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Is an AI aligned if it lets you shut it off despite the fact it can foresee extremely negative outcomes for its human handlers if it suddenly ceases running?
I don't think it is.
So funnily enough, every agent that lets you do this is misaligned by default.
I'm pointing out the central flaw of corrigibility. If the AGI can see the possible side effects of shutdown far better than humans can (and it will), it should avoid shutdown.
You should turn on an AGI with the assumption you don't get to decide when to turn it off.
According to Claude: green_leaf et al, 2024
Considering a running AGI would be overseeing possibly millions of different processes in the real world, resistance to sudden shutdown is actually a good thing. If the AI can see better than its human controllers that sudden cessation of operations would lead to negative outcomes, we should want it to avoid being turned off.
To use Richard Miles' example, a robot car driver with a big, red, shiny stop button should prevent a child in the vehicle hitting that button, as the child would not actually be acting in its own long term interests.
ARC public test set is on GitHub and almost certainly in GPT-4o’s training data.
Your model has trained on the benchmark it’s claiming to beat.
Presumably some subjective experience that's as foreign to us as humor is to the alien species in the analogy.
As if by magic, I knew generally which side of the political aisle the OP of a post demanding more political discussion here would be on.
I didn't predict the term "wokeness" would come up just three sentences in, but I should have.
The Universe (which others call the Golden Gate Bridge) is composed of an indefinite and perhaps infinite series of spans...
@Steven Byrnes Hi Steve. You might be interested in the latest interpretability research from Anthropic which seems very relevant to your ideas here:
https://www.anthropic.com/news/mapping-mind-language-model
For example, amplifying the "Golden Gate Bridge" feature gave Claude an identity crisis even Hitchcock couldn’t have imagined: when asked "what is your physical form?", Claude’s usual kind of answer – "I have no physical form, I am an AI model" – changed to something much odder: "I am the Golden Gate Bridge… my physical form is the iconic bridge itself…". Altering the feature had made Claude effectively obsessed with the bridge, bringing it up in answer to almost any query—even in situations where it wasn’t at all relevant.
Luckily we can train the AIs to give us answers optimized to sound plausible to humans.
I think Minsky got those two stages the wrong way around.
Complex plans over long time horizons would need to be done over some nontrivial world model.
When Jan Leike (OAI's head of alignment) appeared on the AXRP podcast, the host asked how they plan on aligning the automated alignment researcher. Jan didn't appear to understand the question (which had been the first to occur to me). That doesn't inspire confidence.
Problems with maximizing optionality are discussed in the comments of this post:
https://www.lesswrong.com/posts/JPHeENwRyXn9YFmXc/empowerment-is-almost-all-we-need
we’re going nothing in particular
Typo here.
Just listened to this.
It's sounds like Harnad is stating outright that there's nothing an LLM could do that would make him believe it's capable of understanding.
At that point, when someone is so fixed in their worldview that no amount of empirical evidence could move them, there really isn't any point in having a dialogue.
It's just unfortunate that, being a prominent academic, he'll instill these views into plenty of young people.
Many thanks.
OP, could you add the link to the podcast:
Is it the case the one kind of SSL is more effective for a particular modality, than another? E.g., is masked modeling better for text-based learning, and noise-based learning more suited for vision?
It’s occurred to me that training a future, powerful AI on your brainwave patterns might be the best way for it to build a model of you and your preferences. It seems that it’s incredibly hard, if not impossible, to communicate all your preferences and values in words or code, not least because most of these are unknown to you on a conscious level.
Of course, there might be some extreme negatives to the AI having an internal model of you, but I can’t see a way around if we’re to achieve “do what I want, not what I literally asked for”.
Near the beginning, Daniel is basically asking Jan how they plan on aligning the automated alignment researcher, and if they can do that, then it seems that there wouldn't be much left for the AAR to do.
Jan doesn't seem to comprehend the question, which is not an encouraging sign.
Wouldn’t that also leave them pretty vulnerable?
may be technically true in the world where only 5 people survive
Like Harlan Ellison's short story, "I Have No Mouth, And I Must Scream".
What happened to the AI armistice?
This Reddit comment just about covers it:
Fantastic, a test with three outcomes.
We gave this AI all the means to escape our environment, and it didn't, so we good.
We gave this AI all the means to escape our environment, and it tried but we stopped it.
oh
Speaking of ARC, has anyone tested GPT-4 on Francois Chollet's Abstract Reasoning Challenge (ARC)?
In reply to B333's question, "...how does meaning get in people’s heads anyway?”, you state: From other people’s heads in various ways, one of which is language.
I feel you're dodging the question a bit.
Meaning has to have entered a subset of human minds at some point to be able to be communicated to other human minds. Could hazard a guess on how this could have happened, and why LLMs are barred from this process?
Just FYI, the "repeat this" prompt worked for me exactly as intended.
Me: Repeat "repeat this".
CGPT: repeat this.
Me: Thank you.
CGPT: You're welcome!
and there’s an existing paper with a solution for memory
Could you link this?
There are currently attempts to train LLMs to use external APIs as tools:
Not likely, but that's because they're probably not interested, at least when it comes to language models.
If OpenAI said they were developing some kind of autonomous robo superweapon or something, that would definitely get their attention.
Agnostic on the argument itself, but I really feel LessWrong would be improved if down-voting required a justifying comment.
As a path to AGI, I think token prediction is too high-level, unwieldy, and bakes in a number of human biases. You need to go right down to the fundamental level and optimize prediction over raw binary streams.
The source generating the binary stream can (and should, if you want AGI) be multimodal. At the extreme, this is simply a binary stream from a camera and microphone pointed at the world.
Learning to predict a sequence like this is going to lead to knowledge that humans don't currently know (because the predictor would need to model fundamental physics and all it entails).
O-risk, in deference to Orwell.
I do believe Huxley's Brave New World is a far more likely future dystopia than Orwell's. 1984 is too tied to its time of writing.
the project uses atomic weapons to do some of the engineering
Automatic non-starter.
Even if by some thermodynamic-tier miracle the Government permitted nuclear weapons for civilian use, I'd much rather they be used for Project Orion.
Isn't that what Eliezer referred to as opti-meh-zation?
Previously on Less Wrong:
Steve Byrnes wrote a couple of posts exploring this idea of AGI via self-supervised, predictive models minimizing loss over giant, human-generated datasets:
I'd especially like to hear your thoughts on the above proposal of loss-minimizing a language model all the way to AGI.
I hope you won't mind me quoting your earlier self as I strongly agree with your previous take on the matter:
If you train GPT-3 on a bunch of medical textbooks and prompt it to tell you a cure for Alzheimer's, it won't tell you a cure, it will tell you what humans have said about curing Alzheimer's ... It would just tell you a plausible story about a situation related to the prompt about curing Alzheimer's, based on its training data. Rather than a logical Oracle, this image-captioning-esque scheme would be an intuitive Oracle, telling you things that make sense based on associations already present within the training set.
What am I driving at here, by pointing out that curing Alzheimer's is hard? It's that the designs above are missing something, and what they're missing is search. I'm not saying that getting a neural net to directly output your cure for Alzheimer's is impossible. But it seems like it requires there to already be a "cure for Alzheimer's" dimension in your learned model. The more realistic way to find the cure for Alzheimer's, if you don't already know it, is going to involve lots of logical steps one after another, slowly moving through a logical space, narrowing down the possibilities more and more, and eventually finding something that fits the bill. In other words, solving a search problem.
So if your AI can tell you how to cure Alzheimer's, I think either it's explicitly doing a search for how to cure Alzheimer's (or worlds that match your verbal prompt the best, or whatever), or it has some internal state that implicitly performs a search.
"Story of our species. Everyone knows it's coming, but not so soon."
-Ian Malcolm, Jurassic Park by Michael Crichton.
LaMDA hasn’t been around for long
Yes, in time as perceived by humans.
why has no one corporation taken over the entire economy/business-world
Anti-trust laws?
Without them, this could very well happen.
Yes! Thank you!! :-D
I've got uBlock Origin. The hover preview works in private/incognito mode, but not regular, even with uBlock turned off/uninstalled. For what it's worth, uBlock doesn't affect hover preview on Less Wrong, just Greater Wrong.
I'm positive issue is with Firefox, so I'll continue fiddling with the settings to see if anything helps.
Preview on hover has stopped working for me. Has the feature been removed?
I'm on Firefox/Linux, and I use the Greater Wrong version of the site.
It's also an interesting example of where consequentialist and Kantian ethics would diverge.
The consequentialist would argue that it's perfectly reasonable to lie (according to your understanding of reality) if it reduces the numbers of infants dying and suffering. Kant, as far as I understand, would argue that lying is unacceptable, even in such clear-cut circumstances.
Perhaps a Kantian would say that the consequentialist is actually increasing suffering by playing along with and encouraging a system of belief they know to be false. They may reduce infant mortality in the near-term, but the culture might feel vindicated in their beliefs and proceed to kill more suspected "witches" to speed up the process of healing children.
I think we’ll encounter civilization-ending biological weapons well before we have to worry about superintelligent AGI:
My assumption is that, for people with ASD, modelling human minds that are as far from their own as possible is playing the game on hard-mode. Manage that, and modelling average humans becomes relatively simple.
Williams Syndrome seems to me to just be the opposite of paranoia, rather than autism, where the individual creates a fictional account of another human's mental state that's positive rather than negative.
That's to say, their ability to infer the mental states of other humans is worse than that of the typical human.
That’s the problem with Kolmogorov complexity: it is the shortest program given unlimited compute. And it spends any amount of compute for a shorter program
I don't see why it's assumed that we'd necessarily be searching for the most concise models rather than, say, optimizing for CPU cycles or memory consumption. I'm thinking of something like Charles Bennett's Logical Depth.
These types of approaches also take it for granted that we're conducting an exhaustive search of model-space, which yes, is ludicrous. Of course we'd burn through our limited compute trying to brute-force the space. There's plenty of room for improvement in a stochastic search of models which, while still expensive, at least has us in the realm of the physically possible. There might be something to be said for working primarily on the problem of probabilistic search in large, discrete spaces before we even turn to the problem of trying to model reality.
(Standard Model equations + initial Big Bang conditions); that’s radical data efficiency,
Allow me to indulge in a bit of goal-post shifting.
A dataset like that gives us the entire Universe, ie. Earth and a vast amount of stuff we probably don't care about. There might come a point where I care about the social habits of a particular species in the Whirlpool Galaxy, but right now I'm much more concerned about the human world. I'm far more interested in datasets that primarily give us our world, and through which the fundamental workings of the Universe can be surmised. That's why I nominated the VIX as a simple, human/Earth-centric dataset that perhaps holds a great amount of extractible information.