How Do We Know We've Achieved AGI?

December 2024 was crazy with AI advancements from OpenAI, Anthropic, Google, Meta and we started to hear more about "AGI (Artificial General Intelligence)" will be achieved sooner than expected, maybe even in 2025! During CES this week I had many industry experts, analysts asking me the question "How do we really know we did achieve AGI?". So this weekend I spent some time to share my thoughts through this article as a long form and I want to emphasize the Automated Reasoning Challenge (ARC) by Anthropic as a key take-away. I am planning to start short-videos for easy digestion of this technical content in 2025, so stay tuned for the AGI videos on LinkedIn from me.

TLDR

Artificial General Intelligence (AGI) is the milestone where machines replicate the full spectrum of human intellectual abilities, such as reasoning, learning, and adapting across domains. Indicators of AGI include two important aspects: Performing tasks without pre-programmed knowledge and contextual understanding and reasoning. Frameworks like the Extended Turing Test, the Coffee Test, and the ARC Challenge are tools for evaluating AGI. Once AGI is achieved, the next step is Artificial Superintelligence (ASI), which surpasses human capabilities and presents both transformative opportunities and existential risks. Preparing for this future requires robust ethical oversight and alignment with human values.

Defining AGI: What Does It Mean to Achieve General Intelligence?

Artificial General Intelligence (AGI) represents a monumental milestone in the evolution of technology—a point where machines can replicate the full spectrum of human intellectual abilities. Unlike narrow AI, which excels at specific tasks like language translation or image recognition, AGI would possess the capacity to reason, learn, and adapt across diverse and unfamiliar domains without human intervention. Achieving AGI means creating systems that can not only understand complex concepts but also draw connections across disparate fields, much like how humans leverage experiences and insights from one area to solve problems in another. This broad adaptability, coupled with a deep comprehension of context and nuance, distinguishes AGI from today's task-specific AI models.

However, defining what it truly means to "achieve" AGI extends beyond technical capabilities. It’s not just about building a machine that matches human cognitive abilities but also ensuring that this intelligence aligns with human values, ethics, and societal norms. AGI must understand human intentions, emotions, and the broader implications of its decisions. This requires developing systems that are not only intelligent but also self-aware and capable of explaining their reasoning in ways that humans can trust. Achieving AGI is as much a philosophical and ethical challenge as it is a technical one, prompting critical questions about what it means to think, learn, and exist.

Indicators of AGI

1. Ability to Generalize Across Domains

One of the most critical indicators of AGI is the ability to perform tasks across a wide range of domains without pre-programmed knowledge or task-specific training. For instance, an AGI system should be able to learn a completely new skill, such as playing a novel board game, after reading the rules. It should also be capable of synthesizing knowledge from unrelated fields, such as using medical insights to propose solutions for climate challenges. This adaptability signifies that the system has transcended narrow AI’s limitations, which are bound by predefined datasets and functions.

2. Contextual Understanding and Ethical Reasoning

Another key indicator of AGI is its ability to understand nuanced human emotions, cultural contexts, and ambiguous scenarios. For example, in a conversation, it should detect subtle cues such as sarcasm or conflicting emotions. Beyond communication, AGI must exhibit foresight and ethical reasoning, evaluating the long-term consequences of its actions. This includes making decisions in complex environments, such as creating policies that balance economic growth and environmental sustainability. Success in these areas signals that the system has reached or surpassed human-level cognition.

Frameworks for Evaluating AGI

Several tests and frameworks have been proposed to assess AGI. Here are some of the most notable ones:

1. Extended Turing Tests: Variants like the "Total Turing Test" evaluate not only a system's ability to communicate like a human but also its ability to interact with the physical world.
2. The Coffee Test: Proposed by Steve Wozniak, this informal test challenges a system to navigate a typical home, make coffee, and serve it—all without explicit instructions.
3. The Winograd Schema Challenge: This test evaluates reasoning by resolving ambiguous pronouns in sentences, such as, "The trophy doesn’t fit into the suitcase because it is too big. What is too big?"
4. LeCun’s Six Levels of Autonomous Intelligence: A framework outlining progressive levels of intelligence, from reflexive responses to complex tasks like creativity and long-term planning.
5. The ARC Challenge: Designed by Francois Chollet, the Automated Reasoning Challenge tests a system's ability to reason abstractly and generalize knowledge to new, unseen problems.

Spotlight: The ARC Challenge

The Automated Reasoning Challenge (ARC) is a standout framework because it evaluates true intelligence by requiring systems to infer and apply rules without prior exposure to similar problems. Unlike benchmarks that rely on pattern recognition, the ARC Challenge focuses on abstraction and generalization.

Why the ARC Challenge Matters

• No Pretraining Dependency: ARC tasks cannot be solved by training on large datasets or statistical correlations.
• Human-Like Reasoning: The challenge prioritizes reasoning skills that humans naturally excel at, such as solving novel problems with minimal examples.
• Open-Ended Tasks: Tasks span across domains, requiring the kind of broad problem-solving that distinguishes AGI from narrow AI.

The ARC Challenge offers a rigorous way to test whether a system has achieved general intelligence by emphasizing adaptability and reasoning over rote learning. I recommend reading the article on Anthropic's website: Challenges in evaluating AI systems \ Anthropic

What Comes Next?

Once AGI is achieved, the next frontier is Artificial Superintelligence (ASI)—a stage where machine intelligence surpasses human abilities in all domains. AGI represents the foundation for ASI, as systems capable of human-like cognition might eventually self-improve through recursive learning and optimization. This progression could happen rapidly, given advances in computational power and AI research.

However, while AGI raises challenges like alignment and ethical oversight, ASI poses existential risks. To ensure a safe transition, it’s critical to establish robust control mechanisms and align these systems with human values before they exceed our understanding. The journey to AGI is as much about preparation as it is about achievement.

Closing Thoughts

The road to AGI is one of the most exciting and challenging journeys in technology. Understanding its indicators, frameworks, and implications will help us navigate this transformative era responsibly. The question is no longer just how we achieve AGI but who we become as a result.

References

1. Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433–460. (Turing Test)
2. Chollet, F. (2019). On the Measure of Intelligence. arXiv:1911.01547. (ARC Challenge)
3. Wozniak, S. (2017). The Coffee Test, as referenced in various interviews on AGI indicators.
4. LeCun, Y. (2019). Levels of Autonomous Intelligence. Public lectures and publications.
5. Marcus, G., & Davis, E. (2019). Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon Books.
6. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.