Artificial intelligence (AI) systems are becoming smarter every day, beating world champions in games like Go, identifying tumors in medical scans better than human radiologists, and increasing the efficiency of electricity-hungry data centers. Some economists are comparing the transformative potential of AI with other "general purpose technologies" such as the steam engine, electricity, or the transistor. But current AI systems are far from perfect. They tend to reflect the biases of the data used to train them and to break down when they face unexpected situations. They can be gamed, as we have seen with the controversies surrounding misinformation on social media, violent content posted on YouTube, or the famous case of Tay, the Microsoft chatbot, which was manipulated into making racist and sexist statements within hours.

One of most excruciating pain points during Data Exploration and Preparation stage of an Analytics project are missing values. How do you deal with missing values - ignore or treat them? The answer would depend on the percentage of those missing values in the dataset, the variables affected by missing values, whether those missing values are a part of dependent or the independent variables, etc. Missing Value treatment becomes important since the data insights or the performance of your predictive model could be impacted if the missing values are not appropriately handled.The 2 tables above give different insights. The inference from the table on the left with the missing data indicates lower count for Android Mobile users and iOS Tablet users and higher Average Transaction Value compared to the inference from the right table with no missing data. The inference from the data with missing values could adversely impact business decisions.

MaLeS is an automatic tuning framework for automated theorem provers. It provides solutions for both the strategy finding as well as the strategy scheduling problem. This paper describes the tool and the methods used in it, and evaluates its performance on three automated theorem provers: E, LEO-II and Satallax. An evaluation on a subset of the TPTP library problems shows that on average a MaLeS-tuned prover solves 8.67% more problems than the prover with its default settings.