Retro AI: How 2011's AI Might Have Shaped the Modern Web

Back in the early days of AI and machine learning, particularly around 2011, a system like this would have been groundbreaking for several reasons:

1. Automation of Manual Tasks

• Content Categorization: Before automated systems, content categorization was often done manually by editors or through basic keyword matching. A Naive Bayes classifier could automate this process, saving time and reducing human error.

2. Scalability

• Handling Large Volumes of Data: As blogs and news websites grew in popularity, the volume of content increased exponentially. An automated system like this would be able to handle a large number of posts efficiently.

3. Improved User Experience

• Better Search and Navigation: By automatically categorizing content, users could more easily find relevant articles through better search functionality and improved navigation on websites.

4. Innovation in Web Development

• Integration with Modern Frameworks: The use of Flask to create a RESTful API for this system would have been seen as innovative at the time, showcasing how machine learning models could be integrated into web applications.

5. Data-Driven Decision Making

• **Analytics

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