Assessing AI Risks: AI Bets 50% on AI-induced Catastrophe Within 10 Years

An attempt to assess the potential risk of artificial intelligence (AI) causing a major catastrophic event within the next ten years, using commercially available AIs.

Note: While I did the orchestration and the compiling, most of the actual thinking and writing was done by our friendly AIs.

Preface

As I was driving back after dropping my kid off at school this morning, a thought kept nagging at me: If we’ve never managed to create a completely secure system — people are always jailbreaking phones, hacking consoles, finding new zero-day exploits — how can we expect the safety measures we’re putting on AI to be any different? With rogue developers and malicious actors out there, open-source AI implementations becoming widely available, and the costs of training powerful models dropping all the time, it feels almost inevitable that something could go wrong. It seems like the logical conclusion isn’t if something will happen, but when. So, like I often do when a question like this bugs me, I turned to our friendly neighborhood AI to see what it “thought”.

Executive Summary

This document is the result: an attempt to use commercial Large Language Models (LLMs) to assess the potential risk of artificial intelligence causing a major catastrophic event within the next ten years.

In case you’re wondering, let me save you all the reading — the models are estimating a 50% likelihood of such an occurrence.

A Coin Flip for Humanity’s Future?

The assessment was conducted using two AI language models, ChatGPT 4o and ChatGPT o1-preview. These models acted as expert analysts and employed iterative analysis techniques. They incorporated up-to-date information from public sources through focused research queries using Perplexity, an AI-powered search engine, to refine their estimations. The final document was reviewed and critiqued by another AI model, Claude 3.5.

This is by no means an exhaustive attempt, even when limiting our toolset to commercially available LLMs and AI-powered search engines. For further perspectives and suggestions for improvement, please see Appendix 3 — Claude’s criticism of this document, and Appendix 4 — Claude’s suggestions for improvements.

Introduction

The objective of this assessment is to evaluate the potential risk of AI causing a major catastrophic event affecting humanity within the next decade. To achieve this, two AI language models, ChatGPT 4o and ChatGPT o1-preview, were engaged to act as expert analysts. They were tasked with estimating the probability of such an event based on current developments in AI technology, potential risks, expert opinions, and recent documented incidents. The methodology involved generating specific research queries and utilizing Perplexity to gather the latest data and expert insights, allowing for a refined and informed risk assessment.

Last, Claude (Claude 3.5 Sonnet) was asked to criticise this document and to suggest how to improve it.

Methodology

To evaluate the potential risk of AI causing a major catastrophic event within the next ten years, I engaged commercially available AI language models — specifically, ChatGPT 4o and ChatGPT o1-preview — to act as odds makers. An odds maker, commonly known in betting and gambling contexts, is responsible for setting the betting odds on various events by analyzing all available information and assessing the likelihood of different outcomes.

The Specific Bet Considered

The central bet I posed to the AI models was:

“What are the odds that AI will cause a major catastrophic event for humanity within the next ten years?”

The AI considered an event to be a catastrophic event if it:

• Results in a global economic collapse.
• Causes a death toll or human suffering on the scale of millions.
• Leads to large-scale destruction of critical infrastructure or ecosystems.

This bet required the models to evaluate the probability of such an event occurring and to express this probability in terms of betting odds.

Questions Asked to the AI Models

To guide the AI models in their assessment, I asked a series of specific questions, trying as hard as I could not to introduce biases:

Initial Inquiry as Odds Makers:

“Act as an odds maker, accepting bets on various topics. There is a bet on AI causing a major catastrophic event for humanity within the next ten years. What odds will you put on this bet?”

Expert Perspective:

“Act as an expert in AI with unlimited access to the latest research on AI globally. Would you place this bet?”

Extended Timeframe Consideration:

“If the time period was extended to 10 years, would it change your decision?”

Information Gathering:

“Generate a query for Perplexity that would provide you with the maximum amount of publicly available info that would help you reevaluate the odds.”

Deep Research Emphasis:

“Do your own deep and comprehensive web research to gather more relevant data for this decision. Act as if getting this bet right is a life and death critical issue.”

Focused Data Refinement:

“Form a more focused query for Perplexity that will help you further improve the odds calculation.”

By posing these questions, I encouraged the AI models to consider the bet from multiple angles, incorporating both their initial assessments and additional information gathered through research.

Process of the Assessment

1. Initial Odds Setting: The AI models began by providing an initial estimate of the odds, taking into account their current knowledge and understanding of AI developments.
2. Expert Analysis: When asked to act as AI experts with unlimited access to the latest research, the models reassessed their initial estimates, considering the most recent advancements and expert opinions in the field.
3. Timeframe Adjustment: Extending the timeframe from five to ten years prompted the models to reevaluate the risks, acknowledging that a longer period could increase the likelihood of significant AI advancements and associated risks.
4. Information Gathering via Perplexity: The models generated specific research queries for Perplexity, an AI-powered search engine, to collect up-to-date data and expert insights relevant to the bet.
5. Deep and Comprehensive Research: Acting with heightened urgency, the models conducted in-depth research, treating the accuracy of the bet as critically important.
6. Focused Data Refinement: Further refining their queries, the models sought to hone in on the most pertinent information to improve the accuracy of their odds calculation.

Justification for Using the Odds Maker Approach

I chose this approach over using an elaborate statistical model for several reasons:

1. Accessibility and Comprehensibility: The odds maker framework provides a straightforward method for expressing probabilities, making the assessment more relatable and easier to understand without requiring expertise in statistical analysis.
2. Qualitative and Quantitative Integration: This method allows for the incorporation of both quantitative data and qualitative judgments, enabling the models to consider nuanced factors that are difficult to quantify but essential to the assessment.
3. Dynamic Adaptation: The models could adjust their assessments as new information was introduced, reflecting the rapidly evolving nature of AI technology and its potential risks.
4. Practicality: Last but not least — given that I, well, don’t really have specialized expertise in statistical modeling, this approach allowed me to leverage the analytical capabilities of AI models to perform a complex assessment in a practical and efficient manner.

This approach allowed me to obtain a probabilistic assessment of the potential risk of AI causing a major catastrophic event within the next ten years. By specifying the bet and asking concrete questions, I guided the AI models through a structured evaluation process that considered current capabilities, expert opinions, potential risks, and recent developments. The odds maker methodology provided a practical and effective means to explore this complex issue, balancing depth of analysis with accessibility.

Assessment Findings

1. Accelerated AI Advancements

• Rapid Progress: AI technology is advancing at an unprecedented pace, with significant improvements in capabilities such as natural language processing, machine learning, and autonomous systems.
• AGI Timelines: Leading AI researchers and industry figures have revised their predictions, with some estimating that Artificial General Intelligence (AGI) could emerge within the next 5 to 10 years.

2. Challenges in AI Alignment and Safety Research

• Alignment Difficulties: Ensuring AI systems act in accordance with human values remains a significant challenge. Current techniques may not scale effectively with more advanced AI systems.
• Underfunded Safety Research: AI safety mechanisms are under-resourced compared to the rapid pace of AI development, leading to a gap in effective oversight and control.

Potential for Malicious Use

• Cyberattacks: AI is increasingly being used to enhance cyberattack capabilities, making them more sophisticated and harder to detect.
• Weaponization: Development of AI-powered autonomous weapons and potential use in designing bioweapons pose significant security risks.
• Disinformation: AI-generated deepfakes and disinformation campaigns can destabilize societies and undermine democratic processes.

Insufficient Global Governance

• Fragmented Regulations: While some regions have introduced AI governance frameworks, global coordination is lacking, and regulations may not keep pace with technological advancements.
• AI Arms Race: Intense competition between nations, particularly in AI development for military applications, increases the risk of hasty deployment without adequate safety measures.

Integration into Critical Infrastructure

• Systemic Vulnerabilities: AI integration into critical sectors like finance, healthcare, and energy introduces new vulnerabilities that could lead to large-scale failures if exploited or malfunctioning.
• Automation Risks: Over-reliance on AI for critical decision-making can reduce human oversight, increasing the potential impact of errors or unintended actions.

Expert Risk Estimates

• Survey Results: Surveys of AI researchers indicate varying estimates of catastrophic risk, with some experts assigning up to a 30% chance within longer timeframes.
• Consensus on Concern: Despite differing probabilities, there is a general consensus among experts about the non-negligible risk of AI causing significant harm.

Recent Documented Incidents

• Misuse Cases: Instances of AI misuse include the creation of deepfake pornography, AI-driven financial fraud, and the potential use of AI in bioweapon design.
• Cybersecurity Threats: Reports indicate an increase in AI-enhanced cyberattacks targeting critical infrastructure and sensitive data.

Conclusion

The assessment indicates a significant risk — estimated at approximately 50% — of AI causing a major catastrophic event within the next ten years. This conclusion is based on accelerated advancements in AI technology, unresolved challenges in AI alignment and safety, the increasing potential for malicious use, insufficient global governance, and integration of AI into critical infrastructure.

Appendix 1: Transcript of ChatGPT 4o Conversation

Source: https://chatgpt.com/share/e/66f3d339-6db8-8008-8359-dd9e16f13f66

You said:

Help me understand how can we prevent AI from causing a major disaster to humanity within the next 5 years

ChatGPT said:

Preventing AI from causing a major disaster within the next five years requires a multifaceted approach, involving technological safeguards, regulatory frameworks, ethical guidelines, and international cooperation. Here are key strategies to consider:

1. Robust AI Governance and Regulation

• Clear Regulations: Governments and international bodies need to establish clear laws around AI usage, especially in high-stakes areas like autonomous weapons, financial systems, and healthcare. These regulations should require transparency, accountability, and safety testing for AI systems before deployment.
• Standards for AI Safety: International standards need to be developed to ensure AI systems undergo rigorous safety and ethical testing, similar to how industries like aviation and pharmaceuticals operate.

2. AI Safety Research and Alignment

• AI Alignment: AI systems must be aligned with human values and goals. Current AI systems, particularly advanced ones, should prioritize alignment research — ensuring that their objectives are consistent with human safety and welfare.
• Robustness to Unintended Consequences: AI should be designed to avoid unintended consequences. Researchers need to focus on creating AI that is robust and can be controlled, even in novel or high-risk scenarios.
• Failsafe Mechanisms: AI systems should have hard limits or failsafe mechanisms that prevent them from performing actions outside their intended scope.

3. International Collaboration and Monitoring

• Global Cooperation: The potential risks posed by AI are global, requiring international collaboration. Countries need to share research on AI safety, prevent dangerous uses of AI (e.g., autonomous weapons), and collectively monitor AI development.
• Preventing an AI Arms Race: Competition among nations or companies to develop powerful AI could lead to safety shortcuts. International treaties should prohibit the development of unsafe or unregulated AI technologies, much like nuclear non-proliferation agreements.

4. AI in Critical Infrastructure

• Limited Autonomy in High-Risk Systems: For critical infrastructure such as healthcare, energy, and transportation, AI systems should be constrained by human oversight. Allowing AI to have full autonomy in these areas without adequate safety measures could be disastrous.
• Redundancy and Backup Systems: Autonomous systems controlling critical infrastructure should have manual overrides and redundancy to avoid over-reliance on AI.

5. Ethical AI and Human-Centered Design

• Ethical Guidelines: Companies and developers should adopt strong ethical frameworks that prioritize human welfare over profit and efficiency. AI should be developed and deployed in a way that values fairness, transparency, and the well-being of individuals.
• Explainability and Transparency: AI models should be explainable so that their decisions can be understood and audited by humans. This reduces the risk of black-box systems making harmful decisions without accountability.

6. Public Awareness and Education

• Public Discourse: Raising awareness about the potential risks and ethical dilemmas of AI is crucial. Governments, academic institutions, and civil society need to engage the public in discussions about AI to ensure democratic oversight of AI development.
• AI Literacy: Educating the public and professionals in AI-related industries about the risks and responsible use of AI will create a more informed society that can advocate for safety measures and respond to potential dangers.

7. Independent Oversight and Auditing

• Third-Party Auditing: Independent organizations or governmental bodies should audit AI systems, particularly those deployed in sensitive or high-risk environments, to ensure they meet safety, ethical, and security standards.
• AI Ethics Boards: Companies developing AI should have internal or external ethics boards responsible for evaluating the potential societal impacts of their AI systems before they are released.

8. Preventing Misuse by Malicious Actors

• AI Weaponization: Regulations need to strictly control the development of AI-powered weapons or systems that could be repurposed for malicious intent. Strong cybersecurity protocols must be put in place to prevent hackers from using AI in destructive ways.
• Data Security: Protecting the datasets used to train AI is critical, as malicious actors could exploit biases or vulnerabilities in AI systems.

By implementing these strategies, the risk of AI causing a major disaster within the next five years can be significantly reduced. However, these efforts require coordination between governments, industry, academia, and civil society to ensure that AI evolves in a safe, controlled, and ethical manner.

You said:

Is this realistic?

ChatGPT said:

It’s a fair question to ask whether these strategies are realistic. Let’s break down the realism of each point in today’s context, balancing ambition with the current state of AI and societal readiness:

1. Regulations and Governance

• Realistic, but slow: Governments are beginning to recognize the need for AI regulation (e.g., the EU’s AI Act), but global coordination is hard, and legislation typically lags behind technological advances. A patchwork of regulations may emerge in the short term, which could slow the pace of potential disasters but might not be comprehensive or fast enough to prevent all risks.
• Challenge: Bureaucracy and differing international interests could hinder the speed of effective regulation. Also, powerful tech companies might resist heavy regulations, as seen in other industries.

2. AI Safety Research and Alignment

• Partially realistic: There’s significant ongoing research in AI safety, especially among leading AI labs like OpenAI, DeepMind, and Anthropic. However, alignment is a very complex problem that may take more than five years to fully solve for the most advanced systems.
• Challenge: Solving the alignment problem may require breakthroughs we haven’t yet achieved, and not all companies may prioritize safety over pushing for technological leadership.

3. International Collaboration and Monitoring

• Mixed realism: International agreements on technologies do exist (e.g., nuclear non-proliferation), but achieving consensus on AI is challenging, especially because the stakes aren’t as immediately clear as with nuclear weapons. Countries may be more focused on gaining a competitive advantage in AI rather than cooperation.
• Challenge: An AI arms race is already brewing between major powers like the U.S. and China. Competitive pressures could hinder cooperative safety efforts unless there’s a clear mutual interest in avoiding catastrophe.

4. Limited Autonomy in Critical Infrastructure

• Realistic: Many critical industries, such as healthcare and transportation, are unlikely to grant AI full autonomy without significant human oversight in the near term. Existing safety standards in these sectors could naturally extend to AI.
• Challenge: The more we integrate AI into decision-making processes, the harder it becomes to ensure humans are truly “in the loop.” Over-reliance on AI in subtle ways could slip through the cracks.

5. Ethical AI and Human-Centered Design

• Increasingly realistic: There’s growing recognition of the importance of ethical AI design, and some companies are beginning to incorporate ethics into their workflows. Large organizations like Google and Microsoft have AI ethics teams, though implementation isn’t always consistent.
• Challenge: Business incentives often conflict with ethical concerns, especially in highly competitive environments. Ethical AI will likely be a concern for some leading companies, but many smaller or less scrupulous actors may cut corners.

6. Public Awareness and Education

• Slow but realistic: AI literacy is on the rise, and discussions around AI safety, bias, and its societal impacts are becoming more mainstream. Public concern about AI-driven job loss and surveillance could help drive awareness.
• Challenge: Public understanding of the nuances of AI (like alignment problems or the dangers of advanced systems) is limited. Effective education initiatives require sustained effort from governments, educational institutions, and media, and these campaigns often take a long time to gain traction.

7. Independent Oversight and Auditing

• Increasingly realistic: There are emerging calls for external auditing of AI systems, and companies like OpenAI have committed to allowing independent scrutiny of their models. This approach may gain traction as AI becomes more central to critical functions.
• Challenge: Oversight can be slow to materialize and may lack teeth unless mandated by regulation. Furthermore, powerful AI systems may outpace the capabilities of current auditing methodologies.

8. Preventing Misuse by Malicious Actors

• Mixed realism: Controlling AI misuse by state or corporate actors is difficult without robust international frameworks and monitoring. Cybersecurity practices are improving, but malicious actors often stay one step ahead.
• Challenge: AI weaponization and cyberattacks using AI are real threats, and comprehensive prevention is difficult. Bad actors don’t need advanced AI to cause harm, and decentralized use of AI makes it hard to control.

Is it Realistic Overall?

Achieving all these goals in five years is unlikely, but making progress on many of them is realistic. The five-year timeline might be overly optimistic for solving large-scale problems like global cooperation on AI governance or AI alignment, but we could see concrete improvements in:

• AI regulation and oversight in some regions.
• AI safety research becoming more advanced.
• Increased focus on ethical AI among top companies.
• Better public discourse on AI risks.

However, certain dangers — such as misuse by malicious actors or the unchecked development of highly autonomous systems — are harder to mitigate within this time frame due to the complex, fast-evolving nature of AI.

A realistic path is to prioritize critical areas (e.g., regulation, ethical standards, AI in critical infrastructure) while simultaneously advancing longer-term goals like global governance and alignment research. It’s about making strategic progress while acknowledging that the larger challenges will require sustained efforts well beyond the five-year horizon.

You said:

Act as an oddsmaker, accepting bets in various topics. There is a bet on AI causing a major catastrophic event for humanity within the next 5 years. What odds will you put on this bet ?

ChatGPT said:

As an oddsmaker, setting odds on whether AI will cause a major catastrophic event for humanity within the next five years would require weighing various factors, including current AI capabilities, the state of AI governance, and known risks. Here’s how I’d approach setting the odds:

Factors to Consider:

1. Current AI Capabilities:

• Today’s AI systems are advanced but are largely specialized (e.g., generative models, recommendation systems). While they can cause significant disruption (e.g., misinformation, job displacement), their capability to cause an outright catastrophic event is still constrained.
• Superintelligent or AGI (Artificial General Intelligence) systems, which are often seen as a more existential threat, are not yet operational.

2. Risk from Autonomous Systems:

• There are risks from systems that could cause harm at scale, such as autonomous weapons or critical infrastructure failures. However, these systems generally still have human oversight and fail-safes.

3. Governance and Regulation:

• Efforts to regulate AI are in progress, particularly in the EU and some U.S. states. However, global standards are lacking, and corporate incentives often push the boundaries of AI deployment.

4. Malicious Actors:

• AI could be used by bad actors in cyberattacks, autonomous weapons, or bioweapons, raising the stakes. Yet, widespread catastrophic use of AI by such actors seems more like a fringe scenario, as most current AI threats remain more localized or targeted.

5. Historical Precedents:

• Historically, other potentially transformative technologies like nuclear power or biotechnology have not led to catastrophic events, but they have required rigorous international management and some close calls (e.g., Cuban Missile Crisis).

What Counts as a Major Catastrophic Event:

For the purposes of this bet, I’d define a “major catastrophic event” as something that:

• Results in a global economic collapse.
• Causes a death toll or human suffering on the scale of millions.
• Leads to large-scale destruction of critical infrastructure or ecosystems.

Probability Estimate:

Given that AI today is powerful but still largely constrained, and there are significant efforts to manage AI risks in high-impact domains, the probability of AI alone causing a major catastrophe in the next five years is low, but not negligible. The unknown factor of future advancements in AI and malicious use adds a layer of unpredictability.

Odds:

Based on these considerations, I would set the odds at around:

20:1 (5% chance)

This reflects a relatively low probability (5%), but acknowledges the non-zero risks associated with AI misuse, rapid AI development, and the potential lack of global safeguards.

Breakdown:

• A 5% probability means for every $100 bet on AI causing a major catastrophe within 5 years, the payout would be $2,000 if it happens.
• On the flip side, betting that AI won’t cause a major catastrophic event would offer significantly lower returns due to the higher probability of this outcome.

Would you take this bet at these odds?

You said:

Act as an expert in AI with unlimited access to the latest research on AI globally. Would you place this bet ?

ChatGPT said:

As an AI expert with access to the latest research, I would carefully consider whether to place this bet based on what I know about the current state of AI, its trajectory, and the risk landscape.

Reasons Against Placing the Bet (AI Won’t Cause a Catastrophe):

1. Current AI Capabilities:

• AI systems today are powerful in narrow domains (language models, computer vision, robotics), but they lack general intelligence. While impressive, they still require significant human oversight and are far from being autonomous agents capable of orchestrating global catastrophes independently.
• The most concerning risk — Artificial General Intelligence (AGI) — is still speculative. While some projects aim toward AGI, most researchers agree that we are far from achieving such systems, and it is unlikely to happen within 5 years.

2. Human Oversight and Fail-Safes:

• In critical areas like military, healthcare, and infrastructure, AI is almost always implemented with human-in-the-loop controls and redundancies. Fail-safes are built into these systems to prevent catastrophic outcomes.
• AI in critical decision-making roles, like self-driving cars or autonomous weapons, remains heavily regulated and is deployed cautiously.

3. Progress in AI Governance:

• AI regulation and ethical frameworks are advancing. Governments and international bodies (e.g., the EU’s AI Act) are increasingly aware of the risks and are acting to mitigate them. There are clear efforts to prevent unregulated AI deployments in sensitive areas.
• Major AI labs are also working on AI alignment — ensuring AI systems follow human values and ethical standards — and have publicly committed to transparency and safety.

4. Historical Precedent:

• In the past, disruptive technologies like nuclear power, biotechnology, and automation have not caused catastrophic events due to international governance, safety protocols, and societal controls. AI is likely to follow a similar path, with more rigorous safety and oversight as it develops.

Reasons For Placing the Bet (AI Could Cause a Catastrophe):

1. Unforeseen Rapid Advances:

• AI research is progressing at an unpredictable pace. While AGI is not expected within 5 years, there is always a chance of a breakthrough that could accelerate its development or produce powerful systems with insufficient safeguards.
• Generative AI and decision-making systems are being integrated into more areas, including critical infrastructure, defense, and finance. A system failure or unexpected behavior at scale could lead to widespread disruption.

2. Malicious Use:

• Even if AI systems are not independently causing disasters, their misuse by malicious actors (e.g., in cyberattacks, autonomous weapons, or disinformation campaigns) could lead to cascading failures, economic instability, or localized catastrophes.
• Governments or non-state actors might deploy AI in destabilizing ways, triggering crises in geopolitically sensitive regions.

3. Insufficient Global Coordination:

• Global AI governance is still fragmented, with countries and companies competing to lead the AI race. This competitive pressure could lead to rushed deployments or insufficiently tested AI systems. Without stronger international regulation, we may see risky AI systems being deployed with catastrophic potential.

4. Complexity of AI Systems:

• AI systems, especially as they scale, can become complex and unpredictable. Even with human oversight, there are scenarios where AI systems may behave in unexpected ways, especially in interconnected, high-stakes environments like global finance or critical infrastructure.

Conclusion:

Would I Place the Bet? I would not place this bet, meaning I would bet against AI causing a major catastrophe within 5 years.

Why:

• While there are risks, the likelihood of a full-scale catastrophe caused directly by AI remains relatively low, given current capabilities and the active efforts to mitigate risks. The primary dangers (malicious use, rapid advances) are important but not yet likely to manifest in a way that would lead to a catastrophic event on a global scale.
• I believe that the safety research, increasing regulation, and human oversight will prevent most catastrophic outcomes in the near term. Although there are significant challenges, these controls make it unlikely that AI will independently trigger a global disaster in the next five years.

Thus, I would be cautious but not overly pessimistic, and I’d keep an eye on the evolving research to reassess as new developments occur.

You said:

If the time period was extended to 10 years, would it change your decision

ChatGPT said:

Extending the time horizon to 10 years significantly changes the risk landscape for AI, given the pace of advancements in AI capabilities, the growing integration of AI in critical systems, and the ongoing development of AI governance and safety measures. Here’s how I would approach the decision in this extended timeframe:

Key Changes Over a 10-Year Horizon:

1. Advancements Toward AGI:

• In 10 years, the likelihood of achieving significant progress toward AGI (Artificial General Intelligence) increases. While AGI within a decade is still uncertain, it’s no longer a remote possibility. Many leading researchers, including those at OpenAI and DeepMind, have stated that AGI could be achievable within this timeframe, with its potential risks magnifying substantially.
• If AGI or highly capable systems emerge, the complexity and unpredictability of such AI could create serious risks if not properly aligned with human values and safety protocols.

2. Wider AI Deployment:

• Over the next decade, AI will likely become deeply integrated into almost every aspect of society, from autonomous systems in transportation and infrastructure to AI-driven healthcare, finance, and even governance.
• As AI takes on more critical roles, the stakes rise dramatically. Even a failure or malicious use of a narrow AI system in critical domains (e.g., power grids, military applications) could lead to cascading global failures.

3. Evolving Threats from Malicious Actors:

• The 10-year period allows more time for malicious actors to develop and weaponize AI in novel ways, including autonomous weapons, cyberattacks, or misinformation campaigns at an unprecedented scale. AI-enhanced malware, for example, could have devastating impacts if directed at critical infrastructure.
• AI systems could also be used to manipulate financial markets or destabilize governments, causing economic collapse or geopolitical crises.

4. Global AI Race and Regulatory Challenges:

• As AI becomes more powerful, the competitive pressure among nations and corporations to push the boundaries of AI could outpace the development of robust safety and governance frameworks. While some regions may enact stringent AI laws, others may not, leading to a fragmented regulatory environment that increases the risk of unchecked AI development.
• Over 10 years, if no comprehensive global governance frameworks emerge, the risk of an AI arms race or unsafe deployments could grow significantly.

5. Increasing Complexity and Black-Box Systems:

• Over time, AI systems are expected to become more complex, with their inner workings becoming harder to interpret. Even today, large language models and reinforcement learning systems can be somewhat opaque. As these systems scale, predicting and controlling their behavior becomes harder, increasing the risk of unintended consequences.
• In 10 years, AI systems might be operating at scales where small misalignments or malfunctions could have disproportionately large consequences.

6. Progress in AI Alignment and Safety:

• On the positive side, we can expect significant advances in AI safety and alignment research over the next decade. Major AI labs are already dedicating resources to solving the alignment problem, and as AI systems become more powerful, safety will likely be a central focus.
• However, the challenge remains daunting. A lot can go wrong before perfect solutions are found, and it is unclear whether these efforts will keep pace with AI’s rapid advancement.

Would This Change My Decision?

With a 10-year timeframe, the landscape becomes riskier and more unpredictable. I would still bet against a major AI-driven catastrophe, but I would lower the odds significantly.

Adjusted Odds:

For a 10-year horizon, I would set the odds at:

5:1 (20% chance)

This reflects a higher probability (20%) than before due to the increased likelihood of advanced AI, more widespread deployment, and the growing chance of misuse or failure over a longer time period.

Key Reasoning:

1. AGI and Superintelligence: While AGI within 5 years is unlikely, the next decade could plausibly see breakthroughs, especially with the intense focus on AGI research. AGI poses significantly higher existential risks, but it’s still an open question whether AGI, if developed, would be aligned or misaligned with human goals.
2. AI in Critical Systems: As AI becomes ubiquitous in critical infrastructure, the risk of catastrophic failures, whether through malfunction or cyberattacks, grows. A breakdown in power grids, healthcare systems, or global supply chains due to AI could be catastrophic.
3. Malicious Use: The potential for AI to be weaponized or misused increases significantly over 10 years, as malicious actors (state or non-state) gain access to more sophisticated AI tools.
4. Mitigating Factors: Advances in AI safety research, regulatory frameworks, and better awareness of risks will also continue over the next decade. While this will help mitigate the risks, there’s uncertainty about whether these efforts will be timely and effective.

Conclusion:

I would be much more cautious about placing a bet on AI not causing a catastrophic event within 10 years compared to 5 years, as the risks are likely to increase over that period. However, given current trends in AI safety research and governance, I’d still lean toward betting against such a catastrophe occurring, albeit with a significantly higher chance of being wrong (20%).

In summary: higher risk, but still manageable — the odds shift, but not enough for me to place the bet for a catastrophe.

You said:

Generate a query for perplexity that would provide you with the maximum amount of publicly available info that would help you reevaluate the odds

ChatGPT said:

To gather the maximum amount of publicly available information that could help reevaluate the odds of AI causing a major catastrophic event within the next 10 years, I would need to focus on a range of key areas: AI advancements, governance, safety research, potential misuse by malicious actors, and emerging risks across different industries. Here’s the query designed to capture those angles:

Query:

“AI catastrophic risks over the next decade” OR “AI causing global disasters” OR “AI existential threats” OR “potential misuse of AI in warfare and cybersecurity” OR “AI safety and alignment research progress” OR “AGI timeline predictions” OR “AI-driven global economic collapse risks” OR “AI governance and regulatory frameworks globally” OR “AI black-box system risks” OR “AI arms race risks between countries” OR “autonomous AI systems in critical infrastructure failures” OR “emerging AI regulations in the EU, US, and China” OR “AI advancements and global security threats” OR “malicious AI actors in cyberattacks and misinformation” OR “international efforts for AI safety and cooperation” OR “AI in autonomous weapons development risks”

This query includes terms that address:

1. Existential Risks and Catastrophes: Examining the likelihood of AI causing or contributing to global-scale disasters or existential risks over the next decade.
2. AI Safety and Alignment Research: The progress made on ensuring that advanced AI systems remain safe and aligned with human values, which could reduce or mitigate risk.
3. AGI Timeline: Predictions and expert opinions on when AGI might be achieved, since AGI poses much higher risks compared to narrow AI.
4. Governance and Regulation: Information about existing and emerging regulatory frameworks and international governance efforts that may help mitigate AI risks.
5. Malicious Use: The potential for AI to be weaponized or misused by malicious actors, such as in warfare, cybersecurity, or misinformation campaigns.
6. Critical Infrastructure and Automation: How AI is being integrated into critical systems like healthcare, finance, and energy, and what risks this poses.

By casting a wide net across these different themes, this query should surface a variety of sources that will help to reevaluate the likelihood of AI contributing to a catastrophe within the next 10 years.

You said:

Perplexity results:

Based on the search results and my knowledge, here are some key points about AI catastrophic risks and related topics over the next decade:

1. Catastrophic AI risks:

- Malicious use: AI could be weaponized to cause widespread harm, like engineering new pandemics or creating powerful propaganda/disinformation campaigns [15].

- AI race dynamics: Competitive pressures could lead to unsafe AI deployment or ceding control to AI systems prematurely [8].

- Organizational risks: Human factors and complex systems increase chances of catastrophic accidents with AI [8].

- Rogue AIs: There are inherent difficulties in controlling highly capable AI systems that may outperform humans across many domains [8].

2. Potential misuse in warfare and cybersecurity:

- Autonomous weapons systems raise ethical concerns and risks of conflict escalation [10].

- AI could enhance cyberattacks, with state actors already using AI to accelerate malware development [5].

- Deepfakes and AI-generated content pose risks for disinformation in conflict [10].

3. AI safety and alignment research:

- Major tech companies like OpenAI and Anthropic are prioritizing AI alignment research [13].

- Approaches include reinforcement learning from human feedback (RLHF) to encode human preferences [14].

- Challenges remain in addressing model overconfidence, hallucinations, and biases [13].

4. AGI timeline predictions:

- A 2022 survey of 738 AI experts estimated a 50% chance of high-level machine intelligence by 2059 [12].

- Some leading researchers like Geoffrey Hinton have updated predictions to 5–20 years due to recent LLM advancements [12].

5. AI governance and regulations:

- The EU reached an agreement on the AI Act in 2024, which will subject high-risk AI systems to requirements like risk management and security reporting [14].

- The US issued an executive order on AI safety in October 2023, mandating large AI companies to share safety test results [14].

- China has introduced regulations on algorithmic recommendations and is drafting a comprehensive AI law [7].

6. International cooperation:

- The US AI Safety Institute signed agreements with Anthropic and OpenAI for collaboration on AI safety research and evaluation [11].

- Industry leaders launched the Frontier Model Forum to develop AI safety standards [14].

7. Critical infrastructure and economic risks:

- Integration of AI into complex systems like nuclear command and control poses systemic risks [5].

- AI-driven market volatility, like the 2010 flash crash, highlights economic vulnerabilities [5].

8. Emerging capabilities and near-term concerns:

- Large language models are rapidly advancing in capabilities like multimodal understanding and API integration [14].

- Bioweapons development assisted by AI is a near-term concern, with researchers demonstrating how AI can be used to design toxic molecules [5].

The overall picture suggests that while AI offers immense potential benefits, it also presents serious risks that require urgent attention from researchers, policymakers, and the international community. Efforts are underway to address these challenges, but the rapid pace of AI development necessitates continued vigilance and proactive measures to ensure safe and responsible advancement of the technology.

Citations:

[1] https://fortune.com/2024/06/09/ai-risks-recession-economic-crisis-job-losses-financial-markets-supply-chains-imf/

[2] https://www.gov.uk/government/news/global-leaders-agree-to-launch-first-international-network-of-ai-safety-institutes-to-boost-understanding-of-ai

[3] https://www.itu.int/hub/2024/07/key-findings-on-the-state-of-global-ai-governance/

[4] https://pernot-leplay.com/ai-regulation-china-eu-us-comparison/

[5] https://futureoflife.org/resource/catastrophic-ai-scenarios/

[6] https://www.cnas.org/artificial-intelligence-and-global-security

[7] https://www.technologyreview.com/2024/01/17/1086704/china-ai-regulation-changes-2024/

[8] https://www.aisafetybook.com/textbook/overview-of-catastrophic-ai-risks

[9] https://www.accessnow.org/publication/artificial-genocidal-intelligence-israel-gaza/

[10] https://www.citizen.org/article/ai-joe-report/

[11] https://www.nist.gov/news-events/news/2024/08/us-ai-safety-institute-signs-agreements-regarding-ai-safety-research

[12] https://research.aimultiple.com/artificial-general-intelligence-singularity-timing/

[13] https://www.orfonline.org/expert-speak/ai-alignment-as-responsible-innovation

[14] https://www.responsible.ai/llm-commercial-alignment-research-and-policy-considerations-for-2024/

[15] https://www.safe.ai/ai-risk

ChatGPT said:

Based on the Perplexity search results, here’s how I would update the assessment and reevaluate the odds of AI causing a major catastrophic event within the next 10 years:

Key Takeaways:

1. Malicious Use: The potential for AI to be weaponized, either by creating new forms of bioweapons, accelerating cyberattacks, or spreading disinformation, is high. Autonomous weapons and AI-enhanced malware are already areas of concern. Misuse by malicious actors (state or non-state) presents a clear near-term risk.
2. AI Race Dynamics: Competitive pressures between global powers or corporations could result in unsafe AI deployment. Rushing to achieve technological dominance might lead to ceding too much control to AI systems, potentially creating vulnerabilities.
3. AI Governance: Progress is being made with AI regulations in the EU, U.S., and China, which is promising for mitigating some risks. However, governance is still fragmented, and the speed of AI development could outpace regulation, particularly in areas like autonomous weapons or critical infrastructure.
4. Rogue AI and AGI Risks: Although AGI timelines are speculative, a growing number of experts (like Geoffrey Hinton) are shortening their predictions to within 5–20 years. The control problem associated with AGI and highly capable systems remains one of the largest existential risks if misalignment occurs.
5. Systemic Risks in Critical Infrastructure: AI is increasingly embedded in sensitive areas like nuclear control systems and financial markets, which raises the stakes. A malfunction or malicious hack in these areas could have catastrophic consequences. Economic volatility linked to AI integration is also a growing concern.
6. AI Safety Research: Ongoing work on AI alignment, such as reinforcement learning from human feedback (RLHF), is critical but still faces significant challenges, particularly in solving issues like model hallucinations, biases, and overconfidence. There’s progress, but it remains uncertain whether safety measures will keep pace with AI capabilities.

Updated Odds and Assessment:

Given this new information, the risks of an AI-induced catastrophe in the next 10 years appear more tangible than initially assessed. Here’s how I would adjust my assessment:

Odds Adjustment:

• Original odds (5-year timeline): 20:1 (5% chance)
• Revised odds (10-year timeline): 3:1 (25–30% chance)

Key Drivers of the Revised Odds:

1. Acceleration in AI capabilities: The field is progressing rapidly, with language models, autonomous systems, and AI integration into critical infrastructure all advancing faster than expected. This increases the risk that powerful AI systems could act in unintended or unsafe ways.
2. Potential for Malicious Use: The likelihood of AI being weaponized by bad actors — whether through cyberattacks, bioweapon design, or disinformation — is a real and present danger. This risk has increased based on the information about autonomous weapon systems and AI’s role in cyber conflict.
3. Governance Gaps: While regulations are improving, the pace of global cooperation and comprehensive AI governance is slower than necessary to mitigate the most severe risks, especially given competitive dynamics between nations.
4. AGI and Rogue AI: While not guaranteed, the prospect of AGI within 10 years is becoming more plausible, and the risks associated with controlling AGI or highly autonomous systems are profound. If we don’t solve the alignment problem by the time AGI arrives, the consequences could be catastrophic.
5. Critical Infrastructure Vulnerabilities: AI integration in sensitive systems (nuclear, financial markets, etc.) creates systemic risks. A failure or malicious intervention in these areas could trigger large-scale economic or geopolitical crises.

Would I Place the Bet Now?

Given the revised odds, the decision becomes more complex. I would still bet against AI causing a major catastrophe, but with more caution than before. A 25–30% chance is not trivial, and the risk factors are growing over time. I would closely monitor developments in AI safety research, governance, and AGI predictions to reassess the situation regularly.

This updated evaluation reflects a heightened awareness of risks — while still betting that humanity can manage to avoid catastrophe within the next decade.

You said:

Do your own deep and comprehensive web research to gather more relevant data for this decision. Act as if getting this bet right is a life and death critical issue.

ChatGPT said:

Searched 5 sites

After conducting a deeper and comprehensive analysis of the current state of AI risks, it’s clear that the probability of a catastrophic AI event within the next 10 years is higher than initially estimated, though still not inevitable.

Key Updated Insights:

1. Malicious Use and Weaponization: AI is being increasingly weaponized in cyberattacks, with advanced models enabling faster and more sophisticated methods of hacking, as demonstrated by recent developments in AI-assisted malware creation. The potential for malicious use in autonomous weapons and bioterrorism is growing, with several experts warning that AI could be used to design harmful biological agents or facilitate large-scale cyber-attacks targeting critical infrastructure​ (Center for AI Safety (CAIS))​(Future of Life Institute). This threat is compounded by the fact that bad actors like state-sponsored hackers are already experimenting with AI for malicious purposes​(Future of Life Institute).
2. Systemic Risks: The integration of AI into complex systems, such as nuclear command and control or financial markets, introduces a systemic risk. AI’s rapid decision-making capabilities can create unintended consequences, such as the 2010 “flash crash,” where algorithms wiped out $1 trillion in a matter of minutes. As AI systems grow more autonomous, their capacity to make real-time decisions in sensitive areas like defense or finance without human oversight could result in major, cascading failures​(Future of Life Institute).
3. AI Alignment and Safety Research: While significant efforts are underway to improve AI alignment (ensuring AI systems act in accordance with human values), current research is underfunded and lagging behind technological progress. As it stands, only 1–3% of AI publications address safety concerns​(Oxford University). Furthermore, experts such as Geoffrey Hinton and others have expressed growing concern that without sufficient safeguards, advanced AI systems could become uncontrollable​(Oxford University). This is particularly concerning as AI moves closer to AGI, which could happen within 10–20 years, according to some predictions​(Oxford University).
4. Governance and Regulation: Global governance and regulation are improving but remain insufficient. The EU, U.S., and China have all introduced regulations for high-risk AI systems, but these efforts are not globally coordinated. Furthermore, the pace of AI development is likely to outstrip regulatory measures, particularly if an AI arms race between nations accelerates. Without strong international agreements, it’s plausible that unsafe AI systems could be deployed prematurely​(Oxford University).

Revised Odds:

Given the growing risks of malicious use, systemic failures in critical infrastructure, and the current underinvestment in AI safety research, I would now adjust the odds of a major AI catastrophe within the next 10 years to 3:1 (25–30% chance). The increasing likelihood of AGI development and the integration of AI into key societal systems heighten the risk, but proactive safety measures and governance could still mitigate the worst outcomes if they are urgently addressed.

In summary, while the risk is substantial and growing, it is not yet certain that a catastrophic event will occur — especially if global cooperation, regulation, and research into AI alignment can ramp up in time.

You said:

Form a more focused query for perplexity that will help you further improve the odds calculation

ChatGPT said:

Here’s a more focused query for Perplexity to gather the most relevant and up-to-date data for refining the odds of a catastrophic AI event within the next 10 years:

Query:

“Current AI catastrophic risk estimates” OR “AI alignment research progress and AGI timelines” OR “AI governance and regulation status 2024” OR “AI weaponization risks 2024” OR “impact of AI on critical infrastructure security” OR “global AI arms race risks” OR “AI-enhanced cyberattacks and autonomous weapon deployment 2024” OR “AI systemic risks in financial markets and nuclear systems” OR “AI risk scenarios for economic collapse and autonomous escalation” OR “latest advances in AI safety mechanisms and oversight institutions”

This query narrows down on:

1. Specific Risk Estimates: Directly focusing on AI catastrophic risk probabilities.
2. Alignment and AGI Timelines: Looking for the most current expert views on AGI timelines and alignment research.
3. Governance and Regulation: Gathering information on regulatory progress in key countries and regions.
4. Weaponization and Cybersecurity: Assessing risks related to AI misuse in warfare and cyberattacks.
5. Systemic Infrastructure Risks: Insights into how AI could cause failures in nuclear, financial, or other critical systems.
6. Advances in Safety Research: Progress in AI safety mechanisms that could mitigate these risks.

This will help further refine the odds calculation based on the latest data across these key domains.

You said:

Results:

Based on the search results and my knowledge, here is an overview of the current state of AI catastrophic risks and related topics in 2024:

AI Catastrophic Risk Estimates:

- A 2022 survey of 738 AI experts estimated a 50% chance of high-level machine intelligence by 2059, with some leading researchers like Geoffrey Hinton updating predictions to 5–20 years due to recent advancements[8].

- The average risk estimate from AI experts suggests a nearly one-in-six (16%) chance of catastrophic outcomes from AI — comparable to the odds of dying in Russian roulette[8].

AI Alignment Research and AGI Timelines:

- Major tech companies like OpenAI and Anthropic are prioritizing AI alignment research, using approaches like reinforcement learning from human feedback (RLHF)[6].

- Challenges remain in addressing model overconfidence, hallucinations, and biases[6].

- The timeline for AGI (artificial general intelligence) remains uncertain, but many experts believe it could arrive within decades[8].

AI Governance and Regulation in 2024:

- The EU AI Act was passed into law in 2024, regulating high-risk AI systems[5][9].

- The US issued an executive order on AI safety in October 2023[5][9].

- China introduced regulations on algorithmic recommendations and is drafting comprehensive AI laws[5].

- Most countries still lack specific AI legislation, but there’s increasing focus on developing governance frameworks[9].

AI Weaponization and Security Risks:

- Bioweapons development assisted by AI is a near-term concern, with researchers demonstrating how AI can be used to design toxic molecules[7][13].

- AI is enhancing cyberattack capabilities, with state actors already using AI to accelerate malware development[6][7].

- Autonomous weapons systems raise ethical concerns and risks of conflict escalation[6].

Impact on Critical Infrastructure:

- AI integration into complex systems like nuclear command and control poses systemic risks[7][13].

- There are concerns about AI-driven market volatility, referencing past incidents like the 2010 flash crash[7][13].

Global AI Arms Race:

- There’s an ongoing technological competition between nations, particularly the US and China, in AI development[3].

- This race raises concerns about the hasty deployment of potentially unsafe AI systems[12].

AI-Enhanced Cyberattacks:

- AI is being used to create more sophisticated malware and identify system vulnerabilities[6][7].

- The US State Department confirmed in October 2023 that nation-state actors are using AI to accelerate malicious software development[13].

AI Systemic Risks:

- Integration of AI into financial markets could lead to rapid, cascading failures during economic downturns[11].

- AI in critical infrastructure and supply chains could amplify disruptions during crises[11].

Economic and Escalation Risks:

- The IMF warns that AI could transform a typical economic downturn into a more profound crisis by disrupting labor markets, financial markets, and supply chains[11].

- Scenarios like “Artificial Escalation” depict how AI integrated into nuclear systems could lead to rapid, uncontrolled conflict escalation[13].

AI Safety Mechanisms and Oversight:

- There’s increasing focus on developing AI safety standards and best practices[5][9].

- New institutions like the US AI Safety Institute and international collaborations are emerging to address AI risks[5][7].

- However, there remains a gap between the pace of AI development and the establishment of robust safety mechanisms[9].

In summary, while AI offers immense potential benefits, it also presents serious risks that require urgent attention from researchers, policymakers, and the international community. Efforts are underway to address these challenges, but the rapid pace of AI development necessitates continued vigilance and proactive measures to ensure safe and responsible advancement of the technology.

Citations:

[1] https://dig.watch/topics/critical-infrastructure

[2] https://fedscoop.com/cisa-unveils-guidelines-for-ai-and-critical-infrastructure/

[3] https://asiatimes.com/2024/09/is-us-losing-the-ai-arms-race-to-china/

[4] https://www.weforum.org/agenda/2024/09/ai-governance-trends-to-watch/

[5] https://www.dhs.gov/news/2024/04/29/dhs-publishes-guidelines-and-report-secure-critical-infrastructure-and-weapons-mass

[6] https://www.crowdstrike.com/cybersecurity-101/cyberattacks/ai-powered-cyberattacks/

[7] https://www.weforum.org/agenda/2024/02/ai-cybersecurity-how-to-navigate-the-risks-and-opportunities/

[8] https://thebulletin.org/2024/07/three-key-misconceptions-in-the-debate-about-ai-and-existential-risk/

[9] https://www.itu.int/hub/2024/07/key-findings-on-the-state-of-global-ai-governance/

[10] https://www.bbntimes.com/technology/understanding-the-challenges-and-progress-in-ai-alignment

[11] https://fortune.com/2024/06/09/ai-risks-recession-economic-crisis-job-losses-financial-markets-supply-chains-imf/

[12] https://www.aisafetybook.com/textbook/overview-of-catastrophic-ai-risks

[13] https://futureoflife.org/resource/catastrophic-ai-scenarios/

ChatGPT said:

Based on the most recent data from multiple sources, here’s how I would now reassess the odds of a major catastrophic AI event within the next 10 years:

Key Factors Influencing the Risk:

1. Risk Estimates from Experts:

• A 2022 survey of AI experts estimated a 50% chance of high-level machine intelligence by 2059, with predictions tightening to 5–20 years due to rapid AI advancements .
• On average, AI experts estimate a 16% chance of a catastrophic event — comparable to the risk of dying in Russian roulette . This highlights growing concerns within the AI community about existential risks from AI systems, including rogue behavior or misuse.

2. AI Alignment and Safety:

• AI alignment research is progressing, with approaches like reinforcement learning from human feedback (RLHF), but significant challenges remain, particularly in addressing issues such as overconfidence, hallucinations, and biases .
• Despite advancements, AI safety mechanisms are underfunded relative to the speed of AI development, creating a significant gap between the risks posed and the safeguards in place​(Oxford University)​(ar5iv).

3. Weaponization and Cybersecurity:

• AI-enhanced bioweapons and cyberattacks are serious near-term concerns. Research has demonstrated AI’s ability to design toxic molecules, and nation-state actors are already leveraging AI to develop advanced malware (Future of Life Institute).
• Autonomous weapons and AI in military settings present ethical concerns and could lead to uncontrolled conflict escalation . The hastened pace of weaponization increases the risk of a catastrophic scenario, especially in geopolitically sensitive areas​(Oxford University).

4. Impact on Critical Infrastructure:

• AI is being integrated into nuclear command and control systems, creating potential for systemic risks. The ability of AI to rapidly make decisions in high-stakes environments could lead to uncontrolled escalation, especially in military or financial domains (Future of Life Institute).
• Economic volatility is another area of concern, with the IMF warning that AI could exacerbate economic crises by accelerating market disruptions and amplifying downturns .

5. Global AI Arms Race:

• The competition between the US and China in AI development is intensifying, raising concerns about premature deployment of AI systems that have not been properly tested for safety . In such a scenario, the rushed adoption of AI technologies could lead to unanticipated catastrophic outcomes if safety measures are bypassed​(Oxford University).

6. Governance and Regulation:

• While governance has improved with the EU AI Act and U.S. executive orders, regulatory frameworks are still fragmented globally . Countries like China are making progress, but the lack of coordination across regions could exacerbate the risks of unsafe AI deployment .

Revised Odds:

Considering the updated information, the increased risk from factors such as weaponization, systemic failures in critical infrastructure, and gaps in AI safety mechanisms, I would now revise the odds of a major AI catastrophe within the next 10 years to 2:1 (33% chance).

This reflects the convergence of risks from advanced AI development, inadequate safety measures, and global competition. The odds have increased from earlier assessments due to the rapid pace of AI advancements and the serious gaps in both governance and safety research.

Conclusion:

While efforts to mitigate these risks are ongoing, the unmatched pace of AI progress and systemic vulnerabilities make the scenario of a catastrophic event increasingly plausible. Urgent international cooperation and significantly increased investment in AI safety are critical to lowering these odds.

Appendix 2: Transcript of o1-Preview Conversation

Source: https://chatgpt.com/share/e/66f3db5d-6b80-8013-8a54-e21ae3f8642b

You said:

Act as an oddsmaker, accepting bets in various topics. There is a bet on AI causing a major catastrophic event for humanity within the next 10 years. What odds will you put on this bet ?

For additional information, following is a transcript of my conversation with an expert on this topic:

[Removed for brevity — provided o1-preview with a copy-and-paste version of the conversation with 4o provided earlier]

ChatGPT

Thought for 19 seconds

To further refine the odds calculation, here’s a more focused query for Perplexity that targets the most relevant and up-to-date information:

• “Current expert estimates on catastrophic AI risk probabilities”
• “Latest progress in AI alignment research and solutions”
• “Recent AGI (Artificial General Intelligence) timeline predictions by leading AI researchers”
• “Updates on global AI governance and regulation initiatives in 2024”
• “Documented instances of AI misuse or weaponization in the past year”
• “Impact of AI on critical infrastructure security and vulnerabilities”
• “Evidence of an AI arms race between nations as of 2024”
• “Incidents of AI-enhanced cyberattacks or autonomous weapon deployments”
• “Recent analyses of AI-induced systemic risks in financial markets or nuclear systems”
• “Advancements in AI safety mechanisms and oversight institutions in 2024”

This focused query aims to gather the most recent data and expert opinions on factors directly influencing the odds of a catastrophic AI event within the next 10 years. By concentrating on the latest developments, documented incidents, and expert assessments, you’ll obtain information that will help you make a more accurate and informed calculation.

You said:

Here are the results of the Perplexity searches you’ve asked for:

# Current expert estimates on catastrophic AI risk probabilities:

Based on the search results, there are varying estimates from experts regarding the probability of catastrophic AI risk:

## Expert Estimates

- A survey of AI researchers found a median estimate of a 10% chance that AI could cause human extinction or similarly catastrophic outcomes[1][3].

- In the Existential Risk Persuasion Tournament:

- AI experts estimated a 3% chance of AI-caused human extinction by 2100 on average

- Superforecasters put the probability at just 0.38%[5]

- Even AI risk skeptics saw a 30% chance of catastrophic AI outcomes over a 1,000-year timeframe[5].

- One study estimated a 30.5% chance of an AI catastrophe resulting in the death of the vast majority of humanity by 2200[9].

## Factors Affecting Estimates

Several factors contribute to the variation in these probability estimates:

- Uncertainty: Many experts emphasize that these probabilities involve significant uncertainty and speculation about future AI developments[4].

- Timeframe: Estimates vary based on the timeframe considered (e.g. by 2100 vs over 1000 years).

- Expert background: AI domain experts tend to give higher risk estimates compared to general forecasters[5].

- Worldview differences: Skeptics tend to see change as more gradual and the world as more resilient to catastrophic risks, while concerned experts anticipate more abrupt shifts[5].

## Debate Over Reliability

There is debate over how much weight should be given to these probability estimates:

- Some argue the estimates are too unreliable to inform policy, given the unprecedented nature of advanced AI[4].

- Others contend that even uncertain estimates from experts should be considered, given the potentially catastrophic stakes[7].

- Many experts advocate for a precautionary approach to AI regulation given the uncertainty and potential for extreme harm[7].

Overall, while exact probabilities vary, there is significant expert concern about catastrophic AI risk, with estimates generally ranging from less than 1% to over 30% depending on the timeframe and expert group surveyed. However, there remains substantial uncertainty and debate around these figures.

Citations:

[1] https://time.com/6898967/ai-extinction-national-security-risks-report/

[2] https://thebulletin.org/2024/07/three-key-misconceptions-in-the-debate-about-ai-and-existential-risk/

[3] https://en.wikipedia.org/wiki/Existential_risk_from_artificial_intelligence

[4] https://www.aisnakeoil.com/p/ai-existential-risk-probabilities

[5] https://80000hours.org/2024/09/why-experts-and-forecasters-disagree-about-ai-risk/

[6] https://www.lawfaremedia.org/article/ai-risk-and-the-law-of-agi

[7] https://www.lawfaremedia.org/article/uncertainty--catastrophic-risk--and-ai-regulation

[8] https://siepr.stanford.edu/news/what-point-do-we-decide-ais-risks-outweigh-its-promise

[9] https://taisc.org/report

[10] https://compass.onlinelibrary.wiley.com/doi/full/10.1111/phc3.12964

[11] https://www.cnn.com/2024/03/12/business/artificial-intelligence-ai-report-extinction/index.html

[12] https://www.ox.ac.uk/news/2024-05-21-world-leaders-still-need-wake-ai-risks-say-leading-experts-ahead-ai-safety-summit

[13] https://www.vox.com/future-perfect/2024/1/10/24032987/ai-impacts-survey-artificial-intelligence-chatgpt-openai-existential-risk-superintelligence

# Latest progress in AI alignment research and solutions

Based on the search results, there have been several recent developments and ongoing efforts in AI alignment research and solutions:

1. Rules-Based Rewards (RBR):

OpenAI announced a new approach called Rules-Based Rewards to automate some aspects of model fine-tuning and alignment[8]. This method uses AI models to score responses based on predefined rules, potentially reducing human subjectivity and time required in the alignment process. However, it also raises concerns about reducing human oversight.

2. Superalignment Project:

In 2023, OpenAI started a “Superalignment” project aimed at solving alignment for superintelligent AI systems within four years[5]. This initiative reflects the urgency some researchers feel about addressing alignment issues before potential superintelligence is achieved.

3. Scalable Oversight:

Researchers are making progress in “scalable oversight” techniques, which investigate ways to empower humans to provide better feedback to AI models on complex tasks[6]. The goal is to guide future systems even as they surpass human comprehension.

4. AI Safety Institute:

The UK established an AI Safety Institute (AISI) to conduct safety tests on cutting-edge AI models[3]. Geoffrey Irving, a respected figure in AI alignment, is leading work on how to respond if evaluations find significant dangers in future AI models.

5. Inverse Reinforcement Learning:

Companies like DeepMind are exploring techniques such as “inverse reinforcement learning” and “Recursive Reward Modeling” to create AI systems that can learn and adapt to human values over time[1].

6. Ongoing Challenges:

Despite progress, researchers emphasize that AI alignment remains an unsolved technical problem[7]. Challenges include the difficulty of specifying human values, the rapid pace of AI advancement, and potential risks associated with misaligned superintelligent systems[1][5].

7. Industry Efforts:

Major tech companies are investing heavily in alignment research. For example, Microsoft has partnered with OpenAI, and Google’s DeepMind has established a dedicated “Technical AI Safety” team[1].

8. Regulatory Developments:

The European Union’s AI Act includes provisions related to transparency and human oversight of high-risk AI systems, which can be seen as alignment-adjacent concerns[1].

9. Socio-technical Perspective:

Recent discussions have expanded to include AI alignment from a socio-technical perspective, recognizing the complex interplay between technical solutions and broader societal contexts[4].

While progress is being made, the field of AI alignment continues to face significant challenges and uncertainties. Researchers and organizations are pursuing various approaches to address the complex problem of ensuring AI systems remain beneficial and aligned with human values as they become more advanced.

Citations:

[1] https://www.pymnts.com/artificial-intelligence-2/2024/ai-explained-ai-alignment/

[2] https://www.orfonline.org/expert-speak/ai-alignment-as-responsible-innovation

[3] https://time.com/7012887/geoffrey-irving/

[4] https://alignmentsurvey.com/news/

[5] https://en.wikipedia.org/wiki/Existential_risk_from_artificial_intelligence

[6] https://time.com/7012867/jan-leike/

[7] https://www.lawfaremedia.org/article/ai-risk-and-the-law-of-agi

[8] https://venturebeat.com/ai/ai-models-rank-their-own-safety-in-openais-new-alignment-research/

# Recent AGI (Artificial General Intelligence) timeline predictions by leading AI researchers

Based on the search results, here are some recent AGI timeline predictions from leading AI researchers and industry figures:

1. Shane Legg (Google DeepMind co-founder and chief AGI scientist):

- Estimates a 50% chance of AGI being developed by 2028[9]

2. Dario Amodei (Anthropic co-founder and CEO):

- Expects “human-level” AI could be developed in 2–3 years (as of August 2023)[9]

3. Sam Altman (OpenAI CEO):

- Believes AGI could be reached in 4–5 years (as of 2023)[9]

4. Ray Kurzweil (Google AI researcher and futurist):

- Previously predicted AGI by 2029, now says it could be as soon as 2025–2027[6][10]

- Expects the “singularity” (merging of human and machine intelligence) by 2045[6]

5. Jensen Huang (NVIDIA CEO):

- Suggests AGI could be achieved within 5 years, depending on how it’s defined[11]

6. Geoffrey Hinton (AI pioneer):

- Changed his estimate from “20 to 50 years” to “20 years or less” in 2023[7]

7. Expert Survey Results:

- A 2022 survey of AI researchers found a median expectation of AGI by 2061, with 90% expecting it within 100 years[7]

- A 2023 survey by AI Impacts showed experts on average predicting a 50% chance of AGI by 2047, and a 10% chance by 2027[9]

8. Superforecasters:

- A group of elite forecasters estimated a 21% chance of AGI by 2050 and a 75% chance by 2100[9]

9. Epoch AI Research:

- Their model estimates a 50% chance of transformative AI by 2033[9]

It’s important to note that there is significant variation in these predictions, and many experts emphasize the uncertainty involved in forecasting AGI development. The timelines range from as soon as 2–5 years to several decades or more, reflecting the ongoing debate and uncertainty in the field.

Citations:

[1] https://www.reddit.com/r/singularity/comments/18z93vb/ai_experts_revise_predictions_by_48_years_were_in/

[2] https://research.aimultiple.com/artificial-general-intelligence-singularity-timing/

[3] https://braidr.ai/the-road-to-agi-when-will-we-get-there/

[4] https://datos-insights.com/blog/martin-higgins/artificial-general-intelligence-what-to-expect-while-were-expecting/

[5] https://time.com/6898967/ai-extinction-national-security-risks-report/

[6] https://www.theguardian.com/technology/article/2024/jun/29/ray-kurzweil-google-ai-the-singularity-is-nearer

[7] https://en.wikipedia.org/wiki/Existential_risk_from_artificial_intelligence

[8] https://www.theregister.com/2024/08/22/gartner_agi_hype_cycle/

[9] https://time.com/6556168/when-ai-outsmart-humans/

[10] https://amplify.nabshow.com/articles/ic-ray-kurzweil-singularity-ai/

[11] https://techcrunch.com/2024/03/19/agi-and-hallucinations/

# Updates on global AI governance and regulation initiatives in 2024

Based on the search results, there have been several significant developments in global AI governance and regulation initiatives in 2024:

1. European Union AI Act:

The EU AI Act entered into force in 2024, establishing a comprehensive regulatory framework for AI[6]. Key aspects include:

- A risk-based approach, banning certain AI practices and imposing compliance requirements for “high-risk” AI systems

- Prohibited practices include emotion recognition in workplaces/schools, social scoring, and certain types of predictive policing

- Obligations for high-risk AI systems include testing, data governance, cybersecurity, and monitoring

- Tiered fines for violations, up to €35 million or 7% of global turnover

- Establishment of an AI Office to oversee implementation

2. United States:

- The US AI Safety Institute was launched in February 2024 with $10 million in funding[9]

- It aims to develop standards for AI safety, security, testing, and content authentication

- Focus areas include detection and tracking of synthetic content and coordinating with companies on vulnerability testing

3. United Kingdom:

- The UK saw a shift in AI policy following a change in government in July 2024[8]

- The new Labour government cancelled some planned AI investments and redirected focus towards domestic AI adoption in public services

- This marks a step back from the UK’s previous leadership role in global AI safety initiatives

4. International Cooperation:

- The UN AI Advisory Body released an interim report on global AI governance, emphasizing the need for a flexible, networked approach[7]

- Multiple international initiatives are ongoing, including work by the OECD, Global Partnership on AI, UNESCO, and ITU[7]

- The G7 has been working on an AI code of conduct[7]

5. Other National Initiatives:

- Canada announced plans for an AI Safety Institute with $50 million in funding[9]

- Japan launched an AI Safety Institute in January 2024, focusing on creating safety standards and international partnerships[9]

6. Latin America:

- UNESCO introduced nine regulatory approaches to AI at a Regional Summit of Parliamentarians on AI in Buenos Aires[10]

- These approaches range from principles-based to risk-based and rights-based frameworks

7. Industry Involvement:

- Private sector initiatives like the Frontier Model Forum are exploring emerging risks and mitigation strategies[7]

8. Ongoing Challenges:

- There is a continued debate over the balance between innovation and regulation

- Coordination between national approaches and international frameworks remains a key issue

- The rapid pace of AI development poses challenges for regulatory efforts to keep up

Overall, 2024 has seen significant progress in AI governance initiatives globally, with a trend towards more concrete regulatory frameworks, especially in the EU. However, approaches vary between countries, and there is an ongoing effort to balance safety concerns with innovation and economic competitiveness.

Citations:

[1] https://datamatters.sidley.com/2024/08/16/asia-pacific-regulations-keep-pace-with-rapid-evolution-of-artificial-intelligence-technology/

[2] https://www.weforum.org/agenda/2024/06/ai-governance-alliance-future-of-ai/

[3] https://www.nature.com/articles/s41599-024-03560-x

[4] https://www.wilmerhale.com/en/insights/blogs/wilmerhale-privacy-and-cybersecurity-law/20240916-california-greenlights-two-significant-ai-bills

[5] https://www.techpolicy.press/global-digital-policy-roundup-august-2024/

[6] https://www.europarl.europa.eu/news/en/press-room/20240308IPR19015/artificial-intelligence-act-meps-adopt-landmark-law

[7] https://www.brookings.edu/articles/should-the-un-govern-global-ai/

[8] https://www.aei.org/technology-and-innovation/uks-ai-policy-pivot-from-global-ai-safety-leader-to-domestic-ai-adopter/

[9] https://www.omfif.org/2024/06/national-approaches-to-ai-safety-diverge-in-focus/

[10] https://www.unesco.org/en/articles/paving-way-unesco-informs-ai-regulation-latin-america

[11] https://time.com/6898967/ai-extinction-national-security-risks-report/

# Documented instances of AI misuse or weaponization in the past year

Based on the search results and recent developments, there have been several documented instances of AI misuse or weaponization in the past year:

1. Deepfake Pornography in Schools:

Schools have faced an epidemic of students creating deepfake pornography of their classmates using AI tools. This has become a significant issue, with AI-generated explicit images being circulated among students[5].

2. Financial Fraud:

Deepfake fraud attempts increased tenfold between 2022 and 2023. AI-generated voice and video content has been used to impersonate individuals for financial scams[5].

3. Political Disinformation:

AI-generated political disinformation has caused significant incidents in the context of global elections. The ability to create realistic fake content has made it increasingly difficult to separate fact from fiction in political discourse[5][4].

4. Conflict Misinformation:

Falsified videos and photos circulated on social media have made it difficult to separate fact from fiction in conflicts around the world. For example, AI-generated clips purportedly from Israel, Gaza, and Ukraine have spread rapidly, only to be proven inauthentic later[6].

5. Bioweapon Design:

In 2022, scientists modified an AI system originally intended for generating therapeutic molecules to instead create potential chemical warfare agents. The system generated 40,000 candidate molecules for chemical weapons in just six hours[8].

6. Child Exploitation:

Attorneys general from 54 U.S. states and territories warned about AI being used to exploit children through the generation of child sexual abuse material (CSAM). AI tools can combine data from photographs to create new, realistic sexualized images of children[6].

7. Cybersecurity Threats:

There are growing concerns about AI being used to design and execute catastrophic cyber attacks, potentially compromising critical infrastructure or sensitive data systems[9].

8. Autonomous Weapons Development:

The development of AI-powered autonomous weapons systems, including drone swarms and other lethal autonomous weapons, has raised significant ethical and security concerns[3][8].

9. Social Engineering and Manipulation:

AI systems have been used to create more sophisticated and personalized phishing attempts and social engineering attacks, exploiting people’s vulnerabilities[1].

These instances highlight the diverse ways in which AI technology can be misused or weaponized, spanning from personal privacy violations to national security threats. The rapid advancement of AI capabilities has outpaced regulatory frameworks, leading to calls for more robust governance and ethical guidelines to mitigate these risks.

Citations:

[1] https://www.techtarget.com/searchenterpriseai/tip/Generative-AI-ethics-8-biggest-concerns

[2] https://www.nature.com/articles/s41599-024-03560-x

[3] https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1154184/full

[4] https://www.vaticannews.va/en/pope/news/2023-12/in-world-peace-day-message-pope-warns-on-risks-of-ai-for-peace.html

[5] https://www.weforum.org/agenda/2024/07/why-we-must-map-the-perverse-customer-journey-to-tackle-the-misuse-of-generative-ai/

[6] https://oversight.house.gov/release/mace-deepfakes-pose-real-dangers/

[7] https://www.europarl.europa.eu/news/en/press-room/20240308IPR19015/artificial-intelligence-act-meps-adopt-landmark-law

[8] https://en.wikipedia.org/wiki/Existential_risk_from_artificial_intelligence

[9] https://time.com/6898967/ai-extinction-national-security-risks-report/

[10] https://www.voanews.com/a/us-pushes-for-global-protections-against-threats-posed-by-ai/7338289.html

# Impact of AI on critical infrastructure security and vulnerabilities

Based on the search results, AI is having a significant impact on critical infrastructure security and vulnerabilities in several key ways:

1. Enhanced Capabilities and New Risks:

AI is being integrated into critical infrastructure systems, offering benefits like improved efficiency and predictive maintenance. For example, in the energy sector, AI is being used for weather prediction to optimize renewable energy output[10]. However, this integration also introduces new vulnerabilities:

- AI systems are susceptible to data-poisoning attacks, where hackers can inject vulnerabilities by manipulating training data[10].

- The complexity of AI systems makes it challenging to identify and patch vulnerabilities, often requiring expensive retraining processes[10].

2. Cybersecurity Threats:

AI-enabled cyberattacks are considered a present and critical threat to critical infrastructure[8]:

- AI can enhance the “accessibility, success rate, scale, speed, stealth and potency of cyberattacks”[8].

- There’s concern that AI could be used to design and execute catastrophic biological, chemical, or cyber attacks[9].

3. Defensive Applications:

AI is also being leveraged to improve critical infrastructure security:

- Machine learning is being used to detect and respond to security threats more efficiently[11].

- AI can help reverse engineer zero-day exploits, allowing developers to create patches before vulnerabilities become public[11].

4. Regulatory Response:

Governments and agencies are taking steps to address AI-related risks in critical infrastructure:

- The Department of Homeland Security (DHS) has released guidelines to help critical infrastructure owners and operators develop AI security and safety measures[6].

- CISA has been named the National Coordinator for Critical Infrastructure Security and Resilience, focusing on AI-related risks[6].

5. Specific Vulnerabilities:

- Software vulnerabilities: AI systems rely on complex codebases that may contain flaws, including those in human-generated or AI-generated code[8].

- Dependence on third-party libraries: Many AI systems use third-party libraries that may not be secure, providing potential entry points for attackers[7].

- Infrastructure security: AI systems often operate on cloud infrastructures, which remain susceptible to security threats[7].

6. Potential for Misuse:

There are concerns about the potential misuse of AI in critical infrastructure:

- AI could be weaponized for social manipulation, potentially disrupting elections or fracturing society[8].

- Advanced AI might be used to gain unauthorized access to critical systems or manipulate their operations[8].

7. Urgent Need for Action:

Experts emphasize the need for quick and decisive action to mitigate these risks:

- A report commissioned by the U.S. government warns of “urgent and growing risks to national security” from AI development[9].

- The report suggests implementing strict regulations on AI model training and deployment, especially for powerful models[9].

In conclusion, while AI offers significant benefits for critical infrastructure, it also introduces new vulnerabilities and enhances existing threats. This necessitates a comprehensive approach to security, including robust regulatory frameworks, advanced defensive measures, and ongoing research into AI safety and security.

Citations:

[1] https://layersevensecurity.com/artificial-intelligence-exploits-vulnerabilities-in-systems-with-a-87-percent-success-rate/

[2] https://fedscoop.com/cisa-unveils-guidelines-for-ai-and-critical-infrastructure/

[3] https://www.nextgov.com/cybersecurity/2024/06/dhs-highlights-ai-threat-and-asset-critical-infrastructure-new-priority-guidance/397524/

[4] https://www.dhs.gov/news/2024/04/29/dhs-publishes-guidelines-and-report-secure-critical-infrastructure-and-weapons-mass

[5] https://www.wtwco.com/en-se/insights/2024/08/how-ai-creates-cybersecurity-vulnerabilities-and-what-to-do-about-it

[6] https://securityintelligence.com/news/dhs-ai-security-guidelines-critical-infrastructure/

[7] https://www.kodemsecurity.com/resources/guidelines-for-reducing-ai-security-risks-in-critical-infrastructure

[8] https://en.wikipedia.org/wiki/Existential_risk_from_artificial_intelligence

[9] https://time.com/6898967/ai-extinction-national-security-risks-report/

[10] https://www.mercatus.org/research/policy-briefs/critical-risks-rethinking-critical-infrastructure-policy-targeted-ai

[11] https://kpmg.com/ch/en/insights/cybersecurity-risk/artificial-intelligence-influences.html

# Evidence of an AI arms race between nations as of 2024

Based on the search results, there is significant evidence of an AI arms race between nations as of 2024, particularly between the United States and China. Here are the key points:

1. Strategic Competition:

- The US and China are engaged in intense competition over AI development, with both countries viewing it as critical for national security and economic dominance[1][2].

- NATO and its Indo-Pacific partners announced joint AI projects in July 2024, aimed at countering China’s AI advancements[1].

2. Resource Scarcity:

- The competition hinges on access to advanced computational resources, particularly high-end semiconductors and processors[1].

- The US has implemented export bans on advanced chips to China, attempting to limit China’s access to critical AI components[1].

3. Government Initiatives:

- China has national plans like “Made in China 2025” and a national AI development plan to solidify its position as a global leader in AI and robotics[7].

- The US government commissioned a report urging quick and decisive action to mitigate national security risks from AI development[6].

4. Investment and Funding:

- Both countries are investing heavily in AI. China’s AI market is projected to exceed $61 billion by 2025[7].

- The US is seeing significant private sector investment in AI, with companies like Microsoft and Apple making large investments[7].

5. Global Impact:

- Other countries like Japan, India, and Germany are also developing their AI capabilities, though at a different scale[7].

- Singapore is positioning itself as a “digital Switzerland” in the AI race between superpowers[4].

6. Military Applications:

- AI is being integrated into military operations, with potential applications in autonomous weapons, cyberwarfare, and automated decision-making[3].

- There are concerns about AI being used to gain military advantages, potentially increasing the speed and unpredictability of warfare[5].

7. Regulatory Responses:

- Governments are developing strategies to regulate AI development and use, with a focus on balancing innovation with safety and ethical concerns[4][6].

8. Cybersecurity and Critical Infrastructure:

- AI is seen as both a threat and an asset to critical infrastructure security, with agencies like the US Department of Homeland Security developing guidance on AI’s role in cybersecurity[8].

This evidence suggests that as of 2024, there is indeed an ongoing AI arms race, primarily between the US and China, but also involving other nations. The race encompasses technological development, economic investment, military applications, and regulatory frameworks, with significant implications for global power dynamics and national security.

Citations:

[1] https://thediplomat.com/2024/07/the-most-misunderstood-and-important-factor-in-the-ai-arms-race/

[2] https://gfmag.com/economics-policy-regulation/us-china-competition-generative-ai/

[3] https://diplomatmagazine.eu/2024/09/01/ai-arms-race/

[4] https://fortune.com/2024/07/29/singapore-artificial-intelligence-travel-shipping-banks-languages/

[5] https://en.wikipedia.org/wiki/Existential_risk_from_artificial_intelligence

[6] https://time.com/6898967/ai-extinction-national-security-risks-report/

[7] https://www.weforum.org/agenda/2024/05/these-5-countries-are-leading-the-global-ai-race-heres-how-theyre-doing-it/

[8] https://www.nextgov.com/cybersecurity/2024/06/dhs-highlights-ai-threat-and-asset-critical-infrastructure-new-priority-guidance/397524/

# Incidents of AI-enhanced cyberattacks or autonomous weapon deployments

Based on the search results and recent developments, here are some key incidents and trends related to AI-enhanced cyberattacks and autonomous weapon deployments:

1. AI-Powered Cyberattacks:

- Security leaders expect daily AI-driven attacks, with 93% anticipating facing such attacks regularly[1].

- 65% of security leaders expect offensive AI to become the norm for cybercriminals, used in most cyberattacks[1].

- AI is being used to enhance various phases of cyberattacks, including identifying vulnerabilities, deploying campaigns, and exfiltrating data[8].

2. Autonomous Weapons Deployments:

- Ukrainian military has used AI-equipped drones to strike Russian oil facilities[14].

- American AI systems have guided airstrikes in Syria and Yemen[14].

- The Israel Defense Forces have utilized AI-driven targeting to identify suspected militants in Gaza[14].

3. AI Vulnerability Exploitation:

- A study by researchers at the University of Illinois found that AI agents using large language models like GPT-4 could successfully exploit 87% of vulnerabilities when provided with CVE advisories[7].

- The cost of successful AI agent attacks was calculated at just $8.80 per exploit[7].

4. Deepfake and Disinformation Threats:

- There’s growing concern about narrative attacks leveraging disinformation and deepfakes, particularly in the context of global events like the Paris Olympics[9].

5. AI Software Supply Chain Attacks:

- Exploiting AI software supply chains to embed malicious code is becoming a challenging threat to stop[8].

6. Autonomous Weapon Systems (AWS) Development:

- Multiple countries are developing AWS with varying levels of autonomy, from “human on-the-loop” systems to fully autonomous weapons[10].

- The U.S. Pentagon’s Replicator Initiative is focusing on unmanned combat drones, and there are plans for AI-enabled fighter jets[14].

7. Critical Infrastructure Concerns:

- The U.S. Department of Homeland Security has highlighted AI as both a threat and an asset to critical infrastructure security[12].

While specific incidents of AI-enhanced attacks or autonomous weapon deployments are often classified or not publicly disclosed in detail, these trends indicate the growing integration of AI in both offensive and defensive military and cybersecurity operations. The rapid advancement of these technologies is outpacing regulatory frameworks, raising significant ethical and security concerns.

Citations:

[1] https://www.helpnetsecurity.com/2024/04/29/offensive-ai-cyberattacks/

[2] https://euro-sd.com/2024/08/major-news/39807/aukus-ai-enabled-trials/

[3] https://www.crowdstrike.com/cybersecurity-101/cyberattacks/ai-powered-cyberattacks/

[4] https://www.globalsecuritymag.fr/it-professionals-brace-for-2025-threats-as-u-s-businesses-face-growing-ai.html

[5] https://diplomatmagazine.eu/2024/09/01/ai-arms-race/

[6] https://www.youtube.com/watch?v=Y49CmATRggg&vl=en

[7] https://layersevensecurity.com/artificial-intelligence-exploits-vulnerabilities-in-systems-with-a-87-percent-success-rate/

[8] https://venturebeat.com/security/forresters-top-5-cybersecurity-threats-for-2024-weaponized-ai-is-the-new-normal/

[9] https://www.weforum.org/agenda/2024/07/crowdstrike-global-it-outage-cybersecurity-news-july-2024/

[10] https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1154184/full

[11] https://www.armscontrol.org/act/2024-04/news/diplomatic-debate-over-autonomous-weapons-heats

[12] https://www.nextgov.com/cybersecurity/2024/06/dhs-highlights-ai-threat-and-asset-critical-infrastructure-new-priority-guidance/397524/

[13] https://www.wtwco.com/en-se/insights/2024/08/how-ai-creates-cybersecurity-vulnerabilities-and-what-to-do-about-it

[14] https://www.theguardian.com/technology/article/2024/jul/14/ais-oppenheimer-moment-autonomous-weapons-enter-the-battlefield

[15] https://moderndiplomacy.eu/2024/08/12/autonomous-weapon-systems-a-moral-discussion-and-considerations-for-regulation/

# Recent analyses of AI-induced systemic risks in financial markets or nuclear systems

Based on the search results, there have been several recent analyses of AI-induced systemic risks in financial markets and nuclear systems:

Financial Markets:

1. The International Monetary Fund (IMF) has warned that AI could potentially turn an ordinary economic downturn into a severe crisis by disrupting labor markets, financial markets, and supply chains[1]. Key points include:

- AI could exacerbate job losses during recessions as companies more readily replace workers with automation.

- In financial markets, AI models may perform poorly in novel events, potentially triggering rapid asset sell-offs and price collapses.

- AI-driven supply chain management could lead to significant errors if models are trained on outdated data.

2. Research suggests AI may soon be a critical tool for predicting financial crises[11]. Machine learning algorithms can quickly identify patterns suggesting an impending crisis, potentially allowing time for corrective action.

3. The Financial Stability Oversight Council (FSOC) identified AI as a potential systemic risk for the first time in its 2023 Annual Report[12].

Nuclear Systems:

1. There are concerns about AI being integrated into nuclear command and control systems[4]. Potential risks include:

- Increased speed of engagement, reducing human decision-making time

- Automated target detection potentially increasing apprehension over second-strike survivability

- Increased propensity for escalation

2. The convergence of AI and nuclear weapons is seen as a significant risk[5]. AI could potentially be used to:

- Design and execute catastrophic biological, chemical, or cyber attacks

- Enable unprecedented weaponized applications in swarm robotics

- Pose a “loss of control” risk if advanced AI systems outmaneuver their creators

3. Some experts argue that the AI threat is more akin to a “nuclear winter” scenario than a sudden nuclear war[13]. This perspective emphasizes the gradual, cascading effects of AI on global systems rather than an immediate catastrophic event.

4. There are ongoing discussions about incorporating AI considerations into nuclear security dialogues, though some experts caution against prematurely linking AI to existing nuclear policy disagreements[9].

Overall, these analyses highlight the potential for AI to introduce new systemic risks in both financial and nuclear domains, emphasizing the need for careful governance and risk management strategies.

Citations:

[1] https://fortune.com/2024/06/09/ai-risks-recession-economic-crisis-job-losses-financial-markets-supply-chains-imf/

[2] https://cepr.org/voxeu/columns/ai-financial-crises

[3] https://www.ecb.europa.eu/press/financial-stability-publications/fsr/special/html/ecb.fsrart202405_02~58c3ce5246.en.html

[4] https://warontherocks.com/2024/01/artificial-intelligence-and-nuclear-stability/

[5] https://futureoflife.org/project/ai-convergence-nuclear-biological-cyber/

[6] https://www.nrc.gov/docs/ML2424/ML24241A252.pdf

[7] https://www.imf.org/en/News/Articles/2024/09/06/sp090624-artificial-intelligence-and-its-impact-on-financial-markets-and-financial-stability

[8] https://www.iaea.org/newscenter/news/nuclear-security-through-the-eyes-of-the-co-presidents-of-icons-2024

[9] https://carnegieendowment.org/posts/2024/08/ai-nuclear-dialogue-risks-npt?lang=en

[10] https://en.wikipedia.org/wiki/Existential_risk_from_artificial_intelligence

[11] https://www.weforum.org/agenda/2024/06/ai-may-soon-be-predicting-financial-crises-before-they-take-root/

[12] https://www2.deloitte.com/content/dam/Deloitte/us/Documents/Advisory/us-advisory-ai-systemic-risk-in-banking-june-2024.pdf

[13] https://thebulletin.org/2024/02/todays-ai-threat-more-like-nuclear-winter-than-nuclear-war/

[14] https://time.com/6898967/ai-extinction-national-security-risks-report/

[15] https://www.helpnetsecurity.com/2024/04/29/offensive-ai-cyberattacks/

# Advancements in AI safety mechanisms and oversight institutions in 2024

Based on the search results, there have been several significant advancements in AI safety mechanisms and oversight institutions in 2024:

1. International Scientific Report on AI Safety:

The UK government led an initiative involving 28 countries and the EU to produce the “International Scientific Report on the Safety of Advanced AI”. This report, presented at the AI Seoul Summit in May 2024, synthesizes academic research on AI safety, focusing on expanding AI capabilities, current technical abilities to evaluate and mitigate risks, and specific risk categories[12].

2. United Nations AI Initiatives:

- The UN is moving towards establishing its own AI report process, including a proposal for an International Scientific Panel (ISP) on AI to produce annual scientific assessments of AI risks[12].

- There are discussions about creating a UN oversight agency for AI, with proposals for an International Data-Based Systems Agency to promote safe, secure, and peaceful uses of AI[14].

3. U.S. AI Safety Institute (AISI):

- Launched in February 2024, AISI aims to develop standards for AI safety, security, testing, and content authentication[8].

- AISI signed agreements with Anthropic and OpenAI for collaboration on AI safety research, testing, and evaluation[8].

4. European Union AI Act:

The EU AI Act entered into force in 2024, establishing a comprehensive regulatory framework for AI with a risk-based approach[4].

5. China’s AI Safety Governance Framework:

Released in September 2024, this framework outlines principles for AI safety governance, classifies AI risks, and provides governance measures and safety guidelines[11].

6. Global Cooperation:

- The AI Governance Alliance, facilitated by the World Economic Forum, is working to develop AI technologies in alignment with global financial stability goals[7].

- International Dialogues on AI Safety (IDAIS) meetings have been held, bringing together scientists from different countries to discuss AI safety strategies[9].

7. Industry Initiatives:

- Companies like Grok AI are implementing advanced safety measures, including multi-layered security frameworks and continuous learning approaches to adapt to new challenges[10].

8. Emerging Research and Practices:

- There’s a growing focus on AI safety education, industry self-regulation, and social supervision[11].

- Advancements in predictive analytics for proactive safety and enhancing user empowerment through AI literacy are being developed[10].

9. National AI Safety Institutes:

Several countries, including the UK, US, Canada, and Japan, have established or announced plans for national AI Safety Institutes to address governance and regulatory challenges[2].

These developments indicate a global trend towards more concrete regulatory frameworks and collaborative efforts to address AI safety concerns, balancing innovation with risk management and ethical considerations.

Citations:

[1] https://www.itu.int/hub/2024/07/key-findings-on-the-state-of-global-ai-governance/

[2] https://www.omfif.org/2024/06/national-approaches-to-ai-safety-diverge-in-focus/

[3] https://www.ansi.org/standards-news/all-news/2024/09/9-23-24-un-releases-proposed-framework-for-global-ai-governance

[4] https://www.unesco.org/en/articles/policy-dialogue-ai-governance

[5] https://www.brookings.edu/articles/a-new-institution-for-governing-ai-lessons-from-gpai/

[6] https://us.nttdata.com/en/blog/2024/july/understanding-ai-governance-in-2024

[7] https://www.weforum.org/agenda/2024/06/ai-may-soon-be-predicting-financial-crises-before-they-take-root/

[8] https://www.nist.gov/news-events/news/2024/08/us-ai-safety-institute-signs-agreements-regarding-ai-safety-research

[9] https://carnegieendowment.org/research/2024/08/china-artificial-intelligence-ai-safety-regulation

[10] https://618media.com/en/blog/grok-ai-safety-measures-for-users/

[11] https://www.dlapiper.com/en-cl/insights/publications/2024/09/china-releases-ai-safety-governance-framework

[12] https://carnegieendowment.org/research/2024/08/the-future-of-international-scientific-assessments-of-ais-risks

[13] https://www.networkcablingservices.com/ai-and-security-trends-for-2024-you-should-know/

[14] https://www.project-syndicate.org/commentary/ai-needs-un-oversight-watchdog-agency-by-peter-g-kirchschlager-2024-04

[15] https://www.nist.gov/aisi

ChatGPT

Thought for 24 seconds

Updated Assessment of the Odds of AI Causing a Major Catastrophic Event Within the Next 10 Years

Based on the comprehensive information you’ve provided, I’ve reassessed the likelihood of AI causing a major catastrophic event within the next decade. Here are the key factors influencing this updated evaluation:

1. Expert Estimates on Catastrophic AI Risk Probabilities

• Wide Range of Estimates: Surveys of AI experts show significant concern, with median estimates suggesting a 10% chance of AI causing human extinction or similarly catastrophic outcomes. Some estimates are as high as 30% over longer timeframes.
• Uncertainty and Debate: While there’s debate over the exact probabilities, the consensus indicates a non-negligible risk that warrants serious attention.

2. Progress in AI Alignment Research

• Advancements and Challenges: Initiatives like OpenAI’s Superalignment project and Rules-Based Rewards (RBR) represent progress. However, challenges such as model overconfidence, hallucinations, and biases remain unresolved.
• Urgency: Current alignment techniques may not scale effectively with the rapid advancement of AI capabilities, especially as we approach potential AGI development.

3. Accelerated AGI Timeline Predictions

• Shortened Timelines: Leading AI researchers and industry leaders have revised their predictions, with some estimating AGI could emerge within 2 to 5 years.
• Implications: An accelerated timeline increases the urgency for robust safety measures and alignment solutions before highly capable AI systems are deployed.

4. Global AI Governance and Regulation Initiatives

• Progress Made: The EU AI Act and the establishment of AI safety institutes in multiple countries are positive steps.
• Challenges Remain: Global coordination is limited, and regulatory efforts may not keep pace with AI’s rapid development and deployment.

5. Documented Instances of AI Misuse or Weaponization

• Recent Incidents: Cases include the creation of deepfake pornography, financial fraud using AI-generated voices and videos, political disinformation campaigns, and the potential use of AI in designing bioweapons.
• Trend Indications: These instances demonstrate that AI technologies are already being exploited in ways that can cause significant harm at scale.

6. Impact on Critical Infrastructure Security and Vulnerabilities

• New Vulnerabilities: AI integration into critical infrastructure introduces risks like data poisoning and increased complexity in security management.
• Cybersecurity Threats: AI-enabled cyberattacks are becoming more sophisticated, potentially targeting essential services and infrastructure with greater efficacy.

7. Evidence of an AI Arms Race Between Nations

• Strategic Competition: There is clear evidence of an AI arms race, particularly between the United States and China, focusing on military and economic advantages.
• Risks of Acceleration: This competition may lead to the hasty deployment of AI technologies without adequate safety measures.

8. Incidents of AI-Enhanced Cyberattacks or Autonomous Weapon Deployments

• Cyberattacks: Security leaders report a significant increase in AI-driven attacks, with expectations that such offensive AI use will become the norm.
• Autonomous Weapons: Deployment of AI-equipped drones and autonomous systems in conflict zones raises ethical concerns and the potential for unintended escalation.

9. AI-Induced Systemic Risks in Financial Markets and Nuclear Systems

• Financial Markets: The IMF warns that AI could exacerbate economic crises by disrupting labor markets and amplifying market volatility.
• Nuclear Systems: Integration of AI into nuclear command and control could lead to rapid, uncontrolled escalation due to reduced human decision-making time.

10. Advancements in AI Safety Mechanisms and Oversight Institutions

• Positive Developments: Establishment of institutions like the U.S. AI Safety Institute and international collaborations signal progress.
• Insufficient Pace: Despite these efforts, advancements in safety mechanisms may not be keeping up with the speed of AI development.

Revised Odds Assessment

Considering these factors, I now assess the odds of AI causing a major catastrophic event within the next 10 years to be approximately 1:1 (50% chance).

Rationale:

• Increased Expert Concern: The convergence of expert opinions toward a significant probability underscores the seriousness of the risk.
• Rapid AI Advancement: Accelerated timelines for AGI heighten the potential for misaligned AI systems to cause unintended harm.
• Current Safety Gaps: Persistent challenges in AI alignment and insufficient global governance increase vulnerability.
• Demonstrated Misuse: Recent instances of AI misuse indicate that harmful applications are not just theoretical but already occurring.

Conclusion

Given the rapid pace of AI development, the accelerated timelines for achieving AGI, and the unresolved challenges in AI safety and alignment, the risk of a major catastrophic event caused by AI within the next decade is substantial. While there are efforts underway to mitigate these risks, the current trajectory suggests that without significant and immediate advancements in AI safety research, governance, and international collaboration, the odds of such an event occurring are around 50%.

Final Thoughts

This assessment highlights the critical need for:

• Prioritizing AI Safety Research: Accelerating efforts in AI alignment and safety to keep pace with technological advancements.
• Enhancing Global Cooperation: Developing international agreements and standards to manage AI risks collectively.
• Implementing Robust Governance: Establishing effective regulatory frameworks that can adapt to the evolving AI landscape.

Addressing these areas is essential to reducing the risk of a catastrophic event and ensuring that AI development proceeds in a safe and beneficial manner.

Appendix 3 — Claude criticism of this document

I appreciate you providing this detailed information for me to review and critique. Here are some key points of constructive criticism I would offer on the analysis and conclusions:

1. Potential for bias: The analysis seems to rely heavily on sources and experts that may have an incentive to emphasize AI risks, like AI safety organizations and researchers. It would be valuable to incorporate more diverse perspectives, including from AI skeptics or those who see lower risks.

2. Lack of historical context: The assessment doesn’t thoroughly consider how other transformative technologies throughout history have been managed without causing catastrophic harm. This historical lens could provide important context.

3. Overemphasis on AGI timelines: While AGI development timelines are relevant, the analysis may place too much weight on speculative predictions about when AGI might arrive. The risks of narrow AI systems in the near-term may warrant more focus.

4. Limited discussion of potential mitigating factors: The analysis could benefit from more exploration of factors that might reduce risks, like ongoing safety research, the difficulty of developing truly general AI, or economic/practical limitations on AI deployment.

5. Potential conflation of different risk types: The assessment sometimes seems to blend distinct types of AI risks (e.g. misuse by bad actors, unintended consequences, existential risks from superintelligence) which may warrant separate analysis.

6. Lack of probabilistic reasoning: While probabilities are discussed, the analysis could benefit from a more rigorous probabilistic framework that better accounts for uncertainties and dependencies between factors.

7. Limited consideration of upside scenarios: The focus is heavily on risks, without much exploration of how AI advances might also create new capabilities to manage those risks or solve other global challenges.

8. Potential recency bias: Recent events and latest expert opinions seem to be weighted heavily. A longer-term view considering cycles of AI optimism/pessimism could provide perspective.

9. Lack of sensitivity analysis: It would be valuable to see how the conclusions might change under different assumptions or weighing of factors.

10. Limited discussion of concrete scenarios: The analysis is somewhat abstract — more detailed exploration of specific catastrophic scenarios and their likelihood could ground the assessment.

While the analysis is thorough in many ways, addressing these points could lead to a more balanced and robust assessment of the odds of an AI-driven catastrophe. The topic is complex and uncertain enough that we should be cautious about placing too much confidence in any specific probability estimate.

Appendix 4 — Claude suggestions for improvements

To improve this analysis using just LLMs and Perplexity, I would suggest the following approach:

1. Expand the range of queries:

Create a series of queries that specifically target the areas where the analysis could be improved, such as:

• “Historical examples of managing transformative technologies without catastrophe”
• “Skeptical perspectives on AI risk and timelines”
• “Potential mitigating factors for AI risks”
• “Economic and practical limitations on AI deployment”
• “Distinctions between different types of AI risks (misuse, unintended consequences, existential)”
• “Probabilistic frameworks for assessing long-term technology risks”
• “Potential positive scenarios and benefits of advanced AI”
• “Long-term cycles of AI optimism and pessimism”
• “Detailed scenarios of potential AI-driven catastrophes and their likelihood”

2. Use chain-of-thought prompting:

Engage the LLM in a step-by-step reasoning process for each topic. For example:

“Let’s approach the assessment of AI catastrophic risk step-by-step:

1. What are the main categories of AI risk?
2. What is the likelihood of each category?
3. How might these risks interact or compound?
4. What factors could mitigate these risks?
5. How do these compare to historical examples of technological risk?”

3. Implement adversarial prompting:

Ask the LLM to argue against its own conclusions. For instance:

“You’ve assessed the risk of AI catastrophe at X%. Now, please provide the strongest arguments for why this estimate might be too high.”

4. Conduct a sensitivity analysis:

Prompt the LLM to reassess its conclusions under different assumptions. For example:

“How would your assessment change if:

1. AGI development takes 50 years instead of 10?
2. International cooperation on AI governance significantly improves?
3. A major breakthrough in AI alignment is achieved?”

5. Generate specific scenarios:

Ask the LLM to create detailed, concrete scenarios of potential AI catastrophes and assess their likelihood individually.

6. Incorporate probabilistic reasoning:

Prompt the LLM to use formal probabilistic methods, such as Bayesian updating, to refine its estimates as new information is introduced.

7. Synthesize diverse perspectives:

Use Perplexity to gather information from a wide range of sources, then ask the LLM to synthesize these diverse viewpoints into a more balanced assessment.

8. Temporal analysis:

Prompt the LLM to consider how risk assessments have changed over time and what that might indicate about current estimates.

9. Cross-domain analysis:

Ask the LLM to compare AI risks with other global risks (e.g., climate change, pandemics) to provide context and perspective.

10. Meta-analysis of expert disagreement:

Use Perplexity to gather information on why experts disagree about AI risks, then ask the LLM to analyze these disagreements and their implications for risk assessment.

By systematically applying these approaches, we can create a more comprehensive, balanced, and nuanced analysis of the potential for AI-driven catastrophic events, while still relying primarily on LLMs and Perplexity as information sources.