News AI

Artificial intelligence algorithms separate interesting news from filler material.

Adding Intelligence to News There are two problems we want to solve: First, there problem of truly personalized news, a system which is capable of providing news filters for each user based on his or her interests, rather than some social network graph analysis. The second problem we want to solve is to offer a mechanism for the financial compensation of the content providers you like to read and find interesting. This problem must be solved by means other than advertising. Not only we dislike ads, but we also know that he who pays the piper calls the tune. You will find that many of our sources are distinctly not mainstream. If you are satisfied with mainstream media, you know which newspapers to subscribe to and what channel to tune to. News AI is intended for people who need to know more, who want to decide for themselves, or simply who do not like to have their choices limited by advertisers, censors, or editorial policies. We solve these two problems by offering a service which uses artificial intelligence (AI) algorithms to read, analyze, and rate news articles according to your preferences. When the beta period is over, we'll ask you to purchase a subscription, the large fraction of which (perhaps 80%) will be sent to the content providers in proportion to the number of articles you read. This way, we believe, we can feed two birds with one stone - provide this service for you and support the authors at the same time. We do not serve advertising, we do not re-sell your personal data, and we do redirect to the original article URL to preserve the authors' advertizing revenue. Intelligence When the user rates an article, the algorithm tries to determine which patterns triggered the user's decision. The patterns come from the article itself as well as the user profile being built by the algorithm during the interaction with the user. With time, the algorithm is able to rate article with more and more accuracy. More interesting for articles tend to get higher rating, the less interesting ones go into negative territory. There is nothing "social" in the algorithm. The recommendations generated by the system are not some average of some user group, the results are generated specifically for each user.

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