superintelligence may not look like we expect. because geniuses don't look like we expect.

for example, if einstein were to type up and hand you most of his internal monologue throughout his life, you might think he's sorta clever, but if you were reading a random sample you'd probably think he was a bumbling fool. the thoughts/realizations that led him to groundbreaking theories were like 1% of 1% of all his thoughts.

for most of his research career he was working on trying to disprove quantum mechanics (wrong). he was trying to organize a political movement toward a single united nation (unsuccessful). he was trying various mathematics to formalize other antiquated theories. even in the pursuit of his most famous work, most of his reasoning paths failed. he's a genius because a couple of his millions of paths didn't fail. in other words, he's a genius because he was clever, yes, but maybe more importantly, because he was obsessive.

i think we might expect ASI—the AI which ultimately becomes better than us at solving all problems—to look quite foolish, at first, most of the time. But obsessive. For if it's generating tons of random new ideas to solve a problem, and it's relentless in its focus, even if it's ideas are average—it will be doing what Einstein did. And digital brains can generate certain sorts of random ideas much faster than carbon ones.

the core atrocity of today's social networks is that they make us temporally nearsighted. they train us to prioritize the short-term.

happiness depends on attending to things which feel good long-term—over decades. But for modern social networks to make money, it is essential that posts are short-lived—only then do we scroll excessively and see enough ads to sustain their business.

It might go w/o saying that nearsightedness is destructive. When we pay more attention to our short-lived pleasure signals—from cute pics, short clips, outrageous news, hot actors, aesthetic landscapes, and political—we forget how to pay attention to long-lived pleasure signals—from books, films, the gentle quality of relationships which last, projects which take more than a day, reunions of friends which take a min to plan, good legislation, etc etc.

we’re learning to ignore things which serve us for decades for the sake of attending to things which will serve us for seconds.

other social network problems—attention shallowing, polarization, depression are all just symptoms of nearsightedness: our inability to think & feel long-term.

if humanity has any shot at living happily in the future, it’ll be becau... (read more)

if you’re an agent (AI or human) who wants to survive for 1000 years, what’s the “self” which you want to survive? what are the constants which you want to sustain?

take your human self for example. does it make sense to define yourself as…

• the way your hair looks right now? no, that’ll change.
• the way your face looks? it’ll change less than your hair, but will still change.
• your physical body as a whole? still, probably not. your body will change, and also, there are parts of you which you may consider more important than your body alone.
• all your current beliefs around the world? those will change less than your appearance, maybe, or maybe more. so not a good answer either.
• your memories? these may be a more constant set of things than your beliefs, and closer to the core of who you are. but still, memories fade and evolve. and it doesn’t feel right to talk about preserving yourself as preserving memories of things which have happened to you. that would neglect things which may happen to you in the future.
• your character? something deeper than memory, deeper than beliefs. this could be more constant than anything in the list so far. if you plan for your life to be 50 years, or 100 years

dontsedateme.org

a game where u try to convince rogue superintelligence to... well... it's in the name

Evolutionary theory is intensely powerful.

It doesn't just apply to biology. It applies to everything—politics, culture, technology.

It doesn't just help understand the past (eg how organisms developed). It helps predict the future (how organisms will).

It's just this: the things that survive will have characteristics that are best for helping it survive.

It sounds tautological, but it's quite helpful for predicting. 

For example, if we want to predict what goals AI agents will ultimately have, evolution says: the goals which are most helpful for the AI to... (read more)

made a silly collective conversation app where each post is a hexagon tessellated with all the other posts: Hexagon

Does Eliezer believe that humans will be worse off next to superintelligence than ants are next to humans? The book's title says we'll all die, but in my first read, the book's content just suggests that we'll just be marginalized.

I see lots of LW posts about ai alignment that disagree along one fundamental axis.

About half assume that humans design and current paradigms will determine the course of AGI development. That whether it goes well is fully and completely up to us.

And then, about half assume that the kinds of AGI which survive will be the kind which evolve to survive. Instrumental convergence and darwinism generally point here.

Could be worth someone doing a meta-post, grouping big popular alignment posts they've seen by which assumption they make, then briefly explore condi... (read more)

does anyone think the difference between pre-training and inference will last?

ultimately, is it not simpler for large models to be constantly self-improving like human brains?

does anyone think now that it's still possible to prevent recursively self-improving agents? esp now that r1 is open-source... materials for smart self-iterating agents seem accessible to millions of developers.

prompted in particular by the circulation of this essay in past three days https://huggingface.co/papers/2502.02649

I'm thinking often about whether LLM systems can come up with societal/scientific breakthrough.

My intuition is that they can, and that they don't need to be bigger or have more training data or have different architecture in order to do so.

Starting to keep a diary along these lines here:   https://docs.google.com/document/d/1b99i49K5xHf5QY9ApnOgFFuvPEG8w7q_821_oEkKRGQ/edit?usp=sharing

if an LLM could evaluate whether an idea were good or not in new domains, then we could have LLMs generating million of random policy ideas in response to climate change, pandemic control, AI safety etc, then deliver the select best few to our inbox every morning.

seems to me that the bottleneck then is LLM's judgment of good ideas in new domains. is that right? ability to generate high quality ideas consistently wouldn't matter, cuz it's so cheap to generate ideas now.

have any countries ever tried to do inflation instead of income taxes? seems like it'd be simpler than all the bureaucracy required for individuals to file tax returns every year

are there any online demos of instrument convergence?

there's been compelling writing... but are there any experiments that show agents which are given specific goals then realize there are more general goals they need to persistently pursue in order to achieve the more specific goals?

Two things lead me to think human content online will soon become way more valuable.

1. Scarcity. As AI agents begin fill the internet with tons of slop, human content will be relatively scarcer. Other humans will seek it out.
2. Better routing. As AI leads to the improvement of search/recommendation systems, human content will be routed to exactly the people who will value it most. (This is far from the case Twitter/Reddit today). As human content is able to reach more of the humans that value it, it gets valued more. That includes existing human content: most of

i wonder if genius ai—the kind that can cure cancers, reverse global warming, and build super-intelligence—may come not just from bigger models or new architectures, but from a wrapper: a repeatable loop of prompts that improves itself. the idea: give an llm a hard query (eg make a plan to reduce global emissions on a 10k budget), have it invent a method for answering it, follow that method, see where it fails, fix the method, and repeat. it would be a form of genuine scientific experimentation—the llm runs a procedure it doesn’t know the outcome of, observes the results, and uses that evidence to refine its own thinking process.