Deep learning, though rooted in decades of research, gained prominence around 2012 with breakthroughs like the ImageNet classification model. Despite its success in areas like speech and image recognition, concerns about its limitations have emerged. Deep learning excels at pattern recognition but struggles with abstract reasoning, generalization, and handling open-ended tasks. It is data-hungry, lacks transparency, and cannot distinguish causation from correlation. It also has difficulty with hierarchical structures and commonsense reasoning, and is vulnerable to adversarial examples. Additionally, deep learning systems are not inherently robust or interpretable, and their reliance on large datasets makes them less effective in novel or unstable environments. While deep learning is a powerful tool for certain tasks, it is not a universal solution and must be supplemented with other techniques to achieve artificial general intelligence. The field needs to explore alternatives such as unsupervised learning, symbolic AI, and insights from cognitive psychology to develop more robust and flexible AI systems.Deep learning, though rooted in decades of research, gained prominence around 2012 with breakthroughs like the ImageNet classification model. Despite its success in areas like speech and image recognition, concerns about its limitations have emerged. Deep learning excels at pattern recognition but struggles with abstract reasoning, generalization, and handling open-ended tasks. It is data-hungry, lacks transparency, and cannot distinguish causation from correlation. It also has difficulty with hierarchical structures and commonsense reasoning, and is vulnerable to adversarial examples. Additionally, deep learning systems are not inherently robust or interpretable, and their reliance on large datasets makes them less effective in novel or unstable environments. While deep learning is a powerful tool for certain tasks, it is not a universal solution and must be supplemented with other techniques to achieve artificial general intelligence. The field needs to explore alternatives such as unsupervised learning, symbolic AI, and insights from cognitive psychology to develop more robust and flexible AI systems.