# The Truth About General Intelligence: Why AI Remains Lacking
Artificial intelligence (AI) has achieved significant milestones in recent years, excelling at tasks like playing intricate games, crafting human-like text, and producing lifelike images and videos. These developments have sparked conversations regarding the potential arrival of artificial general intelligence (AGI)—a type of AI capable of undertaking any intellectual task that a human can perform. While some assert that AGI is just around the corner, others contend that we are still quite distant from realizing it.
A primary point of contention in this discussion is the absence of a clear definition for AGI. Nevertheless, we can reference an existing illustration of general intelligence: the human brain. When comparing AI with biological intelligence, it becomes clear that today’s AI systems function in fundamentally different manners. These distinctions prompt significant inquiries regarding whether AI, in its current form, can genuinely achieve general intelligence.
## Understanding AGI: A Constantly Shifting Objective
The notion of AGI is frequently defined in ambiguous terms. Some advocates assert that AGI will be realized when AI surpasses human capability in various tasks, while others argue that authentic AGI must exhibit extensive adaptability and problem-solving abilities across multiple fields.
Ariel Goldstein, a researcher at Hebrew University of Jerusalem, proposes that AGI ought to be “more robust, more stable—not necessarily smarter in general but more coherent in its abilities.” He highlights that existing AI systems often excel in one area while underperforming in another seemingly related field. Neuroscientist Christa Baker of NC State University shares this viewpoint, stressing that intelligence should encompass the capacity to generalize knowledge across various contexts.
## How AI and the Brain Differ
Though AI systems draw inspiration from the human brain’s structure, they differ in several important aspects. Grasping these distinctions can elucidate why AI has yet to attain general intelligence.
### 1. **Neurons vs. Synthetic Neurons**
AI systems, especially neural networks, aim to model the architecture of the brain. However, artificial neurons are far more simplistic than biological ones. Actual neurons possess a high level of specialization, utilizing different neurotransmitters and interacting with hormones. They also communicate through complex patterns of electrical signals, while artificial neurons depend on predefined mathematical functions.
Additionally, the brain’s structure is not strictly hierarchical. Instead, it features intricate feedback loops and lateral connections that enable dynamic information processing. In contrast, AI typically adheres to a rigid, layer-based framework.
### 2. **Modularity of the Brain vs. Monolithic AI**
The human brain comprises specialized regions that manage different functions, such as vision, language, and motor control. These areas cooperate seamlessly, facilitating the integration of information from various sources. Conversely, most AI models tend to be monolithic, lacking inherent modularity. Although some AI systems are beginning to adopt modular frameworks, they still do not match the brain’s level of flexibility.
### 3. **Continual Learning in the Brain**
AI systems generally operate in two distinct stages: training and deployment. Once an AI model is trained, its knowledge remains relatively fixed, with minimal updates. In contrast, the human brain is perpetually learning and adapting. For instance, when acquiring a new skill, the brain instantly incorporates feedback to enhance its approach. This capability for real-time learning gives biological intelligence a considerable edge over AI.
### 4. **Complex Memory and Context**
Human memory is intricate, encompassing short-term, long-term, and working memory. This complexity allows individuals to recollect past experiences, identify patterns, and apply knowledge in novel situations. AI, on the other hand, lacks genuine memory. For example, large language models depend on a limited “context window” that retains only recent interactions. Without an advanced memory structure, AI struggles to build upon past experiences in the same manner as humans.
### 5. **Energy Efficiency Comparison**
The human brain functions on approximately 20 watts of power—less than that of a typical light bulb. In contrast, training extensive AI models necessitates considerable computational resources and energy consumption. This inefficiency underscores a fundamental constraint of existing AI methodologies.
## The Path Forward: Is General Intelligence Within AI’s Reach?
Despite these hurdles, AI research is continually advancing. Some scientists believe that AGI may emerge through innovative approaches, such as neuromorphic computing, which aims to closely replicate the brain’s architecture. Others suggest that achieving AGI may require entirely new paradigms beyond current machine learning frameworks.
However, as Christa Baker points out, “We don’t even know how the brain achieves its flexibility and generalizability. So how do you build that into a system?” Until we gain a more profound understanding of biological intelligence, replicating it in machines may remain a challenging aspiration.
## Conclusion
Although AI has made notable progress, it still functions in fundamentally different ways than the human brain. Current AI systems lack the adaptability, memory capacity, and real-time learning abilities that characterize general intelligence. Consequently, assertions that AGI is imminent should be approached with skepticism.
Rather than concentrating solely on replicating human intelligence
