… 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 2021 • AAAI Spring Symposium Series
March 22-24, 2021 | An international virtual AI event. Original venue: Stanford University, Palo Alto, California, USA
- Extended submission due: 30th of November 2020
- Notification of authors: 10th of January 2021
- Camera-ready: 19th of February 2021
- Registration: 26th of February 2021
- Registration for non-authors (FCFS): 5th of March 2021
- 2021 Spring Symposium: 22-24 of March 2021
- 2020 Spring Symposium: 24-25 of March 2021
- January 10, 2021: Due to the vast number of submissions, the review phase and notification will be delayed by a maximum of two weeks.
- October 30, 2020: AAAI decided to convert the Spring Symposium format to virtual for 2021 due to the worldwide pandemic situation and Stanford regulations.
- October 27, 2020: Submission deadline extended until 30th of November 2020.
- September 20, 2020: EasyChair Smart CFP.
- September 17, 2020: WikiCFP.
- August 24, 2020: Call for Participation (CfP).
- August 13, 2020: 2021 Symposium proposal accepted.
- July 21, 2020: AAAI had to cancel the physical meeting of the 2020 Fall Symposium due to the current COVID-19 outbreak.
- July 16, 2020: 2021 Symposium proposal submitted.
- May 5, 2020: Volume I • AAAI-MAKE 2020 Spring Symposium proceedings published on CEUR-WS.
- March 4, 2020: AAAI had to cancel the physical meeting of the 2020 Spring Symposium following the Stanford University policy related to the current COVID-19 outbreak.
#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. In the self-driving car domain, safety and control are fundamental, demanding 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 require compliance with law and regulations. This specific application knowledge needs to be represented and strictly enforced, depending on the application scenario, 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 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 ongoing 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.
AAAI-MAKE 2021 aims for bringing together practitioners and researchers from various companies, research centers, and academia of machine learning and knowledge engineering. Furthermore, participants should reflect on the progress made on combining machine learning and knowledge engineering approaches now two years later, after being raised in the AAAI spring symposium series in 2019 for the first time. The participants should continuously work together on joint AI for practice that is being explainable and grounded in domain knowledge. Last but not least, participants shall benefit from each other to avoid pitfalls on the one hand and provide the ground for synergetic co-operations to identify the most promising areas for better results.