Logan Riggs
Boston, MA, USA

Wiki Contributions

Comments

The AI Countdown Clock

Why did you use the weak AGI question? Feels like a motte-and-Bailey to say “x time until AGI” but then link to the weak AGI question.

Frame for Take-Off Speeds to inform compute governance & scaling alignment

I wonder how much COVID got people to switch to working on Biorisks.

What I’m interested here is talking to real researchers and asking what events would convince them to switch to alignment. Enumerating those would be useful for explaining to them.

I think asking for specific capabilities would also be interesting. Or what specific capabilities they would’ve said in 2012. Then asking how long they expect between that capability and an x-catastrophe.

Gato as the Dawn of Early AGI

I agree. You can even get career advice here at https://www.aisafetysupport.org/resources/career-coaching

Or feel free to message me for a short call. I bet you could get paid to do alignment work, so it’s worth looking into at least.

Prize for Alignment Research Tasks

[Note: this one, steelman, and feedback on proposals all have very similar input spaces. I think I would ideally mix them as one in an actual product, but I'm keeping them separate for now]

Task: Obvious EA/Alignment Advice

  • Context: There are several common mental motions that the EA community does which are usefully applied to alignment. Ex. "Differential Impact", "Counterfactual Impact", "Can you clearly tell a story on how this reduces x-risk?", and "Truly Optimizing for X". A general "obvious advice" is useful for general capabilities as well, but this is intended to have a strong, EA tilt to it.
  • Input Type: A plan for reducing x-risk, either a research direction, upskilling, or field-building
  • Output Type: Useful exploratory questions 

Instance 1

Input:

I'm thinking about taking up a job at Google to upskill in machine learning before I apply to Anthropic or Redwood Research.

Output:

Isn't there a more direct path to upskilling in machine learning? Like taking a course or doing a bunch of dumb, fun projects? Additionally, is programming/machine learning your comparative advantage?

Instance 2

Input:

I'm working on robustness to distributional shift, specifically identifying when an input is out-of-distribution so it can default to a no-op or asking for human feedback on that input.

Output:

How neglected is this field? If you didn't work in this field, would someone else have done the work you'd do, but a year or two later?

Instance 3

Input:

I'm building an interpretability tool that can tell you what the model was thinking when it was making a classification, for instance, what part of the input it was using to make that classification.

Output:

Let's say you succeed in making this tool; can you tell a story on how this tool can reduces AI x-risk?

Prize for Alignment Research Tasks

Task: Steelman Alignment proposals

  • Context: Some alignment research directions/proposals have a kernel of truth to them. Steelmanning these ideas to find the best version of it may open up new research directions or, more likely, make the pivot to alignment research easier. On the latter, some people are resistant to change their research direct, and a steelman will only slightly change the topic while focusing on maximizing impact. This would make it easier to convince these people to change to a more alignment-related direction.
  • Input Type: A general research direction for advancing alignment.
  • Output Type: A steelman of the research direction

Instance 1

Input:

Currently AI systems are prone to bias and unfairness which is unaligned with our values. I work in bias and fairness, specifically in analyzing how the biases in large datasets (such as common crawl) affect the probability distributions in large language models.

Output:

What I'm most excited about for bias and fairness research is understanding the model's concepts. If we can build tools that pin-point the bias or unfairness in a model such that there's a causal explanation and we can manipulate that part of the model to make it unbiased and fair, then we may be able to pin-point other parts of the model we care about. For example, parts of the model that are optimizing for a goal, signs of deception, and human values. Research in this area while asking "how could this work generalize to pin-pointing [human values/deception]" seems promising, especially making use of the current literature.

Instance 2

Input:

AI capabilities will continue to increase, so how do we propose utilizing this to boost alignment research. An Alignment research assistant (ARA) can perform many different tasks for the researcher such as summarizing papers, writing code, and assisting math proofs.

Output:

What I'm most excited about for something like ARA is differential research that mostly helps alignment researchers. Collecting datasets and tasks to e.g. give feedback on alignment proposals and automate mentorships from alignment researchers would be incredibly useful for avoiding dead-ends and scaling up junior researchers, respectively.

Instance 3

Input:

Before we trust the AI, we can prevent it from taking over the world by not giving it internet access or putting it in a faraday cage to avoid causal interactions with the outside world. Another possibility is running the AI in a simulated environment different than our own, so that we could catch it if it starts to perform power seeking.

Output:

What I'm most excited about for boxing an AI is figuring out the minimal capabilities required to perform a pivotal act. We box because we're concerned it may have too many resources (internet access, rich world model, etc) in order to gain more resources, but if we can constrain the capabilities of it enough to ensure a pivotal act, this reduces x-risk. Creating more and more pivotal acts that require less capabilities (or a different variety of capabilities) means we don't have to optimize as much and risk power-seeking.

Instance 4

Input:

We can perform reinforcement learning from human feedback to align the AI to human values. By achieving greater instruct-ability with smaller models and extrapolating trends with larger models, we can more safely build larger models that do what we ask them to.

Output:

What I'm most excited about human-in-the-loop feedback is using this system to accelerate alignment research and create better human-in-the-loop systems that then create better human-in-the-loop systems (i.e. recursive bootstrapping). Additionally, providing lots of human feedback can give the model a better understanding of human values. Although this doesn't imply the model optimizes for those human values, it does mean we have a more coherent concept to point to with e.g. interpretability tools.

Prize for Alignment Research Tasks

Task: Feedback on alignment proposals

  • Context: Some proposals for a solution to alignment are dead ends or have common criticisms. Having an easy way of receiving this feedback on one's alignment proposal can prevent wasted effort as well as furthering the conversation on that feedback.
  • Input Type: A proposal for a solution to alignment or a general research direction
  • Output Type: Common criticisms or arguments for dead ends for that research direction

Instance 1

Input:

Currently AI systems are prone to bias and unfairness which is unaligned with our values. I work in bias and fairness, specifically in analyzing how the biases in large datasets (such as common crawl) affect the probability distributions in large language models.

Output:

Making AI systems un-biased and fair has a positive impact on deployed products, but does not reduce existential risk.

Instance 2

Input:

AI capabilities will continue to increase, so how do we propose utilizing this to boost alignment research. An Alignment research assistant (ARA) can perform many different tasks for the researcher such as summarizing papers, writing code, and assisting math proofs.

Output:

If ARA can summarize, write code, and assist math proofs, then it can also be used to accelerate capabilities research. There are already market incentives to create those types of tools, so it isn't likely that you're able to produce a good research assistant that can perform those tasks before another company does.

Instance 3

Input:

Before we trust the AI, we can prevent it from taking over the world by not giving it internet access or putting it in a faraday cage to avoid causal interactions with the outside world. Another possibility is running the AI in a simulated environment different than our own, so that we could catch it if it starts to perform power seeking.

Output: (from Arbital)

The central difficulty of AI boxing is to describe a channel which cannot be used to manipulate the human operators, but which provides information relevant enough to be pivotal or game-changing relative to larger events. For example, it seems not unthinkable that we could safely extract, from a boxed AI setup, reliable information that prespecified theorems had been proved within Zermelo-Fraenkel set theory, but there is no known way to save the world if only we could sometimes know that prespecified theorems had been reliably proven in Zermelo-Fraenkel set theory.

Instance 4

Input:

We can perform reinforcement learning from human feedback to align the AI to human values. By achieving greater instruct-ability with smaller models and extrapolating trends with larger models, we can more safely build larger models that do what we ask them to.

Output:

An intelligent enough model can optimize for reward by taking over the reward signal directly or manipulating the mechanical turk workers providing the feedback. Having humans-in-the-loop doesn't solve the problem of power-seeking being instrumentally convergent.

Convincing People of Alignment with Street Epistemology

Thanks. Yeah this all sounds extremely obvious to me, but I may not have included such obvious-to-Logan things if I was coaching someone else.

Key things to avoid include isolating people from their friends, breaking the linguistic association of words to reality, demanding that someone change their linguistic patterns on the spot, etc - mostly things which street epistemology specifically makes harder due to the recommended techniques

Are you saying street epistemology is good or bad here? I've only seen a few videos and haven't read through the intro documents or anything.

Convincing All Capability Researchers

I was talking to someone recently who talked to Yann and got him to agree with very alignment-y things, but then a couple days later, Yann was saying very capabilities things instead. 

The "someone"'s theory was that Yann's incentives and environment is all towards capabilities research.

"Mild Hallucination" Test

I think that everyone can see these in theory, but different people focus on different types of information (eg low level sensory information vs high level sensory information) by default. 

I believe drugs or meditating can change which types of information you pay more attention to by default, momentarily or even permanently. 

I've never taken drugs beyond caffeine & alcohol, but meditating makes these phenomena much easier to see. I bet you could get most people to see them if you ask them to e.g. stare at a textured surface like carpet for 20 seconds and describe what they're seeing. 

Most people do not do this normally because why would you focus on these things for long periods of time unless your on drugs, meditate, or a bored kid? 

Productive Mistakes, Not Perfect Answers

I understand your point now, thanks. It's:

An embedded aligned agent is desired to have properties (1),(2), and (3). But, suppose (1) & (2), then (3) cannot be true. Then, suppose (2) & ...

or something of the sort. 

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