To achieve human-like fact-checking, it is necessary to improve natural language processing capabilities. This involves training neural networks to accurately understand human speech and written language.

The first step is to gather large amounts of human-written and spoken data to use as training data. This data can be obtained from a variety of sources such as news articles, books, and transcripts of spoken conversations.

Next, the data must be preprocessed to remove noise and irrelevant information. This can involve removing stop words, punctuation, and other non-relevant information from the text.

After pre-processing, the data can be used to train the natural language processing model. This involves using machine learning algorithms to learn patterns in the data and improve the model's accuracy.

Once the model is trained, it can be used to perform natural language processing tasks such as sentiment analysis, topic modeling, and text classification. These tasks are crucial for fact-checking as they can help identify biases and unreliable sources.

Overall, achieving human-like fact-checking capabilities requires improving natural language processing capabilities through gathering and preprocessing data, training machine learning models, and performing natural language processing tasks. The timelines for achieving this goal will depend on the resources available and the complexity of the task, but continuous improvement and refinement will be necessary to achieve the desired level of accuracy.
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