AI systems tend to operate in the darkness of black boxes. Input data are transformed into decisions without much human-readable justification. Akai Kaeru’s explainable AI software discovers and visually explains interesting patterns and causal relations in complex data, supporting data analysts in the construction of trustable decision-making AI models. Say goodbye to tedious data exploration and instead gain quick access to the hidden information in your data within an easy to understand visual interface.
NEWS: We recently applied our software to predict the risk of certain US counties to experience larger that usual COVID-19 mortality. We gained some very interesting insight on demographic county profiles that expose them to higher health risks. Our study highlights the general capabilities of our software to identify explainable data patterns that exhibit a higher than usual response to a certain target variable, here COVID-19 death rate. Check out the details of our study using the link below. Then imagine how we can help you to gain this type of insight in your own domain. Chances are high that we can help you.
In contrast to what else you may have come across in search of a solution to your pressing data understanding problems – our software is not just an assortment of standard machine learning routines. Rather, it is an evolution of cutting edge research conceived in the university research lab, reduced to practice through numerous sessions with leading data analysts from industry. Use our tools on their own, or plug them into your existing data analytics workflow and give it an instant turbo charge.
Akai Kaeru Simplifies Data Analysis by Combating Information Overload
A common headache for data analysts is how to pick a manageable set of meaningful attributes and features from a list of hundreds or more. Akai Kaeru's new approach to data analysis puts an end to this guessing game.
Akai Kaeru’s Pattern Miner automatically decomposes the data into a manageable set of statistically robust data patterns which each can be concisely described with just a few attributes.
Next, Akai Kaeru's Visual Causal Analyst distills correlations that exist among these patterns into a terse set of causal relations. This process eliminates all spurious correlations and makes it easy to discern the true interactions that exist in the data. This is illustrated in the figure on the right.
Finally, the Data Context Map visual layout visualizes both patterns and relations in an integrated and intuitive fashion. Analysis can freely interact with this visual layout to explore new patterns and relations.
Akai Kaeru Enables Extra Insight by Inviting the Human into the Analytics Loop
Humans are still far superior to robotic data analysis, especially when transforming creative hunches into novel strategies. Let our software do the heavy lifting. Channel its work to help you in carving out new actionable insight. Make use of its wide set of interactive visual tools to inspect and tune the data patterns and test new relations derived from them.
See the Product page to learn more.
Copyright © 2020 Akai Kaeru, LLC - All Rights Reserved.