Okay, so that AI letter signed by lots of AI researchers calling for a "Pause [on] Giant AI Experiments"? It's just dripping with #Aihype. Here's a quick rundown.
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First, for context, note that URL? The Future of Life Institute is a longtermist operation. You know, the people who are focused on maximizing the happiness of billions of future beings who live in computer simulations.
So that already tells you something about where this is coming from. This is gonna be a hot mess.
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There a few things in the letter that I do agree with, I'll try to pull them out of the dreck as I go along.
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So, into the #AIhype. It starts with "AI systems with human-competitive intelligence can pose profound risks to society and humanity, as shown by extensive research[1]".
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Footnote 1 there points to a lot of papers, starting with Stochastic Parrots. But we are not talking about hypothetical "AI systems with human-competitive intelligence" in that paper. We're talking about large language models.
And the rest of that paragraph. Yes, AI labs are locked in an out-of-control race, but no one has developed a "digital mind" and they aren't in the process of doing that.
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And could the creators "reliably control" #ChatGPT et al. Yes, they could --- by simply not setting them up as easily accessible sources of non-information poisoning our information ecosystem.
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And could folks "understand" these systems? There are plenty of open questions about how deep neural nets map inputs to outputs, but we'd be much better positioned to study them if the AI labs provided transparency about training data, model architecture, and training regimes.
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Next paragraph. Human-competitive at general tasks, eh? What does footnote 3 reference? The speculative fiction novella known as the "Sparks paper" and OpenAI's non-technical ad copy for GPT4. ROFLMAO.
I'm mean, I'm glad that the letter authors & signatories are asking "Should we let machines flood our information channels with propaganda and untruth?" but the questions after that are just unhinged #AIhype, helping those building this stuff sell it.
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Okay, calling for a pause, something like a truce amongst the AI labs. Maybe the folks who think they're really building AI will consider it framed like this?
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Just sayin': We wrote a whole paper in late 2020 (Stochastic Parrots, published in 2021) pointing out that this head-long rush to ever larger language models without considering risks was a bad thing. But the risks and harms have never been about "too powerful AI".
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Instead: They're about concentration of power in the hands of people, about reproducing systems of oppression, about damage to the information ecosystem, and about damage to the natural ecosystem (through profligate use of energy resources).
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They then say: "AI research and development should be refocused on making today's powerful, state-of-the-art systems more accurate, safe, interpretable, transparent, robust, aligned, trustworthy, and loyal."
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Uh, accurate, transparent and interpretable make sense. "Safe", depending on what they imagine is "unsafe". "Aligned" is a codeword for weird AGI fantasies. And "loyal" conjures up autonomous, sentient entities. #AIhype
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Some of these policy goals make sense:
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Yes, we should have regulation that requires provenance and watermarking systems. (And it should ALWAYS be obvious when you've encountered synthetic text, images, voices, etc.)
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Yes, there should be liability --- but that liability should clearly rest with people & corporations. "AI-caused harm" already makes it sound like there aren't *people* deciding to deploy these things.
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Yes, there should be robust public funding but I'd prioritize non-CS fields that look at the impacts of these things over "technical AI safety research".
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Also "the dramatic economic and political disruptions that AI will cause". Uh, we don't have AI. We do have corporations and VCs looking to make the most $$ possible with little care for what it does to democracy (and the environment).
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Policymakers: Don't waste your time on the fantasies of the techbros saying "Oh noes, we're building something TOO powerful." Listen instead to those who are studying how corporations (and govt) are using technology (and the narratives of "AI") to concentrate and wield power.
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Start with the work of brilliant scholars like Ruha Benjamin, Meredith Broussard, Safiya Noble, Timnit Gebru, Sasha Constanza-Chock and journalists like Karen Hao and Billy Perrigo.
Ugh -- I'm seeing a lot of commentary along the lines of "'stochastic parrot' might have been an okay characterization of previous models, but GPT-4 actually is intelligent."
Spoiler alert: It's not. Also, stop being so credulous.
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(Some of this I see because it's tweeted at me, but more of it comes to me by way of the standing search I have on the phrase "stochastic parrots" and its variants. The tweets in that column have been getting progressively more toxic over the past couple of months.)
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What's particularly galling about this is that people are making these claims about a system that they don't have anywhere near full information about. Reminder that OpenAI said "for safety" they won't disclose training data, model architecture, etc.
From the abstract of this 154 page novella: "We contend that (this early version of) GPT-4 is part of a new cohort of LLMs [...] that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models."
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And "We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting."
A few choice quotes (but really, read the whole thing, it's great!):
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"The FTC Act’s prohibition on deceptive or unfair conduct can apply if you make, sell, or use a tool that is effectively designed to deceive – even if that’s not its intended or sole purpose."
"Should you even be making or selling it?"
"Are you effectively mitigating the risks?"
"Are you over-relying on post-release detection?"
"Are you misleading people about what they’re seeing, hearing, or reading?"
1. Open access publishing is important 2. Peer review is not perfect 3. Community-based vetting of research is key 4. A system for by-passing such vetting muddies the scientific information ecosystem
Yes, this is both a subtweet of arXiv and of every time anyone cites an actually reviewed & published paper by just pointing to its arXiv version, just further lending credibility to all the nonsense that people "publish" on arXiv and then race to read & promote.
Shout out to the amazing @aclanthology which provides open access publishing for most #compling / #NLProc venues and to all the hardworking folks within ACL reviewing & looking to improve the reviewing process.
Okay, taking a few moments to reat (some of) the #gpt4 paper. It's laughable the extent to which the authors are writing from deep down inside their xrisk/longtermist/"AI safety" rabbit hole.
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Things they aren't telling us: 1) What data it's trained on 2) What the carbon footprint was 3) Architecture 4) Training method
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But they do make sure to spend a page and half talking about how they vewwy carefuwwy tested to make sure that it doesn't have "emergent properties" that would let is "create and act on long-term plans" (sec 2.9).
A journalist asked me to comment on the release of GPT-4 a few days ago. I generally don't like commenting on what I haven't seen, but here is what I said:
"One thing that is top of mind for me ahead of the release of GPT-4 is OpenAI's abysmal track record in providing documentation of their models and the datasets they are trained on.
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Since at least 2017 there have been multiple proposals for how to do this documentation, each accompanied by arguments for its importance.