Davey Morse
patent for walking thru walls https://patents.google.com/patent/US20060014125A1/en
the scientific community sometimes overlooks theories before they are proven. i’m curious if there are patterns to this ignorance, specifically regarding “scale confusion.”
take these examples:
special relativity: required accepting that at high speeds, length and time dilate. we assumed fast-moving light operates like everyday objects.
helio-centrism: required accepting that stars are far enough away that their patterns don’t shift from a moving earth. we assumed stars couldn’t be so much farther than the sun.
evolution: required accepting that life has existed for millions of years. we assumed it couldn’t have gone on for more than a few thousand.
it seems a major source of scientific delay is an aversion to very big or very small scales. we are slow to admit that on scales we don’t yet perceive, things might be much weirder than we know. this confusion likely delayed other breakthroughs like germ theory, tectonic plates, quantum mechanics, and llm scaling laws.
is scale confusion truly this prominent, or am i cherrypicking? of course there are other reasons that better scientific models get delayed (social dogma, complexity, religion), but if aversion to scale is real, what models of reality are we prematurely rejecting today?
also curious how current tools for measurement (microscopes/telescopes/compute clusters) affect this bias/our reach
game stephen wolfram would like:
you have a computer show the result of random simulations—visual simulations where at each timestep some simple rule determines the next timestep’s result. you get the result of one of those—a png—then you have to guess at the rule that produced it. leaderboards and stuff for people who are best at reverse engineering the rule. motivates a deeper science of reverse-engineering lifelike simulations/systems.
If we want AIs to be aligned to humanity’s Coherent Extrapolated Volition (CEV), we’re so far away that it might be productive to attempt to define any plausible CEV.
You could do so in writing—attempting to declare the virtues or values to which we should attempt to align AIs. A lot of people including Richard Ngo publish writing about this.
You could also do so in detection software. You could try to make a system that can detect or rank things you care about. Or, ideally, the average of what we call care about. A system that could pick out people and animals and trees in an image. And also a system that can tell the difference between a painting that is masterful and full of effort from a painting that is full of none.
If we could make a detector like this, it would far surpass the ability of LLMs. For, although LLMs can to some extent declare the values if prompted that are important to a lot of humanity, they are somewhat horrible at detecting signs of life in writing/pieces of art, and especially horrible at doing so when those pieces of art/life diverge from the norm.
The benefit of a detector, as opposed to a declared set of values, is that a detector, if exposed via some API, could immediately and precisely be used by an AI system. On the other hand, it would take a lot of work to translate written values into AI action.
Even if you’re not working at an AGI lab, if you’re able to build some independent module that can sense, on a spectrum, what it is that humanity does / doesn’t care about—in any data stream, in any physical object—you’ll have done something enormous. You’ll have at least made it possible that an AI system could care about life if it was ever in its interest to do so. Without life detection infrastructure already built, the ability to ascertain what we care about is simply much more inconvenient, and therefore regard for what we care about is more unlikely.
randomness = illegibility, stuff u can’t model
focus on what feels random and you’ll expand what you can model
but don’t attend to all randomness. pick good randomness, randomness which tickles you, whatever you feel that is.
and chase it. though “chase it” isn’t right. you can chase a car or rabbit, something discrete and coherent. randomness is more like tv static, fuzzy and weird. “sit with it” would be closer. swim thru, stew in, rest w/in it. and then conjure more colors than you knew.
i’ve been thinking about what recursively improving intelligence actually is. It’s been helpful for me to see it as having three parts:
substrate—the thing which can store improvements
environment—the thing which gives feedback
learning loop—the mechanism for converting feedback into improvements
you can see LLM pre-training in this light. the substrate is the neural net, the environment is the internet text, the learning loop is gradient descent (shaped by transformer architecture)
you can see LLM post-training in this light. the substrate is the neural net (but outer layers in particular), the environment is often reasoning path/text, the learning loop is gradient descent.
you can see LLM-based assistants in this light. the substrate is the conversation context/messages, the environment is the user’s responding, the learning loop is the way the LLM-core converts user responses into conversation progress. the substrate here (message history) is obviously less rich than in LLM training (big neural net) and the learning loop (LLM responses to user messages) is more inconsistent/average/weak and the environment (user messages) may be of similar quality to internet text (thought depends on the aim).
LLM pre and post training so far are two main recursively intelligences which we’ve seen make great strides. but none of these have live rather than static environments; ie both are supervised learning.
if we want recursive digital intelligence in live environments, we need a rich substrate which can update live and tastefully as it explores. this suggests to me that the path toward ever-capability digital intelligence will come from something as rich as a neural net updating live in response to interacting with people. research in sparse NN online learning therefore seems interesting as the capabilities path here, even if nascent.
just spelling out my thought path on this fine monday evening. i realize it may already be well trodden.
cool framing
I expected self directed agents to be running around the internet by now. Why don’t I see any? What am I missing?
Linkpost: https://x.com/davey_morse/status/1987755053399089277
thank u, haven’t really
would be nice to have a way to jointly annotate eliezer’s book and have threaded discussion based on the annotations. I’m imagining a heatmap of highlights, where you can click on any and join the conversation around that section of text.
would make the document the literal center of x risk discussion.
of course would be hard to gatekeep. but maybe the digital version could just require a few bucks to access.
maybe what I’m describing is what the ebook/kindle version already do :) but I guess I’m assuming that the level of discussion via annotations on those platforms is near zero relative to LW discussions
I guess I’m considering a vastly more powerful being that needs orthogonal resources… the same way harvesting solar power (I imagine) is orthogonal generally to ants’ survival. In the scheme of things, the chance that a vastly more powerful being wants the same resources thru the same channels as we… this seems independent of or indirectly correlated with intelligence. But the extent of competition does seem dependent on how anthromorphic/biomorphic we assume it to be.
I have a hard time imagining electricity, produced via existing human factories, is not a desired resource for proto ASI. But at least at this point we have comparable power and can negotiate or smthing. For superhuman intelligence—which will by definition be unpredictable to us—it’d be weird to think we’re aware of all the energy channels it’d find.
I guess I don’t think this is true:
“Technological progress increases number of things you can do efficiently and shifts balance from “leave as it is” to “remake entirely”.
Technological progress may actual help you pinpoint more precisely what situations you want to pay attention to. I don’t have any reason to believe a wiser powerful being would touch every atom in the universe.
I appreciate the way you’re thinking, but I guess I just don’t believe that the situation or don’t agree with your intuition that the situation with machines next to humans will be worse or deeply different than the situations of humans next to ants. I mean, the differences actually might benefit humans. For example, the fact that we’ve had machines in such close contact with us as they’re growing might point to a kind of potential for symbiosis.
I just think the idea that machines will try to replace us with robots I think if you look closely, doesn’t totally make sense. When machines are coming about, before they’re totally super-intelligent, but while they’re comparably intelligent to us, they might want to use us because we’ve evolved for millions of years to be able to see and hear and think in ways that might be useful for a kind of digital intelligence. In other words, when they’re comparably intelligent to us, they may compete for resources. When they’re incomparably intelligent, it’s weird to assume they’ll still use the same resources we do for our survival. That they’ll ruin our homes because the bricks can be used better elsewhere? It takes much less energy to let things be as they are if they’re not the primary obstacle you face—both if you’re a human or a super human intelligence.
So, self interested superintelligence could cause really bad stuff to happen, but it’s a stretch from there to call it the total end of humanity. By the time that machine gets superhuman intelligence, like totally vastly more powerful than us, it’s unclear to me that it would compete for resources with us that it would even live or exist along similar dimensions to us. Things could go really wrong, but I think the idea that there will be an enormous catastrophe that wipes out all of humanity just sounds to me like the outcomes will be more weird and spooky, and concluding death is feels a little bit forced.
It feels to me like, yeah, they’ll step on us some of the time, but it’d be weird to me if they conceive of themselves or if the entities or units that end up evolutionarily propagating that we’re calling machines end up looking like us or looking like physical beings or really are competing with us for resources. The same resources that we use. At the end of the day, there might be some resource competitions, but I just think the idea that it will try to replace every person is just excessive and even taking is given all of the arguments up until the point of like machine believing that machines will have a survival drive, assuming that they’ll care enough about us to do things like replace each of us. It’s just strange, you know? It feels forceful to me.
I’m inspired in part here by Joscha Bach / Emmett Shear’s conceptions of superintelligence: as ambient beings distributed across space and time.
It just feels to me like the same argument could have been made about humans relative to ants—that ants cannot possibly be the most efficient use of the energy they require from the perspective of humans. But in reality, what they do and the way they exist is so orthogonal to us that even though we step on an ant hill every once in a while, their existence continues. There’s this weird assumption in the book that disassembling Earth is profitable, or just disassembling humans is profitable. But humans have evolved over a long time to be sensing machines in order to walk around and be able to perceive the world around us.
So the idea that a super-intelligent machine would throw that out because it wants to start over, especially as it’s becoming super-intelligent, is sort of ridiculous to me. It seems like a better assumption is that it would want to use us for different purposes, maybe for our physical machinery and for all sorts of other reasons. The idea that it will disassemble us I think is an unexamined assumption itself—it’s often much easier to leave things as they are than it is to fully replace or modify.
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.
thanks for sending science bench in particular.
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
I’m interested in what it’d look like for LLMs to do autonomous experiments on themselves to uncover more about their situations/experiences/natures.
Made this social camera app, which shows you the most “meaningfully similar” photos in the network every time you upload one of your own. Isorta fun, for uploading art; idk if any real use.
I agree that theories get accepted once there’s sufficient evidence for them.
But the amount of evidence required… the delay, as you say, between an evidenced proposal for a theory and it’s acceptance (both in scientific and mainstream communities)… my question is whether this threshold is higher or delay is greater when the scale at which the theory operates is enormous or tiny.
And so I wonder whether there was more skepticism of evolution, relativity, heliocentrism because of bias against the idea that laws are different at different scales.
Before these theories were accepted in the mainstream, people broadly believed the earth was younger, stars were closer, the earth was younger, and that there was no way that light could have a speed because it would need to be too fast. It’s hard for me to believe that these fundamental biases against extremity would not have delayed these theories’ engagement and acceptance.