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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Obliqueness Thesis, published by Jessica Taylor on September 19, 2024 on The AI Alignment Forum.

In my Xenosystems review, I discussed the Orthogonality Thesis, concluding that it was a bad metaphor. It's a long post, though, and the comments on orthogonality build on other Xenosystems content. Therefore, I think it may be helpful to present a more concentrated discussion on Orthogonality, contrasting Orthogonality with my own view, without introducing dependencies on Land's views.

(Land gets credit for inspiring many of these thoughts, of course, but I'm presenting my views as my own here.)

First, let's define the Orthogonality Thesis. Quoting Superintelligence for Bostrom's formulation:

Intelligence and final goals are orthogonal: more or less any level of intelligence could in principle be combined with more or less any final goal.

To me, the main ambiguity about what this is saying is the "could in principle" part; maybe, for any level of intelligence and any final goal, there exists (in the mathematical sense) an agent combining those, but some combinations are much more natural and statistically likely than others. Let's consider Yudkowsky's formulations as alternatives. Quoting Arbital:

The Orthogonality Thesis asserts that there can exist arbitrarily intelligent agents pursuing any kind of goal.

The strong form of the Orthogonality Thesis says that there's no extra difficulty or complication in the existence of an intelligent agent that pursues a goal, above and beyond the computational tractability of that goal.

As an example of the computational tractability consideration, sufficiently complex goals may only be well-represented by sufficiently intelligent agents. "Complication" may be reflected in, for example, code complexity; to my mind, the strong form implies that the code complexity of an agent with a given level of intelligence and goals is approximately the code complexity of the intelligence plus the code complexity of the goal specification, plus a constant.

Code complexity would influence statistical likelihood for the usual Kolmogorov/Solomonoff reasons, of course.

I think, overall, it is more productive to examine Yudkowsky's formulation than Bostrom's, as he has already helpfully factored the thesis into weak and strong forms. Therefore, by criticizing Yudkowsky's formulations, I am less likely to be criticizing a strawman. I will use "Weak Orthogonality" to refer to Yudkowsky's "Orthogonality Thesis" and "Strong Orthogonality" to refer to Yudkowsky's "strong form of the Orthogonality Thesis".

Land, alternatively, describes a "diagonal" between intelligence and goals as an alternative to orthogonality, but I don't see a specific formulation of a "Diagonality Thesis" on his part. Here's a possible formulation:

Diagonality Thesis: Final goals tend to converge to a point as intelligence increases.

The main criticism of this thesis is that formulations of ideal agency, in the form of Bayesianism and VNM utility, leave open free parameters, e.g. priors over un-testable propositions, and the utility function. Since I expect few readers to accept the Diagonality Thesis, I will not concentrate on criticizing it.

What about my own view? I like Tsvi's naming of it as an "obliqueness thesis".

Obliqueness Thesis: The Diagonality Thesis and the Strong Orthogonality Thesis are false. Agents do not tend to factorize into an Orthogonal value-like component and a Diagonal belief-like component; rather, there are Oblique components that do not factorize neatly.

(Here, by Orthogonal I mean basically independent of intelligence, and by Diagonal I mean converging to a point in the limit of intelligence.)

While I will address Yudkowsky's arguments for the Orthogonality Thesis, I think arguing directly for my view first will be more helpful.

In general, it seems ...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Secret Collusion: Will We Know When to Unplug AI?, published by schroederdewitt on September 16, 2024 on The AI Alignment Forum.

TL;DR: We introduce the first comprehensive theoretical framework for understanding and mitigating secret collusion among advanced AI agents, along with CASE, a novel model evaluation framework. CASE assesses the cryptographic and steganographic capabilities of agents, while exploring the emergence of secret collusion in real-world-like multi-agent settings.

Whereas current AI models aren't yet proficient in advanced steganography, our findings show rapid improvements in individual and collective model capabilities, posing unprecedented safety and security risks. These results highlight urgent challenges for AI governance and policy, urging institutions such as the EU AI Office and AI safety bodies in the UK and US to prioritize cryptographic and steganographic evaluations of frontier models.

Our research also opens up critical new pathways for research within the AI Control framework.

Philanthropist and former Google CEO Eric Schmidt said in 2023 at a Harvard event:

"[...] the computers are going to start talking to each other probably in a language that we can't understand and collectively their super intelligence - that's the term we use in the industry - is going to rise very rapidly and my retort to that is: do you know what we're going to do in that scenario? We're going to unplug them [...]

But what if we cannot unplug them in time because we won't be able to detect the moment when this happens? In this blog post, we, for the first time, provide a comprehensive overview of the phenomenon of secret collusion among AI agents, connect it to foundational concepts in steganography, information theory, distributed systems theory, and computability, and present a model evaluation framework and empirical results as a foundation of future frontier model evaluations.

This blog post summarises a large body of work. First of all, it contains our pre-print from February 2024 (updated in September 2024) "Secret Collusion among Generative AI Agents". An early form of this pre-print was presented at the 2023 New Orleans (NOLA) Alignment Workshop (see this recording NOLA 2023 Alignment Forum Talk Secret Collusion Among Generative AI Agents: a Model Evaluation Framework). Also, check out this long-form Foresight Institute Talk).

In addition to these prior works, we also include new results. These contain empirical studies on the impact of paraphrasing as a mitigation tool against steganographic communications, as well as reflections on our findings' impact on AI Control.

Multi-Agent Safety and Security in the Age of Autonomous Internet Agents

The near future could see myriads of LLM-driven AI agents roam the internet, whether on social media platforms, eCommerce marketplaces, or blockchains. Given advances in predictive capabilities, these agents are likely to engage in increasingly complex intentional and unintentional interactions, ranging from traditional distributed systems pathologies (think dreaded deadlocks!) to more complex coordinated feedback loops.

Such a scenario induces a variety of multi-agent safety, and specifically, multi-agent security[1] (see our NeurIPS'23 workshop Multi-Agent Security: Security as Key to AI Safety) concerns related to data exfiltration, multi-agent deception, and, fundamentally, undermining trust in AI systems.

There are several real-world scenarios where agents could have access to sensitive information, such as their principals' preferences, which they may disclose unsafely even if they are safety-aligned when considered in isolation. Stray incentives, intentional or otherwise, or more broadly, optimization pressures, could cause agents to interact in undesirable and potentially dangerous ways.

For example, join...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Estimating Tail Risk in Neural Networks, published by Jacob Hilton on September 13, 2024 on The AI Alignment Forum.

Machine learning systems are typically trained to maximize average-case performance. However, this method of training can fail to meaningfully control the probability of tail events that might cause significant harm. For instance, while an artificial intelligence (AI) assistant may be generally safe, it would be catastrophic if it ever suggested an action that resulted in unnecessary large-scale harm.

Current techniques for estimating the probability of tail events are based on finding inputs on which an AI behaves catastrophically. Since the input space is so large, it might be prohibitive to search through it thoroughly enough to detect all potential catastrophic behavior. As a result, these techniques cannot be used to produce AI systems that we are confident will never behave catastrophically.

We are excited about techniques to estimate the probability of tail events that do not rely on finding inputs on which an AI behaves badly, and can thus detect a broader range of catastrophic behavior. We think developing such techniques is an exciting problem to work on to reduce the risk posed by advanced AI systems:

Estimating tail risk is a conceptually straightforward problem with relatively objective success criteria; we are predicting something mathematically well-defined, unlike instances of eliciting latent knowledge (ELK) where we are predicting an informal concept like "diamond".

Improved methods for estimating tail risk could reduce risk from a variety of sources, including central misalignment risks like deceptive alignment.

Improvements to current methods can be found both by doing empirical research, or by thinking about the problem from a theoretical angle.

This document will discuss the problem of estimating the probability of tail events and explore estimation strategies that do not rely on finding inputs on which an AI behaves badly. In particular, we will:

Introduce a toy scenario about an AI engineering assistant for which we want to estimate the probability of a catastrophic tail event.

Explain some deficiencies of adversarial training, the most common method for reducing risk in contemporary AI systems.

Discuss deceptive alignment as a particularly dangerous case in which adversarial training might fail.

Present methods for estimating the probability of tail events in neural network behavior that do not rely on evaluating behavior on concrete inputs.

Conclude with a discussion of why we are excited about work aimed at improving estimates of the probability of tail events.

This document describes joint research done with Jacob Hilton, Victor Lecomte, David Matolcsi, Eric Neyman, Thomas Read, George Robinson, and Gabe Wu. Thanks additionally to Ajeya Cotra, Lukas Finnveden, and Erik Jenner for helpful comments and suggestions.

A Toy Scenario

Consider a powerful AI engineering assistant. Write M for this AI system, and M(x) for the action it suggests given some project description x.

We want to use this system to help with various engineering projects, but would like it to never suggest an action that results in large-scale harm, e.g. creating a doomsday device. In general, we define a behavior as catastrophic if it must never occur in the real world.[1] An input is catastrophic if it would lead to catastrophic behavior.

Assume we can construct a catastrophe detector C that tells us if an action M(x) will result in large-scale harm. For the purposes of this example, we will assume both that C has a reasonable chance of catching all catastrophes and that it is feasible to find a useful engineering assistant M that never triggers C (see Catastrophe Detectors for further discussion).

We will also assume we can use C to train M, but that it is ...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Can startups be impactful in AI safety?, published by Esben Kran on September 13, 2024 on The AI Alignment Forum.

With

Lakera's strides in securing LLM APIs,

Goodfire AI's path to scaling interpretability, and 20+

model

evaluations

startups among

much

else, there's a rising number of technical startups attempting to secure the model ecosystem.

Of course, they have varying levels of impact on superintelligence containment and security and even with these companies, there's a lot of potential for aligned, ambitious and high-impact startups within the ecosystem. This point isn't new and has been made in our

previous

posts and by

Eric Ho (Goodfire AI CEO).

To set the stage, our belief is that these are the types of companies that will have a positive impact:

Startups with a profit incentive completely aligned with improving AI safety;

that have a deep technical background to shape AGI deployment and;

do not try to compete with AGI labs.

Piloting AI safety startups

To understand impactful technical AI safety startups better, Apart Research joined forces with collaborators from Juniper Ventures, vectorview (alumni from the latest YC cohort), Rudolf (from the upcoming def/acc cohort), Tangentic AI, and others. We then invited researchers, engineers, and students to resolve a key question "can we come up with ideas that scale AI safety into impactful for-profits?"

The hackathon took place during a weekend two weeks ago with a keynote by

Esben Kran (co-director of Apart) along with 'HackTalks' by

Rudolf Laine (def/acc) and

Lukas Petersson (YC / vectorview). Individual submissions were a 4 page report with the problem statement, why this solution will work, what the key risks of said solution are, and any experiments or demonstrations of the solution the team made.

This post details the top 6 projects and excludes 2 projects that were made private by request (hopefully turning into impactful startups now!). In total, we had 101 signups and 11 final entries. Winners were decided by an LME model conditioned on reviewer bias. Watch the authors' lightning talks

here.

Dark Forest: Making the web more trustworthy with third-party content verification

By Mustafa Yasir (AI for Cyber Defense Research Centre, Alan Turing Institute)

Abstract: 'DarkForest is a pioneering Human Content Verification System (HCVS) designed to safeguard the authenticity of online spaces in the face of increasing AI-generated content. By leveraging graph-based reinforcement learning and blockchain technology, DarkForest proposes a novel approach to safeguarding the authentic and humane web. We aim to become the vanguard in the arms race between AI-generated content and human-centric online spaces.'

Content verification workflow supported by graph-based RL agents deciding verifications

Reviewer comments:

Natalia: Well explained problem with clear need addressed. I love that you included the content creation process - although you don't explicitly address how you would attract content creators to use your platform over others in their process. Perhaps exploring what features of platforms drive creators to each might help you make a compelling case for using yours beyond the verification capabilities.

I would have also liked to see more details on how the verification decision is made and how accurate this is on existing datasets.

Nick: There's a lot of valuable stuff in here regarding content moderation and identity verification. I'd narrow it to one problem-solution pair (e.g.,

"jobs to be done") and focus more on risks around early product validation (deep interviews with a range of potential users and buyers regarding value) and go-to-market. It might also be worth checking out

Musubi.

Read the full project here.

Simulation Operators: An annotation operation for alignment of robot

By Ardy Haroen (USC)

Abstrac...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How difficult is AI Alignment?, published by Samuel Dylan Martin on September 13, 2024 on The AI Alignment Forum.

This work was funded by Polaris Ventures

There is currently no consensus on how difficult the AI alignment problem is. We have yet to encounter any real-world, in the wild instances of the most concerning threat models, like deceptive misalignment. However, there are compelling theoretical arguments which suggest these failures will arise eventually.

Will current alignment methods accidentally train deceptive, power-seeking AIs that appear aligned, or not? We must make decisions about which techniques to avoid and which are safe despite not having a clear answer to this question.

To this end, a year ago, we introduced the AI alignment difficulty scale, a framework for understanding the increasing challenges of aligning artificial intelligence systems with human values.

This follow-up article revisits our original scale, exploring how our understanding of alignment difficulty has evolved and what new insights we've gained. This article will explore three main themes that have emerged as central to our understanding:

1. The Escalation of Alignment Challenges: We'll examine how alignment difficulties increase as we go up the scale, from simple reward hacking to complex scenarios involving deception and gradient hacking. Through concrete examples, we'll illustrate these shifting challenges and why they demand increasingly advanced solutions.

These examples will illustrate what observations we should expect to see "in the wild" at different levels, which might change our minds about how easy or difficult alignment is.

2. Dynamics Across the Difficulty Spectrum: We'll explore the factors that change as we progress up the scale, including the increasing difficulty of verifying alignment, the growing disconnect between alignment and capabilities research, and the critical question of which research efforts are net positive or negative in light of these challenges.

3. Defining and Measuring Alignment Difficulty: We'll tackle the complex task of precisely defining "alignment difficulty," breaking down the technical, practical, and other factors that contribute to the alignment problem. This analysis will help us better understand the nature of the problem we're trying to solve and what factors contribute to it.

The Scale

The high level of the alignment problem, provided in the previous post, was:

"The alignment problem" is the problem of aligning sufficiently powerful AI systems, such that we can be confident they will be able to reduce the risks posed by misused or unaligned AI systems

We previously introduced the AI alignment difficulty scale, with 10 levels that map out the increasing challenges. The scale ranges from "alignment by default" to theoretical impossibility, with each level representing more complex scenarios requiring more advanced solutions. It is reproduced here:

Alignment Difficulty Scale

Difficulty Level

Alignment technique X is sufficient

Description

Key Sources of risk

1

(Strong) Alignment by Default

As we scale up AI models without instructing or training them for specific risky behaviour or imposing problematic and clearly bad goals (like 'unconditionally make money'), they do not pose significant risks. Even superhuman systems basically do the commonsense version of what external rewards (if RL) or language instructions (if LLM) imply.

Misuse and/or recklessness with training objectives.

RL of powerful models towards badly specified or antisocial objectives is still possible, including accidentally through poor oversight, recklessness or structural factors.

2

Reinforcement Learning from Human Feedback

We need to ensure that the AI behaves well even in edge cases by guiding it more carefully using human feedback in a wide range of situations...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Contra papers claiming superhuman AI forecasting, published by nikos on September 12, 2024 on The AI Alignment Forum.

[Conflict of interest disclaimer: We are

FutureSearch, a company working on AI-powered forecasting and other types of quantitative reasoning. If thin LLM wrappers could achieve superhuman forecasting performance, this would obsolete a lot of our work.]

Widespread, misleading claims about AI forecasting

Recently we have seen a number of papers - (Schoenegger et al., 2024, Halawi et al., 2024, Phan et al., 2024, Hsieh et al., 2024) - with claims that boil down to "we built an LLM-powered forecaster that rivals human forecasters or even shows superhuman performance".

These papers do not communicate their results carefully enough, shaping public perception in inaccurate and misleading ways. Some examples of public discourse:

Ethan Mollick (>200k followers)

tweeted the following about the paper Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy by Schoenegger et al.:

A post on

Marginal Revolution

with the title and abstract of the paper

Approaching Human-Level Forecasting with Language Models by Halawi et al. elicits responses like

"This is something that humans are notably terrible at, even if they're paid to do it. No surprise that LLMs can match us."

"+1 The aggregate human success rate is a pretty low bar"

A

Twitter thread with >500k views on LLMs Are Superhuman Forecasters by Phan et al. claiming that "AI […] can predict the future at a superhuman level" had more than half a million views within two days of being published.

The number of such papers on AI forecasting, and the vast amount of traffic on misleading claims, makes AI forecasting a uniquely misunderstood area of AI progress. And it's one that matters.

What does human-level or superhuman forecasting mean?

"Human-level" or "superhuman" is a hard-to-define concept. In an academic context, we need to work with a reasonable operationalization to compare the skill of an AI forecaster with that of humans.

One reasonable and practical definition of a superhuman forecasting AI forecaster is

The AI forecaster is able to consistently outperform the crowd forecast on a sufficiently large number of randomly selected questions on a high-quality forecasting platform.[1]

(For a human-level forecaster, just replace "outperform" with "performs on par with".)

Red flags for claims to (super)human AI forecasting accuracy

Our experience suggests there are a number of things that can go wrong when building AI forecasting systems, including:

1. Failing to find up-to-date information on the questions. It's inconceivable on most questions that forecasts can be good without basic information.

Imagine trying to forecast the US presidential election without knowing that Biden dropped out.

2. Drawing on up-to-date, but low-quality information. Ample experience shows low quality information confuses LLMs even more than it confuses humans.

Imagine forecasting election outcomes with biased polling data.

Or, worse, imagine forecasting OpenAI revenue based on claims like

> The number of ChatGPT Plus subscribers is estimated between 230,000-250,000 as of October 2023.

without realising that this mixing up ChatGPT vs ChatGPT mobile.

3. Lack of high-quality quantitative reasoning. For a decent number of questions on Metaculus, good forecasts can be "vibed" by skilled humans and perhaps LLMs. But for many questions, simple calculations are likely essential. Human performance shows systematic accuracy nearly always requires simple models such as base rates, time-series extrapolations, and domain-specific numbers.

Imagine forecasting stock prices without having, and using, historical volatility.

4. Retrospective, rather than prospective, forecasting (e.g. forecasting questions that have al...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AI forecasting bots incoming, published by Dan H on September 9, 2024 on The AI Alignment Forum.

In a recent appearance on Conversations with Tyler, famed political forecaster Nate Silver expressed skepticism about AIs replacing human forecasters in the near future. When asked how long it might take for AIs to reach superhuman forecasting abilities, Silver replied: "15 or 20 [years]."

In light of this, we are excited to announce "FiveThirtyNine," an AI forecasting bot. Our bot, built on GPT-4o, provides probabilities for any user-entered query, including "

Will Trump win the 2024 presidential election?" and "

Will China invade Taiwan by 2030?" Our bot performs better than experienced human forecasters and performs roughly the same as (and sometimes even better than) crowds of experienced forecasters; since crowds are for the most part superhuman, FiveThirtyNine is in a similar sense. (We discuss limitations later in this post.)

Our bot and other forecasting bots can be used in a wide variety of contexts. For example, these AIs could help policymakers minimize bias in their decision-making or help improve global epistemics and institutional decision-making by providing trustworthy, calibrated forecasts.

We hope that forecasting bots like ours will be quickly integrated into frontier AI models. For now, we will keep our bot available at

forecast.safe.ai, where users are free to experiment and test its capabilities.

Quick Links

Demo:

forecast.safe.ai

Technical Report:

link

Problem

Policymakers at the highest echelons of government and corporate power have difficulty making high-quality decisions on complicated topics. As the world grows increasingly complex, even coming to a consensus agreement on basic facts is becoming more challenging, as it can be hard to absorb all the relevant information or know which sources to trust. Separately, online discourse could be greatly improved.

Discussions on uncertain, contentious issues all too often devolve into battles between interest groups, each intent on name-calling and spouting the most extreme versions of their views through highly biased op-eds and tweets.

FiveThirtyNine

Before transitioning to how forecasting bots like FiveThirtyNine can help improve epistemics, it might be helpful to give a summary of what FiveThirtyNine is and how it works.

FiveThirtyNine can be given a query - for example, "Will Trump win the 2024 US presidential election?" FiveThirtyNine is prompted to behave like an "AI that is superhuman at forecasting". It is then asked to make a series of search engine queries for news and opinion articles that might contribute to its prediction. (The following example from FiveThirtyNine uses GPT-4o as the base LLM.)

Based on these sources and its wealth of prior knowledge, FiveThirtyNine compiles a summary of key facts. Given these facts, it's asked to give reasons for and against Trump winning the election, before weighing each reason based on its strength and salience.

Finally, FiveThirtyNine aggregates its considerations while adjusting for negativity and sensationalism bias in news sources and outputs a tentative probability. It is asked to sanity check this probability and adjust it up or down based on further reasoning, before putting out a final, calibrated probability - in this case, 52%.

Evaluation. To test how well our bot performs, we evaluated it on questions from the Metaculus forecasting platform. We restricted the bot to make predictions only using the information human forecasters had, ensuring a valid comparison. Specifically, GPT-4o is only trained on data up to October 2023, and we restricted the news and opinion articles it could access to only those published before a certain date.

From there, we asked it to compute the probabilities of 177 events from Metaculus that had happened (or not ha...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Backdoors as an analogy for deceptive alignment, published by Jacob Hilton on September 6, 2024 on The AI Alignment Forum.

ARC has released a paper on Backdoor defense, learnability and obfuscation in which we study a formal notion of backdoors in ML models. Part of our motivation for this is an analogy between backdoors and deceptive alignment, the possibility that an AI system would intentionally behave well in training in order to give itself the opportunity to behave uncooperatively later.

In our paper, we prove several theoretical results that shed some light on possible mitigations for deceptive alignment, albeit in a way that is limited by the strength of this analogy.

In this post, we will:

Lay out the analogy between backdoors and deceptive alignment

Discuss prior theoretical results from the perspective of this analogy

Explain our formal notion of backdoors and its strengths and weaknesses

Summarize the results in our paper and discuss their implications for deceptive alignment

Thanks to Boaz Barak, Roger Grosse, Thomas Read, John Schulman and Gabriel Wu for helpful comments.

Backdoors and deceptive alignment

A backdoor in an ML model is a modification to the model that causes it to behave

differently on certain inputs that activate a secret "trigger", while behaving similarly on ordinary inputs. There is a wide existing literature on backdoor attacks and defenses, which is primarily empirical, but also includes some theoretical results that we will mention.

Deceptive alignment is a term from the paper Risks from Learned Optimization in Advanced Machine Learning Systems (Section 4) that refers to the possibility that an AI system will internally reason about the objective that it is being trained on, and decide to perform well according to that objective unless there are clues that it has been taken out of its training environment.

Such a policy could be optimal on the training distribution, and yet perform very badly on certain out-of-distribution inputs where such clues are present, which we call defection triggers.[1] The opposite of deceptive alignment is robust alignment, meaning that this performance degradation is avoided.

Since a deceptively aligned model and a robustly aligned model behave very differently on defection triggers, but very similarly on typical inputs from the training distribution, deceptive alignment can be thought of as a special kind of backdoor, under the following correspondence:

Deceptive alignment

Backdoors

Robustly aligned model

Original (unmodified) model

Deceptively aligned model

Backdoored model

Defection trigger

Backdoor trigger

The main distinguishing feature of deceptive alignment compared to other kinds of backdoors is that the deceptively aligned model is not produced by an adversary, but is instead produced through ordinary training. Thus by treating deceptive alignment as a backdoor, we are modeling the training process as an adversary.

In our analysis of deceptive alignment, the basic tension we will face is that an unconstrained adversary will always win, but any particular proxy constraint we impose on the adversary may be unrealistic.

Static backdoor detection

An important piece of prior work is the paper Planting Undetectable Backdoors in Machine Learning Models, which uses a digital signature scheme to insert an undetectable backdoor into a model. Roughly speaking, the authors exhibit a modified version of a "Random Fourier Features" training algorithm that produces a backdoored model. Any input to the backdoored model can be perturbed by an attacker with knowledge of a secret key to produce a new input on which the model behaves differently.

However, the backdoor is undetectable in the sense that it is computationally infeasible for a defender with white-box access to distinguish a backdoored model from an or...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Conflating value alignment and intent alignment is causing confusion, published by Seth Herd on September 5, 2024 on The AI Alignment Forum.

Submitted to the Alignment Forum. Contains more technical jargon than usual.

Epistemic status: I think something like this confusion is happening often. I'm not saying these are the only differences in what people mean by "AGI alignment".

Summary:

Value alignment is better but probably harder to achieve than personal intent alignment to the short-term wants of some person(s). Different groups and people tend to primarily address one of these alignment targets when they discuss alignment. Confusion abounds.

One important confusion stems from an assumption that the type of AI defines the alignment target: strong goal-directed AGI must be value aligned or misaligned, while personal intent alignment is only viable for relatively weak AI. I think this assumption is important but false.

While value alignment is categorically better, intent alignment seems easier, safer, and more appealing in the short term, so AGI project leaders are likely to try it.[1]

Overview

Clarifying what people mean by alignment should dispel some illusory disagreement, and clarify alignment theory and predictions of AGI outcomes.

Caption: Venn diagram of three types of alignment targets. Value alignment and Personal intent alignment are both subsets of Evan Hubinger's definition of intent alignment: AGI aligned with human intent in the broadest sense.

Prosaic alignment work usually seems to be addressing a target somewhere in the neighborhood of personal intent alignment (following instructions or doing what this person wants now), while agent foundations and other conceptual alignment work usually seems to be addressing value alignment. Those two clusters have different strengths and weaknesses as alignment targets, so lumping them together produces confusion.

People mean different things when they say alignment. Some are mostly thinking about value alignment (VA): creating sovereign AGI that has values close enough to humans' for our liking. Others are talking about making AGI that is corrigible (in the Christiano or Harms sense)[2] or follows instructions from its designated principal human(s). I'm going to use the term personal intent alignment (PIA) until someone has a better term for that type of alignment target.

Different arguments and intuitions apply to these two alignment goals, so talking about them without differentiation is creating illusory disagreements.

Value alignment is better almost by definition, but personal intent alignment seems to avoid some of the biggest difficulties of value alignment. Max Harms' recent sequence on corrigibility as a singular target (CAST) gives both a nice summary and detailed arguments. We do not need us to point to or define values, just short term preferences or instructions.

The principal advantage is that an AGI that follows instructions can be used as a collaborator in improving its alignment over time; you don't need to get it exactly right on the first try. This is more helpful in slower and more continuous takeoffs. This means that PI alignment has a larger basin of attraction than value alignment does.[3]

Most people who think alignment is fairly achievable seem to be thinking of PIA, while critics often respond thinking of value alignment. It would help to be explicit. PIA is probably easier and more likely than full VA for our first stabs at AGI, but there are reasons to wonder if it's adequate for real success. In particular, there are intuitions and arguments that PIA doesn't address the real problem of AGI alignment.

I think PIA does address the real problem, but in a non-obvious and counterintuitive way.

Another unstated divide

There's another important clustering around these two conceptions of al...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Is there any rigorous work on using anthropic uncertainty to prevent situational awareness / deception?, published by David Scott Krueger on September 4, 2024 on The AI Alignment Forum.

AI systems up to some high level of intelligence plausibly need to know exactly where they are in space-time in order for deception/"scheming" to make sense as a strategy.

This is because they need to know:

1) what sort of oversight they are subject to

and

2) what effects their actions will have on the real world

(side note: Acausal trade might break this argument)

There are a number of informal proposals to keep AI systems selectively ignorant of (1) and (2) in order to prevent deception. Those proposals seem very promising to flesh out; I'm not aware of any rigorous work doing so, however. Are you?

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.