Data-intensive research is still all the rage these days. Breakthroughs in AI (and especially NLP) seem likely to only accelerate its adoption. But can anybody provide examples (preferably from different disciplines) of instances where such research discovered entirely new insights into topics that scholars had been working on for a long time, or where it just totally undermined ruling paradigms in the field?
Given the current situation of the global economy and systemic, we can use AI with semantic treatment and GIS to map, treat and opmitimize healthcare resources.
Social media will be accessed more than today because people cannot afford going to healthcare facilities.
Estimating numerical model errors in engineering and physical sciences. Data-driven models can be used to approximate and compensate model errors in tough situations where, previously, model errors were assumed to be Gaussian a priori. We no longer have such constraints when properly trained neural networks are used.
Enabling statistical estimation techniques via manifolds or generalized surfaces is one of the most hot research up gradational area. In particular its implementation for big and raw data via neural network or Bayesian framework is of particular recent interests. Now a days Humanizing Machine Learning is one of the most thrilling topic of research.
Given the current situation of the global economy and systemic, we can use AI with semantic treatment and GIS to map, treat and opmitimize healthcare resources.
Social media will be accessed more than today because people cannot afford going to healthcare facilities.
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