The ultimate goal, in Dr. Ferrucci's view, is that A.I. becomes a trusted "thought partner," a skilled collaborator at work and at home, making suggestions and explaining them. Elemental Cognition, founded in 2015, is taking measured steps toward that goal with a promising, though unproven, hybrid approach. Its system combines the latest developments in machine learning with a page from the A.I.'s past, software modeled after human reasoning. Newer machine learning programs are remarkable at pattern recognition and predictions, far more powerful than in the "Jeopardy!" They pore through millions of words and word patterns, and generate the most likely interpretations.

In recommender systems, a cold-start problem occurs when there is no past interaction record associated with the user or item. Typical solutions to the cold-start problem make use of contextual information, such as user demographic attributes or product descriptions. A group of works have shown that social media background can help predicting temporal phenomenons such as product sales and stock price movements. In this work, our goal is to investigate whether social media background can be used as extra contextual information to improve recommendation models. Based on an existing deep neural network model, we proposed a method to represent temporal social media background as embeddings and fuse them as an extra component in the model. We conduct experimental evaluations on a real-world e-commerce dataset and a Twitter dataset. The results show that our method of fusing social media background with the existing model does generally improve recommendation performance. In some cases the recommendation accuracy measured by hit-rate@K doubles after fusing with social media background. Our findings can be beneficial for future recommender system designs that consider complex temporal information representing social interests.

Wearable devices such as smartwatches continuously monitor and track user preferences and habits. Not only has this led to impactful applications in the healthtech sector, it also works well for sports and fitness. According to leading tech research firm Gartner, the global wearable device market is estimated to see more than $87 billion in revenue by 2023.