Architect Corner Update : Hazel Cast partners wth Architect Corner – No SQL Database
Profondo – AI Deep Learning Platform
Artificial Intelligence based Deep Learning is an evolving field. Architect Corner has Next Generation AI Deep Learning Platform.
The following are the use cases targeted by Architect Corner:
- The social dynamics in Twitter are characterized by signatures representing the tweet’s popularity, contagiousness, stickiness, and interactivity.
- The social dynamics in Yelp are characterized by signatures representing how different groups of reviewers rate individual businesses. We have found the patterns where theses signatures interact by generating, enhancing, or dominating one another.
- Deep networks have also had spectacular successes for pedestrian detection and
image segmentation and yielded superhuman performance in traﬃc sign classiﬁcation
such as indexation attribution modeling, collaborative filtering, or recommendation
- Detect Emotions from Photos
- recommendation engine predicts the proxy of interest.
- User clicks on ad
- user enters a rating
- user clicks on a “like” button,
- user buys product
- user spends some amount of money on the product
- user spends time visiting a page for the product
- Collaborative ﬁltering systems: when a new item or a new user is introduced, its lack of rating history means that there is no way to evaluate its similarity with other items or users or the degree of association between, say, that new user and existing items. This is called the problem of cold-start recommendations. we get a biased and incomplete view of the preferences of users: we only see the responses of users to the items. they were recommended and not to the other items. In addition, in some cases we may not get any information on users for whom no recommendation has been made (for example, with ad auctions, it may be that the price proposed for an ad was below a minimum price threshold, or does not win the auction, so the ad is not shown at all). More importantly, we get no information about what outcome would have resulted from recommending any of the other items.