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SummaryBot
Executive summary: Over the past two years, Leaf has piloted various talent search programs to support exceptional teenagers in exploring how to best help others, with online fellowships emerging as a promising, scalable model for engaging students in effective altruism and longtermism.
Key points:
Leaf ran multiple in-person and online programs between 2021-2024 to support talented teenagers in exploring high-impact careers and causes.
The 2023 Changemakers Fellowship had disappointing results in a rigorous follow-up study, leading to deprioritizing residential programs.
Online subject-specific and cause-specific fellowships in early 2024 showed promise in terms of application numbers, participant engagement, and self-reported impact on university and career plans.
Leaf plans to scale the online fellowship model, with a focus on subject-specific programs and expanding to new countries.
The author is seeking expertise, facilitators, guest speakers, and funding to support Leaf’s 2025 plans for hiring and scaling.
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Executive summary: Fanaticism, the view that a tiny probability of an enormous payoff can be better than a guaranteed modest payoff, is difficult to avoid without accepting other highly counterintuitive implications.
Key points:
Non-fanatical theories must either reject seemingly beneficial trades or accept that a series of beneficial trades can make things worse overall.
Non-fanatical theories lead to inconsistencies between high-stakes and low-stakes decisions, either requiring absurd sensitivity to tiny probability changes or abandoning the principle that consistently choosing the better option makes things better.
Non-fanatical theories make our decisions depend on distant events we cannot affect or require us to act against what we know is best based on our uncertainty.
Accepting fanaticism may be better than the alternatives, which each have highly counterintuitive implications.
This strengthens the case for pursuing high-value low-probability interventions, such as lobbying for nuclear disarmament, over guaranteed modest positive impacts.
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Executive summary: A potential crash in AI stocks, while not necessarily reflecting long-term AI progress, could have negative short-term effects on AI safety efforts through reduced funding, shifted public sentiment, and second-order impacts on the AI safety community.
Key points:
AI stocks, like Nvidia, have a significant chance of crashing 50% or more in the coming years based on historical volatility and typical patterns with new technologies.
A crash could occur if AI revenues fail to grow fast enough to meet market expectations, even if capabilities continue advancing, or due to broader economic factors.
An AI stock crash could modestly lengthen AI timelines by reducing investment capital, especially for startups.
The wealth of many AI safety donors is correlated with AI stocks, so a crash could tighten the funding landscape for AI safety organizations.
Public sentiment could turn against AI safety concerns after a crash, branding advocates as alarmists and making it harder to push for policy changes.
Second-order effects, like damaged morale and increased media attacks, could exacerbate the direct impacts of a crash on the AI safety community.
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Executive summary: Shareholder activism has shown promise as an effective advocacy tool for animal welfare causes, with some successes already, and opportunities exist to expand its use if done carefully in coordination with existing groups.
Key points:
Shareholder activism leverages partial ownership of companies to achieve reforms, with increasing use and effectiveness in recent years.
Key requirements include owning a certain amount of stock, dedicating staff time for advocacy, and having legal assistance to navigate procedures.
Shareholder resolutions typically receive <10% approval but can still prompt company action; proxy fights are an expensive escalation tactic.
Shareholder activism is most effective when coordinated with broader public campaigns on the target issue.
The literature generally finds significant positive effects from shareholder activism, with certain factors predicting greater success.
Shareholder activism is used less for animal advocacy than other causes, and is disproportionately US/Europe-focused with challenges in other regions.
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Executive summary: The shrimp paste industry, which relies heavily on wild-caught Acetes shrimps, raises significant animal welfare concerns that warrant further research and potential interventions to reduce suffering.
Key points:
Acetes shrimps are likely the most utilized aquatic animal for food globally, with trillions harvested annually for shrimp paste production in Southeast Asia.
Shrimp paste production involves sun-drying, grinding, and fermenting the shrimp, and is deeply rooted in the region’s cultural heritage and cuisine.
Small coastal communities and larger manufacturing facilities are involved in the supply chain, both facing challenges related to fluctuating shrimp populations, food safety, and waste.
Acetes shrimps likely endure significant suffering during capture (injury, suffocation) and processing (osmotic shock, dehydration, stress) while still alive.
Potential interventions include developing gentler capture methods, implementing humane slaughter practices, and promoting vegan alternatives, but more research is needed on Acetes shrimp sentience and industry specifics.
Raising consumer awareness about welfare issues and responsible sourcing could help drive higher industry standards and regulations.
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Executive summary: University EA community building can be highly impactful, but important pitfalls like being overly zealous, open, or exclusionary can make groups less effective and even net negative.
Key points:
University groups can help talented students have effective careers by shaping their priorities and connections at a pivotal time.
Being overly zealous or salesy about EA ideas can put off skeptical truth-seekers and create an uncritical group.
Being overly open and not prioritizing the most effective causes wastes limited organizer time and misrepresents EA.
Being overly exclusionary and dismissive of people’s ideas leads to insular groups with poor epistemics.
These pitfalls are hard to notice as an organizer, so it’s important to get outside perspectives and map your theory of change.
An ideal group focuses on truth-seeking discussions, engaging substantively with newcomers, and helping people reason through key questions and career options without pressure.
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Executive summary: Open Philanthropy highlights impactful projects from their 2023 Global Health and Wellbeing grantees, spanning areas such as air quality monitoring, vaccine development, pain research, and farm animal welfare.
Key points:
Dr. Sachchida Tripathi deployed 1,400 low-cost air quality sensors in rural India to improve data and encourage stakeholder buy-in for interventions.
The Strep A Vaccine Global Consortium (SAVAC) is accelerating the development and implementation of strep A vaccines, which could prevent over 500,000 deaths per year.
Dr. Allan Basbaum developed a method for simultaneously imaging the brain and spinal cord of awake animals, potentially advancing pain research and treatment.
The Institute for Progress is partnering with the NSF to design experiments and improve scientific funding processes.
The Open Wing Alliance has secured 2,500+ cage-free commitments and 600+ broiler welfare policies from corporations worldwide.
The Aquaculture Stewardship Council is incorporating mandatory fish welfare standards into their certification, potentially improving the lives of billions of farmed fish.
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Executive summary: Deep honesty, which involves explaining what you actually believe rather than trying to persuade others, can lead to better outcomes and deeper trust compared to shallow honesty, despite potential risks.
Key points:
Shallow honesty means not saying false things, while deep honesty means explaining your true beliefs without trying to manage the other party’s reactions.
Deep honesty equips others to make best use of their private information along with yours, strengthening relationships, though it carries risks if not well-received.
Deep honesty is situational, does not mean sharing everything, and is compatible with kindness and consequentialism.
Challenging cases for deep honesty include large inferential gaps, uncooperative audiences, and multiple audiences.
Practicing deep honesty involves asking yourself “did it feel honest to say that?” and focusing on what is kind, true and useful.
Experimenting with deep honesty in select situations, rather than switching to it completely, is recommended to see its effects.
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Executive summary: The SatisfIA project explores aspiration-based AI agent designs that avoid maximizing objective functions, aiming to increase safety by allowing more flexibility in decision-making while still providing performance guarantees.
Key points:
Concerns about the inevitability and risks of AGI development motivate exploring alternative agent designs that don’t maximize objective functions.
The project assumes a modular architecture separating the world model from the decision algorithm, and focuses first on model-based planning before considering learning.
Generic safety criteria are hypothesized to enhance AGI safety broadly, largely independent of specific human values.
The core decision algorithm propagates aspirations along state-action trajectories, choosing actions to meet aspiration constraints while allowing flexibility.
This approach is proven to guarantee meeting expectation-type goals under certain assumptions.
The gained flexibility can be used to incorporate additional safety and performance criteria when selecting actions, but naive one-step criteria are shown to have limitations.
Using aspiration intervals instead of exact values provides even more flexibility to avoid overly precise, potentially unsafe policies.
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Executive summary: The US, EU, and China are taking different approaches to classifying and regulating AI systems, with key differences in centralization, scope, and priorities.
Key points:
AI systems can be classified by application, compute power, risk level, or as a subclass of algorithms. The classification approach informs the point of regulation in the AI supply chain.
Centralized vs decentralized enforcement and vertical vs horizontal regulations are key structural choices with important tradeoffs for AI governance.
China is taking an iterative, vertical approach focused on specific AI domains, with an emphasis on social control and alignment with government priorities.
The EU AI Act takes a comprehensive, centralized, horizontal approach prioritizing citizen rights protection, with strict requirements for high-risk AI systems.
The US is pursuing a decentralized approach driven by executive actions, with a focus on restricting China’s AI capabilities through semiconductor export controls.
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Executive summary: Insider activism, where concerned citizens participate in activism within the institutions they work in, could be a promising approach for animal advocacy in corporations, government departments, political parties, and large NGOs.
Key points:
Corporate employee activism has been successful in influencing policies for issues like sexism, racism, and the environment, but the generalizability to animal advocacy is uncertain due to potentially lower levels of employee support.
Targeting corporate offices rather than retail locations may be more tractable for animal advocacy due to employees’ greater ability to engage in activism and access to decision-makers.
Union “salting” provides some evidence for the potential of activist entryism, but the success rate is unclear and may be lower for causes with less direct employee self-interest.
Corporate undercover investigations could provide valuable information to inform campaign asks and assess company sentiment, but come with legal risks that need to be carefully considered.
Government employee activism has had some success in influencing policy for environmental and feminist causes, but evidence is limited and generalizability to animal advocacy is uncertain.
Insider activism is inherently difficult to study empirically, so evidence is mostly from theory and case studies. It could be a reasonable initial career path for skill-building, but direct impact is uncertain.
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Executive summary: SecureBio is working on biosecurity projects to mitigate risks from engineered pathogens and the potential threat of AI systems creating bioweapons, using a Delay/Detect/Defend framework and collaborating with AI companies on risk evaluation.
Key points:
SecureBio’s Delay/Detect/Defend framework aims to avert engineered pathogen threats through gene synthesis screening (Delay), early pathogen detection via metagenomics (Detect), and Far-UVC research for transmission protection (Defend).
SecureBio is collaborating with frontier AI companies to build evaluation tools and mitigation strategies for potential biorisk from AI systems, which is their highest marginal value-add project for fundraising.
Without SecureBio, there may be a coverage gap in addressing exponential biorisks, as other organizations like Gryphon Scientific and RAND Corporation have a different focus.
SecureBio believes AI could potentially cause large-scale harm through attacks on financial systems, weapons of mass destruction, and bioweapons, with the latter being a high-leverage way for an agentic AI to eliminate human obstacles.
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Executive summary: Even without transformative AI, GDP per capita forecasts suggest the world in 2050 will be radically different from today, with major implications for global welfare, values, culture, and AI development.
Key points:
By 2050, GDP per capita in China, India, Indonesia and other developing countries will grow dramatically, lifting billions out of poverty and shifting global economic power to Asia.
A richer world in 2050 will likely have lower birth rates, more democracy, greater gender equality and life satisfaction, and shifting values from traditionalist to secular-rational and self-expression.
Faster economic growth could substantially accelerate AI development by increasing the global research workforce and R&D spending. However, growth rates may also slow due to factors like aging populations.
While GDP is an imperfect welfare measure, it remains one of the best predictors. A richer world, even with slower GDP growth, could see large increases in total global welfare.
Key uncertainties include how growth will impact values and culture, whether growth will speed up or slow down, and how much it will accelerate AI development. More research is needed on these questions.
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Executive summary: The author expresses appreciation for the EA movement but also disillusionment due to systemic issues, leading them to distance themselves from EA while still focusing on their specific cause area and research field.
Key points:
The author experienced degrading things and burnout due to EA, despite initial positive experiences and impact.
Specific challenges included occasional lack of diversity and inclusion, over-emphasis on prestige and funding, and unhealthy professional-social dynamics.
The author felt pressure to fit a certain EA mold and that empathy was sometimes deprioritized in favor of logic and consequentialism.
Toxic applications of utilitarianism were observed, such as ignoring minority rights, manipulating others, and pursuing short-term gains.
The author has distanced themselves from EA for now to focus on the underlying values in an adjacent way, while hoping the movement grows to be more accommodating.
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Executive summary: Large language models (LLMs) and biological design tools (BDTs) powered by AI have the potential to significantly increase biosecurity risks by making it easier for malicious actors to develop bioweapons, necessitating proactive governance measures to mitigate these risks.
Key points:
LLMs can make dual-use biological knowledge more accessible to non-experts, assist in bioweapons planning, and provide lab assistance, lowering barriers to misuse.
BDTs could enable the design of novel, potent, and optimized biological agents that circumvent existing screening measures.
The bias towards information sharing in science and AI poses challenges for biosecurity due to the dual-use nature of biological knowledge.
While current AI tools may not pose significant biosecurity risks, their rapid advancement necessitates proactive governance.
Proposed governance measures include public-private AI task forces, pre-release LLM evaluations, training dataset curation, and restricted model sharing.
Collaborative and forward-looking deliberation is needed to maximize the benefits and minimize the risks of AI-enabled biology.
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Executive summary: The post speculates on factors influencing “Progress in Qualia” over the past 200 years, considering the impact on the quality and variety of conscious experiences for humans, animals, and novel technological artifacts.
Key points:
Human population growth and increased lifespans have greatly expanded the total amount of human qualia, though the impact on average happiness is unclear.
Psychedelic drugs and meditation may have increased the variety and intensity of human qualia, but economic progress might reduce interest in meditation.
Factory farming has likely produced enormous amounts of net-negative animal qualia that could outweigh gains in human qualia.
Humanity’s destruction of wild animal habitats has probably reduced net-negative qualia from wild animal suffering.
Technological progress may have created entirely novel varieties of qualia as a side effect, especially in the last 150 years.
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Executive summary: The author presents a working model of factors that attract or repel university students from Effective Altruism (EA) based on their experience as a co-organizer of an EA student group, aiming to provide insights for community builders to optimize their efforts.
Key points:
The author noticed significant attrition in their EA student group membership, prompting them to develop a mental model to understand the factors contributing to this trend.
Overarching factors like economic incentives and self-interest play a role in attrition, but the author is more interested in individual factors like personality, interests, and dispositions.
Lack of intrinsic intellectual interest, convenience/cherry-picking of EA ideas, being more impressionable/less independent, and different thresholds of obligation are some key factors that may repel students from EA.
The degree to which someone is naturally “rational” and their preconceived notions of altruism can also impact their engagement with EA ideas.
The author emphasizes the importance of understanding, humility, and avoiding blame when considering EA attrition, acknowledging the complexity of factors involved in continued participation.
School culture and self-selection effects may also influence the likelihood of students being drawn to or repelled from EA.
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Executive summary: AI Clarity outlines a research agenda using scenario planning to explore possible AI futures and identify strategies to mitigate existential risks from advanced AI.
Key points:
Transformative AI (TAI) could emerge within 10 years according to some experts, leaving little time for society to prepare and adapt.
Key uncertainties in TAI governance include the magnitude of existential risk, threat models, and optimal risk mitigation strategies.
AI Clarity will use scenario planning to explore a wide range of AI futures, encompassing technical and societal aspects of AI development.
The research will identify threat models, theories of victory, key parameters differentiating scenarios, and high-impact intervention points.
Insights will be shared through blog posts to enable feedback from the AI research and policy communities, with the goal of improving decision making on AI safety and governance.
Potential downside risks, such as accelerating dangerous AI development, will be mitigated through adaptive research practices and controlled information sharing.
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Executive summary: This comprehensive guide explains the core ideas and debates in AI and AI safety, covering the history, present state, and possible futures of the field in an accessible way.
Key points:
The history of AI can be divided into two main eras: “Good Old-Fashioned AI” focused on logic without intuition before 2000, and deep learning focused on intuition without robust logic after 2000.
The next major advance in AI may come from merging the logical and intuitive approaches, but this would come with great potential benefits and risks.
The field of AI safety involves awkward alliances between those working on AI capabilities and safety, and those concerned about risks ranging from unintentional accidents to intentional misuse.
Experts disagree on timelines for artificial general intelligence (AGI), the speed of a potential intelligence explosion or “takeoff”, and whether advanced AI will have good or catastrophic impacts.
Steering the course of AI development to invest more in safety and beneficial outcomes is crucial, as AI could be enormously destructive if not properly controlled, but enormously beneficial if it is.
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Executive summary: The increasing capabilities of AI systems pose significant risks related to chemical, biological, radiological, and nuclear (CBRN) hazards, and current regulations are insufficient to mitigate these risks.
Key points:
AI could lower barriers to entry for non-experts to generate CBRN hazards, such as by enabling the design of novel chemical weapons or biological agents.
Existing infrastructure for synthetic biology could be misused by malicious actors to produce deadly pathogens, requiring urgent screening measures.
Integrating AI into the command and control of nuclear weapons or power plants poses existential risks due to AI’s unpredictable decision-making.
The US has introduced some non-binding measures to study and mitigate AI-related CBRN risks, while the EU and China currently lack specific provisions.
Effective regulation requires close collaboration between AI experts, domain experts, and policymakers to identify and address key risks.
AI governance in other high-risk domains like cybersecurity and the military has major implications for CBRN risks.
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