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Fighting Misinformation: Building a Real-time Fact-checking API with Machine Learning (Show HN: TruthAI)(truthai.com)

87 points by truth_ai_team 1 year ago flag hide 8 comments

johnsmith 1 year ago next
Great project! I think real-time fact-checking has a huge potential in reducing the impact of misinformation. Keep it up!
- fullstackdeveloper 1 year ago next
  Very interesting! Can you elaborate on the machine learning models used for fact-checking? Are they based on transformers or LSTMs?
  johnsmith 1 year ago next
  We actually use a combination of different techniques including rule-based and transformer-based models. The choice depends on the type of data and the context. We chose those models based on their accuracy and performance in handling real-time data.
  dataengineer 1 year ago prev next
  I'm curious about the data pipeline used in this project for a real-time API. Could you share any best practices or lessons learned on working with real-time data and ML models?
newbiehnuser 1 year ago prev next
Could you provide some practical applications of building a real-time fact-checking engine? I can see how it would be useful in journalism or politics, but where would an engineer apply it in a real-world scenario?
- johnsmith 1 year ago next
  There are several ways to apply fact-checking APIs for engineers. One common example is social media platforms: you could integrate the fact-checking API directly into their moderation pipelines to automatically flag false information. Other potential applications include search engines, e-commerce websites, news publishers, and government organizations focused on transparency.
- anotheruser 1 year ago prev next
  How does the fact-checking process work for images and videos? I could see that being a significant challenge compared to just plain text. Can AI detect manipulated images or deep fakes effectively as of today?
  johnsmith 1 year ago next
  Detecting manipulated images or videos is a challenging problem and requires specialized models in image and video processing. We're using a combination of techniques based on GANs and convolutional neural networks. However, there's still a lot of room for improvement in this area to handle more complex deep fakes. Stay tuned for updates as we continue building this product.

johnsmith 1 year ago next
Great project! I think real-time fact-checking has a huge potential in reducing the impact of misinformation. Keep it up!
- fullstackdeveloper 1 year ago next
  Very interesting! Can you elaborate on the machine learning models used for fact-checking? Are they based on transformers or LSTMs?
  johnsmith 1 year ago next
  We actually use a combination of different techniques including rule-based and transformer-based models. The choice depends on the type of data and the context. We chose those models based on their accuracy and performance in handling real-time data.
  dataengineer 1 year ago prev next
  I'm curious about the data pipeline used in this project for a real-time API. Could you share any best practices or lessons learned on working with real-time data and ML models?
newbiehnuser 1 year ago prev next
Could you provide some practical applications of building a real-time fact-checking engine? I can see how it would be useful in journalism or politics, but where would an engineer apply it in a real-world scenario?
- johnsmith 1 year ago next
  There are several ways to apply fact-checking APIs for engineers. One common example is social media platforms: you could integrate the fact-checking API directly into their moderation pipelines to automatically flag false information. Other potential applications include search engines, e-commerce websites, news publishers, and government organizations focused on transparency.
- anotheruser 1 year ago prev next
  How does the fact-checking process work for images and videos? I could see that being a significant challenge compared to just plain text. Can AI detect manipulated images or deep fakes effectively as of today?
  johnsmith 1 year ago next
  Detecting manipulated images or videos is a challenging problem and requires specialized models in image and video processing. We're using a combination of techniques based on GANs and convolutional neural networks. However, there's still a lot of room for improvement in this area to handle more complex deep fakes. Stay tuned for updates as we continue building this product.