👉 Computing, particularly in the context of artificial intelligence and machine learning, faces several inherent limitations. One major constraint is computational power, where the complexity of models and the amount of data required for training can quickly outstrip available resources, leading to longer training times and higher costs. Another significant limitation is memory capacity; large models often require substantial amounts of RAM to store and process data efficiently, which can be a bottleneck in environments with limited resources. Additionally, there's the issue of energy consumption; training and running large-scale models demand considerable electricity, contributing to environmental concerns. Lastly, there's the challenge of generalization; while models can be optimized for specific tasks, they often struggle to generalize well to new, unseen data, limiting their practical applicability in real-world scenarios.
