Humanity is heading towards a crisis. The mammoth task of providing food for 2 billion more people by 2050 is being deliberated by governments, scientists and agriculturists alike. The consequence of climate change has led to erratic and non-uniform crop growth. Floods are inundating agriculture land, drought is making crop cultivation impossible and pests and insects are wiping out entire crop fields. Just as the situation seems to be going out of hand, technology is proving itself to be the guardian angel yet again. With the power of Machine Learning and Artificial Intelligence, scientists are able to understand and predict intolerable growing conditions, identify various weather and pest infestation patterns and provide sustainable solutions, which helps accomplish our ultimate goal—increasing crop production by two-folds in the next thirty years. This paper gives an insight about ways in which artificial intelligence and machine learning are helping humanity overcome one of its biggest challenge.

Natural language problems have gained increased attention in the recent times. There are many special purposes, and carefully constructed evaluations driving the NLP research. Problem solving tests offer an interesting alternative to these evaluations. The primary goal of creating these problem solving tests is to evaluate the reading skills, and there by create bank of training materials and ranking procedures to match the existing measures of human performance. Solving Set Theory problems using NLP exposes once such research problem and helps creating an evaluation method for Natural Language Understanding Systems. This paper describes the possibility to challenge these systems to successively push higher performance levels up to an accuracy of 80% with high speed as an added advantage.