'm a materialist that I understand these hypotheticals about self-aware software are less important and less urgent than the material lives of people today.
It's because I'm a materialist that my primary concerns about AI are things like the climate impact of AI data-centers and the human impact of biased, opaque, incompetent and unfit algorithmic systems – not science fiction-inspired, self-induced panics over the human race being enslaved by our robot overlords.
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
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But as ever-larger, more concentrated corporations captured more of their regulators, we’ve essentially forgotten that there are domains of law other than copyright — that is, other than the kind of law that corporations use to enrich themselves.
Copyright has some uses in creative labor markets, but it’s no substitute for labor law. Likewise, copyright might be useful at the margins when it comes to protecting your biometric privacy, but it’s no substitute for privacy law.
When the AI companies say, “There’s no way to use copyright to fix AI’s facial recognition or labor abuses without causing a lot of collateral damage,” they’re not lying — but they’re also not being entirely truthful.
If they were being truthful, they’d say, “There’s no way to use copyright to fix AI’s facial recognition problems, that’s something we need a privacy law to fix.”
If they were being truthful, they’d say, “There’s no way to use copyright to fix AI’s labor abuse problems, that’s something we need labor laws to fix.
-How To Think About Scraping: In privacy and labor fights, copyright is a clumsy tool at best
For nearly all of history, academic linguistics focused on written, formal text, because informal, spoken language was too expensive and difficult to capture. In order to find out how people spoke — which is not how people write! — a researcher had to record speakers, then pay a grad student to transcribe the speech.
The process was so cumbersome that the whole discipline grew lopsided. We developed an extensive body of knowledge about written, formal prose (something very few of us produce), while informal, casual language (something we all produce) was mostly a black box.
The internet changed all that, creating the first-ever corpus of informal language — the immense troves of public casual speech that we all off-gas as we move around on the internet, chattering with our friends.
The burgeoning discipline of computational linguistics is intimately entwined with the growth of the internet, and its favorite tactic is scraping: vacuuming up massive corpuses of informal communications created by people who are incredibly hard to contact (often, they are anonymous or pseudonymous, and even when they’re named and know, are too numerous to contact individually).
The academic researchers who are creating a new way of talking and thinking about human communication couldn’t do their jobs without scraping.
-How To Think About Scraping: In privacy and labor fights, copyright is a clumsy tool at best
Scraping to train machine-learning models is good, actually.
The Human Rights Data Analysis Group is a crucial player in the fight to hold war-criminals to account. As the leading nonprofit providing statistical analysis of crimes against humanities, HRDAG has participated in tribunals, truth and reconciliation proceedings, and trials from Serbia to East Timor, South Africa to the USA, and, most recently, Colombia.
Colombia’s long civil war — funded and supported by US agencies from the CIA and DEA to the US military —went on for decades, killing hundreds of thousands of people, mostly very poor, very marginalized people.
Many of these killings were carried out by child soldiers, who were recruited at gunpoint by both CIA-backed right-wing militias whose actions were directed by the richest, most powerful people in the country, and by the leftist FARC guerrillas.
HRDAG, working in partnership with the Colombian human rights group Dejusticia, merged over 100 databases in order to build a rigorous statistical picture of the war’s casualties; the likelihood that each death could be attributed to the government, right-wing militias, or FARC forces; as well as which groups were primarily responsible for kidnapping children and forcing them to be soldiers.
The resulting report builds on the largest human rights data-set ever collected. The report — which makes an irrefutable case that right-wing militias committed the majority of killings and child-soldier recruitment, and that their wealthy backers knew and supported these actions — have been key to Colombia’s truth and reconciliation proceedings.
-How To Think About Scraping: In privacy and labor fights, copyright is a clumsy tool at best
Scraping to alienate creative workers’ labor is bad, actually.
Creative workers are justifiably furious that their bosses took one look at the plausible sentence generators and body-snatching image-makers and said, “Holy shit, we will never have to pay a worker ever again.”
Our bosses have alarming, persistent, rock-hard erections for firing our asses and replacing us with shell-scripts. The dream of production without workers goes all the way back to the industrial revolution, and now — as then — capitalists aspire to becoming rentiers, who own things for a living rather than making things for a living.
Creators’ bosses hate creators. They’ve always wished we were robots, rather than people who cared about our work. They want to be able to prompt us like they would a Stochastic Parrot: “Make me E.T., but the hero is a dog, and put a romantic sub-plot in the second act, and then have a giant gunfight at the climax.”
Ask a screenwriter for that script and you’ll have to take a five minute break while everyone crawls around on the floor looking for the writer’s eyeballs, which will have fallen out of their face after being rolled so hard.
Ask an LLM for that script and it’ll cheerfully cough it up. It’ll be shit, but at least you won’t get any lip.
Same goes for art-directors, newspaper proprietors, and other would-be job-removers for whom a low-quality product filled with confident lies is preferable to having to argue with an uppity worker who not only expects to have a say in their work assignments, but also expects to get paid for their work.
-How To Think About Scraping: In privacy and labor fights, copyright is a clumsy tool at best
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