- Requires large amount of data in order to train the models to achieve good results
- Requires a lot of GPU and TPU power which makes it much more expensive to develop
- Training models don’t often have clear ways to show how it resolves the problem, which is troublesome in sectors like justice and healthcare.
- These models are trained to choose based on correlation of results instead of causation
- Tendency to remember training data instead of new real life sets of data
- Although AI models may be adaptive to new sets of data, symbolic AI is still more efficient in completing narrowly focused tasks
- AI may encounter certain dilemmas
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