MAKE stands for Machine leArning and Knowledge Engineering – the challenges that we address during the two-day MAKEathon will benefit from a combination of machine learning and explicit knowledge.
The event will be held mainly physically. Those who have difficulties to join physically can participate virtually. In both cases, registration is mandatory.
The event will take place in the main building of the FHNW University of Applied Sciences And Arts Northwestern Switzerland, Olten (CH). Health protection measures recommended by the Federal Council and the FHNW will be followed. Find the latest news here.
“MAKEathon aims to generate AI-based solutions that address global challenges by combining the strengths of Machine Learning and Knowledge Engineering”
Organizations from both research and industry are more than ever seeking to leverage AI solutions to get a competitive edge. Experience in the practice of Artificial Intelligence has shown that there is no “one-size-fits-all” solution.
Many AI solutions often consider machine learning approaches only. Machine learning helps to solve complex tasks based on real-world data instead of already existing knowledge or pure intuition. It is most suitable for applications in which we are not aware of the knowledge we apply or when knowledge is not known. Think of face recognition, which allows a system to detect and identify people on photos. The larger and the more accurate the training set, the better the outcome. However, if a new scenario (not foreseen in the training set) occurs, the ML-based object detection algorithm may fail by making the wrong classification (e.g. taking the picture of a person on a street advertisement for a real human being). This shows that data-driven solutions are far from being intelligent as they need a huge amount of data to achieve accurate conclusions.
Additionally, there are application areas in which it is important that machines can explain their suggestions. This is particularly the case when decisions can have serious consequences like in banking, insurance, or medicine. In medicine, for instance, a physician might not accept a diagnosis or therapy without adequate explanation. Many businesses are highly regulated and, thus, require compliance with law and regulations. Application-specific domain knowledge can be represented with the help of knowledge-based systems that make knowledge explicit and can explain their conclusions.
Given their complementary strengths and weaknesses, there is increasing attention on AI solutions integrating both approaches – knowledge engineering (KE) and machine learning (ML). During the MAKEathon, we follow this idea to create innovative prototypes and new solution approaches.
MAKEathon includes learning and experts from the AAAI-MAKE Spring Symposium on Combining Machine Learning with Knowledge Engineering. Since March 2019, the AAAI symposium is held at Stanford University on a yearly basis and gathers worldwide AI researchers and practitioners.