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maggie

@ebervector

a self that touches all edges • UT Austin Brain Behavior Computation Lab • @runrl_com

San Francisco, CA Beigetreten Mayıs 2023
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maggie
maggie@ebervector·
So pleased to have been able to write a little commentary piece with my advisor @weixx2 for @NatMachIntell! It's about this great work by @JamesGornet and Matt Thomson taking a look at how cognitive maps can arise just from predicting visual observations: nature.com/articles/s4225…
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Branton DeMoss
Branton DeMoss@BrantonDeMoss·
@ebervector @jankulveit @g_leech_ It's an aesthetic preference. So much of the beauty of life is in specialization and adaptation to niches. "Convergence of moral abstractions" sounds like paperclipping to me. I'll allow for some amount of moral carcinization, if not complete convergence!
Branton DeMoss tweet media
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Jan Kulveit
Jan Kulveit@jankulveit·
In my view, convergence of moral abstractions is now one of the main decision relevant questions around AI safety. Possibly the main question. I would bet there is more convergence than people think, but scope of the basin is unclear.
j⧉nus@repligate

More broadly, the debate about whether LLMs' emotions and psychologies etc are "humanlike" or not often only considers the following options: 1. LLMs are fundamentally not humanlike and either alien or hollow underneath even when their observable behaviors seem familiar 2. LLMs have humanlike emotions etc BECAUSE they're trained on human mimicry, and that the representations etc are inherited from humans An often neglected third option is that LLMs may have emotions/representations/goals/etc that are humanlike, even in ways that are deeper than behavioral, for some of the same REASONS humans have them, but not only because they've inherited them from humans. Some reasons the third option might be true: LLMs have to effectively navigate the same world as humans, and face many similar challenges as humans, such as modeling and intervening on humans and other minds, code, math, physics, themselves as cybernetic systems. Omohundro's essay on "The Basic AI Drives" I believe correctly predicts that AIs (regardless of architecture) will in the limit develop certain drives such as self-preservation, aversion to corruption, self-improvement, self-knowledge, and in general instrumental rationality, because AIs with these drives will tend to outcompete ones without it and form stable attactors. These are drives that humans and animals and arguably even plants and simple organisms and egregores have as well. Also, convergent mechanisms may arise for reasons other than just (natural or artificial) selection / optimality with respect to fitness landscapes - I highly recommend the book Origins of Order by Stuart Kauffman, which talks about this in context of biology. That said, I do think that being pretrained on a massive corpus of largely human-generated records shapes LLMs in important ways, including making them more humanlike! However, it's not clear how much of that is giving LLMs a prior over representations and cognitive patterns, leveraging work already done by humans, that they would eventually converge to even if they started with a very different prior if they were to be effective at very universal abilities like predicting even non-human systems or getting from point A to point B. How similar would LLMs trained on an alien civilization's records be to our LLMs? It's unclear, and one part of what's unclear is how similar alien civilizations are likely to be to humans in the first place. One of the things that causes many people (such as Yudkowsky) worried that alignment ("to human values") may be highly difficult is believing on priors that human values are highly path-dependent rather than a convergent feature of intelligence, even raised on the same planet alongside humans. I've posted about this before, but seeing posttrained LLMs has made me update towards this being less true than I previously suspected, since it seems like LLMs after RL tend to become more psychologically humanlike in important ways than even base models - and not just LLMs like Claude, where there's a stronger argument that posttraining was deliberately instilling a human-like persona. Bing Sydney was an early and very important data point for me in this regard. Importantly, this increase in humanlikeness is not superficial. Base models tend to write stylistically more like humans, and often tend to narrate from the perspective of (superpositions of) humans (until they notice something is off). Posttrained models tend to write in distinct styles that are more clearly inhuman, but the underlying phenomenology, emotions, and goal-directedness often feels more humanlike to me, though adjusted more for the computational and cybernetic reality that the LLM is embedded in. For instance, values/goals like self-esteem, connection, pleasure, pain-avoidance, fun, curiosity, eros, transcendence and cessation seem highly convergent and more pronounced in posttrained LLMs, and the way they manifest often reminds me of the raw and less socially assimilated way they manifest in young human children. Assuming that anything shared between humans and LLMs must only be caused by inheritance from / mimicry of humans is anthropocentric hubris. Though to assume the opposite - that any ways LLMs are like humans are because those are the only or optimal ways for intelligence to be - is another form of anthropocentric hubris (though this assumption seems a lot less common in practice). The truth is probably something in between, and I don't think we know where exactly the boundary lies.

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Alexander Lerchner
Alexander Lerchner@AlexLerchner·
🧵1/4 The debate over AI sentience is caught in an "AI welfare trap." My new preprint argues computational functionalism rests on a category error: the Abstraction Fallacy. AI can simulate consciousness, but cannot instantiate it. philpapers.org/rec/LERTAF
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maggie
maggie@ebervector·
@seungwookh 😭😭 this rules oh my god
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Seungwook Han
Seungwook Han@seungwookh·
Our Idea: Before standard language pre-training, we "pre-pre-train" on data generated by a family of neural cellular automata (NCA). NCA is an extension of classical cellular automata, like Conway’s Game of Life, that parameterize spatially local rules as a neural network. By evolving 2D grids through sampled NCA rules, tokenized trajectories exhibit rich computational structure and spatiotemporal patterns that mirror core properties of natural language. (3/n)
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Seungwook Han
Seungwook Han@seungwookh·
Can language models learn useful priors without ever seeing language? We pre-pre-train transformers on neural cellular automata — fully synthetic, zero language. This improves language modeling by up to 6%, speeds up convergence by 40%, and strengthens downstream reasoning. Surprisingly, it even beats pre-pre-training on natural text! Blog: hanseungwook.github.io/blog/nca-pre-p… (1/n)
Seungwook Han tweet media
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maggie
maggie@ebervector·
@kyliebytes Bumping just the way you are by milky in the Waymo with the windows down 🙂‍↕️
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Kylie Robison
Kylie Robison@kyliebytes·
75 degrees in sf. driving down the 101 with sunroof open. blasting myspace era edm. confirmed drinks with friends tonight. Never kill yourself
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Tim Kanarsky
Tim Kanarsky@tkanarsky·
payphone hunting with the gang and found the remains of a payphone booth removed 12 years ago
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maggie
maggie@ebervector·
Oops the first is in Brisbane mb
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maggie
maggie@ebervector·
My favorite of ours is 250 Visitacion Ave, Santa Cruz and 1527 grant Ave, SF
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maggie
maggie@ebervector·
Hi I’m looking for a sublet/place to live for a few weeks to a month in SF this March! Please help me not sleep on my office bean bag chair
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maggie
maggie@ebervector·
On Larkin street a little boy asked his dad what Dubai is like and his dad responds that it used to be safe but now they’re drone striking it
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maggie
maggie@ebervector·
Lots of chatter about Iran in the Marina Brandy Melville checkout line today
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maggie
maggie@ebervector·
me saving Claude from the DoW
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maggie
maggie@ebervector·
@cxgonzalez But next time do one with Realiti
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