Whitepaper: Artificial General Intelligence: Current State, Progress Measurement, and Foundation Models

Executive Summary

Artificial General Intelligence (AGI) represents a theoretical form of AI capable of performing any intellectual task that a human can. This white paper examines the concept of AGI, assesses our current proximity to achieving it, discusses methods for measuring progress, and explores the contributions of major foundation models towards AGI development.

1. Introduction to Artificial General Intelligence

Artificial General Intelligence (AGI) refers to highly autonomous systems that outperform humans at most economically valuable work[1]. Unlike narrow AI systems designed for specific tasks, AGI aims to replicate the breadth and adaptability of human cognition across diverse domains.

Key characteristics of AGI include:

-            Generalized problem-solving abilities

-            Transfer learning across domains

-            Adaptability to novel situations

-            Autonomy to improve it’s own functioning

-            Self-improvement and continuous learning

The term "Artificial General Intelligence (AGI)" was first used in a 2007 book edited by computer scientist Ben Goertzel and AI researcher Cassio Pennachin[1]. However, the concept has been a longstanding goal in AI research and a popular theme in science fiction.

2. Current State of AGI Development

While significant advancements have been made in AI, true AGI remains a theoretical concept. Current AI systems, including large language models (LLMs), excel in specific domains but lack the generalized intelligence and autonomy and adaptability associated with AGI.

Recent developments in foundation models have sparked debates about our proximity to AGI. Some researchers argue that a significant level of general intelligence has already been achieved with frontier models, while others maintain that we are still far from true AGI[7].

According to Stanford University's 2024 AI index, AI has reached human-level performance on many benchmarks for reading comprehension and visual reasoning[7]. This progress has led to varying predictions about the timeline for achieving AGI:

-            Ray Kurzweil predicts that reaching AGI would signal the start of the technological singularity, potentially occurring by the 2030s[1].

-            Ben Goertzel has predicted that we could reach the singularity by 2027[1].

-            Shane Legg, co-founder of DeepMind, believes AGI could arrive by 2028[1].

-            Elon Musk has predicted that AI will surpass human intelligence by the end of 2025[1].

However, it's important to note that these predictions are speculative and subject to debate within the scientific community.

3. Measuring Progress Towards AGI

Quantifying progress towards AGI is challenging due to the lack of a universally accepted definition. However, several frameworks and metrics have been proposed:

3.1 Google DeepMind's Six Levels of AGI

Google DeepMind has proposed a framework for classifying AI systems based on their performance and generality, defining six levels of AGI[5]:

1. No AI: No artificial intelligence capabilities.

2. Emerging AGI: Equal to or somewhat better than an unskilled human.

3. Competent AGI: At least 50th percentile of skilled adults.

4. Expert AGI: At least 90th percentile of skilled adults.

5. Virtuoso AGI: At least 99th percentile of skilled adults.

6. Superhuman AGI: Outperforms 100% of humans.

This matrix approach allows for a more nuanced assessment of AI systems, considering both their depth of capabilities (performance) and the breadth of tasks they can handle (generality). The framework acknowledges that AI systems may exhibit different levels of performance across various tasks, providing a more comprehensive view of their progress towards AGI.

3.2 Capability Frameworks

Comprehensive AGI benchmarks should encompass a broad suite of cognitive and metacognitive tasks, including:

-            Linguistic intelligence

-            Mathematical and logical reasoning

-            Spatial reasoning

-            Interpersonal and intrapersonal social intelligence

-            Ability to learn new skills

-            Creativity

3.3 Autonomy and Risk Assessment

DeepMind's framework also includes a separate matrix for measuring autonomy and associated risks in AI systems, ranging from Level 0 (human performs all tasks) to Level 5 (fully autonomous AI).

4. Foundation Models and AGI Development

Foundation models are large-scale AI architectures trained on extensive datasets, serving as versatile bases for various applications. They exhibit emergent behaviors not explicitly programmed, playing a crucial role in advancing toward Artificial General Intelligence (AGI).

4.1 Key Foundation Models and Their AGI Status

1. GPT Series (OpenAI): The Generative Pre-trained Transformer series, including GPT-4, demonstrates advanced natural language understanding and generation capabilities. GPT-4 is considered a Level 1 General AI ("Emerging AGI") for most tasks, with Level 2 ("Competent AGI") performance in areas like short essay writing and simple coding.
2. BERT (Google): Bidirectional Encoder Representations from Transformers excels in various natural language processing tasks. While highly effective, BERT is generally considered narrow AI, falling into the lower levels of the AGI scale for most general tasks.
3. DALL-E and Flamingo (OpenAI): These models showcase multimodal capabilities in image generation and understanding. Despite their impressive domain-specific performance, they are still considered narrow AI, likely falling into Level 1 or 2 for their specific tasks.
4. LaMDA (Google): Language Model for Dialogue Applications is a conversational AI model with advanced dialogue capabilities. It remains within the Level 1-2 range for general tasks.
5. PaLM (Google): Pathways Language Model demonstrates strong performance across various tasks, including reasoning. PaLM would likely be classified as Level 1-2 for most general applications.
6. Llama 2 (Meta): An open-source large language model with impressive capabilities across various tasks. Llama 2 is generally considered within the Level 1-2 range for most tasks.[3]
7. Florence-2 (Microsoft): A vision foundation model designed for a variety of computer vision and vision-language tasks, utilizing a unified, prompt-based representation. Florence-2 excels in vision tasks and multimodal understanding but remains within Level 1-2 for general applications.
8. SenseNova (SenseTime): A foundation model set encompassing various models and capabilities in natural language processing, content generation, automated data annotation, and custom model training. SenseNova demonstrates versatility across various AI tasks but is still considered within Level 1-2 for general tasks.
9. GR00T (Nvidia): A general-purpose foundation model for humanoid robots, designed to enable robots to understand multimodal instructions and perform a variety of tasks. GR00T represents a significant step toward embodied AGI but currently operates within Level 1-2 for general applications.

4.2 Contributions of Foundation Models to AGI Development

-            Improved generalization across tasks

-            Enhanced transfer learning capabilities

-            Emergence of complex reasoning abilities

-            Advancements in multimodal learning

-            Scaling effects leading to unexpected capabilities

5. Challenges and Future Directions

Despite significant progress, several challenges remain in AGI development:

-            Achieving human-level common sense reasoning

-            Developing robust and reliable fully autonomous systems

-            Addressing ethical concerns and alignment with human values

-            Overcoming limitations in current deep learning paradigms

Future research directions include:

-            Exploration of hybrid AI architectures

-            Development of more sophisticated reasoning capabilities

-            Integration of multimodal learning approaches

-            Investigation of AGI safety and alignment techniques

6. Conclusion

While true AGI remains a distant goal, significant progress has been made through the development of foundation models and advanced AI systems. Measuring this progress requires multifaceted approaches that consider performance, generality, and potential risks. As research continues, it is crucial to address both the technical challenges and ethical implications of AGI development.

The rapid pace of development in AI, particularly in foundation models, continues to push the boundaries of what's possible. However, achieving higher levels of AGI, especially the "Virtuoso" or "Superhuman" levels, remains a significant challenge that requires further advancements in AI research and development.

References

[1] Eurasia Review. (2024). Artificial General Intelligence: A Definitive Exploration Of AI's Next Frontier - Analysis.

[2] YouTube. (2023). OpenAI Q*: Levels of AGI, Google Deepmind - AI Paper Explained.

[3] Viso.ai. (2025). Foundation Models in Modern AI Development (2025 Guide).

[4] Euronews. (2024). This is all you need to know about artificial general intelligence.

[5] Montreal AI Ethics Institute. (2024). Levels of AGI: Operationalizing Progress on the Path to AGI.

[6] Liquid.ai. (2024). Liquid Foundation Models: Our First Series of Generative AI Models.

[7] Wikipedia. (2024). Artificial general intelligence.

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