AAAI-MAKE: Combining Machine Learning and Knowledge Engineering in Practice

AAAI Spring Symposium on March 23-25, 2020
@ Stanford University, Palo Alto, California, USA

Join us…

… and work together on a combined machine learning and knowledge engineering based AI!

This symposium brings together practitioners and researchers from various companies, research centers and academia of machine learning and knowledge engineering working together on joint AI that is being explainable and grounded in domain knowledge.

AAAI-MAKE 2020: Combining Machine Learning and Knowledge Engineering in Practice

AAAI-MAKE 2020 is part of the Spring Symposium Series of the Association for the Advancement of Artificial Intelligence (AAAI), which is typically held during spring break in March on the west coast. The AAAI Spring Symposium Series is an annual set of meetings run in parallel at the History Building of the Stanford University.

«It is designed to bring colleagues together in an intimate forum while at the same time providing a significant gathering point for the AI community. The two and one half day format of the series allows participants to devote considerably more time to feedback and discussion than typical one-day workshops. It is an ideal venue for bringing together new communities in emerging fields.» (aaai.org)

March 23-25, 2020 @ Stanford University, Palo Alto, California, USA

Important Dates

  • Extended submission due: 17th of November 2019
  • Notification of authors: 6th of December 2019
  • Registration for authors and invitees: 14th of February 2020
  • Camera-ready: 22nd of February 2020
  • Registration for others (FCFS): 28th of February 2020
  • Late registration: 23rd of March 2020
  • Symposium: 23-25 of March 2020

News

Motivation

Many current AI solutions rely on machine learning approaches – with great success. Machine learning helps to solve complex tasks based on real-world data instead of pure intuition. It is most suitable for building AI systems when knowledge is not known, or knowledge is tacit.

While machine learning is now able to master data-intensive learning tasks, there are still some challenges. Many tasks require large amounts of training data, especially tasks where events to be predicted are rare. Often, machine output serves merely as a basis for decisions, which are finally made by humans.

Moreover, many business cases and real-life scenarios demand background knowledge and explanations of results and behavior. In medicine, for instance, physicians will likely overrule suggestions if there is no adequate explanation for them. In the self-driving car domain, where safety and control are fundamental, demand for symbolic approaches that can complement machine learning adequately. Moreover, conversational agents require domain knowledge and contextual information to provide satisfactory responses. Furthermore, application areas such as banking, insurance, and life science, are highly regulated and, thus, require compliance with law and regulations. This specific application knowledge needs to be represented and depending on the application scenario strictly enforced, which is the area of knowledge engineering.

Knowledge engineering and knowledge-based systems, which make expert knowledge explicit and accessible, are often based on logic and thus can explain their conclusions. These systems typically require a higher initial effort during development than systems that use machine learning approaches. However, symbolic machine learning and ontology learning approaches are promising for reducing the effort of knowledge engineering.

Because of their complementary strengths and weaknesses, there is an increasing demand in business to integrate knowledge engineering and machine learning for complex business scenarios. Focusing on only one aspect will not exploit the full potential of AI. Conclusively, recent results indicate that explicitly represented application knowledge could assist data-driven machine-learning approaches to converge faster on sparse data and to be more robust against noise, which results in cost efficiency and effectivity for business.

AAAI-MAKE aims for bringing together practitioners and researchers from various companies, research centers and academia of machine learning and knowledge engineering working together on joint AI for practice that is being explainable and grounded in domain knowledge. Participants shall benefit from each other to avoid pitfalls on one hand side and provide the ground for synergetic co-operations to identify the most promising areas for better results.