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Reality is Difficult



The implementation of machine learning in real-world products calls for knowledge and skills far beyond standard machine learning theory. The Alexandra Institute has filed a research application to explore this field, which we believe will receive considerable attention in the next few years.

Machine learning theory is complex in itself, but just wait until you have to implement it in a real-world product! As this excellent article by Aria Haghighi points out, creating well-functioning products based on machine learning almost invariably involves application-specific problems beyond the standard techniques, and solving such problems calls for the understanding of the application domain just as well as of machine learning theory.

And it’s not just about developing the classifier or recognizer. When a sufficient recognition accuracy has been achieved, there are often several other challenges to attend to: The algorithm must be optimized for the specific platform, the computation must be distributed over several devices, the data is sensitive and must be secured, the user interface must be adapted for possibly inaccurate output, the system must improve by itself from user feedback, and so on.

As an example of what I’m talking about here, take a look at Google Translate. While the translation is impressive in itself, they didn’t stop there: If the user isn’t happy with the translation, he or she has the possibility to choose alternative translations of individual elements and to move words around. The machine and the user are collaborating in finding the best translation through a clever user interface. On top of that, the input from the user is fed back to Google’s database, so that it can be used to improve future translations.

Word cloudIt’s all about making the application useful for the actual usage scenario, and this often requires more than a good recognizer. At the Alexandra Institute, we believe that problems of this kind are an upcoming area of research, simply because it is not until now that the recognizers based on machine learning are becoming so accurate that they are ready be used in many different real products. We have therefore filed a research proposal called Data Mining and Machine Learning in Practice and it’s currently under open evaluation at the website Bedre Innovation (Danish for Better Innovation). If you understand Danish, you are very welcome to take a look at our proposal and leave a comment directly on the website - your feedback is very useful for us in the application process.




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InfinIT er finansieret af en bevilling fra Styrelsen for Forskning og Innovation og drives af et konsortium bestående af:
Alexandra Instituttet . BrainsBusiness . CISS . Datalogisk Institut, Københavns Universitet . DELTA . DTU Compute, Danmarks Tekniske Universitet . Institut for Datalogi, Aarhus Universitet . IT-Universitetet . Knowledge Lab, Syddansk Universitet . Væksthus Hovedstadsregionen . Aalborg Universitet