The AI Doom Calculator: Assessing the Risks of Artificial Intelligence [2024]

Artificial intelligence (AI) holds tremendous promise to transform our world and lives for the better. However, some experts warn that advanced AI could also pose existential risks if misused or poorly controlled. In response, researchers have developed tools like the AI Doom Calculator to help estimate and mitigate these dangers.

What is the AI Doom Calculator?

The AI Doom Calculator is an online application created by researchers at the University of Cambridge’s Center for the Study of Existential Risk. It aims to quantify the risks that uncontrolled artificial intelligence could potentially cause human extinction or civilizational collapse.

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How Does the AI Doom Calculator Work?

The AI Doom Calculator generates risk estimates using a mathematical model that factors in key variables related to AI capabilities, motivations, and regulation. Users provide inputs on sliders for:

  • AI Capability Level: How far AI surpasses human-level intelligence, from slight to extreme superiority.
  • AI Motivation: Whether AI is mainly aligned with human values or indifferent to harming humans.
  • Regulation Strictness: The strength of governance over AI research and development.
  • Convergence Time: How soon advanced AI could be developed, from decades to within years.

Based on these inputs, the calculator runs the risk probabilities through mathematical formulas and distributions. The output is the percent chance of human extinction or civilizational collapse under the given scenario.

Adjusting the sliders visually shows how different assumptions affect the risk projections. For example, an AI with extreme capability and misaligned motivations raises the doom risk significantly compared to a scenario with more limited AI and stronger regulation.

Potential Benefits and Applications

Potential Benefits and Applications

Proponents believe the AI Doom Calculator provides valuable insights for technology leaders, policymakers, and the public. Quantifying existential risks can help focus safety efforts on the most influential factors. The calculator also facilitates discussion on managing these dangers.

Some potential benefits and uses include:

  • Prioritizing safety research: The tool highlights capability control, alignment techniques, and governance as key to minimizing risks. Researchers can focus on these high-impact areas.
  • Informing AI policy: Legislators can craft regulations to address motivation alignment, oversight, and responsible disclosure – variables flagged by the calculator.
  • Raising public awareness: Sharing projections gives citizens a better sense of the risks and the importance of developing AI safely.
  • Scenario planning: Adjusting the inputs allows creating models to stress test different policy proposals or technological measures for mitigating AI hazards.

Criticisms and Concerns

However, the AI Doom Calculator has also drawn skepticism and debate within the technology field. Some common criticisms include:

  • Overconfidence in accuracy: Critics argue the simple model makes overconfident forecasts on an extremely complex, unpredictable issue. The outputs portray a false precision to the risk numbers.
  • Assuming a single trajectory: Some believe modeling one unified AI path ignores the potential for multiple coexisting AIs and trajectories, making single probabilities meaningless.
  • Encouraging alarmism: Detractors suggest the calculator may stoke unnecessary fear of AI or lead to overregulation that stifles innovation. There are concerns it could be misused to support an AI arms race mentality.
  • Limited mitigation guidance: While identifying risks, critics say the calculator does little to illuminate mitigation strategies beyond basic governance. More specifics are needed on safe development approaches.

Reactions from Technology Leaders

The release of the AI Doom Calculator sparked discussions and debates within the technology field. Some perspectives:

  • Elon Musk tweeted the tool was “Necessary but insufficient to consider AI safety.” He advised also focusing on developing aligned AI.
  • MIT professor Max Tegmark argued probability models are valuable for finding ideal policies. He compared it to calculations of climate change risks informing responses.
  • AI researcher Andrew Ng critiqued putting percentages on scenarios. He suggested descriptive warnings avoid treating speculation quantitatively.
  • Google DeepMind CEO Demis Hassabis emphasized the need for nuance. He warned against oversimplifying to a single risk number from such a young field.
  • Policy expert William MacAskill called the calculator an “interesting experiment” but highlighted it can’t quantify key factors like AI timelines and trajectories.

The Difficulty of Predicting the Future of AI

At the core of the disagreements lies the enormous challenge of forecasting the development of cutting-edge technologies like artificial intelligence. AI safety expert Allan Dafoe of Yale outlines some of the difficulties:

  • Pace of progress is uncertain: It remains unclear when key milestones like AGI will emerge and how fast capabilities advance after that.
  • Many trajectories are possible: AI progress could involve one dominant system, many systems, or decentralized development.
  • Motivations are untested: We can’t yet know how advanced AI will behave and resist or amplify human goals and values.
  • Interactions introduce chaos: Complex adaptive systems often exhibit nonlinear feedback loops. Small events can shape trajectories.

These uncertainties make calculating specific probabilities of existential catastrophe precarious. However, Dafoe argues we can still productively assess factors that appear to quantitatively increase risks and work to reduce them.

Mitigating Extreme Risks and Building Resilience

Mitigating Extreme Risks and Building Resilience

Despite limitations, many technology leaders see the AI Doom Calculator as a thought-provoking tool to build awareness of the need for safety measures and oversight. Recommendations for reducing risks include:

  • Prioritize developing safe AI techniques: This includes aligning AI goals, building human oversight, and designing safe learning environments (e.g. Oracle or AGI safety methods).
  • Support strong governance: Self-regulation and government oversight focused on safety best practices and responsible disclosure could help mitigate risks.
  • Diversify AI development: With many AI teams and models, risks are less concentrated. But coordination is needed to share safety practices.
  • Build societal resilience: Reducing global problems like climate change leaves society better able to handle disruptions. Promoting education and social welfare may also help.
  • Remain open and vigilant: Monitoring for unforeseen consequences and keeping diverse viewpoints involved may surface the most serious dangers before it’s too late.

Though estimates vary on the exact risks, engaging thoughtfully with tools like the AI Doom Calculator can help direct attention to where it is most needed – ensuring advanced AI is developed safely and for the benefit of all humanity.

Limitations and Ways to Improve the Calculator

While the AI Doom Calculator introduces a data-driven approach to assessing existential risks, the developers and critics also highlight ways the tool could be improved and expanded:

Narrow Focus on Extinction Risk

  • The calculator only models risks of complete human extinction or civilizational collapse. The likelihood and mitigation of more limited AI hazards are also highly relevant to study.

Difficulty Modeling New Information

  • As AI capabilities and trajectory understanding grow, incorporating new data could require rebuilding models and algorithms from scratch rather than simple recalibration.

Assumptions on Trajectories

  • Allowing modeling of multiple AI systems evolving separately rather than one unified AI could improve scenario analysis.

Randomness and Unknown Unknowns

  • True “fat tail” risks, black swan events, and unknown dynamics are challenging to represent quantitatively.

Economic Disruption Not Factored In

  • Modeling could be expanded to include potential for mass unemployment or inequality from transformative AI.

By diversifying the scenarios evaluated, updating the methodology as new evidence emerges, and collaborating across fields, the accuracy and utility of tools like the AI Doom Calculator can progressively improve. This could pave the way for more robust technology forecasting and risk assessment.

Accessing the AI Doom Calculator

Accessing the AI Doom Calculator

The AI Doom Calculator is available online for anyone to access and experiment with risk estimates. The calculator can be found at:

The interface is simple and intuitive. Users adjust sliders to input assumptions for the key variables of AI capability, motivation, regulation, and timelines. The output displays graphs showing the probability distribution for human extinction under that scenario.

Registration with an email address is optional to save and compare scenarios. Otherwise, the calculator can be used anonymously. The source code is also publicly available for scrutiny and to build derivative models.

Inputs and Assumptions

The calculator generates risk estimates based on user inputs for four key parameters:

AI Capability Level

This indicates how far AI capabilities have advanced beyond the human level. Users can input anywhere from slightly above human capability to extremely superhuman intelligence exceeding humans in all domains.

Higher AI capability levels increase existential risk in the model. However, the researchers emphasize that higher capability alone is not inevitably risky – motivation alignment also plays a critical role.

AI Motivation

This represents the degree to which the goals and motivations of advanced AI systems diverge from human values and well-being.

At one extreme AI is indifferent to human suffering. At the other, AI goals are strongly shaped by and aligned with human ethics. Misaligned motivations are a major risk factor.

Regulation Strictness

The strictness slider indicates the strength of governance, oversight, and safety practices applied to AI development.stricter regulation and more careful development is modeled to reduce risks.

However, regulation can also slow helpful innovation if taken too far. The calculator aims for an ideal balance.

Convergence Time

This indicates the estimated time until key developments like artificial general intelligence. Shorter timelines increase risk by providing less prep time. Long development times allow more opportunity to establish oversight.

Understanding the AI Risk Estimates

Based on the input values, the calculator runs the risk probabilities through mathematical models to generate the percentage chances of extinction or civilizational collapse.

But it is important to understand these as abstract estimates rather than precise predictions. The outputs illustrate how changes in key factors would influence relative risks rather than provide numerical certainties.

The probability distribution graphs also show the range of uncertainty. For example, a 5% chance of extinction may have a range between 1% to 10% likelihood based on current knowledge.

Adjusting Inputs to Assess Different AI Futures

The key value of the AI Doom Calculator is in exploring how tweaks to the inputs shift the risk projections.

For example, stronger oversight paired with high capability and long timelines may bring the chance of disaster down to 1%. But weaker regulation could raise it to over 40% for the same capability.

Lawmakers could simulate the impact of proposed regulations. And researchers can model optimistic and pessimistic scenarios to prioritize safety goals.

By visually interacting with the effects of different variables, users gain intuition for the most influential factors in mitigating existential risk.

Share and Compare Risk Estimates

To foster discussion, the calculator allows users to save scenarios and share their risk estimates with others.

Comparing perspectives from technology, policy, and safety experts can help identify blind spots in current thinking and achieve a more balanced view.

Shared estimates also provide a starting point for deliberating policies and safeguards that may keep risk projections within a consensus acceptable threshold.

Tips for Using the AI Doom Calculator

To glean the most insight from the tool, the researchers suggest keeping a few best practices in mind:

Make Reasonable Assumptions

Avoid extreme or improbable scenarios not grounded in evidence. Staying close to realistic cases makes the outputs most relevant.

Avoid Overconfidence Bias

Remember both the model and your inputs have high uncertainty. Consider multiple views.

Understand Influential Factors

Play with the calculator to directly see how motivation, oversight, and pace of progress affect the risks.

Consider Multiple Perspectives

No one knows the exact probabilities. Comparing estimates from different experts provides a fuller picture.

Share and Discuss Estimates

The tool provides a starting point for deliberating policies that could mitigate the short and long-term risks.

Methodology Behind the AI Doom Calculator

Methodology Behind the AI Doom Calculator

The researchers employed a rigorous process to develop the calculator’s underlying methodology:

Literature Reviews

Surveying the landscape of existing ideas and models on AI risks informed the choice of key variables.

Modeling Different Scenarios

Mathematical models were created to translate input values into risk estimates.

Risk Factor Formulas

Formulas combine capability, motivation, regulation, and timeliness into overall probabilities.

Probability Distributions

Distributions provide a range around estimates to capture uncertainty.

User Testing and Refinement

The researchers iteratively improved the tool based on user feedback before public release.

Ongoing evaluation will allow improving factors like weighting different risks and representing new variables. But the core goal of fostering discussion remains.


Tools like the AI Doom Calculator aim to bring more data-driven assessments to the complex issue of existential risks from artificial intelligence. But they inevitably face limitations in modeling such an emerging technology. Their greatest value is in surfacing influencing variables, highlighting research gaps, and provoking thoughtful exchanges on prudent policies. As AI capabilities progress, we will need continued humility about uncertainty combined with vigilance in monitoring for extreme risks and proactively developing solutions to avoid them. An open and earnest discussion of the hazards ahead, as well as the remarkable potential, can guide technological innovation towards broadly shared prosperity.

Frequently Asked Questions

What is the AI tool that predicts death?

Researchers at the University of Copenhagen created an AI system called Life2Vec that predicts individuals’ remaining lifespan based on health and demographic data. The machine learning model was trained on Danish health records and death data going back over 30 years across millions of people. In tests, Life2Vec achieved 78% accuracy at predicting whether someone would die within the next year. The goal of the research is to provide better end-of-life care recommendations tailored to an individual’s risks. However, ethical concerns exist around making such mortality predictions.

Can I play Doom on a calculator?

Yes, some graphing calculators like those made by Texas Instruments have enough processing power and customizable programming that skilled hobbyists have implemented games like Doom on calculator hardware. This requires in-depth knowledge of the calculator architecture and optimization tricks to run 3D game software on such limited resources. Online tutorials exist for loading Doom and other games onto certain calculator models via link cables or add-on hardware. However, most standard calculators do not have the performance for advanced 3D games without modifications.

How accurate are death calculators?

The accuracy of death calculators and life expectancy predictors varies greatly depending on the methodology. Simple calculators that just compute average remaining life expectancy based on age and gender are not very personalized or precise. More advanced models like Life2Vec that incorporate dozens of health factors using AI/ML techniques now achieve approximately 78% accuracy at predicting one-year mortality risk. However, at an individual level, there is still significant uncertainty and room for error. Predictions are based on population stats and are not definitive forecasts. Accuracy is expected to improve as prediction models incorporate more health data.

Were calculators considered AI?

Early calculators were purely fixed mechanical devices, so would not have been considered AI which implies some degree of learning and autonomy. However, today many calculators integrate AI/ML to extend their capabilities. Examples include natural language processing to interpret typed instructions and Wolfram Alpha integration to look up advanced facts online. Some graphing calculators can even be programmed with AI applications. But most standard calculators remain limited to mathematical operations without rising to the level of machine intelligence. Advanced AI-powered computational tools are more accurately characterized as mathematical assistants rather than mere calculators.

Is AI mostly math?

Math is a significant foundation of AI, especially areas like probability, linear algebra, and calculus which are used in machine learning. But modern AI also relies heavily on areas like data engineering, algorithms, large datasets, and computational power. And applications of AI extend far beyond mathematics into domains like computer vision, natural language processing, robotics, and more. So while math is an integral part of AI, it encompasses a wide range of additional technologies and disciplines. AI leverages math extensively, but cannot be reduced solely to mathematical principles.

What type of math is used in AI?

Some common mathematical fields used in AI include:

  • Probability and statistics – Used extensively in machine learning for modeling uncertainty.
  • Linear algebra – Supports matrix operations for ML models.
  • Calculus – For optimizing model parameters and training algorithms.
  • Graph theory – Enables complex relationship representations.
  • Differential equations – Used for modeling dynamic systems and control theory.
  • Discrete math – Applies to digital logic and computer science foundations.
  • Numerical analysis – Algorithms for efficiently solving complex math problems.

So in summary, AI utilizes a diverse toolkit of mathematical techniques paired with computer science and data-driven approaches to enable intelligent behavior in software. Math provides the fundamental logical language to describe AI systems and processes. But it involves creativity and breakthroughs across many technical domains to practically build and apply AI successfully.

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