Executive summary: Recovering from societal collapse to modern civilization would likely take around 5000 years (range 3400-8500 years), based on historical data on transitions between major stages of development.

Key points:

1. Transition from hunter-gatherer to early agriculture typically took 500-2000 years, based on archaeological population data.
2. Early agriculture to pre-industrial society usually required about 2500 years, according to analysis of historical complexity data.
3. Pre-industrial to industrial society transition historically took around 500 years, though can be faster with existing knowledge.
4. Recovery to pre-industrial levels seems likely within a few thousand years after collapse, but further progress is uncertain.
5. Key uncertainties include environmental conditions, resource availability, and likelihood of reinventing crucial innovations.
6. Frequency of past disturbances may influence recovery speed, with modern society potentially facing slower recovery due to lack of experience.

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Executive summary: Analysis of new wastewater sequencing data from the 2023-2024 flu season provides estimates of influenza detection sensitivity, showing lower relative abundance than previously estimated for SARS-CoV-2.

Key points:

1. New data from Missouri and California allows estimation of influenza A and B detection in wastewater during an active flu season.
2. Relative abundance of influenza in wastewater correlates with positive test rates, but with significant variability.
3. Estimated RAi(1%) for influenza A and B is lower than previous SARS-CoV-2 estimates, varying by sequencing protocol and location.
4. Findings will be used to update a metagenomic biosurveillance simulator, likely resulting in higher variance and less optimistic projections.
5. Further sequencing in the 2024-2025 season is expected to reduce uncertainty in these estimates.

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Executive summary: An insurance-for-AI startup could incentivize safer AI practices and provide valuable risk insights, but faces challenges in market timing, industry connections, and securing reinsurance partners.

Key points:

1. The proposed startup would offer professional indemnity insurance for AI legal work, potentially expanding to other industries.
2. Challenges include slow industry movement, lack of claims history for AI, and difficulty securing reinsurance partners.
3. Customer demand exists among some AI-for-law companies, but many potential customers are not yet deploying AI in high-stakes use cases.
4. Pricing AI risk would involve comparing AI and human work samples, with adjustments for unknown factors.
5. Alternative approaches include partnering with AI labs/Big Tech as reinsurers or focusing on selling risk models.
6. The authors decided not to pursue the idea due to timeline concerns and lack of industry connections, but believe it remains promising for the right team.

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Executive summary: Moral theories could trade influence over agents based on impact potential, leading to different ethical approaches for ordinary people vs leaders, and potentially reducing the influence of aggregative consequentialism in finite worlds.

Key points:

1. Moral theories could trade influence, with deontological theories favoring ordinary people and aggregative theories favoring high-impact individuals.
2. This could result in a world where ordinary people follow strict moral rules while leaders maximize total welfare.
3. Different moral theories may prefer influence over agents based on their potential impact and the size of the world.
4. Aggregative theories might trade away influence in finite worlds for control in potentially infinite ones.
5. The observation that our world is finite may imply reducing the influence of aggregative consequentialism.
6. Moral theories might agree to dedicate resources to physics research to resolve influence disputes based on universe properties.

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Executive summary: Anchovy, chicken eggs, and cod liver oil are identified as the most viable food sources of vitamin D during an Abrupt Sunlight Reduction Scenario, with anchovy emerging as the top option due to its high vitamin D content, bioavailability, and production scalability.

Key points:

1. Weighted matrices were used to evaluate foods based on criteria like vitamin D concentration, bioavailability, nutritional value, and production scalability.
2. Anchovy scored highest overall, with availability in the first 9-18 months of a crisis. Eggs and cod liver oil are viable for the first 3-9 months.
3. Recommended daily portions to meet vitamin D needs were calculated for different age groups and crisis periods.
4. Other potential vitamin D sources like lanolin, enriched yeast, and lichens warrant further research.
5. Limitations include lack of bioavailability data and not evaluating all foods for production scalability.
6. Future work should explore more foods, additional evaluation criteria, and regional production capacities to optimize vitamin D availability during crises.

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Executive summary: A new AI scaling paradigm that allows scaling inference-time compute to compete with training compute could lead to rapid capability increases in both closed and open-source models, potentially enabling misuse by malicious actors.

Key points:

1. OpenAI's new "o1" model demonstrates that scaling inference time can significantly boost AI capabilities.
2. This paradigm could accelerate AI progress for both closed-source labs and open-source communities.
3. Frontier labs may soon be able to run powerful models for days, potentially leading to AGI-level capabilities.
4. Open-source models could be enhanced to perform complex reasoning tasks, lowering barriers to powerful AI.
5. There is a risk of malicious actors using these enhanced models for harmful purposes.
6. The rapid pace of development leaves little time to prepare for potential consequences.

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Executive summary: Impact evaluation methodologies are crucial for assessing the effectiveness of development and social action initiatives, but many small organizations lack the resources to implement them properly; this project reviews methodologies and provides guidance for selecting appropriate approaches.

Key points:

1. Impact evaluations measure significant changes in well-being resulting from interventions, helping improve program design and justify expansion.
2. A table summarizing key impact evaluation methodologies was created, comparing their features, strengths, and weaknesses.
3. A guide with key questions was developed to help organizations select suitable evaluation methodologies based on their specific context and needs.
4. Key factors in impact evaluation include evidence, robustness, appropriate indicators, and consideration of relational, contextual, and causal aspects.
5. Challenges include lack of planning for evaluation, preference for immediate results over long-term assessment, and difficulty applying evidence across contexts.
6. Recommendations include promoting an evaluation culture, using standardized approaches, and leveraging available educational resources on impact evaluation.

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Executive summary: The Centre for AI Safety's claims of "superhuman" AI forecasting capabilities for their "539" bot are not supported by evidence, with experiments revealing significant flaws in the bot's reasoning and predictive abilities.

Key points:

1. The technical report lacks crucial details on methodology and dataset construction.
2. Experiments show 539 struggles with coherent predictions over time, low probability events, and short-term forecasts.
3. 539's performance appears inconsistent and often inferior to human forecasters on various test cases.
4. The bot may be aggregating existing human predictions rather than modeling novel forecasts.
5. CAIS's evidential standards and research practices are called into question by these findings.
6. More rigorous testing and transparency are needed before claims of "superhuman" AI forecasting can be substantiated.

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Executive summary: The Material Innovation Initiative (MII), a nonprofit focused on accelerating the development of animal-free and environmentally preferred materials, is shutting down after 5 years of operation despite significant industry growth and accomplishments.

Key points:

1. MII inspired over $2.31 billion in investments for next-gen materials since 2019.
2. The organization facilitated nearly 400 collaborations between brands and next-gen material companies in 2023.
3. MII's research revealed 92% of US consumers are likely to purchase next-gen products.
4. The next-gen materials industry has grown from 102 companies in 2022 to 141 in 2023.
5. No specific rationale is given for MII's closure, but the announcement suggests the industry is now well-positioned to advance without MII's direct involvement.
6. Stakeholders are encouraged to continue supporting the next-gen materials sector despite MII's closure.

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Executive summary: Longtermist modeling of existential risk reduction relies on several sensitive assumptions that, when examined critically, may significantly reduce the estimated value of such interventions.

Key points:

1. Higher baseline existential risk decreases the value of risk reduction efforts.
2. Projected population decline reduces the number of future lives potentially saved.
3. Intervention decay over time diminishes long-term impact of current efforts.
4. Suffering risks (S-risks) could potentially outweigh benefits of existential risk reduction.
5. The "time of perils" hypothesis attempts to address some critiques but requires strong assumptions.
6. Hedonic lotteries (potential massive wellbeing improvements) may counterbalance S-risks.

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