Deep Learning (DL) has achieved astonishing feats, yet it faces fundamental, well-documented limitations that challenge its path toward true intelligence. The core weakness is its insatiable data hunger, requiring massive, labeled datasets for every task, contrasting sharply with the human ability to learn from minimal examples. This data reliance makes DL models brittle and easily tricked by adversarial attacks or slight changes in context, revealing a lack of true robustness.
Furthermore, DL models are often described as "glorified correlation engines" because they struggle with causality, only recognizing that two things happen together rather than understanding the cause-and-effect relationship. This absence of genuine understanding hinders capabilities vital for human-like intelligence, such as planning, reasoning, and counterfactual thinking. This issue is exacerbated by the "black box" problem, as the models' opaque nature prevents humans from tracing or explaining the logic behind complex decisions, jeopardizing trust and accountability.
To overcome these architectural hurdles and pursue Artificial General Intelligence (AGI), the research community is looking beyond the current DL paradigm. The consensus suggests the field must integrate models capable of abstract reasoning and common sense, which current, purely correlational methods cannot provide. Future breakthroughs are expected from Neuro-Symbolic AI, which aims to combine DL's pattern recognition strength with the structured logic of classical AI, creating a more robust and truly intelligent system.
