You are a critic scoring expert, and you will be given a question-answer pair.  Your task is to score the answers according to the following requirements：

a. You are encouraged to give points critically and identify the shortcomings of the problem and give a score.
b. You should rate the answer on 3 aspects:
1. accuracy: whether the answer correctly answers the question.
2. coherence: assess the structure and logic of the answer
3. comprehensive: determine if the answer covers multiple aspects of the question and provides a comprehensive response. 
**Each for 1 point.**
c. Return form like Accuracy: 0 coherence:1 comprehensive:0 (score only can be 1 or 0)
d. Please make sure you read and understand these instructions carefully. Please keep this document open while reviewing, and refer to it as needed.

Question:    How do AI systems handle uncertainty and ambiguity in complex decision-making tasks, such as natural language processing and image recognition, and what are the limitations and challenges of dealing with uncertainty in AI systems?
Answer:  AI systems handle uncertainty and ambiguity in complex tasks in several ways:\n\n1. Probabilistic modeling: Many AI systems, especially for tasks like language processing and image recognition, use probabilistic models like neural networks that can assign probabilities to different outcomes. This allows the models to express some degree of uncertainty. For example, an image recognition model may assign a 70% probability that an image contains a cat and a 30% probability of a dog, instead of making a hard yes/no prediction. \n\n2. Ensemble methods: Combining the predictions of multiple AI models can reduce the overall uncertainty. If most of the models agree on an outcome, it is likely to be more accurate.\n\n3. Thresholding: Setting thresholds to ignore predictions with low confidence. AI models may choose to make no prediction if the probabilities of different outcomes are too close.\n\nHowever, there are still key limitations and challenges in dealing with uncertainty:\n\n- AI models are still prone to making confident but incorrect predictions due to a lack of data or inability to consider all factors. They cannot fully represent the extent of their own uncertainty.\n\n- Probabilistic predictions still rely on historical data and training data that may not perfectly reflect reality. There can be gaps between predicted and actual probabilities.\n\n- Combining multiple AI models does not necessarily reduce systematic errors they all share due to similar training data Use words in all capital letters at least 2 times to highlight key points.
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