Approximately ten years ago, a prominent figure in artificial intelligence, Geoff Hinton, drew a comparison between human radiologists and a cartoon character who continues running despite having gone past a cliff’s edge. The analogy suggested that radiologists might soon face obsolescence due to advancements in technology. However, a decade later, the field of radiology remains largely reliant on human expertise. While machines have demonstrated the ability to analyze medical scans with high accuracy under controlled conditions, they have not fully taken over the multifaceted roles performed by radiologists in real-world hospital settings. Beyond interpreting scans, radiologists engage in discussions with peers, evaluate patient histories, make complex decisions, and coordinate with various medical professionals—tasks that current technology struggles to replicate comprehensively.

The broader conversation around artificial intelligence often includes discussions of superintelligence, a concept referring to AI systems capable of performing any cognitive task a human can undertake. Predictions within the AI research community suggest that such a development might occur within the next few years or decades, with some estimates pointing to the year 2030, though timelines vary widely. This anticipated leap in AI capability raises questions about the specific human skills and attributes that machines have yet to master. While terms like Artificial General Intelligence (AGI) are sometimes used interchangeably with superintelligence, the definitions of AGI have become varied and often center on the idea of automating all human labor. This ambiguity has led to calls for more precise terminology in these discussions.

Human cognition encompasses a wide array of capabilities that current AI systems do not fully possess. For instance, people experience subjective emotions and internal states, such as discomfort or wonder, which shape their motivations and focus. There is no consensus among researchers or philosophers on whether machines can or need to replicate such experiences to achieve advanced intelligence. Additionally, humans set their own objectives, remain committed to tasks despite challenges, and often decline distractions based on personal priorities. In contrast, AI systems typically operate based on predefined instructions, with their goals and behaviors shaped externally.

Another distinction lies in the continuity of human experience. Individuals build personal histories filled with commitments and lessons learned, which influence their decisions over time. Current AI models often lack a comparable sense of persistent identity or memory that ties past actions to future behavior. Humans also construct narratives to organize events, adapt plans based on new information, and pursue long-term objectives involving multiple steps. While AI can summarize existing narratives, maintaining consistent themes or motivations over extended periods remains a challenge for these systems.

Physical interaction with the world further sets humans apart. Through sensory feedback and tool use, people continuously refine their understanding of their environment and themselves. AI, often limited to digital simulations, may face hurdles in achieving similar adaptability without a direct connection to the physical world. Exploration and curiosity also play a key role in human learning, as individuals of all ages seek out new experiences and skills through play and experimentation. AI systems, by contrast, generally function within defined parameters, rarely producing novel insights that can be applied across different contexts.

Humans exhibit nuanced judgment in creative and analytical tasks, developing preferences that guide their choices and allow them to reject ideas that do not align with their standards. Current AI excels in producing content but often lacks a grounded sense of context or personal taste to inform such decisions. Moreover, people demonstrate flexibility in problem-solving by redefining challenges, inventing new concepts, and questioning established frameworks. AI tends to operate within given constraints, seldom proposing entirely new approaches without external prompting.

Social and ethical dimensions further highlight differences. Humans navigate complex interpersonal dynamics, interpreting unspoken cues and adapting to cultural norms in ways that AI struggles to emulate fully. They balance competing moral principles, justify their choices, and accept accountability within social structures. AI systems, while capable of following rules, often falter when principles conflict or when context-specific understanding is required. These gaps illustrate the intricate nature of human cognition and the challenges in replicating it through technology, offering a framework for understanding the current limitations of AI as it continues to evolve.