There is no such thing as one, universal template for every type of legal contract. These kinds of documents are required in so many institutions and cases that it is impossible to rely on one model paper. Moreover, as tax and insurance law is getting more and more complex, it is increasingly difficult for tax offices or insurance institutions to prepare individual documents for every instance. Thus, in collaboration with Pergamin, a startup specializing in handling eContracts, we developed a tool for automatic semantic analysis of legal contracts. It aims to help Pergamin in the creation of legal contract templates

Few weeks ago MIM Solutions team got a cool news that one of our paper was accepted to International Conference on Machine Learning (ICML 2021). The paper has an interesting story behind which would not happen if we did not keep our minds open. More than a year ago, we were working on a more general solution to a parametric flow problem. Previously only very special cases have been solved efficiently. At first sight this does not seem to have anything in common with decomposable submodular function minimization. However, later on we realized that this was the missing step in

Upcoming Thursday (May 27th, 5:00 pm) MIM Solutions CEO Piotr Sankowski will be a guest on the RDC radio. Piotr will take part in the program “Between East and West” hosted by Mr. Jacek Wan. The topic of the conversation will be artificial intelligence, as well as the technological war between the US and China. More details can be found on the RDC radio website.

Problems with machines, devices and possible failures can affect the condition of any company. These troubles not only disrupt your workflow but can also have financial and market implications. Therefore, it is essential to predict and monitor any minor issues that could turn into major damage. The good news is that all of this is possible with the help of Artificial Intelligence. As MIM Solutions we created Alert.MIM Platform – predictive and monitoring system for companies. Based on the data on the condition of machines and devices as well as the course of processes, we create models that allow predicting

Remember, on April Fools’ Day you should be intelligent, not artificial! We wish you only good jokes!

Years ago when I learned about Google PageRank algorithm, my first reaction was this is not the way it should be done! There should be some proof. This probably just shows that my CS education was too theoretical ;). Years later I have learned that indeed there are some nice tools to argue about the running time of PageRank algorithm. And very recently we were able to give some new parallel (in MPC model) algorithms for computing vanilla PageRank. We improved the number of rounds needed from O(log n) to O(log^2 log n) time. You can hear Solbodan talking out