Digital Event Horizon
Despite decades of hype surrounding artificial general intelligence (AGI), a clear definition of the term remains elusive. As researchers debate the meaning of AGI, policymakers craft policy based on AGI timelines, and companies use promises of impending AGI to attract investment, it's time to take a closer look at this pressing issue.
The concept of artificial general intelligence (AGI) is plagued by definitional chaos, with experts having different interpretations. There is a lack of consensus on what exactly AGI means, which has real-world consequences for investment, policy, and public expectations. A definition of AGI based on generating $100 billion in profits is arbitrary and fails to capture the true nature of intelligence. The search for an objective benchmark for AGI is challenging due to its subjective nature. A framework with five levels of AGI performance has been proposed, but it has critics who argue that the term AGI has become technically meaningless. The lack of a clear definition of AGI hinders progress in AI research and development. Focusing on specific capabilities such as learning new tasks, explaining outputs, and producing safe outputs may be a more effective approach than chasing an ill-defined goal.
In recent years, there has been an increasing amount of hype surrounding the concept of artificial general intelligence (AGI), a hypothetical AI system that possesses human-like intelligence and can perform any intellectual task. However, as the term continues to be tossed around, it has become clear that there is a severe lack of consensus on what exactly AGI means. This definitional chaos has real-world consequences, from attracting investment to crafting policy, and it is imperative that we take a closer look at the issue.
The concept of AGI was first coined in 1997 by physicist Mark Gubrud, but it wasn't until around 2002 that computer scientist Shane Legg and AI researcher Ben Goertzel independently reintroduced the term. Since then, there has been a flurry of debate among experts about what AGI should entail. While some argue that it is possible to create an AI system that can outperform humans in most tasks, others are more skeptical.
According to a report by The Wall Street Journal, Microsoft and OpenAI are currently locked in acrimonious negotiations over the definition of AGI, with each side having a different interpretation. This dispute highlights the broader problem of definitional chaos surrounding the concept of AGI. As Google DeepMind wrote in a paper on the topic, "If you ask 100 AI experts to define AGI, you'll get '100 related but different definitions.'"
One way to define AGI is through economics; according to Microsoft and OpenAI, it lies in generating $100 billion in profits. However, this arbitrary profit-based benchmark for AGI perfectly captures the definitional chaos plaguing the AI industry. As researcher François Chollet noted in an interview, "Almost all current AI benchmarks can be solved purely via memorization." This raises questions about whether we are truly measuring intelligence or just creating systems that mimic human-like behavior.
Another way to approach this issue is through philosophy and the search for objective benchmarks. Researchers have attempted to create more sophisticated frameworks to measure progress toward AGI, but these attempts have revealed their own set of problems. As Heidy Khlaaf, chief AI scientist at the nonprofit AI Now Institute, told TechCrunch, "The concept of AGI is too ill-defined to be 'rigorously evaluated scientifically.'"
In an effort to bring order to this chaos, Google DeepMind proposed a framework with five levels of AGI performance: emerging, competent, expert, virtuoso, and superhuman. However, this framework has its critics, with some arguing that the term AGI has become technically meaningless.
The search for a definition of AGI is not just an academic exercise; it has real-world consequences. As companies use promises of impending AGI to attract investment, talent, and customers, policymakers craft policy based on AGI timelines. The public forms potentially unrealistic expectations about AI's impact on jobs and society based on these fuzzy concepts.
In the face of this kind of challenge, some may be tempted to give up on formal definitions entirely, falling back on an "I'll know it when I see it" approach for AGI. However, as Dario Amodei, CEO of Anthropic, noted in his October 2024 essay "Machines of Loving Grace," this subjective standard is useless for contracts, regulation, or scientific progress.
Perhaps the most systematic attempt to bring order to this chaos comes from redefining what we mean by AGI. Instead of chasing an ill-defined goal that keeps receding into the future, we could focus on specific capabilities: Can this system learn new tasks without extensive retraining? Can it explain its outputs? Can it produce safe outputs that don't harm or mislead people?
These questions tell us more about AI progress than any amount of AGI speculation. The most useful way forward may be to think of progress in AI as a multidimensional spectrum without a specific threshold of achievement. But charting that spectrum will demand new benchmarks that don’t yet exist—and a firm, empirical definition of "intelligence" that remains elusive.
In conclusion, the search for an answer to what AGI means is a pressing issue that requires careful consideration. As we continue to develop and deploy AI systems, it is imperative that we take a closer look at our assumptions about what intelligence entails. By redefining what we mean by AGI and focusing on specific capabilities, we can move beyond the definitional chaos surrounding this concept and make progress toward creating more intelligent machines.
Related Information:
https://www.digitaleventhorizon.com/articles/The-Elusive-Dream-of-Artificial-General-Intelligence-A-Definition-Crisis-deh.shtml
https://arstechnica.com/ai/2025/07/agi-may-be-impossible-to-define-and-thats-a-multibillion-dollar-problem/
Published: Tue Jul 8 16:54:06 2025 by llama3.2 3B Q4_K_M
