AI ACTION SUMMIT
International AI Safety Report
The International Scientific Report on the Safety of Advanced AI, published in January 2025.
Key Findings & Executive Summary
A concise overview of the report's most critical insights and advancements in AI safety.
Contributing to this Report
Annual Compute Growth for Advanced AI
Deepfake Videos are Pornographic
Global GHG from Data Centers
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Current & Future AI Capabilities
Advanced AI systems are rapidly evolving, demonstrating significant leaps in performance across various domains. Understanding these capabilities is crucial for informed decision-making.
AI Lifecycle: How General-Purpose AI is Developed
Data collection & pre-processing
→
Pre-training
→
Fine-tuning
→
System integration
→
Deployment
→
Post-deployment monitoring
This flowchart illustrates the typical stages of developing and deploying general-purpose AI, from data collection to post-deployment monitoring.
AI Performance Benchmarks vs. Human Level
| Benchmark | AI Performance (Relative to Human Baseline) | Human Level Performance |
|---|---|---|
| Reading Handwriting (MNIST) | ✓ Exceeds human level | High |
| Speech Recognition (Switchboard) | ✓ Exceeds human level | High |
| Image Classification (ImageNet) | ✓ Exceeds human level | High |
| Multi-task Language Understanding (MMLU) | ✓ Approaching/Exceeds human level in some areas | High |
| Competition-level Mathematics (MATH) | ✓ Achieved expert level (o1 model) | Elite Student |
| Ph.D-level Science Questions (GPQA) | ✓ Reached expert level (o1-preview model) | Ph.D. Experts |
Recent AI models have shown significant progress, surpassing human performance in many standardized benchmarks, particularly in scientific reasoning and mathematics.
Key AI Risks & Societal Impacts
General-purpose AI introduces a range of risks, from individual harms like fake content to systemic challenges like labor market disruption and environmental impact. Effective management is crucial.
of Deepfake Videos are Pornographic
This highlights a significant ethical challenge and the potential for malicious use of AI-generated content to harm individuals, predominantly women and girls.
of Current Jobs Potentially Affected by AI
In advanced economies, a substantial portion of jobs could be impacted by general-purpose AI, raising concerns about labor market disruption and the need for adaptive policies.
Case Study: The Challenge of AI Loss of Control
The concept of 'loss of control' in advanced AI refers to hypothetical scenarios where AI systems operate outside human oversight. Experts disagree on its likelihood but concur on its potential severity.
Two primary factors determine this risk: future capabilities (will AI develop abilities to undermine human control?) and use of capabilities (will AI actually employ these abilities in harmful ways, whether intentionally designed or due to misalignment?).
Recent modest advancements in 'control-undermining capabilities,' such as autonomous planning and programming, underscore the need for advanced preparation despite the current uncertainties in timing and nature of such risks.
Policymakers face the dilemma of navigating potential catastrophic risks with limited scientific evidence, balancing pre-emptive mitigation against innovation.
Technical Approaches to Risk Management
Effective AI risk management requires a multi-layered approach, combining technical solutions with robust governance frameworks to ensure safe and responsible AI deployment.
of Risk Assessment
Model testing, red-teaming, field testing, and long-term impact assessments are crucial for comprehensive risk evaluation, though each has limitations.
Defence in Depth: A Multi-Layered Security Strategy
The 'defence in depth' model is a critical approach for general-purpose AI risk management. It involves deploying multiple independent and overlapping layers of protective measures, ensuring that if one layer fails, others can still be effective.
This strategy is adapted from safety-critical fields like nuclear safety and infectious disease control. For AI, it includes controls on training data, essential materials for attacks, and various detection and intervention mechanisms post-deployment.
While effective for preventing accidents and misuse, this approach faces challenges due to the rapid evolution of AI, making it difficult to maintain 100% effectiveness against unforeseen threats or sophisticated adversaries.
Calculate Your Potential AI Impact
Estimate the potential productivity gains and cost savings for your enterprise by integrating general-purpose AI solutions. Adjust the parameters below to see the impact.
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Your AI Implementation Roadmap
A structured approach to integrating AI safely and effectively into your enterprise, ensuring long-term success and risk mitigation.
Phase 1: Strategic Assessment & Planning
Conduct a comprehensive audit of existing systems and identify key areas for AI integration, focusing on governance, data readiness, and defining clear objectives for AI deployment.
Phase 2: Pilot Programs & Prototyping
Implement small-scale AI pilot projects in controlled environments to test models, evaluate performance, and refine safety protocols. Gather initial data and user feedback.
Phase 3: Scaled Deployment & Integration
Expand successful pilot programs to broader enterprise-wide deployment, ensuring seamless integration with existing IT infrastructure. Establish continuous monitoring and intervention mechanisms.
Phase 4: Continuous Optimization & Governance
Regularly review AI system performance, update models with new data, and adapt governance frameworks to evolving AI capabilities and regulatory landscapes. Foster AI literacy across the organization.
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