WHISTLEBLOWER Reveals Complete AGI TIMELINE, 2024 - 2027 (Q*, QSTAR)

1. OpenAI's plan to create AGI by 2027 involves training multimodal models.

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OpenAI initiated training a 125 trillion parameter multimodal model in August 2022, with the first stage being rakus or qstar, completed by December 2023.

• The launch was canceled due to high inference costs.
• The document contains verifiable information aligning with known developments.
• Speculation surrounds the delayed plans for achieving human-level AGI by 2027.

2. Renaming of GPT models indicates shifts in OpenAI's AI development.

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The original GPT-5 planned for 2025 was renamed GBT-5, with GPT-6 renamed GPT-7, which was put on hold due to a lawsuit by Elon Musk.

• The lawsuit by Elon Musk caused delays in OpenAI's AI development timeline.
• The document hints at a potential progression towards achieving AGI by 2027.

3. Levels of AGI complexity indicate varying degrees of human-like capabilities.

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AGI encompasses different levels from emerging AGI to artificial superintelligence, each reflecting increasing human-like abilities.

• AGI levels range from emerging AGI to virtuoso AGI and artificial superintelligence.
• Synapse count correlates with intelligence levels in biological and AI systems.

4. Parameter count influences AI performance, showing a relationship to task performance.

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Increasing parameter count in neural network models enhances performance on language-related tasks, with diminishing returns at higher counts.

• Performance on tasks like translation and question-answering improves with higher parameter counts.
• The relationship between parameter count and task performance follows a pattern of diminishing returns.

5. AGI performance correlates with brain size and parameter count.

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AGI performance aligns with human brain size and parameter count, with 100 trillion parameters being a crucial threshold for optimal performance.

• Illustrated extrapolations suggest AGI performance matching human levels when brain size aligns with parameter count.
• Engineering techniques can bridge suboptimal performance gaps in AI models with slightly fewer parameters.

6. Early leaks and speculations hint at the development of GPT models with massive parameter counts.

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Leaked information suggests the development of GPT models with 100 trillion parameters, indicating significant advancements in AI technology.

• Speculations from various sources point towards the creation of GPT models with unprecedented parameter counts.
• Rumors and leaks indicate plans for models with massive parameter sizes, potentially revolutionizing AI capabilities.

7. GPT models are evolving towards multimodal capabilities.

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GPT models are progressing towards multimodal functionalities, including processing videos and audio data, expanding their capabilities beyond text and images.

• The evolution of GPT models includes the ability to process diverse data types like videos and audio for enhanced understanding.
• Multimodal models offer new possibilities, such as understanding natural language and providing cross-language responses.

8. AI training on vast data sources raises AGI possibilities.

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Training AI models on extensive internet data could lead to remarkable advancements in robotics.

• Models like GPT-4 and potential future versions could achieve astonishing robotics performance.
• The idea of training models on trillion-parameter scales hints at significant AI progress.
• AI's ability to generate multiple angles of scenes with accurate physics showcases its potential.

9. Debate surrounds AI's understanding of the physical world.

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Discussions question whether AI systems possess common sense reasoning and a world model.

• Some argue AI systems go beyond pattern recognition to understand the physical world.
• AI's ability to generate images with accurate physics raises questions about its comprehension.
• The debate centers on whether AI truly grasps the world's interactions and reasoning.

10. AGI development timeline reveals ambitious goals.

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OpenAI planned to build a human brain-sized model by 2024, aiming for AGI with 100 trillion parameters.

• Microsoft's $1 billion investment aimed to achieve AGI within 5 years.
• The plan involved training an AI model on images, text, and other data sources.
• The goal was to run a humanized brain model with 100 trillion parameters.

11. Concerns arise over rapid AI advancement.

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AI leaders express caution as AI approaches superintelligence faster than anticipated.

• Greg Brockman warned about the dangers of AI advancing towards superintelligence.
• Jeffrey Hinton left Google to discuss the risks of AI surpassing human intelligence.
• The Future of Life Institute urged a 6-month pause in training systems more powerful than GPT-4.

12. Importance of data quality for AGI development.

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High-quality data is crucial for AGI development, with vast amounts needed to train models effectively and achieve human-level performance.

• Training on vast amounts of high-quality data is essential for AGI success.
• Data quality directly impacts the performance and capabilities of AI models.
• Achieving human-level performance requires extensive training on quality data.

13. Significance of scaling laws in AI model training.

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Understanding and applying scaling laws like the chinchilla model can significantly enhance AI model performance and capabilities.

• Chinchilla scaling laws demonstrate the impact of training models on vast amounts of data.
• Scaling laws help optimize AI model training for improved performance.
• Applying scaling laws can lead to surpassing human-level performance in AI models.

14. Predicting future AI capabilities through less compute-intensive systems.

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Training AI models on less compute-intensive systems can help predict the capabilities of future, more advanced models.

• Training on less compute-intensive systems provides insights into future AI capabilities.
• Predicting future AI performance aids in planning for advancements and developments.
• Understanding current AI capabilities guides the prediction of future model performance.

15. Iterative deployment for societal adaptation to AI advancements.

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OpenAI plans iterative releases of AI models to allow society time to adapt to evolving AI capabilities and understand the technology.

• Iterative deployment strategy aims to manage societal acceptance of AI advancements.
• Releasing AI capabilities gradually helps society reassess timelines and expectations.
• Predictable scaling aids in accurately forecasting AI advancements for societal readiness.

16. AGI development requires massive computational resources.

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Despite reduced compute costs, AGI training demands significant computational power due to compute overhang and scale requirements.

• Compute-intensive nature of AGI training persists despite cost reductions.
• Compute overhang poses a challenge due to insufficient available computational resources.
• Scale is crucial for AGI development, necessitating substantial computational capabilities.

17. AGI progress hinges on predicting capabilities and scale.

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Predicting AGI capabilities and scaling up parameter count are key factors in advancing towards AGI.

• Understanding AGI's predictive capabilities is crucial for development.
• Scaling parameter count to human brain size is a significant goal for AGI progress.
• Balancing scale with computational resources is essential for AGI advancement.