Mathematical Technology answers the question 'what can math do for you'? Much like Artificial Intelligence or Big Data, which are both forms of mathematical technology, it often involves algorithms encoded into software and analysis which can provide intelligence on, guide the development of, or directly automate important business or technological processes.
The possibilities are endless. Examples include predicting trends, personalizing information presentation, modeling physical or social processes, optimizing schedules, improving product design, automatic and real-time detection of risks and opportunities, autonomous robotics and decision making, and extracting meaning from complex data.
A different technique may work more effectively, or be faster to implement. Mathematical models or heuristic algorithms could leverage domain knowledge. Advanced techniques can extract more from data that is difficult or expensive to obtain. Mathematical analysis will often uncover hidden problems, offer solutions, and sometimes reveal new opportunities.
Depending on the application, the capabilities of many standard techniques from AI and elsewhere can be expanded by using other mathematical concepts, addressing weaknesses or improving outcomes.
Artificial Intelligence combines many fields such as machine learning and computer vision, themselves collections of mathematical and computational techniques, into one entity based on what these techniques can accomplish. Mathematical Technology expands this group even further to include domains such as big data and mathematical modeling. This change in paradigm removes us even further from the specifics, focusing instead on what this kind of technology can accomplish. It also opens us up to the idea that these technologies are by nature mathematical, and are themselves open to mathematical analysis and further refinement and adjustment.
The technology can be as simple or complex as necessary. It can involve a few lines of code to add a feature to a software product, or a cutting-edge set of algorithms used to perfect new technologies or to help direct large operations. Improving outcomes can entail a few rapid iterations for immediate results, or ongoing and thorough analysis for continuous improvement.