There is a general consensus among relevant researcher that AI can be enabled in any system through Machine/Deep Learning, Neural Networks, Expert Systems and Evolutionary or Genetic Algorithms.
We are specifically focused to solve society and industry issues through limited budget AI projects. I am fully aware and know how to make success in proactive or reactive research culture. If society or industry formally submits pending issues then we can classify it as reactive in nature. In proactive strategy the Research Lab itself take interest to resolve public and Industry issues through AI.
Proactive AI is what ur objective should be:
in medical analysis the proactive analysis is needed.
in marketing analysis, business prediction, Event processing.
Make multiple projections about the likelihood of engagement, propensity to purchase.
Reactive AI: it means acting in response to a situation
A proactive approach carries an unknown risk while as the reactive approach can sometimes be perceived as.a late response.
Any subset of Artificial Intelligence or Machine Learning is based on input to output representation, or proactive/reactive, action/reaction, give/take, training/testing/validating etc. The overall concept is is to evaluate optimal solution or minimum/shortest distance etc with possibly low cost. For a good literature review on pattern analysis, I suggest you to follow the following attached books.
Not just AI, research in any area can be either proactive or reactive. Reactive if it is targeted at addressing an issue that is already at hand and proactive if it is focusing on a possible future problem. Proactive research in AI is relevant but often falls a victim of cold start due to possible lack of data especially when interested in a data-driven AI approach.
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