To achieve the objective of performing live human-like fact-checking, it is essential to train the AI using a large dataset of verified and credible sources. This will help improve the accuracy of fact-checking and enable the AI to perform in real-time. The steps to achieve this are:

Collect a large dataset of verified and credible sources that cover a wide range of topics and subjects.

Preprocess the dataset by cleaning and formatting the data, ensuring that it is in a standardized format that can be easily processed by the AI.

Use natural language processing (NLP) techniques to extract relevant information from the dataset, such as keywords, concepts, and sentiment.

Train the AI using supervised learning algorithms such as neural networks, decision trees, or support vector machines.

Test the AI's accuracy and performance on a separate dataset of annotated fact-checking examples.

Repeat the training and testing process, adjusting the algorithm and dataset as necessary, until the AI achieves high accuracy and performance levels.

Finally, integrate the AI into a live fact-checking system that can quickly and accurately verify the accuracy of claims and statements in real-time.

The timeline for achieving live human-like fact-checking will depend on the size of the dataset, the complexity of the algorithms used, and the availability of computing resources. However, with the right resources and expertise, this objective can be achieved within a matter of months.
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