… 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
March 23-25, 2020 @ Stanford University, Palo Alto, California, USA
Extended submission due: 17th of November 2019 Notification of authors: 9th of December 2019 Registration for authors and invitees: 14th of February 2020
21st of February 2020(latest on 28th of February 2020)
- Registration for others (FCFS): 28th of February 2020
- Symposium: 23-25 of March 2020
- February 21, 2020: A first preliminary version of the AAAI-MAKE symposium program, which is subject to change, is available.
- February 4, 2020: We are delighted to announce that Doug Lenat, CEO of Cycorp and Fellow of AAAI, CSS and AAAS, will give a second keynote speech.
- January 13, 2020: Registration opens for all accepted authors, invited speakers, symposium participants, and other invited attendees.
- December 9, 2019: Review process completed, and authors notified.
- November 22: We are delighted to announce that Natasha Noy from Google AI and former member of the Stanford Center for Biomedical Informatics Research (BMIR) will give a keynote speech.
- November 18, 2018: The submission deadline has expired, and the review process has started.
- October 28, 2019: Submission deadline extension until November 17, 2019.
- September 6, 2019: WikiCFP.
- September 6, 2019: EasyChair Smart CFP.
- September 6, 2019: Call for Participation (CfP).
- September 5, 2019: SSS-20 EasyChair submission site is open.
- August 1, 2019: Symposium proposal accepted.
- July 12, 2019: Symposium proposal submitted.
- April 26, 2019: AAAI-MAKE 2019 papers published on CEUR-WS.
#AAAIMAKE on Twitter#AAAIMAKE – Curated tweets by IISresearch
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.