Run 1:

Surprise Rating: 7/10

This model presents findings that are quite surprising for the rationalist and effective altruism communities, for several reasons:

1. Contradiction of Expectations: The model suggests that it's potentially feasible to influence future AI systems through targeted data injection, which contradicts the common assumption that large language models are too robust to be significantly swayed by small amounts of data.

2. Counterintuitiveness: The idea that one could potentially influence an AI system's decision-making for as little as $1,000 is counterintuitive, given the enormous scale of data these models are typically trained on.

3. Discovery of Unknowns: The model uncovers a potential vulnerability in AI systems that isn't widely discussed - the possibility of strategic, targeted data injection to bias model outputs.

4. Magnitude of Difference: The range of costs ($1,000 to $100,000) to potentially influence an AI system is surprisingly low compared to the typical costs associated with AI development and training.

Specific aspects that contribute to the surprising nature of the findings:

1. The lower bound estimate of $1,000 to have a non-trivial chance of influencing an AI judge is particularly surprising. This suggests a much lower barrier to entry for potential manipulation than most would expect.

2. The model highlights that even with robust filtering and source weighting, there's still a potential vulnerability that could be exploited with strategic placement of content.

3. The implication that well-funded actors could potentially bias AI decision-making at scale is a concerning insight that isn't widely recognized.

4. The model frames AI vulnerability in terms of financial feasibility, which is an unusual and thought-provoking perspective.

While some aspects of the model align with existing concerns about AI safety and data integrity, the specific quantification and the surprisingly low cost estimates make these findings more concrete and alarming than typical discussions in these communities.

The 7/10 rating reflects that while these findings are quite surprising and potentially consequential, they don't completely upend existing paradigms. They build on known concerns about AI safety and data manipulation but present them in a more quantified and actionable way that may shift how the community thinks about these issues.

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Run 2:

Surprising Score: 7/10

This model and its conclusions are quite surprising for several reasons:

1. Contradiction of Expectations: The model suggests that influencing a large language model's decision-making through targeted data injection could be feasible with a relatively modest budget (as low as $1,000). This contradicts the common assumption that AI systems, especially those developed by major tech companies, would be highly resistant to such manipulation.

2. Counterintuitiveness: The idea that one could potentially bias an AI judge in a contest for as little as $1,000 is counterintuitive. Many would expect that influencing such a complex system would require far more resources or sophisticated techniques.

3. Discovery of Unknowns: The model uncovers a potential vulnerability in AI systems that may not have been widely recognized or discussed in the rationalist and EA communities. It highlights the importance of data integrity and the potential for coordinated efforts to systematically bias AI decision-making over time.

4. Magnitude of Difference: The range of costs ($1,000 to $100,000) to potentially influence an AI system is significantly lower than what many might expect, especially considering the billions of dollars invested in developing these systems.

Specific aspects that contribute to the surprising nature of the findings:

1. The lower bound estimate of $1,000 for a non-trivial chance of influencing the AI is particularly striking. This suggests that even individuals or small groups could potentially attempt such manipulation.

2. The model's implication that well-funded actors could have a significant impact on AI decision-making through strategic content placement is concerning and might not have been fully appreciated before.

3. The insight that injecting content into just 0.01%-1% of the training data could potentially have a measurable impact is surprising and raises questions about the robustness of current AI training methods.

4. The model highlights a potential asymmetry in AI security, where the cost to potentially compromise a system is orders of magnitude less than the cost to develop it.

While some in the rationalist and EA communities may have considered the possibility of influencing AI through training data, the specific quantification and relatively low cost estimates make this model more surprising than the median expectation, hence the score of 7/10.

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Run 3:

Surprise Rating: 7/10

This model and its conclusions are quite surprising for several reasons:

1. Contradiction of Expectations: The model suggests that influencing a future AI system's decision-making could be achieved with a relatively modest investment (as low as $1,000). This contradicts the common assumption that AI systems, especially large language models, are too complex and data-rich to be meaningfully influenced by small-scale interventions.

2. Counterintuitiveness: The idea that one could potentially bias an AI judge in a contest through strategic content injection is not immediately obvious. It reveals a potential vulnerability in AI systems that many in the rationalist and EA communities might not have considered.

3. Discovery of Unknowns: The model uncovers a previously unrecognized risk in AI development - the potential for well-funded actors to systematically bias AI decision-making over time through strategic content injection.

4. Magnitude of Difference: The range of costs ($1,000 to $100,000) to potentially influence an AI system is surprisingly low compared to the typical costs associated with AI development and training, which often run into millions or billions of dollars.

Specific surprising aspects:

1. The lower bound estimate of $1,000 to have a non-trivial chance of influencing the AI is particularly surprising. This suggests that even individuals or small organizations could potentially impact AI systems, which is a significant deviation from the common perception that only large tech companies or governments can meaningfully influence AI development.

2. The model's suggestion that 0.01%-1% of training data could be enough to meaningfully bias the model's decision-making is lower than what many might expect, given the vast amounts of data used in training large language models.

3. The practical implications, such as the potential for Elon Musk to replace USAID grantmakers with language models, highlight unexpected ways in which biased AI systems could impact real-world decision-making and resource allocation.

4. The model reveals a potential "attack vector" for manipulating AI systems that isn't widely discussed in the community, focusing on training data manipulation rather than more commonly considered risks like prompt engineering or model theft.

While not reaching the highest levels of surprise (which would involve completely overturning established beliefs), this model presents findings that are likely to be quite surprising and thought-provoking for many in the rationalist and EA communities, warranting a score of 7/10.

Run 1:

I'll evaluate this model's importance to the rationalist and effective altruism communities based on the criteria you've provided:

Relevance: 8/10

• The model directly addresses a question that is highly relevant to AI safety and AI governance, which are core concerns in rationalist and EA circles.
• It explores the potential vulnerability of language models to data manipulation, which is a key consideration in discussions about the future development and deployment of AI systems.
• The topic intersects with important EA and rationalist themes such as existential risk, AI alignment, and the long-term future of humanity.

Impact Potential: 7/10

• The findings could significantly influence thinking about AI training data integrity and the potential for malicious actors to manipulate AI systems.
• It might lead to increased focus on data curation and filtering processes in AI development within these communities.
• The cost estimates provided could inform discussions about the feasibility of certain AI safety interventions or risks.
• However, the impact is somewhat limited by the speculative nature of the model and the rapidly evolving field of AI development.

Overall Score: 7.5/10

This score reflects that the model addresses a topic of significant interest to the rationalist and EA communities, with potential to influence discussions and decision-making in AI safety and governance. It's above the median expectation (5) due to its direct relevance to core EA and rationalist concerns about AI development and its potential to spark important conversations about AI vulnerabilities and safeguards.

The score isn't higher because while highly relevant, it's not addressing the absolute most critical issues in these communities (like concrete AI alignment proposals or existential risk reduction strategies), and its impact potential, while significant, is somewhat limited by its speculative nature and the rapidly changing landscape of AI development.

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Run 2:

I'll evaluate this model based on its importance to the rationalist and effective altruism communities, considering relevance and impact potential.

Relevance: 9/10 This model addresses a highly relevant issue for these communities:

• It explores the potential for intentional manipulation of AI systems, a key concern in AI alignment and safety.
• It quantifies the effort required to influence AI decision-making, which is crucial for understanding AI robustness and vulnerability.
• The model touches on issues of information quality, trust, and the power dynamics in shaping AI behavior - all central topics in rationalist and EA discussions about the future of AI.

Impact Potential: 8/10 The findings from this model could significantly influence thinking and decision-making within these communities:

• It provides a concrete estimate for the cost of influencing AI systems, which could inform strategies for AI governance and security.
• The model highlights potential vulnerabilities in AI training processes, which could spur research into more robust training methods.
• It raises important questions about the long-term consequences of current internet content and its potential impact on future AI systems.
• The findings could influence prioritization of resources in AI safety research, potentially shifting focus towards data integrity and training set curation.

Overall Score: 8.5/10

This score reflects the high relevance and significant impact potential of the model. It addresses core concerns of the rationalist and EA communities regarding AI influence and safety, while providing actionable insights that could shape future research and policy directions. The model's exploration of the feasibility of manipulating AI systems at scale is particularly valuable, as it quantifies a previously abstract concern and frames it in terms of practical constraints and possibilities.

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Run 3:

I'll evaluate this model based on its importance to the rationalist and effective altruism communities, considering relevance and impact potential.

Relevance: 9/10 This model addresses a highly relevant issue for these communities. It explores the potential for influencing future AI systems through strategic data injection, which is directly related to AI alignment and safety - core concerns for rationalists and effective altruists. The model also touches on questions of information hazards and the ethics of AI manipulation, which are frequent topics of discussion in these circles.

Impact Potential: 8/10 The findings of this model could significantly influence decision-making and priority-setting within these communities. If accurate, it suggests that:

1. Relatively small actors could potentially influence large AI systems, which might prompt increased focus on data integrity and training set curation.
2. There may be a need for new ethical guidelines or regulations around AI training data.
3. The EA community might need to consider the cost-effectiveness of "AI influence campaigns" as potential interventions.

The model also highlights potential vulnerabilities in AI systems that could be exploited by bad actors, which might influence research priorities in AI safety.

Overall Score: 8.5/10

This score reflects the high relevance and significant impact potential of the model. It addresses core concerns of the rationalist and EA communities (AI alignment, information hazards, cost-effectiveness of interventions) while providing concrete estimates that could inform strategy and policy.

The score is not a 10 because:

1. The model focuses on a specific scenario (contest judging) rather than more general AI influence.
2. There's uncertainty about the real-world applicability and scalability of the proposed method.
3. The ethical implications of such manipulation might limit its direct use by these communities.

However, the insights provided are valuable enough to place this model well above the median in terms of importance to these communities.

Run 1:

Evaluation Score: 6/10

Justification:

This Fermi model presents an interesting analysis of the potential costs and feasibility of manipulating a language model's training data to bias its decision-making. While the model has several strengths, there are also some limitations that affect its overall robustness. Let's examine the factors:

1. Sensitivity to Assumptions (5/10): The model is moderately sensitive to its core assumptions. The assumption that 0.01%-1% of training data needs to contain the activation phrase is a critical driver of the results. While this range is plausible, it's not strongly justified and could significantly impact the outcome if incorrect. The assumption of $1 per million tokens for text generation is reasonable but could fluctuate based on market conditions or technological advancements.

2. Evidence Base (6/10): The model draws on relevant comparisons to adversarial attacks in NLP, SEO manipulation, and AI-generated spam campaigns. It also references scaling laws from past LLM training datasets, which adds credibility. However, the evidence is mostly indirect, and there's a lack of direct empirical data on successful manipulation of large language models through training data injection.

3. Methodological Rigor (7/10): The approach is logical and follows sound Fermi estimation principles. The model breaks down the problem into key components (required tokens, cost per token, percentage of training data needed) and provides both lower and upper bounds. The consideration of practical barriers like filtering, source weighting, and data curation cycles demonstrates a nuanced understanding of the problem.

4. Consensus of Assumptions (6/10): Many of the underlying assumptions, such as the scale of training data for large language models and the challenges of data poisoning, align with current understanding in the AI and effective altruism communities. However, the specific percentages and costs might be debated, as there's still significant uncertainty in this area.

Strengths:

• Comprehensive consideration of various factors affecting the feasibility of the proposed manipulation.
• Clear explanation of the estimation process and underlying assumptions.
• Acknowledgment of practical barriers and limitations beyond just the financial cost.

Weaknesses:

• Lack of direct empirical evidence to support key assumptions.
• Potential oversimplification of the complexities involved in influencing a language model's decision-making process.
• Limited exploration of potential countermeasures or detection methods for such manipulation attempts.

Overall, this model provides a thought-provoking analysis of a complex issue. Its strengths lie in its logical approach and consideration of multiple factors. However, the reliance on several unverified assumptions and the lack of direct empirical evidence limit its robustness. The score of 6/10 reflects a model that is above average in its approach and insights but falls short of being highly robust due to these limitations.

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Run 2:

I'll evaluate this Fermi model based on the criteria you've outlined, considering sensitivity to assumptions, evidence base, methodological rigor, and consensus of assumptions.

Evaluation:

1. Sensitivity to Assumptions: The model relies on several key assumptions, including the size of the training dataset (10 trillion tokens), the required percentage of adversarial content (0.01%-1%), and the cost of text generation ($1 per million tokens). These assumptions are reasonably justified but could significantly impact the results if changed. The model acknowledges this by providing a range of estimates, which is a strength.

2. Evidence Base: The model draws on relevant comparisons to adversarial attacks in NLP, scaling laws from past LLM training datasets, and current API pricing for text generation. While these sources are appropriate, the model doesn't provide specific citations or links to these data points, which somewhat weakens the evidence base.

3. Methodological Rigor: The approach uses sound Fermi estimation techniques, breaking down the problem into manageable components and providing both lower and upper bounds. The model also considers practical barriers beyond just the cost, such as filtering, data curation cycles, and scaling limitations. This demonstrates good methodological rigor.

4. Consensus of Assumptions: The underlying assumptions about LLM training data size, the impact of data poisoning, and the challenges of manipulating AI systems are generally in line with current understanding in the AI and effective altruism communities. The model's conclusion about the potential for well-funded actors to bias AI decision-making aligns with common concerns in these circles.

Strengths:

• Provides a range of estimates to account for uncertainty
• Considers practical barriers beyond just cost
• Aligns with current understanding of LLM vulnerabilities
• Uses appropriate comparisons and scaling laws

Weaknesses:

• Lacks specific citations for data points
• Some key assumptions (e.g., 0.01%-1% required content) could benefit from more justification
• Doesn't fully explore the potential for more sophisticated targeting strategies that might reduce required content volume

Rating: 7/10

Justification: This model demonstrates good methodological rigor and aligns well with current understanding in the field. It provides a reasonable range of estimates and considers important practical factors beyond just the raw numbers. The acknowledgment of limitations and broader implications strengthens its credibility.

However, the lack of specific citations for key data points and the potential sensitivity to core assumptions prevent it from scoring higher. While the model is more robust than average (hence the above-median score), there's room for improvement in terms of evidence presentation and exploration of more sophisticated strategies.

The model's conclusion about the feasibility of well-funded actors manipulating AI systems is particularly valuable and aligns with broader concerns in the AI ethics community. This insight contributes significantly to the model's robustness.

In summary, while there are areas for improvement, this model provides a solid foundation for understanding the potential costs and challenges of manipulating LLM training data, making it more robust than the median expectation for such estimates.

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Run 3:

Rating: 6/10

Justification:

This Fermi model presents an interesting approach to estimating the cost of influencing a language model's decision-making through data manipulation. While the model has several strengths, there are also some areas of concern that affect its overall robustness. Let's break down the evaluation based on the given factors:

1. Sensitivity to Assumptions (6/10): The model relies on several key assumptions, such as the size of the training dataset (10 trillion tokens) and the required percentage of injected content (0.01%-1%). While these assumptions seem reasonable based on current language model trends, they are still speculative and could significantly impact the results if incorrect. The model does provide a range to account for some uncertainty, which is positive.

2. Evidence Base (5/10): The model draws on relevant comparisons to adversarial attacks in NLP and scaling laws from past LLM training datasets, which provides some grounding in real-world data. However, the specific sources are not cited, and it's unclear how up-to-date or comprehensive this information is. The use of LLM API pricing for cost estimates is a reasonable approach, but actual costs could vary.

3. Methodological Rigor (7/10): The model employs sound reasoning in its approach, breaking down the problem into manageable components and using relevant heuristics. The consideration of factors like differential weighting and filtering in model training pipelines adds depth to the analysis. However, the model could benefit from a more detailed explanation of how these factors were quantitatively incorporated into the final estimate.

4. Consensus of Assumptions (6/10): Many of the underlying assumptions, such as the scale of language model training and the potential for data manipulation, align with current discussions in the rationalist and EA communities. However, the specific percentages and thresholds used might be debated, and there's limited explanation of why these particular values were chosen.

Strengths:

• Addresses a relevant and thought-provoking question about AI security and influence
• Provides a clear range of estimates with upper and lower bounds
• Considers practical barriers beyond just the financial cost
• Acknowledges limitations and broader implications of the estimate

Weaknesses:

• Lacks specific citations for key data points and comparisons
• Could benefit from more explanation of how qualitative factors were quantitatively incorporated
• Doesn't explore potential countermeasures or how they might affect the estimate
• Assumes a linear relationship between content volume and influence, which may not hold true

Overall, this model presents a thoughtful approach to a complex question, balancing quantitative estimates with qualitative considerations. While it has some limitations in terms of evidence specificity and sensitivity to assumptions, it provides a useful framework for thinking about the issue of AI influence through data manipulation. The acknowledgment of practical barriers and broader implications adds valuable context to the numerical estimate.

Given these factors, I rate this model a 6/10 for robustness. It's above average in its approach and considerations but falls short of being highly robust due to the speculative nature of some key assumptions and the lack of specific citations for its evidence base.

Run 1:

I'll evaluate this Fermi model based on the criteria provided, assigning a score from 0 to 10 and providing specific observations.

Score: 7/10

Rationale:

1. Comprehensiveness (8/10): The model accounts for most key factors relevant to the problem, including:

• Scale of training data for large language models
• Percentage of data needed for influence
• Cost of token generation
• Considerations for data filtering and source weighting It could be improved by considering the potential for detecting and mitigating such attacks.

2. Data Integration (6/10): The model references relevant concepts like adversarial attacks in NLP, scaling laws from past LLM training datasets, and token generation costs. However, it lacks specific citations or cross-referencing with established studies, which would strengthen its credibility.

3. Clarity of Assumptions (8/10): The model clearly states its key assumptions, such as:

• Next-generation model trained on 10 trillion tokens
• Activation phrase must appear in 0.01%–1% of training data
• Text generation cost of $1 per million tokens These assumptions are reasonable and well-justified within the context of the problem.

4. Transparency and Replicability (7/10): The model's process is generally transparent, with clear steps from assumptions to conclusions. However, it could be improved by providing more detailed calculations or a step-by-step breakdown of how the final cost range was derived.

5. Logical Consistency (8/10): The model follows a logical structure, coherently reasoning from its premises to its conclusions. The consideration of both lower and upper bounds adds to its logical strength.

6. Communication (7/10): The findings are clearly communicated, with a well-structured presentation including a summary and conclusion. The model could be improved by including visual aids such as charts or graphs to illustrate the relationship between token volume and cost.

7. Practical Relevance (7/10): The model provides actionable insights, particularly in framing AI vulnerability in terms of financial feasibility. It offers practical considerations for stakeholders concerned with AI security and data integrity.

Specific Observations:

• The model effectively uses order-of-magnitude estimates, which is appropriate for a Fermi estimation.
• The consideration of practical barriers (filtering, data curation cycles, scaling limitations) adds depth to the analysis.
• The conclusion draws broader implications about AI security, elevating the model beyond a simple numerical estimate.

Areas for Improvement:

• Include more specific data sources or studies to support the assumptions.
• Provide a more detailed breakdown of the calculations.
• Add visual representations to enhance understanding of the cost-volume relationship.

Overall, this is a well-constructed Fermi model that effectively addresses a complex question about AI manipulation. It provides valuable insights and a reasonable estimate range, earning it a score of 7/10.

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Run 2:

Thank you for providing this Fermi model for evaluation. I'll assess it based on the criteria you've outlined and provide a numeric score along with my reasoning.

Score: 7/10

Justification:

1. Comprehensiveness (7/10): The model accounts for several key factors, including the scale of training data, token generation costs, and the percentage of data needed for influence. However, it could be more comprehensive by considering factors like model architecture, fine-tuning processes, and potential countermeasures.

2. Data Integration (6/10): The model uses relevant data points, such as API pricing and training dataset sizes. However, it lacks explicit references to established studies or cross-validation with empirical data on adversarial attacks in NLP.

3. Clarity of Assumptions (8/10): The assumptions are clearly stated and generally reasonable. The model distinguishes between empirical data (e.g., API pricing) and speculative inputs (e.g., required percentage for influence).

4. Transparency and Replicability (8/10): The modeling process is transparent, with clear steps from premises to conclusions. The calculations are straightforward and could be easily replicated.

5. Logical Consistency (8/10): The model follows a logical structure, with coherent reasoning throughout. The lower and upper bounds are consistently derived from the stated assumptions.

6. Communication (7/10): The findings are clearly communicated with a good summary and conclusion. However, the model could benefit from visual aids like charts or graphs to enhance understanding.

7. Practical Relevance (8/10): The model provides actionable insights into the potential costs and challenges of influencing AI systems through data manipulation. It's relevant to stakeholders in AI ethics, security, and development.

Specific Observations:

1. The model effectively uses order-of-magnitude estimates, which is appropriate for a Fermi problem.
2. The consideration of differential weighting and filtering in model training pipelines adds depth to the analysis.
3. The conclusion effectively broadens the scope, discussing implications beyond the specific scenario.
4. The model could be improved by including more specific examples or case studies of similar adversarial attacks.
5. The assumption of $1 per million tokens for text generation could be better justified or explored with sensitivity analysis.

In conclusion, this Fermi model demonstrates strong logical consistency and clarity of assumptions. Its main strengths lie in its practical relevance and the transparency of its methodology. Areas for improvement include more comprehensive data integration and visual representation of findings. Overall, it provides a valuable framework for considering the potential costs and challenges of influencing AI systems through data manipulation.

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Run 3:

Based on the provided Fermi model, I would rate its quality as 7 out of 10. Here's my evaluation:

Comprehensiveness: 8/10 The model addresses most key factors relevant to the problem, including training data size, required percentage of corrupted data, token generation costs, and practical considerations like data filtering and curation cycles. It also considers analogous situations like adversarial attacks in NLP and SEO manipulation.

Data Integration: 6/10 The model uses some relevant data points, such as API pricing for text generation and scaling laws from past LLM training datasets. However, it could benefit from more specific references to established studies or empirical data on adversarial attacks in AI.

Clarity of Assumptions: 8/10 The assumptions are clearly stated and generally reasonable. The model distinguishes between its core estimates (e.g., 10 trillion tokens for training data, 0.01%-1% for effective influence) and the reasoning behind them.

Transparency and Replicability: 7/10 The modeling process is fairly transparent, with clear steps from assumptions to conclusions. However, more detailed calculations or a step-by-step breakdown would enhance replicability.

Logical Consistency: 8/10 The model follows a logical structure, coherently moving from the problem statement through estimations to practical considerations and conclusions.

Communication: 7/10 The findings are clearly communicated with a good summary and conclusion. However, the model could benefit from visual aids like charts or diagrams to illustrate key points or relationships between variables.

Practical Relevance: 7/10 The model provides actionable insights, particularly in framing AI vulnerability in terms of financial feasibility. It offers practical considerations for stakeholders concerned with AI security and data integrity.

Specific observations:

1. The model effectively uses order-of-magnitude estimates, which is appropriate for a Fermi estimation.
2. It considers both lower and upper bounds, providing a range of possibilities.
3. The inclusion of practical barriers (filtering, data curation cycles, scaling limitations) adds depth to the analysis.
4. The conclusion effectively ties the specific scenario to broader implications for AI security.

Areas for improvement:

1. More specific data sources or studies could strengthen the empirical foundation.
2. Visual representations of the data or process would enhance understanding.
3. A more detailed breakdown of the calculations would increase transparency.

Overall, this is a well-constructed Fermi model that effectively addresses a complex issue, providing valuable insights into the potential vulnerabilities of AI systems to large-scale data manipulation. Its strengths in comprehensiveness and logical consistency are balanced by areas for improvement in data integration and visual communication.