The rise of artificial intelligence (AI) has been considered as revolutionary. It transformed industries and pushed boundaries of what machines can do. However, some of the recent discussions are bringing to light a concerning trend and it is the collapse of AI models. The least widespread failures are raising important questions like whether there is a future of AI and what could be its long-term viability.
AI models are basically designed to learn from data. The models can identify patterns and simultaneously can make predictions based on the information provided. The accuracy of predictions relies on the volume of data. More data means better accuracy. However, the models are gradually growing into complexity. These are increasingly becoming prone to overfitting. These are becoming closely tailored to the specific data it was trained on and therefore becoming less effective in the real-world scenarios. The overfitting helps the models to perform well in controlled environments, but these collapses when faced with new or unseen data.
One main reason for the collapse is the limitation of the data used to train the models. AI systems rely heavily on vast amounts of data. Hence, quality and diversity of the data are crucial. If the data is biased, incomplete or not representative of the real world, the models are to have flaws.
AI models are also struggling with generalization. The models are good in specific tasks, but often fail to adapt when the task changes. This means lack of flexibility.
Another responsible factor is the complexity of the models. The models are becoming intricate when researchers are pushing for more advanced AI that involves millions or billions of parameters. More complication means the models become harder to understand and predict.
